Performance Evaluation of Routing Protocols in Lossy Links ... · Routing Information Protocol Open Shortest Path First Enhanced Interior Gateway Routing Protocol ... selection of

Performance Evaluation of Routing Protocols

in Lossy Links for Smart Building Networks

Ion Emilian Radoi

Master of Science

Computer Science

School of Informatics

University of Edinburgh

2011

iii

Abstract

The volume of research performed in the domain of Wireless Sensor Networks has increased

substantially in recent years. This is due to the advancement of fabrication technology of low-power

devices containing various sensors along with a wireless interface, allowing costs to be reduced.

However, the deployment of large-scale sensor networks, which could consist of several thousand

nodes, still represents a challenge to researchers.

This thesis aims to evaluate the performance of routing protocols in lossy links for smart building

networks. Special attention is directed towards the RPL protocol which is the IETF candidate for a

standard routing protocol in wireless sensor networks.

RPL was compared against other protocols (DSDV and the four versions of SimpleP) using several

metrics in the SpeckSim simulation environment for four topologies of different sizes.

The results reveal that RPL is suitable for use in large networks (over 200 nodes) because it does not

present any scalability limitations as opposed to the other protocols tested in this thesis. However, for

small to medium-sized networks (less than 100 nodes), RPL’s performance does not outclass that of

the other protocols that were tested. Each protocol has proven to have its strengths, thus when

choosing a protocol for a wireless sensor network deployment, the decision has to be based on the

specific requirements of the WSN application.

iv

Acknowledgements

I would like to thank my supervisor Prof. D.K. Arvind for his constant help and guidance and for

supporting my ideas.

Nevertheless, I would like to thank Mat Barnes for helping me understand the SpeckSim simulator,

guiding me through the implementation process of the routing protocols and statistics gathering

modules, and helping me to move on with the project whenever I got stuck.

v

Declaration

I declare that this thesis was composed by myself, that the work contained herein is my own except

where explicitly stated otherwise in the text, and that this work has not been submitted for any other

degree or professional qualification except as specified.

(Ion Emilian Radoi)

vi

Table of Contents

Introduction ........................................................................................................................ 1

1.1. Wireless Sensor Networks ......................................................................................... 1

1.2. Routing in WSN ......................................................................................................... 2

1.3. Motivation ................................................................................................................ 2

1.4. Previous Work ........................................................................................................... 3

1.5. Outline ...................................................................................................................... 3

Background and Related Work ............................................................................................ 5

2.1. RPL ............................................................................................................................ 5

2.2. SpeckSim ................................................................................................................... 6

2.3. Literature Survey ....................................................................................................... 8

2.4. Routing protocols relevant for comparing to RPL ..................................................... 11

2.4.1. DSDV [15],[17] ....................................................................................................... 11

2.4.2. AODV [13],[14] ...................................................................................................... 12

2.4.3. DSR [18],[19] ......................................................................................................... 12

2.5. Smart Buildings ....................................................................................................... 13

2.5.1. Scenarios [11] where routing protocols are needed for smart building networks 13

Design and Implementation .............................................................................................. 17

3.1. Attempt to integrate ContikiRPL into SpeckSim ....................................................... 17

3.2. RPL .......................................................................................................................... 21

3.2.1. Types of RPL messages ..................................................................................... 22

3.2.2. RPL routing ....................................................................................................... 23

3.2.3. Objective Function............................................................................................ 24

3.2.4. Trickle timer ..................................................................................................... 24

3.2.5. RPL in SpeckSim ................................................................................................ 25

3.3. SimpleP ................................................................................................................... 25

3.3.1. SimplePv1 ......................................................................................................... 26

3.3.2. SimplePv2 ......................................................................................................... 26

3.3.3. SimplePv3 ......................................................................................................... 26

3.3.4. SimplePv4 ......................................................................................................... 26

3.3.5. Limitations of SimpleP ...................................................................................... 28

vii

3.4. DSDV .......................................................................................................................28

3.4.1. DSDV features that differ from SimplePv4 .........................................................31

Validation ..........................................................................................................................33

4.1. Validation Scenario ..................................................................................................33

4.1.2. Cooja ................................................................................................................34

4.1.3. SpeckSim ..........................................................................................................35

4.2. Validation Results ....................................................................................................35

4.2.1. Favourable case ................................................................................................36

4.2.2. Detrimental case ...............................................................................................37

4.3. Conclusions .............................................................................................................38

Testing ...............................................................................................................................39

5.1. Simulation Environment ..........................................................................................39

5.2. Topology types ........................................................................................................40

5.2.1. Random ............................................................................................................40

5.2.2. Grid ..................................................................................................................40

5.2.3. Tree ..................................................................................................................41

5.2.4. Building ............................................................................................................41

5.3. Metrics ....................................................................................................................43

5.3.1. Protocol Overhead ............................................................................................43

5.3.2. Size of table ......................................................................................................43

5.3.3. Coverage ..........................................................................................................43

5.3.4. Delivery Ratio / Packet loss ...............................................................................43

5.3.5. Latency .............................................................................................................44

5.3.6. Power consumption ..........................................................................................44

5.3.7. Fault tolerance (Recovery time) ........................................................................44

5.3.8. Route propagation time within the network .....................................................45

5.3.9. Implementation complexity ..............................................................................46

5.3.10. CPU load .........................................................................................................46

5.4. Types of Traffic ........................................................................................................46

5.5. Statistics gathering process in SpeckSim ..................................................................46

Results and Critical Analysis ..............................................................................................49

6.1. Protocol Overhead...................................................................................................49

6.1.1. Control packet generation flow .........................................................................49

viii

6.2.2. Overhead Size ................................................................................................... 52

6.2. Size of Table ............................................................................................................ 52

6.2.1. Number of Entries ............................................................................................ 52

6.2.2. Memory Usage ................................................................................................. 54

6.3. Coverage ................................................................................................................. 56

6.4. Delivery Ratio .......................................................................................................... 58

6.5. Latency ................................................................................................................... 59

6.6. Power Consumption ................................................................................................ 59

6.7. Fault Tolerance........................................................................................................ 60

6.7.1. Scenario ........................................................................................................... 60

6.7.2. Results .............................................................................................................. 61

6.8. Implementation Complexity .................................................................................... 62

6.9. Overall Results Interpretation.................................................................................. 63

Conclusions and Further Work .......................................................................................... 65

7.1. Conclusions ............................................................................................................. 65

7.2. Future work............................................................................................................. 66

Bibliography ...................................................................................................................... 67

Appendix ........................................................................................................................... 71

A. How to use the Cooja Simulator ................................................................................. 71

B. NoComments Script ................................................................................................... 71

ix

List of Figures

Figure 2.1: SpeckSim GUI Screenshot [9]

.......................................................................................... 7

Figure 2.2: SpeckSim Architecture [9] ............................................................................................... 7

Figure 3.1: Cooja simulator - 1 sink, 6 senders running ContikiRPL ............................................... 18

Figure 3.2: Cooja simulator - Sensor Map and Network Graph ....................................................... 19

Figure 3.3: Solution for ContikiRPL in SpeckSim .......................................................................... 20

Figure 3.4: Why NAK is needed .................................................................................................... 27

Figure 3.5: DSDV periodic updates ................................................................................................ 29

Figure 3.6: Forwarding and Advertised Tables for node 1 (4-node topology - SpeckSim) ................ 30

Figure 4.1: Validation topology – Informatics Forum floor plan – 11 node network deployment ..... 34

Figure 4.2: ContikiRPL Test Topology – The RPL Root in favourable position (Cooja Simulator) .. 34

Figure 4.3: RPL Validation Topology (SpeckSim) .......................................................................... 35

Figure 4.4: Hops to DODAG Root ................................................................................................. 36

Figure 4.5: RPL DODAG .............................................................................................................. 36

Figure 4.6: Overhead ..................................................................................................................... 36

Figure 4.7: Hops to DODAG Root ................................................................................................. 37

Figure 4.8: RPL DODAG .............................................................................................................. 37

Figure 4.9: Overhead ..................................................................................................................... 37

Figure 5.1: Random Topology in SpeckSim ................................................................................... 40

Figure 5.2: Grid Topologies in SpeckSim ....................................................................................... 40

Figure 5.3: Tree Topologies in SpeckSim ....................................................................................... 41

Figure 5.4: Building Topology (Informatics Forum – 24 nodes) in SpeckSim.................................. 42

Figure 5.5: Building Topology (Informatics Forum – 11 nodes) in SpeckSim.................................. 42

Figure 5.6: Fault Toleration Example ............................................................................................. 45

Figure 6.1: RPL Overhead ............................................................................................................. 50

Figure 6.2: DSDV Overhead .......................................................................................................... 50

Figure 6.3: SimplePv1 Overhead .................................................................................................... 50


x



Figure 6.7: Overhead Flow ............................................................................................................. 51

Figure 6.8: Overhead Size .............................................................................................................. 52

Figure 6.9: Table Size (Number of Entries) .................................................................................... 53

Figure 6.10: Table Size – Building ................................................................................................. 53

Figure 6.11: Table Size – Grid ....................................................................................................... 53

Figure 6.12: Table Size – Random.................................................................................................. 54

Figure 6.13: Table Size – Tree ....................................................................................................... 54

Figure 6.14: Memory Usage – 11-node Building Topology (Informatics Forum) ............................. 56

Figure 6.15: Coverage – 11-node Building Topology (Informatics Forum) ...................................... 56

Figure 6.16: Coverage – Building ................................................................................................... 57

Figure 6.17: Coverage – Grid ......................................................................................................... 57

Figure 6.18: Coverage – Random ................................................................................................... 57

Figure 6.19: Coverage – Tree ......................................................................................................... 57

Figure 6.20: Delivery Ratio – Building ........................................................................................... 58

Figure 6.21: Delivery Ratio – Grid ................................................................................................. 58

Figure 6.22: Delivery Ratio – Random ........................................................................................... 58

Figure 6.23: Delivery Ratio – Tree ................................................................................................. 58

Figure 6.24: Latency – 11-node Building Topology (Informatics Forum) ........................................ 59

Figure 6.25: Power Consumption ................................................................................................... 60

Figure 6.26: Fault Tolerance Topology (SpeckSim) ........................................................................ 60

Figure 6.27: Fault Tolerance .......................................................................................................... 61

Figure 6.28: Complexity (Lines of Code) ....................................................................................... 62

Figure 6.29: Complexity (Number of Classes) ................................................................................ 62

xi

List of tables

Table 3.1: DSDV – SimplePv4 differences ..................................................................................... 31

Table 6.1: Protocols – Metrics Summary ........................................................................................ 63

xii

List of Abreviations

WSN

LLN

MANET

IETF

ROLL

RPL

DSDV

AODV

DSR

RIP

OSPF

EIGRP

IS-IS

MAC

DODAG

DIO

DAO

CPU

MCU

ACK

NAK

Wireless Sensor Network

Low power and Lossy Network

Mobile Ad hoc Network

Internet Engineering Task Force

Routing Over Low power and Lossy networks

Routing Protocol for Low power and Lossy networks

Destination-Sequenced Distance Vector

Ad hoc On-Demand Distance Vector

Dynamic Source Routing

Routing Information Protocol

Open Shortest Path First

Enhanced Interior Gateway Routing Protocol

Intermediate System to Intermediate System

Medium Access Control

Destination Oriented Directed Acyclic Graph

DODAG Information Object

Destination Advertisement Object

Central Processing Unit

Microcontroller Unit

Acknowledgement

Negative Acknowledgement

1

Chapter 1

Introduction

The main aim of this project is to investigate the performance of the RPL (IPv6 Routing Protocol for

Low power and Lossy Networks) routing protocol in 6lowPAN (IPv6 low Power wireless Area

Networks) networks, analysing its strengths and weaknesses.

RPL is a routing protocol defined by the IETF Routing Over Low power and Lossy (ROLL) networks

Working Group, and is being promoted as the standard for use in wireless sensor networks. A number

of situational requirements for RPL have been identified by the ROLL group for building services and

industrial automation. The goal of this project is to evaluate the performance of the protocol in

meeting the complex requirements of such applications. The project evaluates the performance of the

RPL protocol against existing wireless sensor network and MANET routing protocols in the

SpeckSim network simulation environment.

The project was developed according to the following three phases: selection of protocols and criteria

for evaluation, implementation of the selected protocols and network traffic models in the simulator,

and performance evaluation and analysis.

1.1. Wireless Sensor Networks

Wireless Sensor Networks (WSN) consist of distributed sensors connected as a mesh, with the

purpose of monitoring an area of interest and the surrounding environment. The sensors are able to

capture various factors of the environment such as temperature, sound, pressure, movement and air

composition, among others. Usually, the sensors are dispersed in large numbers in the area of interest.

Information collection is often transmitted to a central collection point during, or after, an experiment.

Today, sensors hold a wide area of usage such as in industry (for monitoring and controlling the

industrial process), environmental monitoring, healthcare applications and military applications. More

advanced networks support bidirectional communication so that the activity of the sensors can be

controlled.

2 Chapter 1. Introduction

The nodes in a WSN are multi-functional, low-power and low-cost devices. Each node can have one

or more sensors, a radio transceiver, a microcontroller and runs on a battery. The range of the radio

communication between the nodes is usually up to 30 meters.

An important challenge is represented by the scalability of network protocols when dealing with a

large number of nodes. However, an equally important challenge is developing simple and efficient

protocols to be used for various operations within a WSN. These design requirements are necessary in

order for the protocols to be suitable to run on the network nodes, which are limited in terms of

hardware (processing power, memory and battery size).

1.2. Routing in WSN

Routing within mesh-connected wireless sensor networks is important as most applications designed

to run over these types of networks are based on the process of intra-node communication, as well as

the process of transferring data from individual nodes to a central collection point (sink).

Routing within wireless sensor networks differs from routing in IP networks in the following ways:

The addressing scheme is often not important for data driven applications

For most of the applications, data is collected from multiple sensors and sent to a central

control point

WSN nodes have serious resource constraints such as processing power, memory size,

energy storage and communication bandwidth

Frequently, nodes in a WSN are deployed statically, requiring low mobility. Thus, compared to

Mobile Ad hoc Networks (MANET) deployments (high rates of mobility that translates to frequent

topological changes), there are very few changes in the topologies.

1.3. Motivation

The need for a standard for wireless sensor network routing led the IETF organization to promote the

RPL protocol as its candidate. The results of this dissertation are a contribution to the consultation

process.

The main aims of the project are discussed next.

The goal of the project is to investigate the behaviour and evaluate the performance of RPL in WSNs.

The project also evaluates the performance of RPL against existing WSN and MANET routing

protocols in the SpeckSim network simulator.

1.4. Previous Work 3

In respect to the proposed goal, the RPL implementation provided in SpeckSim was validated against

the ContikiRPL implementation from the Cooja network simulator. After the validation stage, RPL

was tested and evaluated against the DSDV protocol and the four versions of the SimpleP protocol in

the SpeckSim simulator.

1.4. Previous Work

This project has benefited from the previous work conducted in the same domain as it. The simulation

environment used was the SpeckSim simulator [9] which was developed by the Speckled Computing

group [9], an early version of the RPL implementation in the SpeckSim simulator was provided by

Ryan McNally, and the Cooja simulator along with the ContikiRPL [24] implementation was developed

by the Swedish Institute of Computer Science [24].

The bulk of the previous and related work that was considered for this project is presented in Chapter

2 - Background and Related Work.

1.5. Outline

The rest of the thesis is structured as follows:

Chapter 2: Background and Related Work – the RPL protocol and the SpeckSim network

simulator are described; a brief literature survey is presented, along with routing protocols

that are relevant for comparison to RPL; introduces the concept of Smart Buildings and

offers examples of Smart Building scenarios to show the importance of routing in Smart

Building networks.

Chapter 3: Design and Implementation – presents an early attempt to integrate the

ContikiRPL into the SpeckSim simulator, and describes the implementation of the tested

routing protocols: RPL, SimplePv1, SimplePv2, SimplePv3, SimplePv4 and DSDV.

Chapter 4: Validation – provides a validation for the SpeckSim RPL implementation

against the ContikiRPL implementation (which was used as a benchmark). ContikiRPL was

tested with the help of the Cooja simulator.

Chapter 5: Testing – describes the testing process and methods used in this project; it

describes the settings of the simulation environment, the types of topologies and the metrics

used for evaluation, the types of traffic generated and how the statistics are gathered from the

SpeckSim simulator.

Chapter 6: Results and Critical Analysis – presents the results obtained, along with a short

discussion for each of the metrics tested; it concludes with an overall interpretation of the

results in the form of protocol comparisons.

Chapter 7: Conclusions and Future Work – presents the final remarks, conclusions and

observations, along with possible extensions to the project.

4

5

Chapter 2

Background and Related Work

This section sets the context of the project, provides a description for RPL and for other protocols that

are considered relevant for comparison to RPL, describes the simulator used for evaluation and

presents a survey of past research regarding routing protocols in Wireless Sensor Networks. It finally

describes the concept of Smart Building networks and provides examples of several applications for

such networks.

2.1. RPL

RPL has been proposed by the IETF ROLL (Routing Over Low power and Lossy networks) Working

Group as a standard routing protocol for IPv6 routing in low-power wireless sensor networks.

According to Tripathi et al [21], “existing routing protocols such as OSPF, IS-IS, AODV, and OLSR

have been extensively evaluated by the working group and have been found to not satisfy, in their

current form, all specific routing requirements for LLNs (Low power and Lossy Networks)”[21]. These

networks (LLNs) are mainly characterized by high loss rates, low data rates and instability and can

consist of up to several thousand nodes. The nodes in these networks are constrained in terms of

processing power, memory and energy.

The RPL routing protocol is tree-oriented, with RPL topologies generated starting from the Root

nodes and organized as directed acyclic graphs. RPL forms non-transitive, non-broadcast, multiple

access network topologies. RPL offers flexibility in building a topology, but is complex as described

in detail in the specification of the IETF draft [1].

The predominant traffic patterns in LLNs are point-to-multipoint and multipoint-to-point. The RPL

protocol supports these traffic flows, as well as point-to-point.

Point-to-point traffic is used between the devices within the LLN; point-to-multipoint pattern is used

between a central control point and a set of sensor nodes; multipoint-to-point pattern is used between a

set of sensor nodes and a central control point.

6 Chapter 2. Background and Related Work

RPL separates the process of routing optimization (such as minimizing energy consumption and

latency) from the process of packet routing (packet processing and forwarding). Bidirectional links are

required in order for a LLN to run the RPL protocol, because RPL has to verify whether a router is

reachable before using it as a parent. (During the parent selection process, an external mechanism is

used to verify the neighbour reachability and link properties)

An “instance” defines a topology built with a unique metric within a network. A network can run one

or more instances of RPL. “Each such instance may serve different and potentially antagonistic

constraints or performance criteria.”[1]

“In compliance with the layered architecture of IP, RPL does not rely on any particular features of a

specific link layer technology. RPL is designed to be able to operate over a variety of different link

layers, including ones that are constrained, potentially lossy, or typically utilized in conjunction with

highly constrained host or router devices, such as but not limited to, low power wireless or PLC

(Power Line Communication) technologies.” [1]

2.2. SpeckSim

The simulator chosen for this project is the SpeckSim network simulator. It was developed in Java by

the Speckled Computing group at the University of Edinburgh and is dedicated for simulating wireless

sensor networks. “SpeckSim is designed to perform algorithm-level simulations on the behaviour of a

field of numerous mobile computing devices called Specks. The main design goal was to make it as

easy as possible for new behaviours and capabilities to be added to the simulator.” [9] On the

SpeckSim website, documentation is provided for using the SpeckSim GUI, Extending SpeckSim,

Configuration, Visualisation and Logging.

Figure 2.1 represents a preview of SpeckSim’s graphical user interface.

2.2. SpeckSim 7

Figure 2.1: SpeckSim GUI Screenshot [9]

The simulator has three main components: the SpeckSim kernel which deals with communication, the

simulator state which holds/gathers data that can be used for generating simulation statistics, and the

graphical user interface which provides the user with the means to interact with the simulator.

The core of the simulator is event based, and the simulator holds a queue of these events with a

timestamp. (An example of such an event may be a change in the state of a device.) All events are

held in the queue, and are processed at appropriate times. The simulator time is advancing from one

such event to the other. Thus it is safe to say that the core of the simulator is actually based on this list

of queues of events.

Figure 2.2: SpeckSim Architecture [9]


Figure 2.2 presents a diagram showing the components of the simulator. As shown in the diagram, the

simulator is made up of:

Specks – any simulation entity

Environments – used by devices for communication, such as a radio channel

The Movement Model – positions the specks in the environment (3D space) and its movement (if any) over time

For further details and specifications refer to the SpeckSim website [9].

2.3. Literature Survey

According to “A survey on routing protocols for wireless sensor networks” [2] almost all routing

protocols for WSNs can be classified as data-centric, hierarchical or location-based. Even though the

majority of routing protocols should fit in one of the above mentioned categories, some of them

pursue slightly different approaches. Thus, a fourth category was created: “Network flow and QoS-

aware protocols”. The paper provides a classification for the WSNs routing protocols as follows:

Data-centric protocols – A data-centric protocol is query based and depends on the naming

of the data. The process works like this: a node, which is a central control point, queries

particular regions and then expects to receive data from these sensors. As most of the data

collected will be redundant, it is up to the routing protocol to save energy by eliminating the

redundant data. Data-centric routing differs from traditional routing (which is address-based)

because routes are not created between addressable nodes. Due to the large number of nodes,

for many applications for WSNs it is not practical to assign global identifiers (such as IP

addresses).This makes it difficult to query a particular set of nodes. Since there is great

redundancy of the data transmitted, routing protocols capable of data aggregation help by

saving a significant amount of energy.

Example of data-centric protocols: SPIN (Sensor Protocols for Information via Negotiation),

Directed Diffusion, Rumor routing, GBR (Gradient-based routing), CADR (Constrained

anisotropic diffusion routing), COUGAR and ACQUIRE (ACtive QUery forwarding In

senoR nEtworks).

Hierarchical protocols – Hierarchical protocols mainly address the issue of scalability. A

single gateway architecture is not scalable because increasing the density of sensors may lead

to an overload of the gateway, thereby resulting a high latency for the communication within

the network. Hierarchical routing decreases energy consumption by using multi-hop

2.3. Literature Survey 9

communication within a cluster and afterwards aggregating and performing fusion on the

collected data in order to eliminate any redundant data.

Example of hierarchical protocols: LEACH (Low-Energy Adaptive Clustering Hierarchy),

PEGASIS (Power-Efficient Gathering in Sensor Information Systems), TEEN (Threshold

sensitive Energy Efficient Network protocol) and APTEEN (Adaptive Threshold sensitive

Energy Efficient Network protocol).

Location-based protocols – These protocols use location/position information to determine a

particular region based on the collected data, so that queries are diffused only in that specific

region.

Example of location-based protocols (some of the protocols mentioned are designed for ad-

hoc networks, but are applicable to WSNs): MECN (Minimum Energy Consumption

Network), SMECN (Small MECN), GAF (Geographic Adaptive Fidelity) and GEAR

(Geographic and Energy-Aware Routing).

Network flow and QoS-aware protocols – This category consists of protocols that pursue

slightly different approaches such as network flow and QoS. (QoS-aware protocols are

concerned with end-to-end delay requirements when selecting paths in WSNs).

This paper [2] surveys recent routing protocols and discusses each under their appropriate category.

The aim is to provide a better understanding of the domain and to highlight outstanding open issues.

The paper “Routing Techniques in Wireless Sensor Networks: A Survey” [4] focuses on

routing techniques and provides classification and comparison of routing protocols in WSNs. It

reveals design challenges for routing protocols in wireless sensor networks such as: node deployment,

energy consumption without losing accuracy, data reporting model, node/link heterogeneity, fault

tolerance, scalability, network dynamics, transmission media, connectivity, coverage, data

aggregation, and quality of service.

This paper structures routing protocols in a manner similar to [2], and it presents about the same

protocols. It divides the protocols into two major categories: “Network Structure Based Protocols”

and “Routing protocols based on Protocol Operation”. The first category is divided into three

subcategories which coincide with the classification provided by [2]. This paper provides well

referenced future directions for routing in wireless sensor networks.


A paper highly relevant to this project is “Low-Power Wireless IPv6 Routing with ContikiRPL” [3].

The paper aims to test the RPL protocol, and is based on an implementation of RPL for the Contiki

operating system, called ContikiRPL. This implementation was tested both in simulation and in a low-

power wireless network. The protocol was evaluated in terms of power efficiency and implementation

complexity. The results show that the lifetime of the network with RPL routing on Tmote Sky motes

can be several years. Additionally, the protocol has proven to be lightweight and power-efficient. This

dissertation presents a different approach for evaluating RPL by taking into account other metrics than

the ones mentioned in paper [3], and thereby providing a more comprehensive evaluation. It also

provides a comparison between RPL and other relevant routing protocols in order to highlight RPL’s

strengths and weaknesses.

The paper “A Performance Evaluation Study of RPL: Routing Protocol for Low Power and

Lossy Networks”[21] provides an evaluation of the RPL protocol, simulated using the OMNET++

simulator with the Castalia2.2 framework that allows for wireless sensor networks simulation within

the OMNET++ simulator. The nodes in the simulator used TelosB CC2420 radio and the 802.15.4

MAC protocol. The scenario simulated an outdoors network of medium size (with a total of 86

nodes). The topology was inspired from a real-life deployment and the metrics that were used for

RPL’s evaluation are the following:

“Path quality metrics”

“Control plane overhead”

“Ability to cope with unstable situations (link churns, node dying)”

“Required resource constraints on nodes (routing table size, etc.)”

The paper concludes by stretching out the usefulness of the Trickle timer used by RPL which keeps

the control overhead to a lower level than the actual data packet count. Also, Global repair was used

for managing broken links, which turned out to have “a significant effect on the number of control

packets, which may be used to upper bound the overhead for trade off with time with no

connectivity”[21]. In other words, Global repair provides a trade off between amount of control traffic

generated and the time for loss of connectivity. Since this paper provided the evaluation of the RPL

protocol based on an outdoor network, it is relevant to mention that the paper also concluded that the

protocol operation has to be modified to better adapt to this type of networks.

2.4. Routing protocols relevant for comparing to RPL 11

2.4. Routing protocols relevant for comparing to RPL

2.4.1. DSDV [15],[17]

Destination-Sequenced Distance-Vector (DSDV) is a highly dynamic routing protocol for ad-hoc

networks. It is classified as a distance-vector protocol and is based on multi-hop routing and the

Bellman-Ford algorithm. By definition, an ad-hoc network comprises of mobile devices/nodes called

hosts that communicate within the network without the intervention of any centralised Access Point.

To form such a network, each device/host has to operate as a specialized router. When the DSDV

protocol was created it has been taken into account that the topologies in ad-hoc networks may be

highly dynamic and that most users will desire not to have to perform any administrative actions in

order to set up the network.

The protocol’s main aim is to solve the routing loop problem. The solution that it provides regarding

this problem is the usage of a sequence number for each of the routes within the routing table. For

differentiating between valid and non-valid links the protocol uses even (valid link) and odd (invalid

link) numbers. These sequence numbers are generated by the destination.

Since wireless sensor networks can be quite dynamic, it seemed logical to compare the RPL protocol

to a MANET routing protocol such as DSDV. Other routing protocols (such as RIP, EIGRP, OSPF,

IS-IS) that are used by regular computer networks could not be taken into account for comparison

with RPL because they place too heavy a computational burden on the device running them, thus they

are not suitable for use on wireless sensors (which have very limited resources). Even though RIP is

somewhat similar to DSDV, since both are rather simplistic distance vector protocols and both use the

number of hops as a metric, RIP will not be suitable for an ad-hoc environment because its design

cannot handle highly frequent topology changes. And, according to Perkins and Bhagwat [17], the

split horizon and poison reverse techniques used by RIP were appropriate for use in wired networks,

and do not work in a wireless environment where the packets are sent via broadcast. DSDV avoids the

looping problem by tagging each route with a sequence number. Based on the sequence number,

nodes will be able to differentiate old and new routes.

DSDV, being a distance vector protocol, “in order to keep the distance estimates up-to-date, each node

monitors the cost of its outgoing links and periodically broadcasts, to each one of its neighbours, its

current estimate of the shortest distance to every other node in the network”[17].

Before the work of Perkins and Bhagwat [17], there were numerous objections concerning the use of

the Bellman-Ford algorithm as the basis for routing protocols. These objections targeted the poor

looping properties of the algorithm caused by link failures. They provided the specification for the

DSDV protocol which addresses some of these objections. Besides the thorough description of the

protocol and useful and comprehensive use examples, the paper also provides a theoretical


demonstration as to why DSDV guarantees loop-free paths to each destination and a comparison of

DSDV to several other routing protocols.

DSDV is described as an “innovative approach which models the mobile computers as routes which

are cooperating to forward packets as needed to each other. It makes good use of the properties of the

wireless broadcast medium and it can be utilized at either the network layer (layer 3), or below the

network layer but still above the MAC layer software in layer 2”[17]. The paper’s findings is that

mobile computers (modelled as routers) can effectively cooperate to build ad-hoc networks. This

dissertation aims to discover how DSDV performs in wireless sensor networks and to compare its

performances with that of RPL.

2.4.2. AODV [13],[14]

The Ad hoc On-Demand Distance Vector (AODV) routing protocol is designed for ad hoc mobile

networks. The protocol manages to adapt to low processing and low available memory conditions and

to the high dynamic nature of networks. It enables mobile devices that are part of the network to

quickly acquire routes to new destinations.

As it is a distance vector protocol, it uses multi-hop routing. However, it avoids the “counting to

infinity” problem of the Bellman-Ford algorithm and provides loop-free routing by using destination

sequence numbers for each route entry in the routing table. The destination sequence number is seen

as a local route metric, thus in case of multiple routes for one destination, the route that holds the

greatest destination sequence number is preferred. AODV offers quick convergence in case of

network changes.

AODV and RPL have several similarities, as they are both distance vector routing protocols, and tree

based.

2.4.3. DSR [18],[19]

Dynamic Source Routing (DSR) is a beacon-less routing protocol for ad hoc mobile networks. It

uses source routing, implying that the sender specifies the route that a packet takes through the

network. The sender deposits the address of the intermediate nodes (which are part of the route that

the packet must follow) in the packets.

The protocol is based on two mechanisms: “Route Discovery” and “Route Maintenance”. These

mechanisms have the purpose of discovering and maintaining routes to arbitrary nodes within the

network.

2.5. Smart Buildings 13

DSR is a simple and efficient protocol, which provides loop-free routing and can run on networks

with unidirectional links. It is intended to have low overhead and be able to react quickly to network

changes. The protocol determines and maintains all routing within its network, thereby enabling the

network to be self-organizing and self-configuring. DSR is designed to scale to hundreds of nodes and

is suitable for networks with a high degree of mobility.

2.5. Smart Buildings

A “Smart Building” network consists of spatially distributed autonomous devices to monitor and react

to physical or environmental conditions such as temperature, vibration, pressure, motion or pollutants.

Collaborative processing between the distributed nodes allows for fault tolerant and robust networks.

The potential benefits include maximising energy efficiency and assistive services for people with

disabilities. Within “Smart Buildings”, the people and the environment are equipped to be part of a

network of devices which combine sensing, processing and wireless networking capabilities.

Smart building networks can cost-effectively provide detailed monitoring of the conditions inside a

building. This allows for the environmental systems of a building to deliver just enough heat, air, or

cooling when and where it is needed. “Smart buildings equipped with an integrated array of sensors

can also monitor such things as the amount of sunlight coming into a room and adjust indoor lighting

accordingly. Advanced smart buildings can know who is visiting a building after hours (based on the

key swipe from the security system) and turn on the appropriate lights, equipment, and environmental

controls.” [11]

Smart buildings are occurring as a result of major financial, technical and environmental changes.

HOBNET represent an example of a research project in this domain that has the main objective to

“ease and maximize the use of FIRE platforms by multidisciplinary developers of Future Internet

applications focused on automation and energy efficiency for smart/green buildings”. [11]

The HOBNET scenario analysis report document [11] presents a number of scenarios along with their

description. Some of these scenarios have been selected for simulation this dissertation.

2.5.1. Scenarios [11] where routing protocols are needed for smart

building networks

2.5.1.1 Local adaptation to presence

This scenario adapts the room environment on detection of human presence. The room environment

consists of lighting, temperature and electric appliances. Thus, motion/presence sensors, temperature

and light sensors are necessary.


In this scenario the routing protocol is tested for its reliability and its latency.

2.5.1.2. Emergency management

In case of a fire emergency, the building occupants will have to be provided with a safe path to exit

the building. This application will analyze information provided by the sensors to build a safe path to

an exit by using visible or audible output devices to guide people. The guidance information can also

be received on an occupant’s mobile device.

The sensors necessary for this process are temperature and smoke detection sensors.

The routing protocol, for this scenario, should have the following qualities:

Low latency (fast routing is required)

Fault tolerance (in the case of failure of some of the nodes, alternative routes to the safe exit

has to be found quickly)

Short time for updating the routing table

Short time for a route to propagate in the network

2.5.1.3. CO2 monitoring

By using CO2 sensors, it is intended to adapt the heating, ventilating, and air-conditioning system to

renew the air based on the CO2 level. This approach is considered more appropriate for meeting rooms

and office spaces. (CO2 level low – no need for ventilation, CO2 level high – increase ventilation)

This scenario requires a network of CO2 sensors to be distributed in the space. The sensors sense the

CO2 level and send it to a central sink node. Based on the results of comparing the CO2 level to the

established thresholds for personal comfort, the HVAC system is adjusted. The routing protocol is

tested for reliability and latency.

2.5.1.4. Maintenance control

This scenario uses sensors to detect abnormal activities and provide alerts to the maintenance team.

According to [11], some examples of abnormal activities are:

Lights not working

A leak is detected

An abnormal electricity consumption is detected

A window remains open in an empty room

A tap remains open in an empty room

An abnormal temperature is detected

2.5. Smart Buildings 15

2.5.1.5. Resources tracking and monitoring

This scenario requires the tracking of high-value portable items and sending an alert should the item

leave the building (either the item is misplaced or has been borrowed without permission). For the

notification in case an item leaves the building to be as close as possible to real time, it is required for

the routing protocol to have a low latency.

The next chapter presents an early attempt to integrate the ContikiRPL into the SpeckSim simulator,

and describes the implementation of the tested routing protocols.

16

17

Chapter 3

Design and Implementation

This chapter presents the rationale for attempting to integrate ContikiRPL into the SpeckSim

simulator and the reason why this path was not pursued further. It also provides details regarding the

design and implementation of the tested protocols (RPL, SimplePv1-v4 and DSDV).

3.1. Attempt to integrate ContikiRPL into SpeckSim

I was made aware early in the project that the Swedish Institute of Computer Science (SICS) provided

an open-source RPL implementation in compliance with the ROLL IETF draft [1] that was integrated

with the Contiki operating system, and ran on the Tmote Sky platform. The ContikiRPL

implementation can be tested in conjunction with the Cooja network simulator.

According to [23], “In March 2010, an interoperability event hosted by the IPSO Alliance took place

in Anaheim, CA. ContikiRPL participated in this event and was tested against a number of emerging

RPL implementations”. Since ContikiRPL had been tested it was reasonable to treat it as a trustworthy

and reliable implementation of the protocol.

This led me to use this RPL implementation in SpeckSim instead of creating a new one. For achieving

this, the natural course of action was to first understand the ContikiRPL code and then port it into

SpeckSim. ContikiRPL was implemented in C using the APIs of the Contiki operating system. (“All

parts of the implementation except for the uIP-specific ICMP message processing are portable to other

operating systems and the routing protocol uses Contiki’s modular IPv6 routing interface.”[3])

The first problem that was encountered was that the ContikiRPL is implemented in C whereas the

SpeckSim simulator was written in Java. However, when I tried to run the ContkiRPL, the most

convenient way was to run it in the Cooja simulator which was also written in Java. This led to the

idea of finding out how the Cooja simulator (Java) called the ContikiRPL code (C) and try to do the

same for the SpeckSim network simulator.

18 Chapter 3. Design and Implementation

The easiest way to get Cooja along with the ContikiRPL implementation is to download a virtual

machine that comes with all installations of Contiki software, development tools which can be

downloaded from [24].

Different examples (“rpl-udp”, “rpl-collect”, “rpl-border-router”) that use ContikiRPL can be found in

the virtual machine in “∼/contiki-2.x/examples/ipv6”. When Cooja starts, it automatically loads some

projects including these examples. Unfortunately, at this stage a problem appears due to the fact that

Cooja is trying to load its project files from a different directory than the one they are stored in. This

can be resolved by copying the needed files into the directory required by Cooja (this is more simple

than changing the required directory in Cooja). When opening the ContikiRPL scenario examples,

Cooja first compiles them. This also raises a problem. In order to not get errors at compilation, some

small changes to the Makefile files for each of the examples are required. These changes consist of

modifying the paths provided in the first line of the Makefile files to match the actual intended

location/path. These problems were encountered after performing the update of the virtual machine in

June 2011. It is possible that later/subsequent updates have solved these problems.

Figure 3.1: Cooja simulator - 1 sink, 6 senders running ContikiRPL

3.1. Attempt to integrate ContikiRPL into SpeckSim 19

Figure 3.2: Cooja simulator - Sensor Map and Network Graph

Figures 3.1 and 3.2 are screenshots of the GUI of the Cooja simulator while running a small scenario

consisting of a7-node topology running the RPL protocol.

After looking through a Cooja tutorial [25] for setting a low-power IPv6/RPL network, I could

observe that when starting a new project and loading sink and sender nodes, the sources for these

types of nodes need to be compiled and created in the simulator. The user interface provides a

“Compile” button that changes into a “Create” button if the source file is compiled successfully. I

have noticed that the files that the simulator loads in order to create sink and sender nodes were the

ones linking the simulator to the ContikiRPL code. Both files were written in C and were calling the

ContikiRPL code. I figured that I should find the answer of how Cooja uses the ContikiRPL code by

understanding how these files are compiled when loaded in the simulator. In order to do this I first

needed to find where the code for Cooja’s “Compile” button is. After analyzing Cooja’s source files, I

noticed that the main method is in the GUI.java file, and afterwards I searched for the “Create” and

“Compile” buttons. The code for these buttons can be found in AbstractCompile.java file. This had the

purpose of finding out how the udp-sink.c and udp-sender.c are compiled and further used in the

simulator.

After researching the ContikiRPL and the Cooja simulator I came to the following conclusions:

In Cooja, the complete firmware of a device is loaded and used within the simulator; there is no

interaction between network layers or device behaviour coded for the Cooja simulator and those coded

for Contiki. The best approach in this case consists in extracting the RPL layer and creating a library

containing it. The library should contain sufficient JNI implementation to make its functions available

to SpeckSim. The approach of using a library developed for a hardware platform requires two


components: one in Java in the simulator and a wrapper in C that provides the JNI calls. The C

wrapper is required to expose the functions of the library without modifying the library itself. The

Java class then maps between the simulator APIs and the exported JNI calls. The issue of storing and

loading fields from, and to, the native library would also need to be addressed. This is handled in

Cooja by storing the entire memory of the mote device (ContikiMote.java). This would not be needed

in the case of SpeckSim.

Even thought comments within the source files of the Cooja network simulator mention the use of JNI

to call C code, I could not find any trace of JNI code in Cooja’s source files (nor in the ContikiRPL’s

.c files). I have noticed that the files that use ContikiRPL (such as udp-sink.c and udp-sender.c from

“∼/contiki-2.x/tools/examples/ipv6/rpl-collect”) which are loaded by the Cooja simulator, are kernel

modules or pieces of code that run in kernel space and are compiled together with the Contiki kernel /

operating system.

A possible solution for achieving this (by using JNI calls) can be seen in the following example:

Consider the 3 layers of behaviour: application, routing (RPL) and MAC. The application and MAC

are implemented in SpeckSim and the network layer would consist of a native compiled RPL (from

Contiki). Sending a packet would have the flow presented as in Figure 3.3:

Figure 3.3: Solution for ContikiRPL in SpeckSim

3.2. RPL 21

Some additional parameters would be required to keep track of which device is active but the

approach is feasible.

However, I chose not to go through with this approach because of the fact that I would end up

spending far too much time to understand and use Contiki rather than the SpeckSim simulator. Also,

the approach is rather complex and complicated, the code is difficult to follow and it requires deep

knowledge of Contiki and the simulator architecture. Even though this is possible to achieve, since the

time to complete this project is quite limited, there was no guarantee of success.

Compilation of the Contiki RPL implementation into a usable native library may also be a problem as

the standard Contiki “build setup” will use cross compilers for microprocessors, although it is

expected to be gcc compliant.

My original reasoning for considering the integration of ContikiRPL into SpeckSim in the first place,

was that I could obtain a validated RPL implementation (the ContikiRPL) in SpeckSim quickly and I

could spend more time in testing the protocol instead of dealing with its implementation. Since this

proved to be far more challenging than expected, and also more time-consuming, I took the decision

not to pursue this goal any further.

3.2. RPL

RPL was developed to be used as a standard routing protocol in WSNs deployed in different

environments such as: ”urban networks, smart grid networks, industrial networks, building and home

networks”[21].

It is optimised for collection networks (which are based on Multipoint-to-Point (MP2P) and Point-to-

Multipoint (P2MP) traffic) with occasional Point-to-Point (P2P) traffic. The collection networks have

multiple nodes which report periodically to few collection/sink nodes, and the sink nodes choose to

communicate with one of the sender nodes in a P2P fashion.

RPL uses MP2P traffic for the process of collecting data (measurements) and uses P2MP traffic for

configuration purposes for the nodes that have to report to the sink nodes.

Starting from a node called Root (a sink node), RPL builds a DODAG (Destination Oriented Directed

Acyclic Graph) with the help of the DIO (DODAG Information Object) messages generated by the

Root. The DODAG has the role of minimising the cost of reaching the Root from any node in the

network. Each node within the DODAG holds a rank value which is calculated based on the

information received in DIO messages. The rank for a node increases as the node is further away from


the Root node. The routing towards the DODAG Root is based on the rank values, as a node roughly

picks the neighbour with the lowest rank when sending a packet up the DODAG.

Nodes within a RPL network exchange information with the purpose of maintaining the DODAG. The

messages that carry DODAG information are called DIO (DODAG Information object) messages.

(“All the nodes in the network regularly issue a DIO which serves as a heartbeat to indicate their rank.

The instant at which a node issues a DIO is governed by a Trickle timer which is reset only when an

inconsistency arises. This way, when the topology is stable, very few DIOs are exchanged.”[22])

RPL uses another type of messages called DAO (Destination Advertisement Object) messages, which

are sent from each node in the network towards the Root node. Based on the information contained in

these messages (a list of node identifiers from all nodes that were traversed by this message), the Root

node knows which nodes a packet has to go through in order to reach a certain destination.

3.2.1. Types of RPL messages

DIO – DODAG Information Object. DIO messages are sent periodically by the Root node and carry

information that allows a node to “discover a RPL Instance, learn its configuration parameters, select

a DODAG parent set, and maintain the DODAG.”[1] The most important information contained by this

message is the DODAG-ID, the Objective Function and the rank of the broadcasting node (which is

used by the receiving node to calculate its own rank). The DIO messages are sent periodically with an

increasing sequence number in order to trigger the parent selection process (described in Section

3.2.2.1. Upward routing) that leads to recalculation of the DODAG that further leads to repairing

existing broken links.

DIS – DODAG Information Solicitation. It is used to trigger a DIO transmission from a RPL node.

This type of message is usually used when nodes join a network. As an alternative to waiting to

receive a DIO message, a node can choose to broadcast a DIS message so that other nodes will

immediately trigger a DIO transmission upon receiving the DIS message.

DAO – Destination Advertisement Object. DAO is used to propagate information up the DODAG

towards the Root node. Depending on the type of routing, DAO messages can be unicast messages

sent from children to selected parents (storing mode) or unicast messages sent to the DODAG Root

(non-storing mode). In either case, the DAO messages follow a common structure. The most

significant information that is carried by a DAO message is the “Reverse Route Stack”. As a DAO

message travels up the DODAG towards the Root node, each node that it passes through appends its

unique ID to this stack.

3.2. RPL 23

DAO ACK – Destination Advertisement Object Acknowledgement. This type of message is

optional and is sent as an unicast message to a DAO recipient which can either be a DAO parent or the

DODAG Root.

CC – Consistency Check. “The CC message is used to check secure message counters and issue

challenge/responses. A CC message MUST be sent as a secured RPL message.”[1]

3.2.2. RPL routing

3.2.2.1. Upward routing

RPL provides routes up the DODAG towards the DODAG Root, constructing a DODAG optimized

according to an Objective Function. This is accomplished by sending DIO messages from the

DODAG Roots down the DODAG, towards the leaf nodes. With the help of these messages the

DODAGs are formed and maintained. A node is able to join a DODAG by discovering neighbours

that are already part of the DODAG of interest and by having built a parent set. The parent set

represents a subset of the candidate neighbour set (which contains the nodes reachable via link-local

multicast). Any neighbour with a lower rank than the node itself is considered as a candidate parent.

From the parent set, a preferred parent is elected which will be used as the next hop for all upward

routes.

This allows for Multipoint-to-Point communication within a WSN that runs RPL.

Regarding the Upward routing process, the construction of the DODAG is of great importance. The

process of the DODAG construction is based on the Distributed Algorithm Operation and it involves

assigning / configuring some nodes as DODAG Roots. These nodes send DIO messages (link-local

multicast) to all the RPL nodes with the intention of advertising their “presence, affiliation with a

DODAG, routing cost and related metrics” [1]. The nodes always listen for DIO messages and they use

the information provided by these messages to join a new DODAG or maintain the existing one.

(“Nodes provision routing table entries, for the destinations specified by the DIO message, via their

DODAG parents in the DODAG Version. Nodes that decide to join a DODAG can provision one or

more DODAG parents as the next-hop for the default route and a number of other external routes for

the associated instance.”[1])

3.2.2.2. Downward routing

RPL can also provide routes down the DODAG in order to reach certain nodes (that are not DODAG

Roots). This is accomplished by sending DAO messages up the DODAG towards the DODAG Root.

Usually downward routes are Point-to-Multipoint routes (from a DODAG Root towards leaf nodes),

but with the help of the DAO messages, Point-to-Point routing can also be supported. (“Point-to-


Point messages can flow toward a DODAG Root (or a common ancestor) through an upward route,

then away from the DODAG Root to a destination through a downward route.”)

The downward routes can be maintained in two modes, called “storing” and “non-storing”. In

“storing” mode, all non-root and non-leaf nodes store a routing table for the downward routes for

their sub-DODAG. The routing tables are formed based on the information provided by the DAO

messages. When a message is travelling on a downward route in “storing” mode, routing decisions

(determine the packet’s next hop) are taken at every hop (based on each hops’ stored routing table).

In “non-storing” mode, the non-root nodes do not have to store a routing table. Only the DODAG

Root has to store and maintain a routing table, thus making it responsible for all downward routing

decisions. The messages are routed down the DODAG based on source routes provided by the

DODAG Root. This mode is highly dependent on the DODAG Root node (if the Root node fails to

store some information, then some of the destinations may not be reached).

Point-to-Point routing can be supported by both “storing” and “non-storing” modes.

3.2.3. Objective Function

The Objective Function (OF) indicates the method that must be used to construct the DODAG. Nodes

become aware of the OF when receiving a DIO message. (“An OF defines how nodes translate one or

more metrics and constraints into a value called Rank, which approximates the node's distance from a

DODAG Root. An OF also defines how nodes select parents.”[1])

RPL supports a variety of Objective Functions, due to its large number of applications. The OF allows

customising the way RPL builds the routing topology based on various metrics and constraints. RPL

was developed offering this flexibility in order to be able to better accommodate networks used by a

wide range of applications.

The Objective Function used by the RPL implementation in the SpeckSim simulator mainly consists

in calculating a node’s rank based on simple hop counting.

3.2.4. Trickle timer

(“The Trickle algorithm allows wireless nodes to exchange information in a highly robust, energy

efficient, simple, and scalable manner. Dynamically adjusting transmission windows allows Trickle to

spread new information on the scale of link-layer transmission times while sending only a few

messages per hour when information does not change. A simple suppression mechanism and

transmission point selection allows Trickle's communication rate to scale logarithmically with

density.”[6])

3.3. SimpleP 25

Trickle is an efficient algorithm for propagating data changes in a network. RPL uses Trickle to

ensure that nodes in a given neighbourhood have consistent, loop-free routes.

The RPL implementation (in SpeckSim) uses the Trickle timer for the DIO transmissions. The

transmission of this type of message is periodic and it is triggered by the Trickle timer. The timer

requires setting two intervals between two DIO transmissions: a minimum interval and a maximum

interval. It starts from the minimum interval and with every transmission it doubles its duration until

it reaches the maximum interval. When the maximum interval is reached, the transmissions will be

maintained at a constant rate (which is dictated by this maximum interval). However, the timer is

forced to reset to the minimum interval with any change that occurs in the DODAG structure.

3.2.5. RPL in SpeckSim

In SpeckSim two different specks were used: one to simulate a RPL Root node (called RPLRoot) and

another that can take the place of both a router node or a leaf node (called RPLRelay). The routing

logic for the RPL protocol was implemented as a behaviour (called RPLRouting).

3.3. SimpleP

SimpleP is a basic routing protocol. It is regardless of the memory possessed by the nodes or the

machine that runs a simulation environment for this protocol.

It sends triggered updates. The trigger is represented by the occurrence of a change in a node’s routing

table. The important fields carried by this message are the source ID, a sequence number and the

sending node’s entire routing table. In order to avoid routing loops, the protocol uses the number of

hops as the main (and single) metric. The routing table stores only one route to a destination, the best

route (the route with the lowest number of hops to the destination).

A routing table entry contains the following fields: “Destination”, “Next Hop” and “Hops”.

“Destination” – holds the ID of the speck that needs to be reached

“Next Hop” – represents the next hop to which packets for the specified destination should

be forwarded

“Hops” – represents the number of hops that the packet has to pass through to get to the

destination

The SimpleP protocol has three versions based on the number of update messages that it sends. Since

the update messages carry a node’s entire routing table, and for a large number of nodes the routing

tables become very large in terms of routing table entries, memory and bandwidth consumption, the

number of such packets exchanged by the nodes of a network is very important.


3.3.1. SimplePv1

The first version of SimpleP, SimplePv1 enables nodes to send an update message every time a

change of a routing table entry occurs. This is the version with the worst performance.

3.3.2. SimplePv2

The second version of the SimpleP protocol provides a slight improvement over the first version.

When a node receives an update message, it makes all the necessary changes (imposed by the received

update) to its routing table before it sends out an update message. These changes could consist in

modifying, adding or deleting several routing entries from the routing table. Since all these changes

are sent out in a single message, it reduces the number of update messages exchanged in the network.

3.3.3. SimplePv3

The third version is an optimized version of the SimplePv2. It uses a timer for delaying the

transmission of an update message for five seconds after a change in the node’s routing table occurs.

In this case, if in those five seconds the node receives other update messages from its neighbours and

its routing table is changed several times, the protocol sends only one update that contains all these

changes.

3.3.4. SimplePv4

3.3.4.1. Bidirectional Links

Since unidirectional links exist in reality and some of the channel models will also have them in

simulation, a small adaptation to the previous versions of SimpleP (because they are building tables

based on reception) consists in only using bidirectional links for routes. An example of how this can

be done is the following: Consider 2 nodes (Node_A and Node_B), if Node_A receives from Node_B,

it does not add Node_B to the routing table but instead sends an acknowledgement to Node_B.

Node_B can then add Node_A to its table. Then Node_B sends an acknowledgement back to Node_A.

When Node_A receives the acknowledgement from Node_B, it adds Node_B to the routing table.

This process is used only for establishing connections between neighbouring nodes (nodes that are in

radio range of each other).

3.3.4.2. Routing Table Maintenance

Another important improvement that SimplePv4 has over its previous version consists in the process

of maintaining the routing tables. This process consists of verifying if the routes learned from a

3.3. SimpleP 27

neighbour still exist in that neighbour’s table. When node A receives an update from node B, it checks

for each destination that has B as the next hop, if B still has a route to that destination in its table. If B

no longer has a route to that destination in its table, then A will also remove it from its table. In this

way, when a broken link occurs, the information can propagate through the network.

3.3.4.3. Dealing with Broken Links

This process is based on the relationship between neighbouring nodes. If a node doesn’t receive any

messages from a neighbour in period of time, it assumes that his neighbour is inactive and it deletes it

from its routing table as well as all the routes that have this neighbour as the next hop. In order to

avoid this from happening, all nodes send periodic “hello” messages. In the current implementation of

SimplePv4, a node has to miss three hello messages from a neighbour in order to consider it inactive.

The protocol also considers the case when two nodes (A and B) establish a neighbour adjacency and

after that, one of the nodes (A) moves and settles in a position so that it can still receive messages

from its neighbour (B), but B cannot receive any messages from A. An illustrative example can be

observed in Figure 3.4.

Figure 3.4: Why NAK is needed

In this case, B will remove A from its routing table, as well as all routes that have the next hop A, but

A will still keep B as a neighbour and all routes through B. The solution for correcting this behaviour

is to make B send a negative acknowledgement (NAK) to A when it erases A from its routing table.

When A receives the NAK, it removes B and every route that had B as a next hop from its routing

table.

In order to achieve all of these improvements in SimplePv4, the protocol no longer sends only one

type of message (the update message), but is also exchanges three other types: Hello messages (send

periodically), ACK messages (used for establishing neighbour adjacencies/connections) and NAK

messages (used for terminating neighbour connectivity).


By having these last features in SimplePv4, the protocol can also be tested in fault tolerance

scenarios along side of RPL.

3.3.5. Limitations of SimpleP

SimpleP does not scale well with the size of the topology due to the following limitations:

Node Memory: As the topology increases in size, the routing tables get larger. In a WSN the nodes

are sensors which are constrained in terms of most resources, especially memory. Thus, every node

storing a routing table containing every other node in the network is not the best approach for a WSN.

Bandwidth consumption: Since SimpleP sends the entire routing table in every update message, for

a large number of nodes in a topology, thus a large routing table, the protocol’s overhead will have a

significant bandwidth usage.

Processing power and time consumption: When a SimpleP node receives an update, it has to

sequentially go through all routes in the table received in the update message. For each route in the

received routing table it has to iterate through its own table to see if it already knows that route, or if it

is a new route. For very large routing tables and a large number of updates received by a node, this

proves to be costly in terms of processing time and processing power.

3.4. DSDV

The routing information is sent in broadcast messages to all neighbours of a node. These messages are

sent periodically once every few seconds (periodic updates) and when topology changes are detected

(immediate or triggered updates).

The broadcast update message contains the following information:

Message Sequence Number (used to identify the update message, each update message

generated by one node contains a new sequence number)

Source Address (the address of the node that is sending the update message)

The Advertised Routing Table (which can be incremental (contains just the routes that

changed since the last full dump update) or full (contains all the routes stored by the node’s

Forwarding Table)) which contains route entries with the following information:

o Destination Address

o Metric = Number of Hops (the number of hops required to reach the destination)

o Route Sequence Number (the sequence number for the route to the specified

destination, as originally stamped by the destination)

3.4. DSDV 29

DSDV uses as a main/primary metric the Route Sequence Number. This helps nodes to distinguish

if a route is more recently generated than another one regarding a specific destination. Even sequence

numbers are assigned to valid routes and odd numbers to the invalid ones. The route with the larger

sequence number is preferred for making forwarding decisions, but not necessarily advertised.

The secondary metric used by DSDV is the number of hops to reach a destination. If a node has to

choose between routes for a destination that have the same sequence number, than the route with the

smaller number of hops is preferred.

When a node adds a route into its forwarding table, it increases the number of hops by 1.

DSDV has four types of update messages: periodic and triggered, full dump and incremental.

Periodic updates are scheduled as can be observed in Figure 3.5.

Figure 3.5: DSDV periodic updates

Periodically, between each full dump update are sent incremental updates (as mentioned earlier,

incremental updates contain just the information that changed since the last full dump update).

Both full dump and incremental updates can be periodic and both can be triggered.

Incremental updates are triggered when significant changes occur. These significant changes

consist in adding a new route in the table or changing the next hop or the metric (= number of hops)

for a destination. Just changing the sequence number for a route in the table (with the next hop and

metric remaining the same) does not count as a significant change, and can wait to be advertised with

the next periodic scheduled update.

Full dump updates are triggered when due to the information that should be sent in an incremental

update, the update packet exceeds the NPDU (Network Protocol Data Unit) size. This usually happens

when movement of nodes becomes frequent.

Broken links. The adjacency between neighbour nodes is maintained through the periodic broadcast

messages. If a node does not receive any broadcasts from one of his neighbours for a defined period of


time, it considers the link with its neighbour to be broken. The process that needs to be followed this

case has three phases:

1. Changing the metric for the route to the inactive neighbour and for all routes that have this

neighbour as the next hop to “∞".

2. Assign a new odd sequence number for these routes

3. Trigger an immediate update

An invalid route can be replaced when an update that contains a route with a greater sequence number

for that destination is received.

All DSDV nodes deal with two types of tables: Forwarding Table and Advertised Table. Figure 3.6

exposes both these tables. The Advertised table is sent in the update messages and it is generated from

the Forwarding Table before an update is sent. The Forwarding Table is used by each node in its

forwarding decisions. All changes caused by information received in update messages are made in this

table (the Forwarding Table).

Figure 3.6: Forwarding and Advertised Tables for node 1 (4-node topology - SpeckSim)

This implementation of DSDV contains a feature not specified in the paper [17]. This was previously

described for the SimplePv4 protocol with the intention of using only bidirectional links for routes.

This small adaptation was implemented because the protocol is building tables based on reception.

(This problem was previously explained in Section 3.3.4.1. Bidirectional Links.) The solution consists

of establishing the neighbour adjacencies by exchanging acknowledgement (ACK) messages and

terminating it by using negative acknowledgement (NAK) messages.

3.4. DSDV 31

3.4.1. DSDV features that differ from SimplePv4

DSDV SimplePv4

1. Type of updates (periodic - triggered)

Periodic update messages

Triggered update messages

Periodic hello messages (to maintain

neighbour adjacencies)

Triggered update messages (triggered by topology changes)

2. Metric

Uses 2 metrics:

Main metric – sequence number

Secondary metric – number of hops

Uses only one metric

Number of hops

3. Type of updates (full dump - incremental)

Incremental updates

Full dump updates

Full dump updates

4. Broken links

Uses something close to poison reverse (when

a node detects a broken link, it keeps the

routes to and through that neighbour with a

changed metric of ∞ and a new odd sequence

number, and then triggers an immediate

update in order to advertise the routes as

invalid)

When a node detects a neighbour as inactive,

it removes the route to and through that

neighbour from its table, and sends an update

message. Neighbours that receive the update

message from this node will perform the

routing table maintenance process as

described previously in Section 3.3.4.2.

Table 3.1: DSDV – SimplePv4 differences

According to [15] [16], DSDV is one of the earliest algorithms available. It provides loop-free routing

and it is suitable for creating small-size ad hoc networks.

The next chapter describes the validation of the RPL implementation in the SpeckSim simulator

against the ContikiRPL implementation.

32

33

Chapter 4

Validation

This chapter describes the validation of the SpeckSim RPL implementation against the ContikiRPL

implementation provided by the Swedish Institute of Computer Science. It presents the validation

scenario, the results that were obtained and the conclusion drawn from them.

4.1. Validation Scenario

The first step in RPL’s evaluation was to validate the implementation in SpeckSim against a correct

RPL implementation before further testing, evaluation and comparisons with other protocols. In this

regard, the ContikiRPL protocol provided in the Cooja simulator was chosen to play the role of a

validation benchmark for RPL.

The ContikiRPL is a trustworthy RPL implementation since it was tested successfully against other

RPL implementations in 2010 at an interoperability event hosted by the IPSO Aliance.

The validation process works as follows: the same network topology (see Figure 4.1), inspired by a

real-life scenario in the deployment of a wireless sensor network in the Informatics Forum, was

implemented in both SpeckSim and Cooja simulators.

In both simulators (SpeckSim and Cooja) will be deployed the same network topology. This topology

is inspired from a real-life scenario, consisting in the deployment of wireless sensors on one of the

floors of the Informatics Forum. This topology is illustrated in Figure 4.1.

It is possible to have this topology design in both SpeckSim and Cooja by limiting the radio ranges of

the nodes so that they can only reach their closest neighbours.

34 Chapter 4. Validation

Figure 4.1: Validation topology – Informatics Forum floor plan – 11 node network deployment

4.1.2. Cooja

The ContikiRPL data was collected after running the protocol on the topology presented in Figure 4.2.

For this topology two sets of data were gathered: one having the RPL Root node placed in the most

favourable position and another having it placed in the most detrimental position. In Figure 4.2 the

Root node (node 11) is placed in the most favourable position because it will form a well balanced

DODAG. The second test has the Root node placed in the most detrimental position which is the

position of node 5. This position is the most detrimental because it leads to forming the most poorly

balanced DODAG that can be formed based on this topology.

Figure 4.2: ContikiRPL Test Topology – The RPL Root in favourable position (Cooja Simulator)

4.2. Validation Results 35

Section A of the Appendix describes how to use the Cooja simulator in order to build RPL

simulations.

4.1.3. SpeckSim

The same topology was built in SpeckSim and can be viewed in Figure 4.3. When the RPL data was

collected, the RPL Root once took the position of node 6 and once the position of node 11, thus

achieving both the most favourable and most detrimental RPL scenarios based on this topology.

Figure 4.3: RPL Validation Topology (SpeckSim)

4.2. Validation Results

In both simulators, for both test cases (the most favourable and most detrimental), the deployed RPL

network was monitored for the first 350 seconds since all nodes started. The results gathered from this

simulation are presented in this section.


4.2.1. Favourable case

4.2.1.1. Number of hops from every node to the RPL Root

Figure 4.4: Hops to DODAG Root

4.2.1.2. RPL DODAG

Figure 4.5: RPL DODAG

4.2.1.3. Protocol Overhead

Figure 4.6: Overhead

Cooja Overhead SpeckSim Overhead

6964 packets / 350 sec 6898 packets / 350 sec

4.2. Validation Results 37

4.2.2. Detrimental case

4.2.2.1. Number of hops from every node to the RPL Root

Figure 4.7: Hops to DODAG Root

4.2.2.2. RPL DODAG

Figure 4.8: RPL DODAG

4.2.2.3. Protocol Overhead

Figure 4.9: Overhead

Cooja Overhead SpeckSim Overhead

9031 packets / 350 sec 6958 packets / 350 sec


The figures presenting the ContikiRPL data were generated by the Cooja Simulator and imported in

this document. The figures presenting the SpeckSim – RPL implementation were separately built

based on the data gathered from the simulator.

4.3. Conclusions

As can be seen in the previous section (4.3. Validation Results), the results obtained for the two

implementations of RPL are fairly similar. When looking over the figures presenting the results, keep

in mind that the nodes’ numbers differ for the topologies used in Cooja and the topologies used in

SpeckSim. Thus, relate the nodes’ numbers with those in the corresponding topology figure.

The number of hops from every node in the topology to the RPL Root is identical for both RPL

implementations. The graphs presented in the left part of figures 4.4 and 4.7 were generated by the

Cooja simulator, and the tables presented in the right part of these figures were created based on data

gathered from the SpeckSim simulator.

The RPL DODAG formed by the Root node is also identical for both RPL implementations. The trees

presented on the left side of figures 4.5 and 4.8 were generated by the Cooja simulator. The trees

presented on the right side of figures 4.5 and 4.8 were built based on the data gathered from the

SpeckSim simulator (each node’s preferred parent and rank value).

The overheads of the two RPL implementations are similar. In the favourable case, the overhead is

almost the same (the difference is less than 1%). For the detrimental case, the overhead for the two

protocol implementations differs slightly, but it does not raise a concern because this can be dependent

on various aspects of the implementation. It is expected that two separate implementations of the same

protocol cannot have identical results, yet they may have similar ones. A 22% difference in the

generated overhead still makes the overhead of the ContikiRPL similar to that of the SpeckSim RPL

implementation (in the detrimental case). It can also be observed that in the detrimental case, both

RPL implementations generate a larger number of control packets compared to the favourable case.

These results conclude the validation of the SpeckSim RPL implementation against the ContikiRPL.

Since the SpeckSim RPL implementation has quite similar behaviour to that of the ContikiRPL when

tested on an 11-node Building topology, it can be considered to be a faithful implementation of the

RPL protocol.

The next chapter describes the testing process and methods used for evaluating the protocols in

wireless sensor networks.

39

Chapter 5

Testing

This chapter provides details about the methods used to test the protocols. It presents the simulation

environment, the topology types used for the different testing scenarios, the metrics used to evaluate

the protocols, the types of network traffic generated and the process of gathering statistics in the

SpeckSim simulator.

5.1. Simulation Environment

The simulation environment is the SpeckSim network simulator. SpeckSim was configured to

simulate a wireless sensor network with the following characteristics:

Environment characteristics:

Specks’ movement: static (not mobile)

Specks’ position: the positions of the specks in the simulator are taken from an external input

file: Movement Models – Current Model – File Position – InputFile.txt. InputFile.txt holds

the coordinates of the specks in the following format: every line represents the coordinates of a speck and it contains three numbers separated by commas “x, y, z” with x, y, z having

values between 0 and 1.

Sensor/Speck characteristics:

Battery: Configurable Capacity Battery (mAh capacity: 1; Voltage: 3)

MCU: Configurable MCU (Active current: 0,005; Sleep current: 0,001; Off current: 0)

MAC Protocol: CarrierSenseMAC (802.15.4 CSMA MAC protocol)

Radio: Selectable Shell Radio (Perfect Radio Shell; Range 0,35)

Power up delay: Min=0, Max=1

Application Start delay: 0

40 Chapter 5. Testing

5.2. Topology types

5.2.1. Random

Many real world applications use random topologies. Such applications use wireless sensors scattered

/ dispersed in certain areas of interest with the intention of collecting data from that environment. A

random topology built in SpeckSim can be observed in Figure 5.1.

Figure 5.1: Random Topology in SpeckSim

5.2.2. Grid

The grid topologies shown in Figure 5.2 ensure that all nodes are within reach (there are no isolated

nodes). Different grid topologies may be observed in Figure 5.2. Even though the topologies differ

based on the number of specks, the distance between any two adjacent specks is 0.2 units.

Figure 5.2: Grid Topologies in SpeckSim

5.2. Topology types 41

5.2.3. Tree

Since RPL is a tree–oriented protocol, testing the protocol on tree-shaped networks represents a

natural step in RPL’s performance evaluation. It is expected that RPL’s performance should be

enhanced on these types of networks compared to the SimpleP and DSDV protocol. Several tree-

shaped topologies built in SpeckSim can be seen in Figure 5.3.

Figure 5.3: Tree Topologies in SpeckSim

5.2.4. Building

Building topologies are meant to simulate real-life deployments of wireless sensor networks. As a test

example, the floor structure of the Informatics Forum was chosen. As can be observed in Figure 5.4, it

is supposed that the corridor (suggested by the thick green line) is equipped with wireless sensors

placed at approximately equal distance between them. It is possible to simulate this scenario in

SpeckSim by limiting the radio ranges of the nodes so that each node can reach just its closest

neighbours. This is suggested by the purple circles which represent the radio ranges of the nodes. The

topology built in SpeckSim can be observed in the right part of the figure. The distance between the

nodes is of approximately 0.1 units and the nodes’ ranges are 0.11 units.

All topologies/scenarios except the Building ones were tested with the speck configurations described

in section 5.1. Simulation Environment. In this case (Building scenarios), the radio ranges of the nodes

had to be altered in order to better accommodate these topologies.


Figure 5.4: Building Topology (Informatics Forum – 24 nodes) in SpeckSim

The protocols were tested on two topologies that are based on the Informatics Forum floor plan. These

topologies differ in terms of the number of nodes. The topology in Figure 5.5 has only 11 nodes. In

reality, if one were to equip the corridor of a floor in the Informatics Forum with wireless sensors, the

deployment in Figure 5.5 would have been chosen due to cost considerations. Thus the deployment

presented in Figure 5.5 is closer to reality than the one in Figure 5.4. However, the topology

presented in Figure 5.4 was used for the purpose of testing the protocols on a denser network in order

to reveal if their behaviour or performance change when the number of nodes of the network is

increased.

Figure 5.5: Building Topology (Informatics Forum – 11 nodes) in SpeckSim

For both Building topologies (presented in Figures 5.4 and 5.5) the RPL protocol was tested when the

Root node was placed once in the most favourable position and once in the most detrimental one. The

most favourable position to place the Root node is where it allows the forming of the most balanced

DODAG. The most detrimental position is one in which the DODAG formed by the Root is as poorly

balanced as possible.

For the topology in Figure 5.4, the most favourable position for placing the RPL Root is the position

of node 12, and the most detrimental one is the position of node 24. For the topology in Figure 5.5,

the most favourable position for placing the RPL Root is the position of node 6, and the most

detrimental one is the position of node 11.

5.3. Metrics 43

5.3. Metrics

Choosing the metrics for evaluation is important and should be relevant to routing in wireless sensor

networks.

5.3.1. Protocol Overhead

This metric is measured in terms of the number of control packets exchanged by the nodes in the

topology. Since WSNs are highly constrained networks, a high protocol overhead could place a heavy

burden on the network. This being the case, the protocol overhead is an important metric to be taken

into account when evaluating a routing protocol for WSNs.

5.3.2. Size of table

Since nodes of WSNs are constrained in terms of memory, a metric that measures the memory usage

per node should be taken into account. And since the routing table is the most memory consuming,

this metric is solely based on the size of the routing table.

5.3.3. Coverage

The purpose of a routing protocol is to find a route (or multiple routes) from a source to a destination.

The Coverage metric indicates the percentage of the total devices/nodes in the network that each node

can reach. This metric is calculated based on the number of routes towards different destinations in the

network that each node has in its routing table.

5.3.4. Delivery Ratio / Packet loss

The Delivery Ratio is defined as the ratio between the number of packets received by a destination

node and the number of packets sent to this destination by a source node.

The routing layer does not really discard a packet; it just tries to find a route. Thus, if a packet has not

turned up at the destination, it means that it probably got dropped in the MAC layer. However,

Delivery Ratio is an important metric often used for the routing process as well and it can be further

used in Fault Tolerance scenarios to reveal the packet loss from the time a nodes fails to the time the

network reconverges.


5.3.5. Latency

This metric is of great concern to almost any types of networks, not just WSNs. In some WSNs (even

Smart Building Networks) applications, immediate/fast responsiveness of the network is quite crucial.

An example is the Emergency management scenario described in section 2.5.1.2.

The latency in the Routing layer is different from the latency in the MAC layer. In the case of the

MAC layer, it can be calculated as an average latency per hop, but the latency in the Routing layer has

to be calculated over several hops. The Routing layer latency is strongly related to the number of hops

a packet has to go through and the time that a node takes to search its routing table (which is based on

the size of the table).

5.3.6. Power consumption

Since wireless sensors are power constrained as they run on batteries, the power consumption is one

of the most important concerns. The power consumption is primary related to the radio transceiver

(especially when it is used to transmit packets, not so much when it just receives) and then to the CPU

when it is used intensively. Thus, the power consumption is significantly dependent on the routing

protocol:

Its overhead affects the power consumption by using the transceiver to send control packets (Update/Hello/ACK/NAK packets)

Its processing of the information received in Update packets (for maintaining the routing

tables) affects the power consumption by intensively using the CPU

5.3.7. Fault tolerance (Recovery time)

In the case when there are at least two paths towards a destination, a routing protocol chooses the best

one with respect to the metrics that it uses. But, if a node on the chosen path fails, the failure needs to

be detected and propagated throughout the network. This will lead to choosing an alternative path

towards that destination if one exists. A suggestive example is presented in Figure 5.6. In Phase 1,

node 1 can reach node 4 through both node 2 and 3, but the best path is chosen through 2. In Phase 2

node 2 becomes inactive (fails), so node 1 needs to detect this. Phase 3 reveals that node 1 terminated

the link to node 2 (it changes from green to blue), and established a route towards node 4 through

node 3.

5.3. Metrics 45

Figure 5.6: Fault Toleration Example

A system is fault tolerant if it continues to operate properly in the event of failure of some of its

components. Thus, in terms of computer/sensor networks, it is useful to know the number of packets

that are lost due to the occurrence of a device failure before the network reconverges (detects the

failure, propagates the information through the network and tries to find other routes between the

nodes that were initially linked by the failed device).

Faults occur frequently in WSNs, and for various real-life WSNs applications it is required an

evaluation that reveals to what extent the deployed networks are fault tolerant. An example of such

applications is presented in section 2.5.1.2. Emergency management.

For the purpose of simulating failure scenarios, the SpeckSim simulator allows the user to force a

node (or an area consisting of several nodes) to fail. This is possible by accessing the following

menus:

Environments – Failure

o Cause ID failure

o Cause area failure

5.3.8. Route propagation time within the network

The route propagation time is the time it takes for a change in the network topology to propagate so

that each of the network’s nodes will be informed about this change. An example that might be used

for testing is introducing a new node in the network, and measuring the time that passes until all the


other nodes in the network learn about its presence. However, in RPL’s case it is measured the time

that passes until the information about the new node reaches the Root node.

Unfortunately, there wasn’t enough time allocated to the project to measure this metric, therefore the

evaluation based on this metric is left for future work.

5.3.9. Implementation complexity

It is useful to know the implementation complexity of a routing protocol if it is desired to run it on

specific devices, thus requiring to port the protocol or to implement it from scratch on a different

platform. This metric is measured in terms of lines of code and since the protocols are implemented in

Java, it is also measured in terms of the number of classes used.

5.3.10. CPU load

Since wireless sensors are constrained in both energy and processing power, it is useful to know how

big is the burden that the routing protocol places on the sensors’ CPU.

However, as in the case of the route propagation time metric, this metric has also been left for future

work.

5.4. Types of Traffic

There are three possible types of traffic:

MP2P (MultiPoint-to-Point)

P2MP (Point-to-MultiPoint)

P2P (Point-to-Point / Peer-to-Peer)

o The full test of Peer-to-Peer traffic pattern consists in any random node trying to

send to every other node.

The evaluation of the protocols focuses more on Peer-to-Peer traffic.

5.5. Statistics gathering process in SpeckSim

In order to gather statistics in SpeckSim two classes are needed: a State class and Statistics class.

5.5. Statistics gathering process in SpeckSim 47

The State class takes the state of the simulator which is a snapshot with everything at that instant.

Everything (a speck, a protocol, a behaviour) has a getState method. The state of the simulator is all

handled and done automatically; it’s only needed to specify in the state the information of interest.

The Statistics class is used to calculate and generate statistics based on the information gathered in the

state object.

For gathering statistics for the routing protocols, RoutingState.java and RoutingStatisticsModule.java

were implemented in SpeckSim. These classes are used for calculating and generating the following

statistics:

1. The number of data packets sent by all nodes in the topology

2. The number of data packets received at their destinations

3. The delivery ratio

4. The coverage percentage (how many nodes can it reach of the total nodes in the network) for

each node in the topology

5. The average size of the routing table for each node in the topology

6. The average latency for a data packet transmission

For measuring other features such as Power Consumption and the number of control packets

generated by the protocol, the simulator already had the necessary Statistics classes implemented.

In order to use the State and Statistics classes, the user has to go through the following menus of the

SpeckSim GUI: Interface – Generate Data – X Variable (Variable name: Time; Start value: 0; Data

points: 40; Stop value: 200; Output name: <a_name>).

The mentioned settings lead to 40 different measures to be taken within the first 200 seconds since the

nodes are activated and the protocol is started. These settings were used for almost all test scenarios.

For processing the gathered data and generate the statistics: Interface – Data post process:

Dataset search root: select the file <a_name>

Active processes – Add element: Statistics

Run processes

This will generate a .csv file containing the desired statistics.

The next chapter presents the results obtained, along with a short discussion, for each of the metrics

tested. It concludes with an overall interpretation of the results in the form of a protocol comparison

48

49

Chapter 6

Results and Critical Analysis

This chapter presents the results gathered from running the tests described in Chapter 5, and is

structured on the metrics that are evaluated for each of the protocols. After presenting the results and

their interpretation for each metric, the chapter concludes with an evaluation of all the tested

protocols.

6.1. Protocol Overhead

The protocol overhead is measured in terms of the number of control packets exchanged by a network

running the protocol for a period of time.

The statistics regarding the overhead of the protocols were gathered from grid topologies of different

sizes. The sizes of the topologies, measured in terms of the number of nodes, were 8, 27 and 64 nodes.

Based on these statistics, graphs were constructed to reveal the flow of the network overhead over

time and the size of the overhead for each of the protocols tested.

6.1.1. Control packet generation flow

6.1.1.1. Protocol Overhead Flow

The following graphs (Figures 6.1 - 6.6) show how the overhead of the protocols over time scales

with the size of the network.

50 Chapter 6. Results and Critical Analysis

Figure 6.1: RPL Overhead

Figure 6.2: DSDV Overhead

Figure 6.3: SimplePv1 Overhead

Figure 6.4: SimplePv2 Overhead

Figure 6.5: SimplePv3 Overhead Figure 6.6: SimplePv4 Overhead

0

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6.1. Protocol Overhead 51

From the graphs presented in this section, it can be seen that RPL and the SimplePv4 protocol have a

similar behaviour. This consists of a high overhead in the initial stage due to the fact that the nodes

exchange a large number of control messages in order for the network to converge. After the network

converges, the nodes still exchange control messages between them, but the number of messages

exchanged in the network is significantly reduced. However, SimplePv4 has a higher overhead than

RPL after the network converged because it sends periodical keep-alive messages (Hello messages).

SimplePv1, SimplePv2 and SimplePv3 have similar behaviours amongst themselves, but they do not

compare with RPL and SimplePv4 because even if they generate a high initial overhead, after the

networks converged SimplePv1-v3 nodes stop sending any control packets. These protocols send only

update messages triggered by the occurrence of topology changes.

DSDV’s behaviour is different from all of the other protocols’. DSDV has constant high control

packet burst intervals due to the generation of new sequence numbers assigned to the routes. These

new sequence numbers are periodically advertised in incremental and full dump updates.

6.1.1.2. Overhead Flow Comparison

The graph presented in Figure 6.7 exposes the overhead flow for all the six tested protocols. Based on

this graph it is possible to observe how the behaviour of each protocol (in terms of the overhead flow)

relates to the others. In order for this graph to be more comprehensive, a logarithmic scale was used

for the “No. of Control Packets” axis.

Figure 6.7: Overhead Flow

50

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6.2.2. Overhead Size

Figure 6.8: Overhead Size

Based on the graph presented in Figure 6.8 it is possible to compare the size of the overhead for each

of tested protocols. Based on these results, RPL’s performance, in terms of the size of the generated

overhead, is better than most of the other protocols. For a better exposure of the overhead sizes, this

graph also uses a logarithmic scale for the “No. of Control Packets” axis.

6.2. Size of Table

6.2.1. Number of Entries

The number of entries in the routing table is important because when a node needs to forward a

packet, it performs a sequential search over all the entries in its routing table until it finds the route for

the desired destination. If the route is not found, the node either uses a default route if one is available,

or discards the packet. In a very large network where the nodes have very large routing tables, when a

node receives many packets that it needs to forward, it will have to perform the sequential search for

every packet. This will create a delay in the forwarding process thus affecting the network’s latency.

Also, it will place a significant burden on the nodes’ CPU.

The statistics presented in Figure 6.9 are gathered from all protocols tested on the 11-node Building

(Informatics Forum) topology.

10

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6.2. Size of Table 53

Figure 6.9: Table Size (Number of Entries)

It can be observed that RPL in storing mode has a considerable lower average table size (measured in

terms of the number of entries of the table) than the other protocols, even for this small 11-node

topology. This is due to the fact that the size of the routing table for RPL nodes decreases towards the

leaf nodes, because each node stores routes only towards the nodes that are below it in the DODAG.

RPL in non-storing mode has a very low average table size due to the fact that only the Root node

stores routes towards the other nodes in the topology. This saves up a lot of memory for all the nodes

in the topology except the Root node, but will lead to an increase in the network’s latency because all

packets will have to be routed through the Root node.

The following graphs (Figures 6.10 – 6.13) reveal how the average table size increases with the

number of nodes in the network for each of the protocols, in the different types of topologies.

Figure 6.10: Table Size – Building Figure 6.11: Table Size – Grid

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Figure 6.12: Table Size – Random Figure 6.13: Table Size – Tree

6.2.1.5. Results Analysis

In all these cases, RPL using non-storing routing has an average table size of approximately one

entry, because only the Root node stores entries to all other node in the network. The other nodes will

just store the next hop (DODAG parent) and the path metrics values for each DODAG parent.

Throughout all the Table Size graphs presented in this section, it can be observed that the size of the

routing table for the DSDV protocol is slightly bigger than the table sizes in the case of the four

versions of the SimpleP protocol. This is because every DSDV node stores an additional route

compared to SimpleP nodes. This additional route is a route to itself with the metric zero. (It has as

destination and next hop the current node and the metric (number of hops to the destination) zero.)

6.2.2. Memory Usage

The size of the table in bits of memory can be determined based on the number of entries in the table.

The number of entries is linear with the size of the table, thus it is possible to relate the size of the

table with the number of entries it has, only if the sizes of the entries are the same.

Unfortunately, the protocols evaluated in this study have entries of different sizes (except the versions

of SimpleP which have the same routing table entries). Thus for each entry, the size of each element it

contains is estimated in order to calculate the full size of the entry. For the values assigned below, it is

assumed that an integer value is stored in 32 bits.

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R_27 nodes R_64 nodes R_125 nodes

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T_21 nodes T_91 nodes

6.2. Size of Table 55

SimplePv1-v4 routing table entry contains 3 elements:

1. Destination Address (IPv6) – 128 bits

2. Next Hop Address (IPv6) – 128 bits

3. Number of hops to the destination – 32 bits

SimplePv1-v4 routing table size = 288 bits

DSDV forwarding table entry contains 5 elements:

1. Destination Address (IPv6) – 128 bits

2. Next Hop Address (IPv6) – 128 bits

3. Metric (= number of hops) – 32 bits

4. Sequence Number

a. Sequence Number – 32 bits

b. Creator of the sequence number – 32 bits

DSDV forwarding table size = 320 bits

Each RPL node stores the following information:

1. Next Hop (DODAG parent; IPv6 Address) – 128 bits

2. Path metrics values for each DODAG parent – 32 bits (the size of this field can change for

different topologies; it depends on the number of parents of a node; for the topology used

(Building topology – Informatics Forum – 11 nodes), each node has only one parent)

Size of the information stored by all nodes = 160 bits

And RPL storing nodes store a DAO routing table entry that contains the following elements:

1. IPv6 Address – 128 bits

2. Retry Counter – 32 bits

3. Logical equivalent of DAO Content:

a. DAO-Sequence – 32 bits

b. Path Sequence – 104 bits (this has a variable size, but for the topology used, the value was calculated based on the height of the DODAG)

c. DAO Lifetime -32 bits

d. DAO Path Control – 32 bits

4. Destination Prefix (or Address or Multicast Group – IPv6 Address) – 128 bits

DAO routing table entry size = 488 bits

Figure 6.14 presents the memory usage by taking into account the size of the routing tables for each

of the tested protocols, calculated based on the number of entries and the values calculated previously.


Figure 6.14: Memory Usage – 11-node Building Topology (Informatics Forum)

According to the results displayed in Figure 6.14, even though RPL nodes store fewer entries in their

routing tables compared to the other protocols, the difference in memory usage is not that significant

because RPL stores more information for each entry. However, with an increase in the number of

nodes, the other protocols will have a significant increase of entries in their tables compared to RPL.

Thus, the difference between RPL’s memory usage and that of the other protocols will become more

evident.

6.3. Coverage

A node’s coverage of the network is strongly related to the size of the table in terms of the number of

route entries. The results presented in this section were gathered from different types and sizes of

topologies.

Figure 6.15: Coverage – 11-node Building Topology (Informatics Forum)

0500

1000150020002500300035004000

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SimplePv1

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6.3. Coverage 57

The graphs presented in Figures 6.16 – 6.19 reveal how the average coverage of each protocol varies

for different types and sizes of topologies.

Figure 6.16: Coverage – Building

Figure 6.17: Coverage – Grid

Figure 6.18: Coverage – Random

Figure 6.19: Coverage – Tree

The results suggest that a better coverage is obtained for networks with a larger number of nodes.

RPL’s average coverage in non-storing mode is given by the following formula:

𝑅𝑃𝐿_𝑐𝑜𝑣𝑒𝑟𝑎𝑔𝑒 =1

Topology_Size(Number_Of_Nodes)

This means that RPL’s average coverage in non-storing mode decreases with the size of the topology.

In all the cases, DSDV appears to have a higher coverage than the other protocols.

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6.4. Delivery Ratio

In order to test the delivery ratio of the protocols, the test code generates a stream of data packets.

Thus, after the network converged, each node in the topology was configured to send one data packet

to every other node in the topology.

The graphs in Figures 6.20 – 6.23 presenting the delivery ratio reveal that the delivery ratio is

unrelated to the routing protocol and strongly related to the MAC protocol. For each of the protocols,

all graphs show a decrease in the delivery ratio with an increase of the number of nodes in the

network, even though the average network coverage percentage is high (above 80%). This is due to

MAC issues that lead to a high rate of collisions at the MAC Layer. However, the drop in the delivery

ratio is not completely related to the MAC layer. It is also related to the interference caused by the

large number of packet transmissions from different sources.

Figure 6.20: Delivery Ratio – Building Figure 6.21: Delivery Ratio – Grid

Figure 6.22: Delivery Ratio – Random Figure 6.23: Delivery Ratio – Tree

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6.5. Latency 59

6.5. Latency

The latency was measured on the 11-node Building Topology. The amount of traffic generated was

the same as in the case of measuring the delivery ratio (after the network converged, each node sends

a data packet to every other node in the network).

Figure 6.24: Latency – 11-node Building Topology (Informatics Forum)

The graph in Figure 6.24 reveals that RPL has a higher latency than the other protocols. This is due to

the fact that RPL is a hybrid protocol; it does not store full routing tables (i.e. that every RPL node

does not store a route to every other node in the topology). In the case of RPL there is a trade-off

between the memory that the protocol uses and the latency of the network. It is interesting to test the

latency metric for RPL because the Root node has very good routes to every other node, but non-root

nodes may have very bad routes to the other nodes depending on their position in the tree. (A bad

route is considered a route that will have a large number of hops for its advertised destination.)

The latency for the DSDV and SimplePv1-v4 protocols is about the same due to the fact that every

one of these protocols stores full/complete routing tables (having a route for all the other nodes in the

topology).

6.6. Power Consumption

The simulation scenario for evaluating the power consumption consisted of using the same type of

specks, each with the same type of battery, and measuring the average time it takes for the specks to

run out of power while running, in turns, each of the routing protocols. The scenario consisted of a 64-

node grid topology, with the specks equipped with batteries of capacity 1mAh and a voltage of 3V.

00.010.020.030.040.050.060.07

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RPL_Storing_F

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SimplePv4


The power consumption was calculated from the current consumption and the voltage drawn by the

node. The current consumption was determined from the battery’s capacity and the lifetime of the

battery.

Figure 6.25: Power Consumption

6.7. Fault Tolerance

6.7.1. Scenario

The fault tolerance scenario used for the evaluation of the protocols is the one presented in Chapter 5,

section 5.3.7. Fault tolerance (Recovery time). The topology used for this scenario is very small and

simple, and can be seen in Figure 6.26.

Figure 6.26: Fault Tolerance Topology (SpeckSim)

Both node 1 and 2 are in range with node 3 and 4, but they are not in range with each other. Thus,

when the simulation starts, node 1 establishes a route to node 2 through node 3. Then, node 1 starts

generating traffic towards node 2. It sends one packet per second. While node 1 is constantly sending

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6.7. Fault Tolerance 61

packets to node 2, node 3 fails. Node 1 still forwards packets to node 3 with the destination node 2

until it detects node 3’s failure. When this happens, it stops sending packets and tries to find another

route to 2. Thus, the route through node 4 is established, and then node 1 continues to send packets.

All packets transmitted after node 3 failed and until node 1 detected the failure are lost. Since the

packets were sent at a constant rate of 1 packet / second, the packet loss also represents the time

(measured in seconds) that it took the network to reconverge.

This time and the packet loss will increase with the distance (in terms of number of hops) between the

sending node and the node that fails. This is due to the additional delay necessary for the information

regarding the failure to propagate through the network in order to reach the sending node so it will

stop transmitting.

6.7.2. Results

The results for the fault toleration scenario are presented in Figure 6.27.

Figure 6.27: Fault Tolerance

SimplePv1, SimplePv2 and SimplePv3 were not designed to cope with the occurrence of failures, thus

they were not included in this test.

According to the IETF Draft for RPL, by contrast with other routing protocols, “RPL does not define

any 'keep-alive' mechanisms to detect routing adjacency failures: this is because in many cases such a

mechanism would be too expensive in terms of bandwidth and even more importantly energy (a

battery operated device could not afford to send periodic Keep alive). Still RPL requires an external

mechanism to detect that a neighbour is no longer reachable. Such a mechanism should preferably be

reactive to traffic in order to minimize the overhead to maintain the routing adjacency and focus on

links that are actually being used.”[1] The current RPL implementation available in SpeckSim is able

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to detect when a node fails, but the DODAG is not automatically readjusted. Thus, the SpeckSim RPL

implementation is not fault tolerant.

The only two protocols which are able to detect and handle failures successfully are DSDV and

SimplePv4. The results for them are presented in Figure 6.27, and can be observed that DSDV

performs better than SimplePv4 for this scenario because it has a lower packet loss. This is probably

due to the following reasons:

1. SimplePv4 exchanges two ACK messages before establishing a neighbour relationship

between two nodes. Thus, it takes longer for SimplePv4 to form an adjacency between two

nodes than for DSDV.

2. The time in which a node does not receive messages from a neighbour and still keeps the

connection active is different for DSDV and SimplePv4.

6.8. Implementation Complexity

The implementation complexity was measured based on the number of lines of code of the source files

and the number of classes used. In order to measure the number of lines of code, I have made a Linux

script that for a list of input files counts the number of lines of code, ignoring the java comments and

empty lines. The script can be found in section B. NoComments Script of the Appendix.

Figure 6.28: Complexity (Lines of Code)

Figure 6.29: Complexity (Number of Classes)

Figure 6.28 presents the complexity of the protocols in terms of lines of code and Figure 6.29

presents the complexity in terms of the number of classes. It can be observed that RPL is by far the

most complex, followed by DSDV and SimplePv4.

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6.9. Overall Results Interpretation 63

6.9. Overall Results Interpretation

Table 6.1 presents a summary of the protocols’ performance with respect to every metric that was

used for their evaluation. The delivery ratio metric was not included in the table because it was

concluded based on the obtained results that it is not related to the routing layer, but more to the MAC

layer.

The table highlights the protocol with the best (green colour) and worst (red colour) performance for

each metric.

In RPL’s case, the values presented in the table for the Table Size, Coverage and Latency metrics,

represent an average value of the protocol’s results using storing and non-storing routing with the

Root node placed in the most favourable position as well as in the most detrimental position in the

network.

No Metric Protocol


1 Overhead Size

(125node Topology) 900 54578 9972 1996 337 2558

2 Avg. Table Size

(11node Topology) 1.9 11 10 10 9.7 10

3 Avg. Coverage

(11node Topology) 19.3 100 100 100 92.7 100

4 Avg. Latency

(11node Topology) 0.041 0.026 0.025 0.025 0.022 0.025

5 Power Consumption

(64node Topology) 61 33.75 30 30 30 32.72

6 Fault Tolerance

(4node Topology) - 36 - - - 48

7 Implementation

complexity 1406 823 362 360 378 621

Table 6.1: Protocols – Metrics Summary

For different types of WSN applications, some of the metrics may be more important than the others.

For most WSN applications, the Overhead Size, Table Size, Fault Tolerance and Power Consumption

are of great importance.

All protocols, except RPL, are limited for use only in small to medium-sized networks due to the

table size. DSDV and SimplePv1-v4 will have full coverage up until the nodes run out of memory. In

their case, the table size grows linearly with the size of the network measured as the number of nodes

in the network. Thus, they cannot be used in networks that lead to routing table sizes larger than the

available memory of each node.


The evaluation of the average coverage for the DSDV and SimplePv1-v4 protocols was done in an

ideal case in which the specks that were running the protocols were not constrained in terms of

memory. This is the reason why the average coverage of these protocols turned up to be this high.

As for RPL, it has 100% coverage across the RPL instance because there should always be a route

through the tree. However, the average coverage of all the RPL nodes is significantly lower compared

to the results of the other protocols that were tested.

DSDV and SimplePv1-v4 protocols do not scale with the size of the network for the following

reasons:

The table size, measured in terms of the number of bytes, which affects the coverage of the

protocols

The table size, measured in terms of the number of entries in the table, which affects the

latency and CPU power

With this taken into account, the RPL protocol is the only protocol (from the ones that this project

tested) suitable for use in networks with a large number of nodes.

The next chapter presents the final remarks, conclusions and observations, along with suggestions for

future work.

65

Chapter 7

Conclusions and Further Work

This chapter summarises the conclusions drawn based on the results presented in Chapter 6 and ends

with some suggestions for future work.

7.1. Conclusions

This dissertation has presented the implementation, characterisation and comparison of a selection of

routing protocols for wireless sensor networks in terms of protocol behaviour and performances. Its

focus has been on the RPL routing protocol which is the proposed IETF candidate for a standard

routing protocol in wireless sensor networks.

Based on the results obtained in the previous chapter it was argued that RPL is the only protocol from

the ones that were tested which is suitable for use in networks with a large number of nodes (over 200

nodes). The other protocols (DSDV, SimplePv1, SimplePv2, SimplePv3 and SimplePv4) are not

scalable with the size of the network due to their table sizes which grow linearly with the number of

nodes in the topology.

When considering small to medium-sized networks (less than 100 nodes), RPL’s performance does

not outclass that of the other protocols that were tested. Table 2 in Chapter 6 reveals that each

protocol has its strengths. Thus when choosing a protocol for a wireless sensor network deployment,

the decision should be based on the specific requirements of the WSN application, in order to choose

the protocol with the most suitable qualities for the application. For instance, in the case of the

“Emergency management” scenario described in Section 2.5.1.2, low latency and fault tolerance are

the most important qualities that the routing protocol should posses. Therefore, DSDV or SimplePv4

would be the appropriate protocols to use in this scenario (if the size of the network does not cause the

routing tables to exceed the nodes’ memory).

This thesis tested the mentioned protocols’ behaviour and performance only in the case of static

wireless sensor networks. The RPL protocol is targeted for use in static networks. However as the

66 Chapter 7. Conclusions and Further Work

need for mobile wireless sensor networks increases, it may be appropriate for the chosen standard

routing protocol for wireless sensor networks to be able to cope with both static and mobile networks.

In this regard, a very recent paper [26] claims to provide a study of an extension which allows RPL to

handle the presence of nodes’ mobility and also an implementation of a mobility-aware RPL version.

If this will be proven to satisfy the needs of both static and mobile wireless sensor networks

applications, it will make RPL a complete protocol for use in wireless sensor networks.

From all protocols evaluated in this thesis, only DSDV and SimplePv4 may be suitable to run in

mobile networks. However, in order for the DSDV protocol to adequately run in real-life wireless

sensor networks, the protocol has to support a feature that is not included in its original specification.

This feature involves establishing only bidirectional links between DSDV neighbours, due to the fact

that real-life radio shells are not perfect spheres.

The aim of this thesis was to evaluate the behaviour and performance of the RPL protocol and

compare it to other protocols suitable for use in wireless sensor networks. The project managed to

achieve its goal by simulating the protocols in the SpeckSim simulator in various WSN scenarios and

by providing valid analysis and interpretation for the data that was collected.

7.2. Future work

This section provides a list of the possible extensions to the work done as part of this thesis.

Possible extensions to this project may include:

1. Testing and evaluating the protocols with respect to other metrics such as CPU load and

route propagation time in the network, as mentioned in Chapter 6.

2. Implement other MANET protocols relevant to compare to RPL such as AODV and DSR,

and evaluate RPL against them for statics scenarios.

3. Evaluate the performance of the protocols regarding the delivery ratio metric using

different MAC protocols in order to reveal which is the most suitable MAC protocol.

4. Re-evaluate the comparison of the overhead size of the protocols considering the different

types and sizes of the control packets exchanged by the protocols (not only the number of packets).

5. Reconsider the integration of ContikiRPL into the SpeckSim simulator, due to the fact that

the current RPL implementation provided in SpeckSim is not fault tolerant. As an alternative

to this approach, the current SpeckSim RPL implementation could be improved to become

fault tolerant.

6. Use real devices (sensors) to deploy a small-size real-life wireless sensor network in order

to validate the results collected so far from the simulator. This can be done by deploying the

same scenario both in reality and in the simulator, and then observing the extent of the

results’ similarity. It can be assumed that if for small topology scenarios the results are

considerably similar, then this similarity might scale with increases in number of nodes.

67

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70

71

Appendix

A. How to use the Cooja Simulator

How to start the Cooja Simulator

1. Open an Ubuntu console/terminal

2. Go to the following path: “ ~/contiki-2.x/tools/cooja ”

3. Execute the “ant run” command

Once the simulator has started, it is possible to create a new simulation by accessing the File menu.To

add nodes into the simulation: Mote – Add mote of type (and choose a mote type).

Create and add RPL nodes in the simulation

Access the upper-left menu bar: Mote Types – Create mote type – Sky Mote Type. This will trigger

the appearance of a pop-up menu. In the “Contiki process / Firmware” field of the pop-up menu, it has

to be chosen either the “udp-sender.c” file (for creating a RPL router or leaf) or “udp-sink.c” (for

creating a RPL Root) file from the “rpl-collect” project that can be found at the following path

“~/contiki-2.x/examples/ipv6/rpl-collect”. After selecting one of these files, it needs to be compiled by

using the “Compile” button, and if the compilation is successful, a “Create” button will appear that

can be used to import the desired type of speck into the simulator. The next step is to choose the

number of the selected type of sensors and the type of positioning and the position interval of the

sensors.

B. NoComments Script

This script was used to eliminate the Java comments and the empty lines from the source code files.

#!/bin/bash

TOTAL=$((0))

for file in $@

do

LINES=`cat ${file} | grep -v ^$ | grep -v /\*.*?\*/ | grep -v

^[[:blank:]]*'\*'.*$ | wc -l`

echo ${file}: ${LINES}

TOTAL=$((${TOTAL}+${LINES}))

done

echo

echo "Total lines: "${TOTAL}

Documents

Performance Evaluation of Routing Protocols in Lossy Links ... · Routing Information Protocol Open Shortest Path First Enhanced Interior Gateway Routing Protocol ... selection of