Networked Systems Research @ Nimbus

Wireless Sensor and Vehicular Ad-hoc Networks

Dirk Pesch

Head of Centre

NIMBUS Centre for Networked Embedded Systems

Cork Institute of Technology

dirk.pesch@cit.ie http://www.nimbus.cit.ie

Overview

1. Overview of CIT and Nimbus Centre

2. Selected research in wireless sensor networks

for indoor applications and localisation

3. Protocol design for vehicular ad-hoc networks

in road safety applications

Cork Institute of Technology

• Ireland’s second largest Institute of Technology located

in Cork (south of Ireland)

• CIT offers Bachelor, Masters and PhD degrees in

Science, Engineering, Business, Art and Music

• CIT has ca. 15000 students and approx. 1000 staff

The NIMBUS Centre

• Three research groups

– Adaptive Wireless Systems

• Wireless Network Design

• Algorithms & Protocols

• Real-time Localisation & Tracking

– Smart Systems Integration

• Sensor Device Integration,

• Miniaturisation and Embedding of Electronics

• Integral Sensing networks

– TEC Centre industry R&D group

• Focus on networked systems

research with applications in

• Energy Management

• Vehicular/Traffic

• Infrastructure Security

• Water Management

PDR

Map

Filtering

GPS

Main Industry & Academic Partners

• Industry (national/international)

– Intel, UTRC, Bord Gais, Benetel, Redmere, Cylon

Controls, Decawave, SocoWave, Alanya, Lincor,

Eurotech, Seftec, IHG, Viva

– Philips, Schneider Electric, Honeywell, ANA,

BijoData, Daimler, HSG Zander, Arup, Gemalto,

Ennovatis, STM

• Academic(national/international)

– UCC/Tyndall, UCD, TCD, NUIG

– Univ. of Bremen, TU Hamburg, CEA LETI,

Fraunhofer IIS, Embedded Systems Institute/TU

Eindhoven, TU Dresden, Univ. College Antwerp, VTT

Wireless Sensor Networks - WSN

Open Issues for Protocol Design for WSN

• Reliability of wireless channel is a concern in many applications

• Node life-times currently one to two orders of magnitude shorter than required for many sensing applications– Requires careful duty cycle adaptation

• Standards based WSN protocols are non-optimal compared to proprietary proposals

• Limited understanding of deployment issues for WSN– No wireless network design for deployment

– Limited understanding of WSN lifetime once deployed

• No integrated network management approach

• No communication protocol framework to deal with diverse range applications and QoS requirements– Results in custom designs every time which increases cost

Indoor Wireless Network Design

Methodology and Tool• First tool to support systematic design and deployment of WSAN in

buildings

– Integrates with IFC BIMs

– Reduces equipment costs by > 20%

– Order of magnitude reduction in design time for non-expert

Wireless Network Design Process

Requirements

Gathering

Automatic Design

& OptimisationDeployment

PHASE 1 PHASE 2 PHASE 3 PHASE 4

Verification

• A. Guinard, M. S. Aslam, D. Pusceddu, S. Rea, A. McGibney, D. Pesch, “Design and Deployment Tool for In-Building Wireless Sensor Networks: a

Performance Discussion”, in Proc. 7th IEEE Performance & Management of Wireless and Mobile Networks (P2MNET 2011), Bonn, Germany, Oct. 2011

• A. Mc Gibney, A. Guinard, D. Pesch, “Wi-Design: A Modelling and Optimization Tool for Wireless Embedded Systems in Buildings”, in Proc. 7th IEEE

Performance & Management of Wireless and Mobile Networks (P2MNET 2011), Bonn, Germany, October 2011

• A. Guinard, A. McGibney, D. Pesch, “A Wireless Sensor Network Design Tool to Support Building Energy Management”, in Proc. of 1st ACM BuildSys (in

conjunction with ACM SenSys), Berkeley, CA, USA, November 2009

Design Tool Case StudyExperienced DesignerNovice Designer WSAN Design Tool

53% Sensor

Traffic

47% Routing

Traffic78% Sensor

Traffic

22% Routing

Traffic 71% Sensor

Traffic

29% Routing

Traffic

Sensing Data

Delivery Ratio

Data transmission

cost (# packets)

Design

cost

Cost

Savings

Design

TimeComments

Novice Designer 97.0 % 1.85 € 3300 € 0 4 hNo previous WSN design experience, follows EnOcean Range

Planning Guide

Experienced Designer 97.6 % 1.21 € 2940 € 360 30 min WSN Design Expert, Sun SPOT developer

WSAN Design Tool 98.2 % 1.46 € 2620 € 680 40 min WSAN Design Tool

3 Gateways 5 Repeaters 3 hops max 3 Gateways 1 Repeater 3 hops max 2 Gateways 2 Repeaters 2 hops max

Experienced DesignerNovice Designer WSAN Design Tool

53% Sensor

Traffic

47% Routing

Traffic78% Sensor

Traffic

22% Routing

Traffic 71% Sensor

Traffic

29% Routing

Traffic

Sensing Data

Delivery Ratio

Data transmission

cost (# packets)

Design

cost

Cost

Savings

Design

TimeComments

Novice Designer 97.0 % 1.85 € 3300 € 0 4 hNo previous WSN design experience, follows EnOcean Range

Planning Guide

Experienced Designer 97.6 % 1.21 € 2940 € 360 30 min WSN Design Expert, Sun SPOT developer

WSAN Design Tool 98.2 % 1.46 € 2620 € 680 40 min WSAN Design Tool

3 Gateways 5 Repeaters 3 hops max 3 Gateways 1 Repeater 3 hops max 2 Gateways 2 Repeaters 2 hops max

DCLA protocol• The DCLA protocol is based

on Q-learning

• DCLA explores and selects

new actions adaptively

according to the rewards

received

• DCLA adapts duty cycle in

event-based scenarios

• Implemented in OPNET and

on telosB motes

START

Any frames received?

Preliminary exploration phase

No

Yes

END

Select next action based on

round-robin

Yes

No Increase learning rate

Select max inactive period

max(ai)

Select next action based on

e-greedy

Stable state (e = 0)

Yes

No

Update r(ai)

Greedily selected a different action?

Decrease exploration rate

No

Increase exploration rate

Yes

Has the reward changed?No

Select next action based on traffic change & last

stable

Increase learning rate

Increase exploration rate

R. de Paz Alberola, D. Pesch, “Duty Cycle Learning Algorithm (DCLA) for

IEEE 802.15.4 Beacon-Enabled Wireless Sensor Networks”, Ad-hoc

Networks, Elsevier, (http://dx.doi.org/10.1016/j.adhoc.2011.06.006)

Average Duty Cycle (DC) selection Average end-to-end delay (D)

Probability of Success (PS) Energy Efficiency

Event-based traffic

30m

30m

Event detection

• Nodes generate traffic following ON/OFF model– ON/OFF distribution follows

Pareto distribution

– Packet arrivals follow truncated normal distribution

• A number of PIR sensors detect the event and report to the sink

• Other nodes generate CBR

Instantaneous DC selection

Probability of Success Energy Efficiency

Distributed Duty Cycle Management (DDCM)

• Distributed Duty Cycle Management (DDCM) for IEEE 802.15.4

Beacon-Enabled Wireless Mesh Sensor Networks.

– DDCM uses DCLA to adapt node’s duty cycle to the network traffic and

manages the allocation of time slots as well as the prevention and

resolution of possible slot conflicts within a mesh network in a

distributed fashion.

Beacon Interval (BI)

Coordinator 1

(BO= 3)SD

Transmit ted

Beacon

Tracked

Beacons

Coordinator 2

(BO= 4)

Coordinator 3

(BO= 5)

ESD BSD SD

SD BSD

SD BSD

ESD

Mult i-superframe durat ion (MD)

Superframe

durat ion (SD)

BSD

BSD

BSD

Beacon Interval (BI)

SD ESD

SD

Broadcast

SD

Extended

SD

R. de Paz Alberola, B. Carballido Villaverde, D. Pesch, “Distributed Duty Cycle Management (DDCM) for IEEE

802.15.4 Beacon-Enabled Wireless Mesh Sensor Networks”, in Proc. of 5th IEEE International Workshop on

Enabling Technologies and Standards for Wireless Mesh Networking, Valencia, Spain, October 2011

Evaluation Results

Probability of SuccessAverage Duty Cycle Selected

Energy Efficiency

IEEE802.15.4 TinyOS Implementation

16

Duty Cycle

Adaptation

CC2420 Power

Consumption

Estimation

Clock Drift

Adjustment

Radio

CAP Sleep

DCLA

Localisation and Tracking

MapUme is an opportunistic localisation system which fuses location related

sensor information that is readily available to localise people and objects

PDR

Map

Filtering

GPS

MapUme

MapUme – OLS ServerSmart

Phone

WiFi TagSensor data

Clients Server

Camera networks

Key components:

Sensor and actuators networks – (Loc. + surveillance +environment system)

Context awareness – (moving objects and unexpected events)

Advanced real-time processing – for collision avoidance and navigation services.

Distributed middleware –scalability, predictability, configurability and continuous commissioning,

Platform for Safety and Security

Enhancement

(D)GPS

IIS active RFID,

Symeo LPR,

UWB,

CIT Opportunistic localisation to cover the rest

WiFi

WSAN

Cellular net.

for large critical infrastructures like airports

Opportunistic Localisation

8

7

6

5

4

3

2

1

0

Mean Location Error of Different Technologies

Ground Floor First Floor

No PDR

GPS

GPS

Outdoor

Ground Floor First Floor

Outdoor

WiFiWiFi WiFi

GSM

GSM GSM

All

UMTS

WiFi Hotspot

V2V: 802.11p, IR

WiMax

Variable

Message Sign

Satellite

Terrestrial

Broadcast

V2I

Vehicular Communication Network

Vehicular Adhoc NETworks - VANETs

• Vehicular communications has been primarily

motivated by safety

• Advent of Active Safety Applications

Goal!

Vehicular Communications - VC

• Relevant Standards

– WAVE: Wireless Access in Vehicular Environments

• IEEE 1609 set of standards (incl. 802.11p) for VC

– IEEE 802.11p: 802.11a modification for VC

• V2V: Vehicle-to-Vehicle Communication

• V2I: Vehicle-to-Infrastructure Communication

• Our Focus: Development of a Broadcast protocol

for active safety applications

– Reliable Vehicular Geo-broadcast protocol (RVG)

Challenges for Broadcasting in VANETs

• Broadcasting is an extremely expensive

technique

– It floods the medium with a high number of redundant

transmissions

– Making an already unreliable medium more

unreliable

• Broadcasting for Safety Applications MUST

satisfy:

– High Packet Delivery

– Low End-to-End delay

– Minimal Overhead

Reliable Vehicular Geo-broadcast protocol

(RVG)

• RVG can disseminate any type of application

data but it has been optimised for the

dissemination of safety related messages

• RVG is focused on high packet delivery, low

delay and low overhead

• Compliant with the IEEE 1609 standards

• M. Koubek, S. Rea, D. Pesch, “Reliable Broadcasting for Active Safety Applications in Vehicular Highway Networks”,

in Proc. of IEEE International Symposium on Wireless Vehicular Communications (WiVeC) 2010, Taipeh, Taiwan,

April 2010M. Koubeck, S. Rea, D. Pesch, “Increasing Multi-Hop Broadcasting Reliability in VANETs”, EURASIP

Journal on Advances in Signal Processing, May 2010

• G. Pastor Grau, D. Pusceddu, S. Rea, O. Brickley, M. Koubek, D. Pesch, “Vehicle-2-Vehicle Communication

Channel Evaluation using the CVIS Platform”, In Proc. of IEEE/ IET International Symposium on Communication

Systems, Networks, and Digital Signal Processing, Newcastle, UK, July 2010

Reliable Vehicular Geo-broadcast in Comparison

Advantages Disadvantages

Simple Flooding

• C2C-CC, NEC, GeoNet

• Simplicity

• Low latency

• Low reliability

• Redundancy

Area-based, neighbour

elimination (NE)

• DRG, UMB

• Medium

redundancy

• Low latency

• Algorithms fail in

real environ.

Multipoint Relaying (MPR)

• TRADE

• Low redundancy

• Low latency

• Unreliable

Combination of NE & MPR

• RVG

• Low redundancy

• Low latency

• High reliability

Environments

• Urban

– 600 x 600m

– 20 - 320 vehicles

– Free flow & Accident

• Highway

– 60 x 2000m

– 50 – 500 vehicles

– Free flow & Accident

RVG: Delivery RatioUrban Free Flow Scenario

RVG: Delivery RatioUrban Free Flow Scenario

Proximity Zone (125m)

Veh. density 20 55 150 230 320

Flood 0.17 0.49 0.92 0.92 0.89

TRADE 0.11 0.24 0.43 0.21 0.21

DRG 0.24 0.48 0.90 0.92 0.99

RVG 0.40 0.74 0.95 0.99 1.00

Achv. [%] 135 51.0 3.3 7.6 12.4

RVG: End-to-End DelayUrban Free Flow Scenario

100ms Services

RVG: OverheadUrban Free Flow Scenario

PACK: Delivery RatioUrban Free Flow Scenario

PACK: End-to-End DelayUrban Free Flow Scenario

End-to-End Delay [ms]

Veh. density 20 55 150 230 320

SRMB 16 16 25 31 30

RR-ALOHA 118 287 588 1072 2007

SFR 24 27 33 46 83

RVG 16 21 31 36 36

Achv. [%] 5 29 24 19 19

Summary and Outlook

• Nimbus research focuses on networked

systems with emphasis on wireless sensor and

vehicular ad-hoc networks

• The main application spaces include WSN for

building energy management and VANET for

traffic safety

• Future plans include to combine building energy

management with electric vehicle charging

• Challenges here include the integration of

widely heterogeneous wireless/mobile ad-hoc

networks to manage these applications