rcti sensors
TRANSCRIPT
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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
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(b)0 5 10
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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
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Connectivity
Me
dianError(r)
(a) Range-based: 3 anchor nodes
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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
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Connectivity
M
edianError(r)
(a) Range free: 3 anchor nodes
5 10 15 20 25 300
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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
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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
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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.