rcti sensors

8/2/2019 RCTI Sensors

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Wireless Sensor Networks

RCTI Seminar Day Presentations

Roshdy Hafez

Thomas Kunz

Marc St.-Hilaire

Ionnis Lambadaris

Richard Yu


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Roshdy Hafez

Systems and Computer Engineering


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Thomas Kunz

Professor and Director

Technology Innovation Management Program


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Mobile Computing Group

Facilitate the development of innovative next-generation mobileapplications on resource-constraint, mobile devices

Develop the required network architectures (MANETs, wirelessmesh networks, wireless sensor networks)

Research into network protocols (MAC, routing, Mobile IP, QoSsupport, transport), and middleware runtime support

Licensed technology to EION Inc. in 2005 (Adaptive IntelligentRouter)

Research funded by federal (NSERC) and provincial grantingagencies (OCE, NCIT), as well as industry

Worked with Bell, Nortel, Motorola in the past

Currently cooperating with CRC, Alcatel-Lucent


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High-Level Architecture: multiple WSN, fixed Core

(Examples: surveying multiple airports, border crossings, etc.)

Base Station

SensorXML

Router

Monitored Area

IP Router

XML Routed Network

Event

collection &

presentation

Monitoring data processing

Event dissemination

1st responder notification

sensor data collection

and archive:

information madeavailable via web

services

IP

Wireless Sensor Networks:

dynamic retasking, new

sensor types/data, improved

algorithms and protocols

Fixed Networking:

distribute sensor data to

(different) recipients, discover

sensors and their capabilities


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Core Functionality: Clock Synchronization, Localization

Clock sync is critical at many layers

Beam-forming, localization, distributed DSP, MAC

Tracking; data aggregation & caching

Similarly, localization is fundamental

Routing, security

Tracking; data aggregation & caching

t=3

t=2t=1

t=0


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Localization

Key requirements: high accuracy, no additional hardware (GPS, etc.),

support fast deployment (minimum # of anchors), range-free or range-based

Another important point: should work well for typical mission-critical deployments

0 2 4 6 8 100

2

4

6

8

10

(a)

0 5 100

2

4

6

8

10

(b)0 5 10

0

2

4

6

8

10

(c)

Random topology, 200 nodes

C-shaped network,

160 nodes

Uniform grid (with

small placement errors)

100 nodes


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Localization: Cooperative Localization,

based on Curvilinear Component Analysis

5 10 15 20 25 300

0.05

0.1

0.15

0.2

Connectivity

Me

dianError(r)

(a) Range-based: 3 anchor nodes

5 10 15 20 25 300

0.05

0.1

0.15

0.2

Connectivity

MedianError(r)

(b) Range based: 10 anchor nodes

MDS-MAP(P,R)

CCA-MAP

MDS-MAP(P,R)

CCA-MAP

5 10 15 20 25 300.1

0.2

0.3

0.4

0.5

Connectivity

M

edianError(r)

(a) Range free: 3 anchor nodes

5 10 15 20 25 300

0.1

0.2

0.3

0.4

Connectivity

MedianError(r)

(b) Range free: 10 anchor nodes

MDS-MAP(P,R)

CCA-MAP

MDS-MAP(P,R)

CCA-MAP

Results for Random Network Topology


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Clock Synchronization

mutual, low overhead, compatible withWiFi, WiMax, Zigbee standards (i.e., based

on periodic beacons)

key idea: adjust slope of local clocks, rather than timestamp value -> converge over time

Max time difference in a 5x5 network using

CSMNS

c.d.f of max time difference in a 5x5 network

using the IEEE 802.11 TSF


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Steps Forward

Defined and evaluated fundamental algorithms through simulations

Plan to implement and evaluate them in a real testbed

Additional research questions

Localization:

Optimal anchor locations (non-trivial and non-obvious)

Apply NN structure to track mobile sensors

Reduce computational complexity

Bound worst-case performance

Synchronization:

Use external clock references

Reflect hierarchical network structure

Ongoing: work on fixed-network aspects, gateway to interconnect WSNand core, XML-based description and discovery, etc.


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Marc St-Hilaire

School of Information Technology


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Wireless Sensor Networks (WSN)

Research in planning algorithms (both static & dynamic)

How to design new WSN in a cost effective way

How to update an existing WSN infrastructure

Organisation (re-organisation) of the nodes to maximize the life time of the network

Research on network protocols

Routing scheme with different objectives

Save energy, minimise delay or combination

Re-organise the route in case of node/link failure

Correlation of events both in space and time

Clock synchronisation

Localization algorithm


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Wireless Sensor Networks (WSN)

Research on data association

How to follow multiple moving targets such as in military applications, border

defence and so on.

Research on data aggregation/fusion

Aggregate data in order to save bandwidth, computing resources, battery life, etc.


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Ioannis Lambadaris

Systems and Computer Engineering


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Overview: Research/Academic InterestsJohn Lambadaris

Associate ProfessorDepartment of Systems and ComputerEngineering

Carleton UniversityOttawa, Ontario K1S 5B6email: [email protected]: (613) 520-2600 x1974Performance Analysis of Computer Communication Networks

Congestion control of IP networks, Differentiated services and Quality of Service

Resillient Packet Ring protocols and performance evaluation

Resource allocation and Quality of Service in optical networks

Real time packet content inspection engines

Security

Endpoint-Driven Intrusion Detection and Containment of Fast Spreading Worms inEnterprise Networks

Mobile/Wireless Networks

High Speed Downlink Packet Access (HSDPA)

Sensor and Ad-Hoc Networks

Zigbee/IEEE 802.15.4 networking

Practical Design for wireless sensor nodes Design, performance analysis and prototyping of nodes based on popular wireless

transceivers such as TI/Chipcon (CC1100, CC1110), Freescale semiconductors(MC13201-2-3 ), Cypress Semiconductors (CYRF69103, CYRF69213)

Distinctions:

Ontario Premiers Excellence Award 1999 -- Carleton Research Achievement Award2000-01.

Patents:20060089113 - Radio control receiver system for multiple bands, frequencies and modulation protocol coverage.Authors: John Lambadaris, A. Elahi and J. Perez
mailto:[email protected]:[email protected]


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Topics to address:

High Speed Downlink Packet Access (HSDPA) systems

Sensor/wireless ad-hoc networks

-Node Location Estimation

-Low Bit rate video for surveillance


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Objective

-To find the optimal

scheduling policythat controls the allocation of the time-code resources.

An optimal policy should be:

-Fair; Divide the resources fairly between all the activeusers.

-Maximize the overall cell throughput.

-Provide channel aware (diversity gain) and high speed

resource allocation.

Optimal Scheduling in High Speed Downlink Packet Access

(HSDPA)


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Methodology-Markov Decision Processes and Dynamic Programming (two user analysis)

-OPNET based simulations for verification

Optimal Scheduling in HSDPA: Analysis and

Validation

Optimal policy (two user case) Comparison with heuristic policies


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Optimal Scheduling in HSDPA: Further research

-Realistic channel modeling

-Packet retransmissions

-Scalability issues

-Extension to more than two users

Recent publications:

Hussein Al-Zubaidy, Ioannis lambadaris, Code Allocation Policy Optimization in HSDPA Networks

Using FSMC Channel Model,IEEE Wireless and Networking Conference (IEEE WCNC), March 31-

April3, 2008.


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Sensor Location Estimation: Problem Statement

The sensor localization problem.

Given a set of sensors deployed in a field, in which

some of them are anchors and the remaining areunknown sensors, we may want to estimate the nodes

positions of the unknown sensors.

Anchors: Nodes that know their positions.

Unknown sensors: Nodes that do not know theirpositions.


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Sensor Location Estimation:Range-based and Range-free algorithms

In order to study the sensor localization problem, researchershave proposed schemes that lie on one of the followingcategories:

Range-based algorithms rely on computing point-to-point distance estimates.Range-free algorithms propose solutions without theavailability of inter-distance measurements.

Our hybrid approach: We will use a range-free approachcoupled with a range-based refinement.


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Sensor Location Estimation:

APIT Algorithm

a is an unknown sensor.

A,B,C,D are audible anchors for a.

Step:

1. Generation of triangles.

3 combinations from the set of

4 audible anchors = 4 triangles

-> {ABC,BCD,ACD,ABD}

2.Acquisition of beaconinformation.

3. APIT Test

4. APIT Aggregation.

5. Position estimation (COG).


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Sensor Location Estimation: Simulation Setup

Random distribution Deterministic distribution of anchors

Black nodes ->anchors,

White nodes -> unknown sensors

Random distribution

Densenetworks

Sparse Networks


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A Propagation Model for Sensors: RIM(Radio Interference Model)

DOI (Degree of Irregularity)

parameter

Maximum path loss percentagevariation per unit degree change

in the direction of radio

propagation.

RIM Model

Model that introduces theDOI parameter.

Anisotropic model.

Radio variations depend with

both distance and direction.


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Sensor Location Estimation: Results

M=200, N=40, R=1.5 [m]

N=40, R=1.5 [m]

DOI=0.1 DOI=0.7


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Sensor Location Estimation: Further research

Time varying interference patterns

Extensions of the location algorithms to include obstacles(e.g. terrain irregularities) between nodes

Complexity and scalability of the algorithms

Extensions to include node/sensor mobility


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Low bit-rate Video Transmission over Wireless Zigbee Networks

Challenges

Video application requirements High data rate for high quality (compression is used)

Bandwidth-efficient codecs are the most computationally intensive

Limitations of Zigbee networks

Low Power (Battery operated)

Maximum nominal rate for IEEE 802.15.4 standard is 250 kbps

Realistic throughput is much lower (CSMA/CA, overhead, multi-hop, etc.)

Video applications may be implemented over Zigbee Using advanced video encoders, video segmentation and rate-control algorithms

Using the multiple channels available in the IEEE802.15.4 and using multiple NICs

Using MDC and multi-hopping over multi-channel multi-interface network topologies

Recent Publication: Ahmed Zainaldin, Ioannis Lambadaris, Bis Nandy Adaptive Rate Control MPEG4 Video Transmission overWireless Zigbee Networks, IEEE International Conference on Communications (ICC), May 19-23 2008


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Solutions for Video Transmission over Zigbee Networks

1. Rate Control Variable bit-rate over Wireless Zigbee Networks (RCVBR)

2. Region of Interest (ROI) Encoding

3. Multi-channel Multi-radio over Wireless Zigbee Networks

4. Multiple Description Coding (MDC) over a multi-channel multi-interface Zigbee networks

VideoSource

MPEG-4Encoder

Packetizer

Rate

Controller

b

r

Q

R(n)

111

111

m m


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Summary: Research expertise and personnel

Simulations, traffic modeling and performance analysis

-NS-2 and OPNET based simulations

Matlab computations for propagation and interference models

Prototyping sensor node/development from concept to manufacturing (PCB design,firmware programming, RF design)

Personnel: Faculty, graduate students, research associates and a group of

professional contractors


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Secure Wireless Biosensors Networking for

Authentication and Life Support of Field

Personnel

Richard Yu

RCTI, Carleton University

Helen Tang and Peter Mason

DRDC - Ottawa


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Military tactical mobile ad hoc networks (MANETs) challengesecurity design.

As the front line of defence, authentication is the corerequirements for integrity, confidentiality and non-repudiationin networked centric warfare.


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Biometrics from biosensors provide some promising solutions tothe authentication problems.

Fingerprint FaceIris Retina

VoiceFinger vein Cardio-based
http://www.aladdin.com/news/2006/etoken/Aladdin_Biometric_Technology.asp


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Patient/citizen centered healthcare based on wireless biosensors


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Sensor data

MultimodalBiometrics

Physiological status

monitoring

Encryption

User

authentication

A unified framework approach


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Research: Wireless networking for biosensors, biometric-basedauthentication for tactical MANET, biosensor data processing,biosensor scheduling and management.

rcti sensors

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