Chapter 4

Routing ProtocolsRouting Protocols

1

Overview

� Routing in WSNs is challenging due to the inherent

characteristics that distinguish these networks from other

wireless networks like mobile ad hoc networks or cellular

networks.

� First, due to the relatively large number of sensor nodes, it is not possible

to build a global addressing scheme for the deployment of a large number

of sensor nodes. Thus, traditional IP-based protocols may not be applied of sensor nodes. Thus, traditional IP-based protocols may not be applied

to WSNs. In WSNs, sometimes getting the data is more important than

knowing the IDs of which nodes sent the data.

� Second, in contrast to typical communication networks, almost all

applications of sensor networks require the flow of sensed data from

multiple sources to a particular BS.

� Third, sensor nodes are tightly constrained in terms of energy, processing,

and storage capacities. Thus, they require careful resource management.

2

Overview (cont.)

� Fourth, in most application scenarios, nodes in WSNs are generally

stationary after deployment except for, may be, a few mobile nodes.

� Fifth, sensor networks are application specific, i.e., design requirements

of a sensor network change with application.

� Sixth, position awareness of sensor nodes is important since data

collection is normally based on the location.

� Finally, data collected by many sensors in WSNs is typically based on � Finally, data collected by many sensors in WSNs is typically based on

common phenomena, hence there is a high probability that this data has

some redundancy.

3

Overview (cont.)

� These routing mechanisms have taken into consideration the

inherent features of WSNs along with the application and

architecture requirements. The task of finding and maintaining

routes in WSNs is nontrivial since energy restrictions and

sudden changes in node status (e.g., failure) cause frequent and

unpredictable topological changes.unpredictable topological changes.

� To minimize energy consumption, routing techniques proposed

for WSNs employ some well-known routing tactics as well as

tactics special to WSNs, e.g., data aggregation and in-network

processing, clustering, different node role assignment, and

data-centric methods were employed.

4

Outline

� 4.1 Routing Challenges and Design Issues in WSNs

� 4.2 Flat Routing

� 4.3 Hierarchical Routing

� 4.4 Location Based Routing

� 4.5 QoS Based Routing

� 4.6 Data Aggregation and Convergecast � 4.6 Data Aggregation and Convergecast

� 4.7 Data Centric Networking

� 4.8 ZigBee

� 4.9 Conclusions

5

Chapter 4.1

Routing Challenges and Design Routing Challenges and Design

Issues in WSNs

6

Overview

� The design of routing protocols in WSNs is influenced by

many challenging factors. These factors must be overcome

before efficient communication can be achieved in WSNs.

� Node deployment

� Energy considerations

� Data delivery model

Node/link heterogeneity� Node/link heterogeneity

� Fault tolerance

� Scalability

� Network dynamics

� Transmission media

� Connectivity

� Coverage

� Data aggregation/convergecast

� Quality of service7

Node Deployment

� Node deployment in WSNs is application dependent and

affects the performance of the routing protocol.

� The deployment can be either deterministic or randomized.

� In deterministic deployment, the sensors are manually placed

and data is routed through pre-determined paths.

� In random node deployment, the sensor nodes are scattered � In random node deployment, the sensor nodes are scattered

randomly creating an infrastructure in an ad hoc manner. If the

resultant distribution of nodes is not uniform, optimal

clustering becomes necessary to allow connectivity and enable

energy efficient network operation.

8

Energy Considerations

� Sensor nodes can use up their limited supply of energy

performing computations and transmitting information in a

wireless environment. Energy conserving forms of

communication and computation are essential.

� Sensor node lifetime shows a strong dependence on the battery

lifetime. In a multihop WSN, each node plays a dual role as lifetime. In a multihop WSN, each node plays a dual role as

data sender and data router. The malfunctioning of some sensor

nodes due to power failure can cause significant topological

changes and might require rerouting of packets and

reorganization of the network.

9

Data Delivery Model

� Time-driven (continuous)

� Suitable for applications that require periodic data monitoring

� Event-driven

� React immediately to sudden and drastic changes

� Query-driven

� Respond to a query generated by the BS or another node in the network

� Hybrid

� The routing protocol is highly influenced by the data reporting method in terms of energy consumption and route stability.

10

Node/Link Heterogeneity

� Depending on the application, a sensor node can have a

different role or capability.

� The existence of a heterogeneous set of sensors raises many

technical issues related to data routing.

� Even data reading and reporting can be generated from these

sensors at different rates, subject to diverse QoS constraints, sensors at different rates, subject to diverse QoS constraints,

and can follow multiple data reporting models.

11

Fault Tolerance

� Some sensor nodes may fail or be blocked due to lack of power,

physical damage, or environmental interference.

� It may require actively adjusting transmit powers and signaling

rates on the existing links to reduce energy consumption, or

rerouting packets through regions of the network where more

energy is available.energy is available.

12

Scalability

� The number of sensor nodes deployed in the sensing area may

be on the order of hundreds or thousands, or more.

� Any routing scheme must be able to work with this huge

number of sensor nodes.

� In addition, sensor network routing protocols should be

scalable enough to respond to events in the environment.scalable enough to respond to events in the environment.

13

Network Dynamics

� Routing messages from or to moving nodes is more

challenging since route and topology stability become

important issues.

� Moreover, the phenomenon can be mobile (e.g., a target

detection/ tracking application).

� On the other hand, sensing fixed events allows the network to � On the other hand, sensing fixed events allows the network to

work in a reactive mode while dynamic events in most

applications require periodic reporting to the BS.

14

Transmission Media

� The traditional problems associated with a wireless channel

may also affect the operation of the sensor network.

� In general, the required bandwidth of sensor data will be low,

on the order of 1-100 kb/s. Related to the transmission media is

the design of MAC.

� TDMA (time-division multiple access)� TDMA (time-division multiple access)

� CSMA (carrier sense multiple access)

15

Connectivity

� High node density in sensor networks precludes them from

being completely isolated from each other.

� However, may not prevent the network topology from being

variable and the network size from shrinking due to sensor

node failures.

� In addition, connectivity depends on the possibly random � In addition, connectivity depends on the possibly random

distribution of nodes.

16

Coverage

� In WSNs, each sensor node obtains a certain view of the

environment.

� A given sensor’s view of the environment is limited in both

range and accuracy.

� It can only cover a limited physical area of the environment.

17

Data Aggregation/Convergecast

� Since sensor nodes may generate significant redundant data,

similar packets from multiple nodes can be aggregated to

reduce the number of transmissions.

� Data aggregation is the combination of data from different

sources according to a certain aggregation function.

� Convergecasting is collecting information “upwards” from the � Convergecasting is collecting information “upwards” from the

spanning tree after a broadcast.

18

Quality of Service

� In many applications, conservation of energy, which is directly

related to network lifetime.

� As energy is depleted, the network may be required to reduce

the quality of results in order to reduce energy dissipation in

the nodes and hence lengthen the total network lifetime.

19

Routing Protocols in WSNs: A taxonomy

Network Structure Protocol Operation

Flat routing• SPIN

• Directed Diffusion (DD)

Negotiation based routing• SPIN

Multi-path network routing

Routing protocols in WSNs

20

• Directed Diffusion (DD)

Hierarchical routing• LEACH

• PEGASIS

• TTDD

Location based routing• GEAR

• GPSR

Multi-path network routing• DD

Query based routing• DD, Data centric routing

QoS based routing• TBP, SPEED, MERR

Coherent based routing• DD

Aggregation• Data Mules, CTCCAP

Reference

� J. N. Al-Karaki and A. E. Kamal, “Routing techniques in

wireless sensor networks: a survey,” IEEE Wireless

Communications, vol. 11, no. 6, pp. 6-28, Dec. 2004.

21

Chapter 4.2

Flat RoutingFlat Routing

22

Overview

� In flat network, each node typically plays the same role and

sensor nodes collaborate together to perform the sensing task.

� Due to the large number of such nodes, it is not feasible to

assign a global identifier to each node. This consideration has

led to data centric routing, where the BS sends queries to

certain regions and waits for data from the sensors located in certain regions and waits for data from the sensors located in

the selected regions. Since data is being requested through

queries, attribute-based naming is necessary to specify the

properties of data.

� Prior works on data centric routing, e.g., SPIN and directed

diffusion, were shown to save energy through data negotiation

and elimination of redundant.

23

4.2.1

SPINSPINSensor Protocols for Information via Negotiation

24

SPINMotivation

� Sensor Protocols for Information via Negotiation, SPIN

� a Negotiation-Based Protocols for Disseminating Information in Wireless

Sensor Networks.

� Dissemination is the process of distributing individual sensor

observations to the whole network, treating all sensors as sink

nodesnodes

� Replicate complete view of the environment

� Enhance fault tolerance

� Broadcast critical piece of information

25

SPIN (cont.) Motivation

� Flooding is the classic approach for dissemination

� Source node sends data to all neighbors

� Receiving node stores and sends data to all its neighbors

� Disseminate data quickly

� Deficiencies

� Implosion

� Overlap

� Resource blindness

26

SPIN (cont.) Implosion

A

CB

x x

Node

The direction

of data sending

The connect

between nodes

CB

D

x x

27

SPIN (cont.) Overlap

q

r

s

Node

(q,r) (s,r)

Node

The direction

of data sending

The connect

between nodesThe searching

range of the

node

A B

C

28

SPIN (cont.) Resource blindness

� In flooding, nodes do not modify their activities based on the

amount of energy available to them.

� A network of embedded sensors can be resource-aware and

adapt its communication and computation to the state of its

energy resource.energy resource.

29

SPIN (cont.) Sensor Protocols for Information via Negotiation

� Negotiation

� Before transmitting data, nodes negotiate with each other to overcome

implosion and overlap

� Only useful information will be transferred

� Observed data must be described by meta-data

Resource adaptation� Resource adaptation

� Each sensor node has resource manager

� Applications probe manager before transmitting or processing data

� Sensors may reduce certain activities when energy is low

30

SPIN (cont.) Meta-Data

� Completely describe the data

� Must be smaller than the actual data for SPIN to be beneficial

� If you need to distinguish pieces of data, their meta-data should differ

� Meta-Data is application specific

� Sensors may use their geographic location or unique node IDSensors may use their geographic location or unique node ID

� Camera sensor may use coordinate and orientation

31

SPIN (cont.) SPIN family

� Protocols of the SPIN family

� SPIN-PP

� It is designed for a point to point communication, i.e., hop-by-hop routing

� SPIN-EC

� It works similar to SPIN-PP, but, with an energy heuristic added to it

� SPIN-BC

� It is designed for broadcast channels

� SPIN-RL

� When a channel is lossy, a protocol called SPIN-RL is used where adjustments

are added to th SPIN-PP protocol to account for the lossy channel.

32

SPIN (cont.) Three-stage handshake protocol

� SPIN-PP: A three-stage handshake protocol for point-to-point

media

� ADV – data advertisement

� Node that has data to share can advertise this by transmitting an ADV with

meta-data attached

� REQ – request for data

� Node sends a request when it wishes to receive some actual data

� DATA – data message

� Contain actual sensor data with a meta-data header

� Usually much bigger than ADV or REQ messages

33

SPIN (cont.)

B

C

F

REQ

ADV

ADV

REQ

REQ

34

A

B

D

E

data

ADV

REQ

ADV

ADVREQ

REQ

REQ

data

datadatadatadata

SPIN (cont.) SPIN-EC (energy-conserve)

� Add simple energy-conservation heuristic to SPIN-PP

� SPIN-EC: SPIN-PP with a low-energy threshold

� Incorporate low-energy-threshold

� Works as SPIN-PP when energy level is high

� Reduce participation of node when approaching low-energy-

thresholdthreshold

� When node receives data, it only initiates protocol if it can participate in

all three stages with all neighbor nodes

� When node receives advertisement, it does not request the data

� Node still exhausts energy below threshold by receiving ADV

or REQ messages

35

SPIN (cont.) Conclusion

� SPIN protocols hold the promise of achieving high

performance at a low cost in terms of complexity, energy,

computation, and communication

36

SPIN (cont.) Reference

� J. Kulik, W.R. Heinzelman and H. Balakrishnan, “Negotiation-

based protocols for disseminating information in wireless

sensor networks,” Wireless Networks, Vol. 8, pp. 169-185, 2002.

37

4.2.2

Directed DiffusionDirected DiffusionA Scalable and Robust Communication Paradigm

for Sensor Networks

38

Directed Diffusion

� Properties of Sensor Networks

� Data-centric routing

� No central authority

� Resource constrained

� Nodes are tied to physical locations

� Nodes may not know the topology� Nodes may not know the topology

� Nodes may fail or move arbitrarily

39

Directed Diffusion (cont.)

� Directed Diffusion is an important milestone in the data centric routing research of sensor networks

� Data centric

� Individual nodes are unimportant

� Request driven

� The sinks requests data by broadcasting interests

� Sources satisfying the interest can be found

� Intermediate nodes route data toward sinks

� Localized repair and reinforcement

� Multi-path delivery for multiple sources, sinks, and queries

40

Directed Diffusion (cont.)

� Sinks broadcast interest to neighbors

� Initially specify a low data rate just to find sources for minimal energy

consumptions

� Interests are cached by neighbors

� Gradients are set up pointing back to where interests came

from from

� Once a source receives an interest, it routes measurements

along gradients

41

Directed Diffusion (cont.)

� Gradients from Source to Sink are initially small

� Increased during reinforcement

Source

Event Event

Source

Event

Sink

Interest propagation

Source Source

Sink

Initial gradients set up

Source

Sink

Data delivery along re-

inforced path

42

Interest Propagation

� Flood interest

� Constrained or Directional flooding based on location is possible

� Directional propagation based on previously cached data

Gradient

Source

Sink

Interest

Gradient

Event

43

Data Propagation

� Multipath routing

� Consider each gradient’s link quality

GradientEvent

Source

Sink

Interest

GradientEvent

44

Reinforcement

� Reinforce one of the neighbor after receiving initial data.

� Neighbor who consistently performs better than others

� Neighbor from whom most events received

GradientEvent

Source

Sink

Interest

GradientEvent

45

Negative Reinforcement

� Explicitly degrade the path by re-sending interest with lower data rate

� Time out: Without periodic reinforcement, a gradient will be torn down

GradientEvent

Source

Sink

Interest

GradientEvent

46

Design Considerations

� Design Space for Diffusion

47

Conclusions

� Directed Diffusion provides a data-centric communication

protocol between sink and sources.

� Directed Diffusion has some novel features - data-centric

dissemination, reinforcement-based adaptation to the

empirically best path, and in-network data aggregation and

caching.caching.

48

Reference

� C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed

Diffusion: A Scalable and Robust Communication Paradigm

for Sensor Networks,”in the Proceedings of the Sixth Annual

International Conference on Mobile Computing and Networks

(MobiCom’00), August 2000.

49

Chapter 4.3

Hierarchical RoutingHierarchical Routing

50

Overview

� In a hierarchical architecture, higher energy nodes can be used

to process and send the information while low energy nodes

can be used to perform the sensing in the proximity of the

target.

� Hierarchical routing is mainly two-layer routing where one

layer is used to select cluster heads and the other layer is used layer is used to select cluster heads and the other layer is used

for routing.

� Hierarchical routing (or cluster-based routing), e.g., LEACH,

PEGASIS, TTDD, is an efficient way to lower energy

consumption within a cluster and by performing data

aggregation and fusion in order to decrease the number of

transmitted messages to the base stations.

51

4.3.1

LEACHLEACHLow-Energy Adaptive Clustering Hierarchy

52

LEACH

� LEACH (Low-Energy Adaptive Clustering Hierarchy), a

clustering-based protocol that minimizes energy dissipation in

sensor networks.

� LEACH outperforms classical clustering algorithms by using

adaptive clusters and rotating cluster-heads, allowing the

energy requirements of the system to be distributed among all energy requirements of the system to be distributed among all

the sensors.

� LEACH is able to perform local computation in each cluster to

reduce the amount of data that must be transmitted to the base

station.

� LEACH uses a TDMA/CDMA MAC to reduce inter-cluster

and intra-cluster collisions.

53

LEACH (cont.)

� Sensors elect themselves to be local cluster-heads at any given

time with a certain probability. These cluster-head nodes

broadcast their status to the other sensors in the network.

� Each sensor node determines to which cluster it wants to

belong by choosing the cluster-head that requires the minimum

communication energy.communication energy.

� Once all the nodes are organized into clusters, each cluster-head creates a schedule for the nodes in its cluster.

� Being a cluster-head drains the battery of that node. In order to

spread this energy usage over multiple nodes, the cluster-head

nodes are not fixed; rather, this position is self-elected at

different time intervals.

54

LEACH Architecture

55

Dynamic Cluster

cluster-head nodes = C at time t1 cluster-head nodes = C’ at time t1 + d

All nodes marked with a given symbol belong to the same cluster, and

the cluster head nodes are marked with a

56

Algorithm Details

� Two main phases

� Set-up phase

� the clusters are organized and cluster heads are selected

� Steady-state phase

� the data transfers to the BS (Base Station)

57

Algorithm Details (cont.)

� Set-up phase

� Node n choosing a random number m between 0 and 1

� If m < T(n) for node n, the node becomes a cluster-head where

1 [ * mod(1 / )]( )

Pif n G

P r PT n

∈

−=

� where P = the desired percentage of cluster heads (e.g., P= 0.05), r=the

current round, and G is the set of nodes that have not been cluster-heads

in the last 1/P rounds. Using this threshold, each node will be a cluster-

head at some point within 1/P rounds. During round 0 (r=0), each node

has a probability P of becoming a cluster-head.

1 [ * mod(1 / )]( )

0 ,

P r PT n

otherw ise

−=

58

Algorithm Details (cont.)

� Set-up phase

� Cluster heads assign a TDMA schedule for their members

where each node is assigned a time slot when it can transmit.

� Each cluster communications using different CDMA codes to

reduce interference from nodes belonging to other clusters.

59

Algorithm Details (cont.)

� Steady-state phase

� All source nodes send their data to their cluster heads

� Cluster heads perform data aggregation/fusion through local transmission

� Cluster heads send them back to the BS using a single direct transmission

60

Conclusion

� Advantages

� Increases the lifetime of the network

� Even drain of energy

� Disadvantages

� Highly dynamic environments

� Nodes use single-hop communication� Nodes use single-hop communication

61

Reference

� W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient

communication protocol for wireless sensor networks”, Proceedings of the

33rd Hawaii International Conference on System Sciences, January 2000.

62

4.3.2

PEGASIS PEGASIS Power-Efficient Gathering in Sensor Information

Systems

63

PEGASIS

� Power-Efficient Gathering in Sensor Information Systems

(PEGASIS) is a near optimal chain-based protocol.

� In order to extend network lifetime, nodes need only communicate with

their closest neighbors and they take turns in communicating with the

base station.

� When the round of all nodes communicating with the base station ends, a

new round will start and so on.new round will start and so on.

� This reduces the power required to transmit data per round as the power

draining is spread uniformly over all nodes.

� Two main objectives for PEGASIS

� Increase the lifetime of each node by using collaborative techniques and

as a result the network lifetime will be increased.

� Allow only local coordination between nodes that are close together so

that the bandwidth consumed in communication is reduced.

64

Main Procedures

� PEGASIS assumes that each sensor node can be able to communicate with the BS directly. It also assumes that all nodes maintain a complete database about the location of all other nodes in the network.

� Greedy Algorithm to Construct Chain

� To construct the chain, start with the furthest node from the BS

� Add to chain closest neighbor to this node that has not been visited� Add to chain closest neighbor to this node that has not been visited

� Repeat until all nodes have been added to chain

� Node i (mod N) will take turns as the leader in round i, (N

represents the number of nodes), and then the leader transmits

data to BS.

65

Main Procedures (cont.)

C0

C3

C1

C2

BS

Chain construction using the greedy algorithm

66

Main Procedures (cont.)

� When a node dies, the chain is reconstructed in the same manner to bypass the dead node

� For gathering data in each round, each node receives data from one neighbor, fuses with its own data and transmits to the other neighbor on the chain

� In a given round, we can use a simple control token passing approach initiated by the leader to start the data transmission from the ends of the chain

67

Main Procedures (cont.)

token token

c2 is a leader

Token passing approach

68

Main Procedures (cont.)

� PEGASIS performs data fusion at every node except the end

nodes in the chain

� Each node will fuse its neighbor’s data with its own to generate

a single packet of the same length and then transmit to its other

neighborneighbor

69

Main Procedures (cont.)

token token

c2 is leaderOperational flow

70

PEGASIS Architecture

Base Station

End node

Leader

End node

71

Conclusion

� Advantages

� Minimizing the total sum of transmission distances

� Increase the lifetime of each node

� Disadvantages

� The single leader can cause higher delay

� Uneven drain of energy� Uneven drain of energy

� An extension to PEGASIS, called Hierarchical-PEGASIS was

introduced with the objective of decreasing the delay incurred

for packets during transmission to the BS.

72

References

� S. Lindsey and C. Raghavendra, “PEGASIS: Power-Efficient Gathering in

Sensor Information Systems,” IEEE Aerospace Conference, Vol. 3, pp. 3-

1125 to 3-1130, Big Sky, MT, USA, 9-16 Mar. 2002.

� S. Lindsey, C. Raghavendra, and K. Sivalingam, “Data gathering in sensor

networks using the energy*delay metric,” the IPDPS Workshop on Issues in

Wireless Networks and Mobile Computing, 2001.

73

4.3.3

TTDDTTDDTwo-Tier Data Dissemination

74

Basic Design

� Each sensor node is aware of its own location.

� Once a stimulus appears, the sensors surrounding it collectively

process the signal and one of them becomes the source to

generate data reports.

� Sinks (users) query the network to collect sensed data. It can be

multiple.multiple.

� In addition, TTDD design assumes that the sensor nodes are

aware of their missions.

� TTDD design uses a grid structure so that only sensors located

at grid points need to acquire the forwarding information.

75

Basic Design (cont.)

� The data source proactively builds a grid structure throughout

the sensor field and sets up the forwarding information at the

sensors closest to grid points.

� With this grid structure in place, a query from a sink traverses

two tiers to reach a source.

� The lower tier is within the local grid square of the sink's� The lower tier is within the local grid square of the sink's

current location (called cells), and the higher tier is made of the

dissemination nodes at grid points

� The sink floods its query within a cell, when the nearest

dissemination node for the requested data receives the query, it

forwards the query to its upstream dissemination node toward

the source.

76

Basic Design (cont.)

� Grid Lifetime

� A source includes a Grid Lifetime in the data announcement message

when sending it out to build the grid.

� If the lifetime elapses and the dissemination nodes on the grid do not

receive any further data announcements to update the lifetime, they clear

their states and the grid no longer exists.

77

Grid Construction

Source

Dissemination Node

78

Query and Data Forwarding

Source

Dissemination Node

Sink

Immediate Dissemination Node

79

Multiple Sinks

Source

Dissemination Node

Sink

Immediate Dissemination Node

Sink2

80

Multiple Sinks (cont.)

Source

Dissemination Node

Sink

Immediate Dissemination Node

Sink2

81

Trajectory Forwarding

Source

Dissemination Node

SinkPrimary agent (PA)

Immediate agent (IA)

82

Trajectory Forwarding (cont.)

Source

Dissemination Node

Sink

Immediate Dissemination Node

Primary agent (PA)

Primary agent (PA)

83

Grid Maintenance

Source

Dissemination Node

84

Grid Maintenance (cont.)

Source

Dissemination Node

85

Conclusion

� TTDD can enable efficient data dissemination in large-scale

wireless sensor networks with sink mobility.

� Instead of passively waiting for queries from sinks, TTDD

exploits the property of sensors being stationary and location-

aware to let each data source build and maintain a grid

structure in an efficient way.structure in an efficient way.

� Sources proactively propagates the existence information of

sensing data globally over the grid structure, so that each sink's

query flooding is confined within a local gird cell only.

� Queries are forwarded upstream to data sources along specific

grid branches, pulling sensing data downstream toward sink.

� TTDD is a good way to building an infrastructure in stationary

sensor networks.86

Reference

� F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang, “A Two-

Tier Data Dissemination Model for Large-scale Wireless Sensor Networks,”

in Proceedings of the ACM/IEEE 6th International Conference on Mobile

Computing and Networking (MobiCom’02), 2002.

87

Chapter 4.4

Location Based RoutingLocation Based Routing

88

Overview

� Sensor nodes are addressed by means of their locations.

� The distance between neighboring nodes can be estimated on the basis of

incoming signal strengths.

� Relative coordinates of neighboring nodes can be obtained by

exchanging such information between neighbors.

� To save energy, some location based schemes demand that

nodes should go to sleep if there is no activity.

� More energy savings can be obtained by having as many

sleeping nodes in the network as possible.

� Hereby, two important location based routing protocols, GEAR

and GPSR, are introduced.

� Geographical and Energy Aware Routing (GEAR)

� Greedy Perimeter Stateless Routing (GPSR)

89

4.4.1

GEARGEARGeographical and Energy Aware Routing

90

Geographical and Energy Aware Routing (GEAR)

� The protocol, called Geographic and Energy Aware Routing

(GEAR), uses energy aware and geographically-informed

neighbor selection heuristics to route a packet towards the

destination region.

� The key idea is to restrict the number of interests in directed

diffusion by only considering a certain region rather than diffusion by only considering a certain region rather than

sending the interests to the whole network. By doing this,

GEAR can conserve more energy than directed diffusion.

� The basic concept comprises of two main parts

� Route packets towards a target region through geographical and energy

aware neighbor selection

� Disseminate the packet within the region

91

Energy Aware Neighbor Computation

� Each node N maintains state h(N, R) which is called learned cost to region R, where R is the target region

� Each node infrequently updates neighbor of its cost

� When a node wants to send a packet, it checks the learned cost to that region of all its neighbors

� If the learned cost of a neighbor to a region is not available, the � If the learned cost of a neighbor to a region is not available, the

estimated cost is computed as follows:

c(Ni, R) = αd(Ni, R) + (1-α)e(Ni)

where

α = tunable weight, from 0 to 1.

d(Ni, R) = normalized distance of neighbor to region

e(Ni) = normalized consumed energy at node i

92

Energy Aware Neighbor Computation (cont.)

� When a node wants to forward a packet to a destination, it

checks to see if it has any neighbor closer to destination than

itself

� In case of multiple choices it aims to minimize the learned cost

h(Ni, R)

� It then sets its own cost to:� It then sets its own cost to:

h(N, R) = h(Ni, R) + c(N, Ni)

c(N, Ni) = combination of remaining energy of N and Ni and the

distance between them

93

Forwarding Around Holes

F G H I J

K L T

C – T = 2B – T =

x5

5A B C D E

S

h(C,T) = h(B,T)+c(C,B)

94

α is set to 1. Initially, at time 0, at node S, among all neighbors of S, B, C, D

are closer to T than S. h(B,T)=c(B,T)= , h(C,T)=c(C,T)=2, h(D,T)=c(D,T)= .5 5

Recursive Geographic Forwarding

� Once the target region is reached, the packets are disseminated within the region by recursive geographic forwarding

� Forwarding stops when a node is the only one in a sub-region

95

Ni

Recursive Geographic Forwarding (cont.) Pathologies

� Inefficient Transmission

� Recursive geographic forwarding vs. Restricted flooding

ARecursive Geographic

Forwarding 3 times for sending

and 3 times for receiving =

Restricted flooding 1 times for

sending and 4 times for receiving

= consuming

5 units of energy

F

E B

C

D

and 3 times for receiving =

consuming 6 units of energy5 units of energy

96

Recursive Geographic Forwarding (cont.) Pathologies

� Non-Termination

� When network density is low compared to (sub) target region size

K

C

B

F

L

A

E H

97

Recursive Geographic Forwarding (cont.) Proposed solution for pathologies

� Node degree is used as a criteria to differentiate low density

networks from high density ones

� Choice of restricted flooding over recursive geographic

forwarding is made accordingly

98

Conclusion

� GEAR strategy attempts to balance energy consumption and

thereby increase network lifetime

� GEAR performs better in terms of connectivity after initial

partition

99

References� Y. Yu, D. Estrin, and R. Govindan, “Geographical and Energy-Aware Routing:

A Recursive Data Dissemination Protocol for Wireless Sensor Networks”, UCLA Computer Science Department Technical Report, UCLA-CSD TR-01-0023, May 2001.

� Nirupama Bulusu, John Heidemann, and Deborah Estrin. “Gps-less low cost outdoor localization for very small devices”. IEEE Personal Communications Magazine, 7(5):28–34, October 2000.

� L. Girod and D. Estrin. “Robust range estimation using acoustic and multimodal sensing”. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2001), Maui, Hawaii, October 2001.

� Nissanka B. Priyantha, Anit Chakraborty, and Hari Balakrishnan. “The cricket location-support system”. In Proc. ACM Mobicom, Boston, MA, 2000.

� Andreas Savvides, Chih-Chieh Han, and Mani B. Strivastava. “Dynamic fine-grained localization in adhoc networks of sensors”. In Proc. ACM Mobicom, 2001.

100

4.4.2

GPSRGPSRGreedy Perimeter Stateless Routing

101

Greedy Perimeter Stateless Routing (GPSR)

� Greedy Perimeter Stateless Routing (GPSR) proposes the

aggressive use of geography to achieve scalability

� GEAR was compared to a similar non-energy-aware routing

protocol GPSR, which is one of the earlier works in geographic

routing that uses planar graphs to solve the problem of holes

� In case of GPSR, the packets follow the perimeter of the planar � In case of GPSR, the packets follow the perimeter of the planar

graph to find their route.

� Although the GPSR approach reduces the number of states a

node should keep, it has been designed for general mobile ad

hoc networks and requires a location service to map locations

and node identifiers.

102

Algorithm & Example

� The algorithm consists of two methods:

greedy forwarding + perimeter forwarding

� Greedy forwarding, which is used wherever possible, and

perimeter forwarding, which is used in the regions greedy

forwarding cannot beforwarding cannot be

103

Greedy Forwarding (cont.)

� Under GPSR, packets are marked by their originator with their destinations’ locations

� As a result, a forwarding node can make a locally optimal, greedy choice in choosing a packet’s next hop

� Specifically, if a node knows its radio neighbors’ positions, the locally optimal choice of next hop is the neighbor geographically closest to the packet’s destinationgeographically closest to the packet’s destination

� Forwarding in this regime follows successively closer geographic hops, until the destination is reached

104

Greedy Forwarding (cont.)

D

x

y

105

Greedy Forwarding (cont.)

� A simple beaconing algorithm provides all nodes with their

neighbors’ positions: periodically, each node transmits a beacon

to the broadcast MAC address, containing only its own

identifier (e.g., IP address) and position

� Position is encoded as two four-byte floating point quantities,

for x and y coordinate valuesfor x and y coordinate values

� Algorithm jittered each beacon’s transmission by 50% of the interval B between beacons, such that the mean inter-beacon transmission interval is B, uniformly distributed in [0.5B, 1.5B]

� Upon not receiving a beacon from a neighbor for longer than timeout interval T, a GPSR router assumes that the neighbor has failed or gone out-of-range, and deletes the neighbor from its table

106

Greedy Forwarding (cont.) The Problem of Greedy Forwarding

D

v z

|xD|<|wD|and|yD|x will not choose to forward to w or y using greedy

x

w y

v z using greedy forwarding

void

xx

107

The Right-Hand Rule: Perimeters

� Mapping perimeters by sending packets on tours of them, using

the right-hand rule. The state accumulated in these packets is

cached by nodes, which recover from local maxima in greedy

forwarding by routing to a node on a cached perimeter closer to

the destination

� This approach requires a heuristic, the no-crossing heuristic, to � This approach requires a heuristic, the no-crossing heuristic, to

force the right-hand rule to find perimeters that enclose voids

in regions where edges of the graph cross

108

x

y

z

Planarized Graphs

� While the no-crossing heuristic empirically finds the vast

majority of routes in randomly generated networks, it is

unacceptable for a routing algorithm persistently to fail to find

a route to a reachable node in a static, unchanging network

topology

� Motivated by the insufficiency of the no-crossing heuristic, we � Motivated by the insufficiency of the no-crossing heuristic, we

present alternative methods for eliminating crossing links from

the network

109

Planarized Graphs (cont.) Relative Neighborhood Graph (RNG)

u vw

110

Planarized Graphs (cont.) Gabriel Graph (GG)

u v

w

111

Planarized Graphs (cont.)

Gabriel Graph (GG)

Relative Neighborhood Graph (RNG)

Original

112

Combining Greedy and Planar Perimeters

� All data packets are marked initially at their originators as greedy mode

� GPSR packet headers include a flag field indicating whether the packet is in greedy mode or perimeter mode

� Packet sources also include the geographic location of the destination in packets

� Only a packet’s source sets the location destination field, it is � Only a packet’s source sets the location destination field, it is left unchanged as the packet is forwarded through the network

� Upon receiving a greedy-mode packet for forwarding, a node

searches its neighbor table for the neighbor geographically

closest to the packet’s destination

� When no neighbor is closer, the node marks the packet into

perimeter mode

113

Combining Greedy and Planar Perimeters (cont.)

� GPSR packet header fields used in perimeter mode forwarding

114

Combining Greedy and Planar Perimeters (cont.)

D

Lp

Lf

e0

xIf forwarding node to D < Lp to D, returns a packet to greedy mode

115

Conclusion

� GPSR generates routing protocol traffic in a quantity independent of the length of the routes through the network

� GPSR generates a constant, low volume of routing protocol messages as mobility increases

� GPSR doesn’t suffer from decreased robustness in finding routes

116

References� B. Karp and H. T. Kung, “Greedy Perimeter Stateless Routing for

Wireless Networks”, Proc. 6th Annual ACM/IEEE Int'l. Conf. Mobile Comp. Net., Boston, MA, pp. 243-54, August 2000.

� G. G. Finn, “Routing and addressing problems in large metropolitan-scale internetworks”, Tech. Rep. ISI/RR-87-180, Information Sciences Institute, March 1987.

� S. Floyd and V. Jacoboson, “The synchronization of periodic routing messages”, IEEE/ACM Transactions on Networking, Vol. 2, pp. 122-136, April 1994.messages”, IEEE/ACM Transactions on Networking, Vol. 2, pp. 122-136, April 1994.

� B. Karp “Greedy perimeter state routing”, Invited Seminar at the USC/Information Sciences Institute, July 1998.

� J. Saltzer, D. P. Reed, and D. Clark, “End-to-end arguments in system design”, ACM Transactions on Computer Systems, Vol. 2, No. 4, Pages: 277-288, November 1984.

117

Chapter 4.5

QoS Based RoutingQoS Based Routing

118

Overview

� In QoS-based routing protocols, the network has to balance

between energy consumption and data quality.

� In particular, the network has to satisfy certain QoS metrics,

e.g., delay, energy, bandwidth, etc. when delivering data to the

BS.

119

Outline

� 4.5.1 TBP (QoS of Bandwidth)

� Ticket-Based Probing

� 4.5.2 SPEED (QoS of Transmission time)

� A Stateless Protocol for Real-Time Communication

� 4.5.3 MERR (QoS of Energy)4.5.3 MERR (QoS of Energy)

� Minimum Energy Relay Routing

120

4.5.1

TBP (Ticket-Based Probing)TBP (Ticket-Based Probing)

QoS of Bandwidth

121

Ticket-Based Probing

� There are numerous paths from source to destination, we shall

not randomly pick several paths to search

� We shall not use any flooding path-discovery approaches,

which may send routing messages to the entire network

� On the other hand, the flooding algorithms can handle

information imprecision but have prohibitively high overheadinformation imprecision but have prohibitively high overhead

� We want to make an intelligent hop-by-hop path selection to

guide the search along the best candidate paths

122

Ticket-Based Probing (cont.)

S

D

123

Ticket-Based Probing (cont.)

� A ticket is the permission to search one path. The source node issues a number of tickets based on the available state information

� More tickets are issued for the connections with tighter requirements

� Probes (routing messages) are sent from the source toward the destination to search for a low-cost path that satisfies the QoSrequirementdestination to search for a low-cost path that satisfies the QoSrequirement

� Each probe is required to carry at least one ticket

124

Ticket-Based Probing (cont.)

S

i

Dj

k

125

Ticket-Based Probing (cont.)

SD

A

C

3 3

3 2x

Demand = 3

B

C

E

3

32

2

2

6

5

x

126

Ticket-Based Probing (cont.)

SD

A

C

3 3

3 2

Demand = 4(1-1,3) (1-1,3)

B

C

E

3

22

2

2

6

5(1-2,1)

(1-2,1)

(1-2,1)

127

Ticket-Based Probing (cont.)

SD

A

C

3 3

3 2

(1.1,3) (1.1.1,2)

Demand = 4

B

C

E

3

22

2

2

6

5(1.2,1)

(1.1.2,1)(1.1.2,1)

(1.2,1)

(1.2,1)

128

Ticket-Based Probing (cont.)

T1

S D

129

T2

Ticket-Based Probing (cont.)

T2

T1

S D

130

Ticket-Based Probing (cont.)

xT2

T1

S D

x

131

Ticket-Based Probing (cont.)

SD

A

C

4 3

3 2

xDemand = 4

(1,4)

(2.1,3)

B

C

E

3

24

2

3

6

5x(2.2,1)

(2.1,3)

(2.1,3)

(2.1,3)

(2.2,1)

(2.2,1)

132

Conclusion

� The routing overhead is controlled by the number of tickets,

which allows the dynamic tradeoff between the overhead and

the routing performance. Issuing more tickets means searching

more paths, which results in a better chance of finding a

feasible path at the cost of higher overhead.

� A distributed routing process is used to avoid any centralized � A distributed routing process is used to avoid any centralized

path computation that could be very expensive for QoS routing

in large networks.

� This approach not only increases the chance of success but also

improves the ability to tolerate the information imprecision

because the intermediate nodes may gradually correct a wrong

decision made by the source.

133

Conclusion (cont.)

� Ticket-based probing scheme achieves a balance between the

single-path routing algorithms and the flooding algorithms. It

does multipath routing without flooding.

� The basic idea is to achieve a near-optimal performance with

modest overhead by using a limited number of tickets and

making intelligent hop-by- hop path selection.making intelligent hop-by- hop path selection.

134

References� S. Chen and K. Nahrstedt, “On finding multi-constrained paths,” in Proc.

IEEE ICC’98, pp. 874-879.

� R. Guerin and A. Orda, “QoS-based routing in networks with inaccurate information: Theory and algorithms,” in Proc. IEEE INFOCOM’97, Japan, pp. 75-83.

� Q. Ma and P. Steenkiste, “Quality-of-service routing with performance guarantees,” in Proc. 4th Int. IFIP Workshop Quality of Service, May 1997, pp. 115-126.1997, pp. 115-126.

� Z. Wang and J. Crowcroft, “QoS routing for supporting resource reservation,” IEEE J. Select. Areas Commun., Sept. 1996.

� S. Chen and K Nahrstedt, “Distributed Quality-of-Service Routing in Ad Hoc Networks,” IEEE J. Select. Areas Commun, vol.17, no. 8, pp. 1488-1505, Aug. 1999.

135

4.5.2

SPEED (QoS of Transmission time)SPEED (QoS of Transmission time)QoS of Transsion time

136

SPEEDMotivation

� Freshness of data

� Promptness of Command and Control

137

SPEED (cont.)Design Objectives

� Stateless Architecture

� Soft Real-Time

� Minimum MAC Layer Support

� QoS Routing and Congestion Management

� Traffic Load Balancing

� Localized Behavior

� Void Avoidance

138

SPEED (cont.)Architecture

139

� Neighbor Set of Node I

� NSi = {node| distance (node, node i ) ≤ R}

� Forwarding Set of Node I

� FSi (Destination) = {node ∈ NSi | L – L_next > 0}

SPEED (cont.)SNGF (Stateless Non-deterministic Geographic Forwarding)

L

j L-L_Next

NSFS

i D

m

k

140

SPEED (cont.) NFL (MAC Layer Feedback)

SELF NeighborsSELF NeighborsSELF NeighborsSELF Neighbors

� Delay Estimation: Delay= Round Trip Time – Receiver Side Processing Time

� On/Off Switch

� Back-Pressure Rerouting

Last Mile Process

SNGFBackpressure

ReroutingNFL

BeaconExchange

APIUniCast MultiCast AnyCast

MAC

DelayEstimation

Neighbor

Table

� Relay Ratio Control01 >∀−=

∑i

ieif

N

eKu

01 =∃= ieifu

- SNGFNeighbor

Nodes

BeaconBeaconBeaconBeaconMR Setpoint

Neighborhood Table

Delay Estimation Beacon

SELF NeighborsSELF NeighborsSELF NeighborsSELF Neighbors

MAC Feedback

Back Pressure Beacon

Relay RatioController

RelayRatiomiss

ratio

on/off

141

Backpressure Rerouting based on MAC Layer

Feedback & SNGF

7 11

SPEED

20

110

30

115

Delay

0.5s

0.1s

0.4s

0.1s

ID

9

7

10

3 Packet Destination

2

3

5

9

10

DelayBoo

Node 5's NTNode 5's NTNode 5's NTNode 5's NT

Packet

Source

Destination

142

Backpressure Rerouting based on MAC Layer

Feedback & SNGF

7

6

ID Delay

5 0.1S

7 0.4SNode 6's NTNode 6's NTNode 6's NTNode 6's NT Packet (to 4)

2

3

5

9

10

Delay BooID Delay

5 0.5S

2 0.1S

4 0.1SNode 3's NTNode 3's NTNode 3's NTNode 3's NT4

11

12Packet 1

Packet 1

Beacon

Packet 2

Packet 2

Packet 2

Packet 2

Packet 2

143

SPEED (cont.) Void Avoidance

� In a similar way, it deals with traffic congestion.

� Backpressure beacon (ID, Destination, Positive Infinity)

� Greedy: It may not find a path even if it exists in the worst case

Last Mile Process

SNGFBackpressure

ReroutingNFL

BeaconExchange

APIUniCast MultiCast AnyCast

MAC

DelayEstimation

Neighbor

Table

1

2

3

4 5

144

SPEED (cont.) Last Mile Process

� AreaMulticastSend(Center position, radius, deadline, packet)

� AreaAnyCastSend(Center position, radius, deadline, packet)

� UnicastSend(Global_ID,deadline,packet)

� SpeedReceive()

Last Mile Process

SNGFBackpressure

ReroutingNFL

BeaconExchange

APIUniCast MultiCast AnyCast

MAC

DelayEstimation

Neighbor

Table

� SpeedReceive()

145

Conclusion

� SPEED maintains a desired delivery speed across the network

through a novel combination of feedback control and non-

deterministic QoS-aware geographic forwarding

� This combination of MAC and network layer adaptation

improves the end-to-end delay and provides good response to

congestion and voidscongestion and voids

146

References

� T. Hea, J. A Stankovic, C. Lu, and T. Abdelzaher, “SPEED: a

stateless protocol for real-time communication in sensor

networks,” in Proc. IEEE International Conference on

Distributed Computing Systems, pp. 46-55, May 2003.

� G. S. Ahn, A. T. Campbell, A. Veres, and L.H. Sun. “SWAN:

Service Differentiation in Stateless Wireless Ad Hoc Networks,” Service Differentiation in Stateless Wireless Ad Hoc Networks,”

In Proc. IEEE INFOCOM'2002, June 2002.

147

4.5.3 MERR (Minimum Energy Relay Routing)MERR (Minimum Energy Relay Routing)

QoS of Energy

148

MERRSystem Model

� Since a railroad train has a global linear structure by nature, we

consider in this paper linear WSNs as sensor networks having,

roughly, a linear topology

� such as sensors embedded in the outer surface of a pipeline or mounted along the

supporting structure of a bridge

� Aiming at such networks, we introduce two routing schemes

that efficiently utilize energy: Minimum Energy Relay Routing

(MERR) and Adaptive MERR (AMERR)

149

MERR (cont.)Energy Model

)(),(

)(

)(),(

γ

γ

drdrP

rrP

drdrP

txrxrelay

rxrx

txtx

εαα

α

εα

++=

=

+=

)(),( γεαα drdrP txrxrelay ++=

)( γεα dr +≡

150

)(),( drdrP txrxrelay εαα ++=

)( γdr εα +≡

γ

γ )1( −=

=

ε

αchar

charchar

opt

d

d

Dor

d

DK Kopt: optimal number of hops

MERR (cont.)Energy Model

� The base station is assumed to have unlimited energy supply.

� For a sensor to transmit a bit-stream of rate r over a distance d,

the transmitter power Ptx(r, d) is

� where αtx is the energy per bit consumed in the transmitter circuit, and ε

)(),( γεα drdrP txtx +=

� where αtx is the energy per bit consumed in the transmitter circuit, and ε

accounts for the energy dissipated in the transmit amplifier. The path loss

exponent γ typically ranges between 2 and 6; it is closer to 2 if there is a

perfect line-of-sight between transmitter and receiver and can go up to 6

in dense urban areas.

� The power Prx(r) needed to receive a bit-stream of rate r is

� where αrx is the energy per bit consumed by the receiver circuit.

151

txrx rrP α=)(

MERR (cont.)Energy Model

� For a sensor to receive a bit-stream of rate r and to forward it a

distance d onward, the power consumption is given

)(),( γεαα drdrP txrxrelay ++=

)( γεα dr +≡

� As Ptx, Prx, and Prelay scale linearly with r, we omit this term in

the following and implicitly assume r=1 bit/s.

152

MERR (cont.)Minimum Energy Path

� Suppose that a sensor S is located at distance D from the base

station BS and that S wants to deliver some data to BS. The

goal is to minimize the power needed on the entire path from S

to BS.

153

MERR (cont.)Minimum Energy Path

� S should transmit directly to BS if

� Otherwise, it is best to select (Kopt − 1) equally spaced,

intermediate nodes for retransmission. Kopt is the optimal

number of hops which is

γγεα /11 )))21(/(( −−≤D

number of hops which is

� where dchar is the characteristic distance

154

γ

γ )1( −=

=

ε

αchar

charchar

opt

d

d

Dor

d

DK

MERR (cont.)System Model

� We consider a linear WSN to be a sensor network having,

roughly, a linear topology.

� Data propagation is assumed to be unidirectional.

155

MERR (cont.) Minimum Energy Relay Routing

|| chardDE − || chardCD −= || chardBD −=‘

BSA B C D E

156

AMERRAdaptive Minimum Energy Relay Routing

157

Conclusion

� Linear topology networks often appear in pipeline monitoring and structural health monitoring, but the application that motivated this work is telemetry and control for freight railroad trains

� Using sensor networks to provide more timely information, the goal of this commercial application is to attain greater visibility of the rolling assets and cargo to allow for real-time failure of the rolling assets and cargo to allow for real-time failure prediction of a train’s components

158

References

� M. Zimmerling, W. Dargie, J. Reason, “Energy-Efficient Routing in Linear

Wireless Sensor Networks,” The Fourth IEEE International Conference on

Mobile Ad-hoc and Sensor Systems, Pisa, Italy, 8-11 October, 2007.

� M. Zimmerling, W. Dargie, and J. Reason, “Localized power-aware routing

in linear wireless sensor networks,” In CASEMANS ’08: Proceedings of the

2nd ACM international conference on Context-awareness for self-managing

systems, pp. 24-33, New York, 2008,.systems, pp. 24-33, New York, 2008,.

159

Chapter 4.6

Data Aggregation and ConvergecastData Aggregation and Convergecast

160

Outline

� 4.6.1 The Impact of Data Aggregation

� 4.6.2 Data Mules

� 4.6.3 Convergecasting Tree Construction and Channel

Allocation Problem (CTCCAP)

� 4.6.4 Distributed Time-Optimal Scheduling for Convergecast

161

Outline

� 4.6.1 The Impact of Data Aggregation

� 4.6.2 Data Mules

� 4.6.3 Convergecasting Tree Construction and Channel

Allocation Problem (CTCCAP)

� 4.6.4 Distributed Time-Optimal Scheduling for Convergecast

162

4.6.1 The Impact of Data Aggregation

� Impact of Data Aggregation in Wireless Sensor Networks

(Krishnamachari, Estrin, & Wicker, 2002)

� Aggregation in Sensor Networks

� Theoretical Results on Aggregation

� Aggregation Techniques

� Performance study� Performance study

163

Aggregation in Sensor Networks

� Traditional Address-Centric routing

� IP address routing

� Not suitable in large scale sensor networks

� Data-Centric Routing

� Content-based routing

� Enhance the data aggregation opportunity� Enhance the data aggregation opportunity

164

Source 1 Source 2

A B

Sink

Source 1 Source 2

A B

Sink

a) Address-Centric (AC) Routing b) Data-Centric (DC) Routing

1

1

2

2

21

1+2

DataAggregation

Theoretical Results on Aggregation

� Let there be k sources located within a diameter X, each a distance di from

the sink. Let NA and ND be the number of transmissions required with AC

and optimal DC protocols, respectively.

1. The following are bounds on ND:

( 1) min( )

min( ) ( 1)

D i

D i

N k X d

N d k

≤ − +

≥ + −

2. Asymptotically, for fixed k, X, as d = min(di) is increased,

3. Although the problem is NP-hard in general, the optimal data aggregation

tree can be formed in polynomial time when the sources induce a

connected subgraph on the communication graph.

min( ) ( 1)D i

N d k≥ + −

1lim D

d

A

N

N k→∞ =

165

Aggregation Techniques

� In general the formation of the optimal aggregation tree is NP-

hard. Some suboptimal DC routing heuristics as follows:

� Center at Nearest Source (CNSDC)

� All sources send the information first to the source nearest to the sink, which

acts as the aggregator.

� Shortest Path Tree (SPTDC)

Opportunistically merge the shortest paths from each source wherever they � Opportunistically merge the shortest paths from each source wherever they

overlap.

� Greedy Incremental Tree (GITDC)

� Start with path from sink to nearest source. Successively add next nearest

source to the existing tree.

� Address Centric (AC)

� No aggregation, distinct shortest paths from each source to sink.

166

Performance Study

Event-Radius model Random Sources model

167

Performance Study (cont.)

Energy Costs

Event-Radius model Random Sources model

168

Conclusions

� Data aggregation can result in significant energy savings for a

wide range of operational scenarios.

� The gains from aggregation are paid for with potentially higher

delay. It should be possible to design routing algorithms for

sensor networks in which this tradeoff is made explicitly.

169

Reference

� Bhaskar Krishnamachari, Deborah Estrin, and Stephen B. Wicker, "The

impact of data aggregation in wireless sensor networks," In Proceedings of

the 22nd International Conference on Distributed Computing Systems

Workshops (ICDCSW'02), pp. 575-578, Vienna, Austria, July 02-05 2002.

170

Outline

� 4.6.1 The Impact of Data Aggregation

� 4.6.2 Data Mules

� 4.6.3 Convergecasting Tree Construction and Channel

Allocation Problem (CTCCAP)

� 4.6.4 Distributed Time-Optimal Scheduling for Convergecast

171

4.6.2 Data Mules

� Data MULEs: Modeling a Three-tier Architecture for Sparse

Sensor Networks (Shah, Roy, Jain, & Brunette, 2003)

� Three-tier architecture

� Data MULEs approaches

172

Three-tier Architecture

Access points – ample resources

Mobile nodes – renewable resources for

intermittent connectivity

Source nodes – limited resources

173

� A top tier of WAN connected devices

� A middle tier of mobile transport agents

� A bottom tier made of fixed wireless sensor nodes

Data MULEs approach

� Data Mules

� Exploit mobile nodes (called MULEs)

� MULEs collect data when near sensor

� Transfer data to an access point when close

174

access pointsensor

Discussions

� Benefits

� Energy efficient

� Short distance communication

between sensor and MULE

� Scalable

� Addition of sensors or MULEs

requires no configuration

� Limitations

� No guarantees on data delivery

� MULEs may lose data

� MULEs may not arrive at a

sensor

� MULEs may not arrive at an

access pointrequires no configuration

� Simple

� Least functionality in sensors

� No forwarding, no global

discovery

access point

175

Reference

� Rahul C. Shah, Sumit Roy, Sushant Jain, and Waylon Brunette, "Data

MULEs: modeling and analysis of a three-tier architecture for sparse sensor

networks," Ad Hoc Networks, Volume 1, Issues 2-3, pp. 215-233, September

2003.

176

Outline

� 4.6.1 The Impact of Data Aggregation

� 4.6.2 Data Mules

� 4.6.3 Convergecasting Tree Construction and Channel

Allocation Problem (CTCCAP)

� 4.6.4 Distributed Time-Optimal Scheduling for Convergecast

177

Convergecasting in WSN

� WSN are mainly used for monitoring

� Monitoring involves data collection and request dissemination

� Convergecasting� Process of data collection from all or a set of sensors in the network

towards the base station (Many to one communication)

� Energy and latency minimization is required for WSNs

178

Convergecasting

� Route construction plays a major role during convergecasting

� Criterion for route construction

� Energy consumption

� Latency incurred

� Choice of MAC layer – since traffic is many to one

179179

Collisions

� Results in packet loss

� Need reliability, use retransmissions

� Retransmission increases energy consumption and latency

� Avoided by using a contention based or contention free MAC

protocolBS

180

1

BS

2

3 4 5

6

CollisionCollision CollisionCollision Coverage Area

or

Sensing Range

Data

Data

Energy & LatencyEnergy

� Energy consumed at a node is used for� Running the transceiver circuitry for transmitting a bit (Etrx)

� Amplifying a bit of data to be transmitted (Eamp)

It depends on the transmission distance

BS BS

E = 4nj Eamp= 4nj

181

3 hops

2 hopsP1

P2

P1

P2

n n

Eamp= 4nj

Eamp= 4nj

Eamp= 5nj

Eamp= 4nj

Eamp= 5nj

Energy consumed for running transceiver

To transmit k data bits from n to BS

P1: 3 * Etrx * k

P2: 2 * Etrx * k

Amplification energy consumed for

transmitting k data bits from n to BS

P1: (4nj + 4nj + 5nj) * k = 13 * k nj

P2: (4nj + 5nj ) * k = 9 * k nj

P1 and P2: paths

BS: Base Station

Energy & Latency (cont.) Energy

� Transceiver startup time

� Frequently switching the transceiver on leads to higher energy wastage

� Aggregation reduces packet header overhead

Time-slot =1 Time-slot = 2

Time-slot = 3

BSBS – Base Station

Aggregation reduces

transmitter startup energy

wastage

Slots allocated to children should

reduce cumulative startup time

of parent’s receiver 1

4 5

2

3

182

Energy & Latency (cont.) Latency

� Time taken to gather data at the base station

� Latency = No. of time-slots × Length of one-slot

� Balanced tree helps in reducing total number of time-slots and length of time-slots

Unbalanced Tree Balanced Tree

BS : Base StationBS

183

Number of slots = 4

Length of each slot = 4 packets

Latency = 16 units

Number of slots = 3

Length of each slot = 3 packets

Latency = 9 units

BS : Base Station

t =1 t = 2 t = 3

t = 4t = 1

BS

1

4 5

2

3

t =1 t = 2 t =1

t = 2t = 3

BS

1

4 5

2

3

Energy & Latency (cont.) Summary

� Energy and latency minimized by avoiding collisions

� Energy consumption can also be minimized by

� Reducing the number of hops

� Choosing path that minimizes amplification energy

� Reducing energy wastage due to transceiver startup time by performing

data aggregationdata aggregation

� Latency minimization by building a balanced routing tree

184

Main Procedures

� Assumptions

� Etrx < Eamp

� One transceiver per node

� Nodes have maximum transmission range (MEamp)

� Clock synchronization mechanism exists

� Builds the tree and allocates channel for the nodes� Builds the tree and allocates channel for the nodes

� Allocates channel for two different convergecast patterns

� Synchronous: Used for realtime data. Enables aggregation. Therefore

parent transmits after it receives from children (parent time-slot > child

time-slots)

� Asynchronous: Used for non-realtime data. Enables aggregation only if

data does not depend in time.

185

Synchronous Convergecast

� Data collection starts from leaf nodes

� Each parent waits for data from its children before sending its data

� Reordering based on timestamp is not necessary at base station

2 3

BS BS

<4, 1> <3, 1>

Note: Weights indicate the

amplification energy expended

Network

13

3

3

1.4

2.2

2

1 2

3

4

5

Convergecast Tree

1 2

3

4

5

<4, 1> <3, 1>

<3, 1>

<2, 1>

<1, 1>

amplification energy expended

to transmit a data bit over that

link

186 <t, c>: a tuple of time-slot t and CDMA code c

Asynchronous Convergecast

� Data collection takes place at independent and not interfering parts of the

network

� Reordering necessary at base station

� Latency will be low

Network 2 3

BS

Convergecast Tree

BS

Note: Weights indicate the Network

1

2 3

3

3

3

1.4

2.2

2

1 2

3

4

5

1 2

3

4

5

<1, 1>

<2, 1>

<3, 1>

<1, 1><2, 1>

Note: Weights indicate the

amplification energy expended

to transmit a data bit over that

link

187

Channel Allocation Criterion 1

� Each node has one transceiver

� Therefore a parent with two children cannot receive from both

of them at the same time using two different codes

� Therefore children transmit at different time instants

ParentX = ParentYParentX = ParentY

X Y

ParentX = ParentY

X Y

ParentX = ParentY

<t1,c1> <t2,c1><t1,c1> <t1,c2>

188

Channel Allocation Criterion 2

� Avoid exposed terminal problem

ParentX ParentYTransmission

range

If X and Y useCollisionCollision CollisionCollision

X Y

If X and Y use

the same channel

If X and Y transmit

at different time-slots

If X and Y transmit

using different CDMA

codes

189

Channel Allocation Criterion 3

� Parent cannot receive the same time it is transmitting

� Therefore, we have parent time-slot ≠ child time-slot

ParentParentxParentParentx

X

ParentX

<t1,c1>

<t1,c2>

X

ParentX

<t1,c1>

<t2,c1>

190

Algorithm

� Build the tree and allocate the channel (is a tuple of time-slot tand CDMA code c, <t, c>)

� Tree constructed in a top down manner

� Use channel allocation criteria defined earlier

� Additional criterion for synchronous convergecasting� child time-slot < parent time-slot

� Since tree construction is top down, it is not possible to allocate � Since tree construction is top down, it is not possible to allocate a valid time-slot for children

� Channel allocation in two phases� Phase I

� Construct tree and allocate channel in increasing order of time-slots

� Phase II� Reverse mapping of time slots to enable synchronous convergecast

191

CTCCAA: Phase I (Tree Construction)

� Construct the tree by reducing number of hops and then choose the path that consumes minimum amplification energy� Reason: Etrx < Eamp since transmission range of sensors are small

� Start constructing the tree with Base station (BS) as the root node

� Maintain a possible parent and a possible child list� Possible Parent List (PPL) = {All nodes recently added to the tree} � Possible Parent List (PPL) = {All nodes recently added to the tree}

� Possible Children List (PCL) = {x | there exists y ∈ PPL such that Eamp(x,y) < MEamp}

� Parent selection� For all x ∈ PCL parentx = arg Minforall y ∈ PPL Eamp(x,y)

� If for all x ∈ PCL parentx ≠ null, then copy PCL to PPL

192

Example: Phase I

Initially current level is 0

PPL = {BS}

PCL = {1, 2}

Since BS is the only possible

parent both 1 and 2 choose 1

2 3

BS

1 2

Weights on links indicate the

amplification energy expended

to transmit a data bit

parent both 1 and 2 choose

BS as their parent.

13

3

3

1.4

2.2

23

4

5

193

CTCCAA: Phase I (Channel Allocation)

� Use a combination of CDMA codes and time-slots

� Allocate children a time-slot that is greater than parent (will do

reverse mapping in phase II)

194

Example: Phase I

Weights on links indicate the

amplification energy expended

to transmit a data bitThis example assumes channel to be

divided over time.

Initially current level is 0

PPL = {BS}

BS

1<1, 1>PPL = {BS}

PCL = {1, 2}1

2

3

4

5

<1, 1><2, 1>

13

3

3

1.4

2.2

2

195

Example: Phase I

Weights indicate the

amplification energy

BS

1

Initially current level is 0

PPL = {1, 2}

PCL = {3, 4, 5}

<1, 1> 12

3

4

5

<1, 1>

<3, 1>

<4, 1>

<2, 1>

<2, 1>

13

3

3

1.4

2.2

2

196

CTCCAA: Phase II

� Only executed for synchronous convergecast

� Use maximum time-slot (Maxts) allocated in the network

� Actual time-slot = Maxts – allocated time-slot

BSMaxts = 4

197

<1, 1> 2

3

4

5

<2, 1>

<4, 1>

<3, 1>

<2, 1> <1, 1>

<4, 1>

<3, 1>

<2, 1>

<3, 1>1

Example

� This shows the advantage of divided channel over time and

CDMA Codes. CDMA codes help in reducing latency by

increasing time-slot reuse

BS

1 2

3

4

5

<3, 2><2, 2>

<1, 1>

<2, 1>

<1, 2>

198

Conclusions

� Convergecast will be preceded by broadcast in monitoring applications

� Measured energy and latency incurred during convergecastover a broadcast tree and a tree constructed by CTCCAA

� Similarly we measured energy and latency for broadcasting over both the trees

199

Reference

� V. Annamalai, S.K.S. Gupta, and L. Schwiebert, “On tree-based

convergecasting in wireless sensor networks,” IEEE Wireless

Communications and Networking, vol. 3, pp.1942-1947 , March 2003.

200

Outline

� 4.6.1 The Impact of Data Aggregation

� 4.6.2 Data Mules

� 4.6.3 Convergecasting Tree Construction and Channel

Allocation Problem (CTCCAP)

� 4.6.4 Distributed Time-Optimal Scheduling for Convergecast

201

Outline

� 4.6.4. Distributed time-optimal scheduling for convergecast

� 4.6.4.1. System model and Assumptions

� 4.6.4.2. Convergecast in tree networks

� 4.6.4.2.1. Linear Networks

� 4.6.4.2.2. Multi-line Networks

� 4.6.4.2.3. Tree Networks

� 4.6.4.2.4. Sleep schedule for energy conservation

� 4.6.4.3. Convergecast in general networks

� 4.6.4.4. Convergecast in other scenarios

202

4.6.4. Distributed Time-optimal Scheduling for

Convergecast

� Convergecast is a typical many-to-one communication pattern

in sensor network applications.

� In convergecast many, or all nodes in the network send data to

a base station during a relatively short time period.

� Using CSMA MAC layer, the convergecast latency incurred by

radial coordination is far from the optimal.radial coordination is far from the optimal.

� TDMA schedule such that the entire convergecast can be

completed in minimal number of timeslots.

203

4.6.4.1. System Model and Assumptions

� Assumptions

� the nodes and the associated base station are static

� the nodes (including the base station) cannot transmit and receive at the

same time

� the bandwidth of every wireless link in the network is assumed to be the

same

the network connectivity is fixed over time� the network connectivity is fixed over time

� the maximum length of a packet is fixed

� the drift in the clock of a node is bounded all the time.

204

4.6.4.2. Convergecast in Tree Networks

� There are three considers of sensor nodes and propose an

optimal convergecast scheduling algorithm.

� linear networks

� multi-line networks

� tree networks

205

4.6.4.2.1. Linear Networks

� We define the following states that a node can be in each

timeslot during the convergecast

� R: The node may receive from a neighboring node.

� T: The node can transmit.

� I : The node neither transmits nor receives.

206

Linear Networks (cont.)

A Linear Network

Convergecast Schedule

207

4.6.4.2.2. Multi-line Networks

� Convergecast scheduling algorithm for multi-line networks is

to schedule transmissions parallelly along multiple branches

Next timeslot

R T

208

State transition for convergecast scheduling

Next timeslotNext timeslot

R T

I

� The network consists of branches A, B, C and D (A < B < C <

D) with 3, 2, 2, and1 nodes

Multi-line Networks (cont.)

A multi-line network

209

Multi-line Networks (cont.)

Convergecast schedule for multi-line networks

210

The Pkts Left field is used to track the number of packets remaining in each branch.

The Last Slot field shows the last timeslot in which a branch has forwarded a

packet to the base station (two less than the last active timeslots).

4.6.4.2.3. Tree Networks

� Convergecast scheduling algorithm for tree networks is based

on the observation that a tree network can be reduced to a

multi-line network with each line represented as a combination

of linear branches of nodes.

211

Tree Networks (cont.)

Reduction of a tree network into linear branches.

(a) (b)

212

4.6.4.2.4. Sleep Schedule for Energy

Conservation

� Energy spent in sleep state is negligible.

� At most 3N timeslots are required to finish the convergecast.

� The total energy consumption in the network is

Joules. Hence, we conclude that the sleep schedule

results in about 50% energy conservation in linear networks.

2

)1(3 eNN +

213

4.6.4.3. Convergecast in General Networks

214

4.6.4.4. Convergecast in Other Scenarios

� We show that our convergecast scheduling algorithm is

applicable even when these assumptions do not hold.

� Base station initiated convergecast

� Nodes with multiple packets

� Non-ideal radio characteristics

215

Convergecast in Other Scenarios (cont.)

Base station initiated convergecast in linear networks.

216

Convergecast in Other Scenarios (cont.)

(a)

(b)

Multi-packet network.

Non-ideal radio propagation characteristics.

217

Conclusions

� A minimal time distributed convergecast scheduling algorithm

for sensor networks.

� The optimal convergecast schedule consists of

3N -3 timeslots, where N is the number of nodes in the network.

� More than 50% of the energy can be saved by using the

proposed sleep schedule.proposed sleep schedule.

218

Reference

� S. Gandham, Y. Zhang, and Q. Huang, “Distributed time-optimal scheduling

for convergecast in wireless sensor networks,” Computer Networks, vol. 52,

pp. 610-629, 2008.

219

Chapter 4.7

Data centric networkingData centric networking

220

Outline

� 4.7.1 Data centric routing

� 4.7.2 Data-centric storage

� 4.7.2.1 One-dimensional data storage

� 4.7.2.2 Multi-dimensional data storage

� 4.7.2.3 Hierarchical data storage

221

4.7.1 Data Centric Routing

� A central querier/data sink (or collection of queriers/sinks)

issues queries that sources in the network respond to. Due to

energy constraints it is desirable for much of the data

processing to be done in-network, and this has led to the

concept of data centric information routing, in which the

queries and responses are for named data.queries and responses are for named data.

� A sensor node is not an identity (address)

� Content based and data centric

� Where are nodes whose temperatures will exceed more than 10 degrees for

next 10 minutes?

� Tell me in what direction that vehicle in region Y is moving?

� Give me periodic reports about animal location in region A every 30 seconds.

222

Data Centric Routing (cont.)

� Depending on the applications, there are likely to be different

kinds of queries in these sensor networks.

� The types of queries can be categorized in many ways, for

example:

� Continuous queries, which result in extended data flows (e.g. “Report

the measured temperature for the next 7 days with a frequency of 1 the measured temperature for the next 7 days with a frequency of 1

measurement per hour”) versus One-shot queries, which have a simple

response (e.g. “Is the current temperature higher than 70 degrees?”)

� Aggregate queries, which require the aggregation of information from

several sources (e.g. “Report the calculated average temperature of all

nodes in region X”) versus Non-aggregate Queries which can be

responded to by a single node (e.g. “What is the temperature measured by

node x?”)

223

Data Centric Routing (cont.)

� Complex queries, which consist of several nested or batched sub-queries

(e.g. “What are the values of the following variables: X, Y, Z?”) versus

simple queries, which have no sub-queries (e.g. “What is the value of the

variable X?”)

� Queries for replicated data, In which the response to a given query can

be provided by many nodes (e.g. “Is there at least one target in the

area?”) and queries for unique data, in which the response to a given area?”) and queries for unique data, in which the response to a given

query can be provided only by one node.

224

Data Centric Routing (cont.)

� SPIN

� One-shot interactions

� 3-stage handshake protocol

� ADV – new data advertisement

� REQ – request for data

� DATA – data messageADV ADVREQDATA

225

ADVREQDATA

ADV

ADV

ADV

ADV

ADV

REQ

REQ

REQ

REQ

DATA

DATA

DATA

DATA

The SPIN protocol

Data Centric Routing (cont.)

� Directed Diffusion

� Repeated interactions

A simplified schematic for directed diffusion

226

4.7.2 Data-centric Storage

� Data centric storage

� Data is stored inside the network.

� All data with the same name (or data range) will be stored at the same

sensor network location

� E.g. an elephant sighting.

� Why data centric storage?� Why data centric storage?

� Energy efficiency

� Robustness against mobility and node failures

� Scalability

227

4.7.2

Data-centric storageData-centric storageOne-dimensional data storage

228

One-dimensional Data Storage

� Data-Centric Storage in Sensornets with GHT, a Geographic

Hash Table(GHT [Ratnasamy et al. 2003])

� Data Storage and Retrieval

� Perimeter Refresh Protocol

� Structured Replication

229

Data Storage and Retrieval

� GHT

� Put(k,v)-stores v (observed data)

according to the key k

� Get(k)-retrieve whatever value is

associated with key k

� Hash function

Hash the key in to the

(12,24)data

� Hash the key in to the

geographic coordinates

� Put() and Get() operations on the

same key “k” hash k to the same

location

user

queryresponse

Hash (‘elephant’)=(12,24)

Put (“elephant”, data) Get (“elephant”)

Hash (‘elephant’)=(12,24)

An example for GHT

230

Perimeter Refresh Protocol

� Assume key k hashes at

location L

� A is closest to L so it

becomes the home node

E

D

Replica

Replica

F

B

D

A

C

L

home

231

Structured Replication

� Augment event name with

hierarchy depth

� Given root r and given

hierarchy depth d

� Compute 4d – 1 mirror images

of r

(100, 100)(0, 100)

of r

(0, 0) (100, 0)

root point

level 1 mirror points

level 2 mirror points

Example of structured replication

with a 2-level decomposition

232

Conclusions

� Data centric storage entails naming of data and storing data at

nodes within the sensor network

� GHT uses Perimeter Refresh Protocol and structured

replication to enhance robustness and scalability

� DCS is useful in large sensor networks and there are many

detected events but not all event types are Queried detected events but not all event types are Queried

233

4.7.2

Data-centric storageData-centric storageMulti-dimensional data storage

234

Multi-dimensional Data Storage

� Multi-Dimensional Range Queries in Sensor Networks (DIM

[Li et al. 2003])

� Building Zones

� Data Insertion

� Query Propagation

235

Building Zones

� Divide network into zones.

� Each node mapped to one

zone.

� Encode zones based on

division.

� Each zone has a unique 6

5

1110

1111

43

21

1100111

0110

010

L∈∈∈∈[1/2, 1)L∈∈∈∈[0, 1/2)

T∈∈ ∈∈

[1/2

, 1

)

T∈∈ ∈∈

[3/4

, 1)

T∈∈ ∈∈

[1/2

, 3/4

)

code.

� Map m-d space to zones.

� Zones organized into a

virtual binary tree.

78

10

90001

0000 001 10

T∈∈ ∈∈

[0, 1

/2)

L∈∈∈∈[0, 1/4) L∈∈∈∈[1/4, 1/2) L∈∈∈∈[1/2, 3/4) L∈∈∈∈[3/4, 1)

T∈∈ ∈∈

[1/4

, 1/2

)T

∈∈ ∈∈[0

, 1/4

)

L: Light, T: Temperature

236

Data Insertion

� Encode events

� Compute geographic

destination

� Hand to GPSR

� Intermediate

nodes can refine

1110

1111

L∈∈∈∈[1/2, 1)

1

110

L∈∈∈∈[0, 1/2)

0111

0110

010

T∈∈ ∈∈

[1/2

, 1

)

T∈∈ ∈∈

[3/4

, 1)

T∈∈ ∈∈

[1/2

, 3/4

)

2

34

5

6

E1= <0.8, 0.7>

nodes can refine

the destination

estimation

L∈∈∈∈[0, 1/4)

0001

0000 001 10

T∈∈ ∈∈

[0, 1

/2)

L∈∈∈∈[1/4, 1/2) L∈∈∈∈[1/2, 3/4) L∈∈∈∈[3/4, 1)

T∈∈ ∈∈

[1/4

, 1/2

)T

∈∈ ∈∈[0

, 1/4

)

987

10

Store E1

L: Light, T: Temperature

237

Query Propagation

� Split a large query into smaller subqueries.

� Encode each subquery.

� Process subqueries separately, resolving locally or

1110

1111

L∈∈∈∈[1/2, 1)

1

110

L∈∈∈∈[0, 1/2)

0111

0110

010

T∈∈ ∈∈

[1/2

, 1

)

T∈∈ ∈∈

[3/4

, 1)

T∈∈ ∈∈

[1/2

, 3/4

)

2

34

5

6Q = <.75-1, .5-.75>

Q12= <.75-1, .75-1>Q11= <.5-.75, . 5-1>

locally or forwarding to other nodes based on their codes.

L∈∈∈∈[0, 1/4)

0001

0000 001 10

T∈∈ ∈∈

[0, 1

/2)

L∈∈∈∈[1/4, 1/2) L∈∈∈∈[1/2, 3/4) L∈∈∈∈[3/4, 1)

T∈∈ ∈∈

[1/4

, 1/2

)T

∈∈ ∈∈[0

, 1/4

)

987

10

Q10= <.75-1, .5-.75>

Q1= <0.5-1, 0.5-1>

L: Light, T: Temperature

238

Conclusions

� DIM resolves multi-dimensional range queries efficiently.

� Work that still needs to be done

� Skewed data distribution

� These can cause storage and transmission hotspots.

� Existential queries

� Whether there exists an event matching a multi-dimensional range.

� Node heterogeneity� Node heterogeneity

� Nodes with larger storage space assert larger-sized zones for themselves.

239

4.7.2

Data-centric storageData-centric storageHierarchical data storage

240

Hierarchical Data Storage

� Load balance and Efficient Hierarchical Data-Centric Storage

in Sensor Networks (HVGR [Zhao et al. 2008])

� Constructing the hierarchical architecture

� Storage load balancing

� Data Storage and retrieval

241

Constructing Hierarchical Architecture

� Assumptions

� Large

� Static

� density

•Each node knows the shortest root

•Each node knows the shortest

path to their first level landmark

242

Constructing Hierarchical Architecture (cont.)

� Assumptions

� Large

� Static

� density

•Each node knows the shortest

L2

L3

root

• Nodes know the shortest

path to their second level landmark

•Termination: the subregion only

contains the owner landmark and

the landmark’s one-hop neighbors.

•Each node knows the shortest

path to their first level landmark

L1

L4

L5

L4.1

L4.2

L4.3

243

Storage Load Balancing

N2N3

L2:[0.1,0.3)L3:[0.3,0.6)

N: the total number node in WSN

Ni: the number node in Ni’s subregion

N=50

0.1

0.2 0.3

N1

N4

N5

L1: [0,0.1)

L4:[0.6,0.8)L5:[0.8,1)

L4.1:[0.6,0.67)

L4.2:[0.67,0.73) L4.3:[0.73,0.8)

Node A table:

L1st:{L1:(0,0.1],L2 :(0.1,0.3],L3:(0.3,0.6],

L4:(0.6,0.8],L2 :(0.8,1]}

L2nd:{L4.1:(0.6,0.67], L4.2:(0.67,0.73],

L4.3:(0.73,0.8]}

L3rd:{L4.2.1: (0.67,0.73],

L4.2.2: (0.70,0.73]}

A

0.1

0.2

0.2

244

Data Storage and Retrieval

L1st:{L4 :(0.6,0.8],…}

Source(0.68)

Query (0.68)

L2 L3

Query (0.68)

L1st:{L4:(0.6,0.8],…}

L2nd:{L4.2:(0.67,0.73],…}

L1

L4L5

L4.1

L4.2L4.3

A

DL4.2.1

L4.2.2

L1st:{L4 :(0.6,0.8],…}

L2nd:{L4.2:(0.67,0.73],…}

L3rd:{L4.2.1: (0.67,0.73],L4.2.2: (0.70,0.73]}

245

Conclusions

� HVGR is very scalable, as the initialization overhead and

routing table size of each node is O(logN).

� HVGR design a simple hash mechanism so that HVGR can

provide a well load balanced data-centric storage system.

246

References� S. Ratnasamy, B. Karp, S. Shenker, D. Estrin, R. Govindan, L. Yin,

and F. Yu, “Data-Centric Storage in Sensornets with GHT, A Geographic Hash Table,” in Journal of Mobile Network Applications, vol. 8, no. 4, pp. 427-442, 2003.

� X. Li, Y. J. Kim, R. Govidan, and W. Hong, "Multi-Dimensional Range Queries in Sensor Networks," in Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys'03), Los Angeles, CA, USA, pp.63-75, Nov. 2003.International Conference on Embedded Networked Sensor Systems (SenSys'03), Los Angeles, CA, USA, pp.63-75, Nov. 2003.

� Y. Zhao, Y. Chen, and S. Ratnasamy, "Load Balanced and Efficient Hierarchical Data-Centric Storage in Sensor Networks," in Proceedings of the 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, San Francisco, California, USA, pp.560-568, June 2008.

247

Chapter 4.8

ZigBeeZigBee

248

Outline

� 4.8 The ZigBee Standard

� 4.8.1 Zigbee frame format

� 4.8.2 The Network Layer

� 4.8.3 The Application Layer

249

Outline

� 4.8 The ZigBee Standard

� 4.8.1 Zigbee frame format

� 4.8.2 The Network Layer

� 4.8.2.1 Network Formation and Address Assignment

� 4.8.2.2 ZigBee Routing protocol

� 4.8.2.3 Route Discovery

� 4.8.3 The Application Layer� 4.8.3 The Application Layer

250

The ZigBee Standard

� ZigBee is a low cost, low power, low complexity, and low data rate wireless

communication technology at short range. Based on IEEE 802.15.4, it is

mainly used as a low data rate monitoring and controlling sensor network

Applications

802.15.4

Zigbee

Specification

Application Framework

Network & Security

Medium Access Control (MAC) Layer

Physical (PHY) Layer

Application

Zigbee stack

Hardware

251

Zigbee Frame Format

� General frame format

Octets: 2 2 2 1 1 Variable

Frame Control Destination Source Radius Sequence Frame Frame Control Destination Source

Address

Radius Sequence

Number

Frame

Payload

Routing Fields

NWK Header NWK

Payload

252

Zigbee Frame Format (cont.)

� General frame format

Frame control field

Frame type setting

253

Zigbee Frame Format (cont.)

� Command frame format

Octets: 2 1 Variable

Frame control Routing fields NWK command

identifier

NWK command

payload

NWK header NWK payload

Command frame format

NWK header NWK payload

Frame type

254

Zigbee Frame Format (cont.)

� RREQ command

RREQ command

payload format

Command options field

255

Zigbee Frame Format (cont.)

� RREP command

RREP command

payload format

256

Outline

� 4.8 The ZigBee Standard

� 4.8.1 Zigbee frame format

� 4.8.2 The Network Layer

� 4.8.2.1 Network Formation and Address Assignment

� 4.8.2.2 ZigBee Routing protocol

� 4.8.2.3 Route Discovery

� 4.8.3 The Application Layer� 4.8.3 The Application Layer

257

The Network Layer

� ZigBee identifies three device types

� The ZigBee coordinator (one in the network) is an FFD managing the

whole network

� A ZigBee router is an FFD with routing capabilities

� A ZigBee end-device corresponds to a RFD or FFD acting as a simple

device

� The ZigBee network layer supports three types of network

configurations:

� Star topology

� Tree topology

� Mesh topology

258

The Network Layer (cont.)

ZigBee coordinator ZigBee router ZigBee end device

(a) Star network (b) Tree network (c) Mesh network

259

Outline

� 4.8 The ZigBee Standard

� 4.8.1 Zigbee frame format

� 4.8.2 The Network Layer

� 4.8.2.1 Network Formation and Address Assignment

� 4.8.2.2 ZigBee Routing protocol

� 4.8.2.3 Route Discovery

� 4.8.3 The Application Layer� 4.8.3 The Application Layer

260

Network Formation and Address Assignment

� Before forming a network, the coordinator determines

� Maximum number of children of a router (Cm)

� Maximum number of child routers of a router (Rm)

� Depth of the network (Lm)

� Note that a child of a router can be a router or an end device, so � Note that a child of a router can be a router or an end device, so

Cm ≥ Rm

� The coordinator and routers can each have at most Rm child

routers and at least Cm − Rm child end devices

261

Network Formation and Address Assignment

(cont.)

� For the coordinator, the whole address space is logically

partitioned into Rm + 1 blocks

� The first Rm blocks are to be assigned to the coordinator’s

child routers and the last block is reserved for the coordinator’s

own child end devices

� From Cm, Rm, and Lm, each router computes a parameter � From Cm, Rm, and Lm, each router computes a parameter

called Cskip to derive the starting addresses of its children’s

address pools

( )( )

1

1 1 ,if 1

1Otherwise,

1

Lm d

Cm Lm dRm

Cskip d Cm Rm Cm Rm

Rm

− −

+ × − −=

= + − − ×

−

262

Network Formation and Address Assignment

(cont.)

� The coordinator is said to be at depth d = 0, and d is increased

by one after each level

� Address assignment begins from the ZigBee coordinator by

assigning address 0 to itself

� If a parent node at depth d has an address Aparent , the n-th child

router is assigned to address router is assigned to address

� Aparent + (n − 1) × Cskip(d) + 1

� n-th child end device is assigned to address

� Aparent + Rm × Cskip(d) + n

263

Network Formation and Address Assignment

(cont.)

Addr = 12

Addr = 8

Addr = 9

Addr = 10

Cm = 5

Rm = 4

Lm = 2

A2

Addr = 7

Cskip = 1

ZigBee coordinator ZigBee router ZigBee end device

B1

Addr = 25

Addr = 0

Cskip = 6

A4

Addr = 19

Cskip = 1

A3

Addr = 13

Cskip = 1

Addr = 24

A1

Addr = 1

Cskip = 1

Addr = 6

Addr = 3

Addr = 2

264

Outline

� 4.8 The ZigBee Standard

� 4.8.1 Zigbee frame format

� 4.8.2 The Network Layer

� 4.8.2.1 Network Formation and Address Assignment

� 4.8.2.2 ZigBee Routing protocol

� 4.8.2.3 Route Discovery

� 4.8.3 The Application Layer� 4.8.3 The Application Layer

265

ZigBee Routing protocol

� In a ZigBee network, the coordinator and routers can directly

transmit packets along the tree

� When a device receives a packet, it first checks if it is the

destination or one of its child end devices is the destination

� If so, this device will accept the packet or forward this packet

to the designated child. Otherwise, it forwards the packet to its

parent

266

ZigBee Routing protocol (cont.)

� Assume that the depth of this device is d and its address is A.

This packet is for one of its descendants if the destination

address Adest satisfies A < Adest < A+ Cskip(d − 1), and this

packet will be relayed to the child router with address

( )( )

11

destA AA A Cskip d

− += + + ×

� If the destination is not a descendant of this device, this packet

will be forwarded to its parent

( )

( )( )

11

dest

r

A AA A Cskip d

Cskip d

− += + + ×

267

ZigBee Routing protocol (cont.)

Cm = 6

Rm = 4

Lm = 3

Addr = 125

Addr = 30

Addr = 126

Addr = 92

Addr = 63

Cskip = 7

Addr = 64

Cskip = 1

Addr = 1

( )

( )( )

11

dest

r

A AA A Cskip d

Cskip d

− += + + ×

ZigBee coordinator ZigBee router ZigBee end device

Addr = 31

Addr = 38

Addr = 1

Cskip = 7

Addr = 32

Cskip = 7Addr = 33

Cskip = 1

Addr = 40

Cskip = 1

Z

? ?

( )( )1A A Cskip d

Cskip d= + + ×

BC

A < Adest < A+ Cskip(d − 1)

A

268

Outline

� 4.8 The ZigBee Standard

� 4.8.1 Zigbee frame format

� 4.8.2 The Network Layer

� 4.8.2.1 Network Formation and Address Assignment

� 4.8.2.2 ZigBee Routing protocol

� 4.8.2.3 Route Discovery

� 4.8.3 The Application Layer� 4.8.3 The Application Layer

269

Route Discovery

Field Name Description

Destination Address 16-bit network address of the destination

Next-hop Address 16-bit network address of next hop towards destination

Entry Status One of Active, Discovery or InactiveEntry Status One of Active, Discovery or Inactive

Routing Table in ZigBee

270

Route Discovery (cont.)

Field Name Description

RREQ ID

(route request)

Unique ID (sequence number) given to every RREQ

message being broadcasted

Source Address Network address of the initiator of the route request

Sender Address Network address of the device that sent the most recent

lowest cost RREQ

Forward Cost The accumulated path cost from the RREQ originator to

the current device

Residual Cost The accumulated path cost from the current device to the

RREQ destination

Route Discovery Table

271

Route Discovery (cont.)

RREQ message

Create RDT entry and

record fwd path cost

RDT entry

exists for this

RREQ ?

NoYes

Is

RREQ for local

node or one of

end-device

children ?

Create RT entry

(Discovery_Underway)

And rebroadcast RREQ

Update RDT entry with

better fwd path cost

Does

RREQ report

A better fwd

path cost ?

Send RREPDrop RREQ

Yes

No

Yes

No

The RREQ processing

272

Route Discovery (cont.)

A

C

BDiscard route

request

route reply

S

D

T

Unicast

Broadcast

Without routing capacity

273

Outline

� 4.8 The ZigBee Standard

� 4.8.1 Zigbee frame format

� 4.8.2 The Network Layer

� 4.8.2.1 Network Formation and Address Assignment

� 4.8.2.2 ZigBee Routing protocol

� 4.8.2.3 Route Discovery

� 4.8.3 The Application Layer� 4.8.3 The Application Layer

274

The Application Layer

� A ZigBee application consists of a set of Application Objects (APOs) spread over

several nodes in the network

� The ZigBee Device Object (ZDO) is a special object which offers services to the

APOs

� The Application Sub layer (APS) provides data transfer services for the APOs and

the ZDO

275

References� P. Baronti, P. Pillai, V. Chook, S. Chessa, and F. Gotta, A. andFun Hu. Wireless sensor

networks: a survey on the state of the art and the 802.15.4 and zigbee standards.

Communication Research Centre, UK, May 2006.

� J. Bruck, J. Gao and A. A. Jiang, “MAP: Medial Axis Based Geometric Routing in Sensor

Network,” in Proceedings of ACM MobiCom, 2005.

� Q. Fang, J. Gao, L. Guibas, V. de Silva, and L. Zhang. GLIDER: Gradient landmark-based

distributed routing for sensor networks. In Proc. of the 24th Conference of the IEEE

Communication Society (INFOCOM’05), March 2005.

� B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris. Span: An energy-efficient coordination

algorithm for topology maintenance in ad hoc wireless networks. In International Conference

on Mobile Computing and Networking (MobiCom 2001), pages 85–96, Rome, Italy, July 2001.

� Y. Xu, J. Heidemann, and D. Estrin. Geography-informed energy conservation for ad hoc

routing. In Proceedings of the ACM/IEEE International Conference on Mobile Computing and

Networking, pages 70–84, Rome, Italy, July 2001.

276

Conclusions

� Routing in sensor networks is a new area of research, with a

limited but rapidly growing set of research results

� We highlight the design trade-offs between energy and

communication overhead savings in some of the routing

paradigm, as well as the advantages and disadvantages of each

routing technique

277

routing technique

� Overall, the routing techniques are classified based on the

network structure into four categories: flat, hierarchical, and

location-based routing, and QoS based routing protocols.

� Although many of these routing techniques look promising,

there are still many challenges that need to be solved in sensor

networks