scalable and reliable wireless sensor network systems

Scalable and reliable wireless sensor network systems

Vinod Kulathumani

Dept. of Computer Science and Electrical EngineeringWest Virginia University

CS/EE 796 Graduate seminar series

Embedded systems

Found in variety of devices Aircraft, radar systems, nuclear and chemical plants Vehicles, TVs, camcorders, elevators > 90% of CPUs used for embedded devices

Part of a larger system Application known apriori

Little flexibility in programming

Networked embedded systems

What if embedded processors were connected ? Not wired but wireless

Enter Wireless Sensor Networks

- Really a network of embedded systems

Enabling technology

Micro-sensors (MEMS, Materials, Circuits) acceleration, vibration, gyroscope, tilt, motion magnetic, heat, pressure, temp, light, moisture, humidity, barometric chemical (CO, CO2, radon), biological, micro-radar actuators (mirrors, motors, smart surfaces, micro-robots)

Communication short range, low bit-rate, CMOS radios

The Vision for WSNs

Combine wireless networks with sensing / actuation

Ubiquitous computing Fine-grained monitoring and control of environment Network and interact with billions of embedded computers

Reasons Wireless communication - no need for infrastructure setup Drop and play Nodes are built using off-the-shelf cheap components Feasible to deploy nodes densely

A new class of computing

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streaming informationto/from physical world

Number CrunchingData Storage

productivityinteractive

Mainframe

Minicomputer

Workstation

PC

Laptop

PDA

Slide courtesy: Murat Demirbas

Application areas

Science: oceanography, seismology

Engineering: industrial automation, structural monitoring

Daily life: health care, disaster recovery

Emerging applications

Combination of sensors with mobile devices Social networking Participatory urban sensing

Assisted living – health monitoring Vehicular networks with variety of sensors Control systems using sensor networks

Trends

Increasing in scale

Increasing in complexity Middle America Subduction Experiment

ExScalIntel Developer Forum

Intel Hillsboro Fab

Outline of talk

Research challenges / goals

Summary of contributions Centralized classification / tracking [SRDS’05, Computer Comm’03] Distributed vibration control [MSNDC’05] Sensor network service for object tracking [EWSN’07, IPSN’06] Distance sensitive snapshot service [OPODIS’07]

Details of a specific contribution Sensor network service for object tracking

Future research interests

My research focus

Interests

Distributed systems / networking Fault-tolerance Self-healing systems Scalability

Sensor networks pose plenty of problems in these areas !

Research challenge

Application

Network

Resource constrained nodes

Low bandwidth, fading, interference

Harsh, malicious environments

Network abstraction layerMiddleware services

Network design

How to design scalable, reliable WSN applications despite unreliable networks ?

Rising in scale, complexity

Performance crucial

Industrial, medical, military

Observation based / control based

Static / mobile

Scales: < 100 to 10000

Unreliable

Classification and tracking (monitoring)

Scenario – asset protection Dense deployment; Resource and bandwidth constrained Goal: classify and observe tracks of objects

Application design Reliable estimation of influence fields [SRDS ‘05] Influence field (IF) – region over which an object can be detected IF estimated using binary detections Classification – Estimating size of IF Tracking – Estimating shape of IF

Soldier and vehicle influence fields wrt magnetometer

Scenario – asset protection Dense deployment; Resource and bandwidth constrained Goal: classify and observe tracks of objects

Application design Reliable estimation of influence fields [SRDS ‘05]

Network design Network abstractions for IF separation

Distance insensitivity, contention insensitivity

Network abstractions for IF shape Routing uniformity

Network parameters (density)

Aggregator

Scenario – asset protection Dense deployment; Resource and bandwidth constrained Goal: classify and observe tracks of objects

Application design Reliable estimation of influence fields [SRDS ‘05]

Network design Network services for separation Network services for uniformity Network parameters (density)

Deployment and testing Line in the sand [Computer Communications’ 03] ExScal (RTSS’05)

Scenario – asset protection Dense deployment; Resource and bandwidth constrained Goal: classify and observe tracks of objects Requirement : low latency (<2 s), high accuracy (> 99%)

Distributed vibration control

Scenario Control vibrations during payload launch Sensors / actuators distributed across surface Low computational resource, fault-prone Experimental study on Boeing fairing simulator [MSNDC’05]

Faults impact – potentially severe Hard to detect in real time

Requirement – mission critical stability

Scenario Control vibrations during payload launch Sensors / actuators distributed across surface

Application design Use on-off control scheme Model plant as linear system; vibration modes assumed Model unreliability as Byzantine behavior of actuators

Worst input to plant at all times

Scenario Control vibrations during payload launch Sensors / actuators distributed across surface

Application design Use on-off control scheme Model plant as linear system; vibration modes assumed Model unreliability as Byzantine behavior of actuators

Worst input to plant at all times

Network design Determine actuator placement for plant to be stable despite

Byzantine actuators [MSNDC’ 05]

Distributed tracking – optimal interception

Scenario WSN laid to protect asset Evader’s goal: minimize distance to asset Pursuer’s goal: intercept evaders at maximum distance Pursuers query sensor network for mobile evader locations

Scenario WSN laid to protect asset Pursuers query sensor network for mobile evader locations

Application design Model as zero-sum game Formulation of optimal pursuit control strategies [IPSN’06]

Presence of delay Under discrete sampling rate

Nash equilibrium conditions for successful pursuit

information of nearer objects required at faster rate

information of nearer objects required with lower delay

Scenario WSN laid to protect asset Pursuers query sensor network for mobile evader locations

Application design Model as zero-sum game Formulation of optimal pursuit control strategies [IPSN’06]

Network design Trail – a distance sensitive network service O(d) find time, cost for object distance d away O(d*log(d)) update time, cost for distance d moved Fault-tolerant, energy-efficient, family of tunable protocols

Scenario WSN laid to protect asset Pursuers query sensor network for mobile evader locations

Application design Model as zero-sum game Formulation of optimal pursuit control strategies [IPSN’06]

Network design Trail – a distance sensitive network service

Deployed and tested in Catch Me If You Can Demonstrated at Richmond Field Station, Berkeley, August 05

Distance sensitive snapshots in WSN

Scenario Distributed object tracking using WSN Goal: Pursuers should eventually catch all evaders

Application design Perfect information not necessary State of evaders distance sensitive in error, latency and rate

eventual catch

Network design Network service for distance sensitive snapshots [OPODIS 07] Exploit alternate forms of compression to gain efficiency

State of nearby nodes is fresher State of nearby nodes more precise State of nearby nodes refreshed more often

Systems built

ExScal (Extreme Scaling Experiment) Goal: classify between person, soldier, SUV and ATV and track Deployment area: 1,260m x 288m 1000+ sensor nodes, 200+ Stargates Technology transferred to Northrup Grumman

10,000 node experiment using ExScal software

Roles Classification / tracking subsystem Integrating communication chain Yield studies [ICNP’05]

Identify and study impact of faults

ExScal field

Other systems built

Kansei WSN testbed at Ohio State 432 TelosB, 150 Stargates, 150 XSM, 100

i-mote2 Software services for data injection, data

collection

Mobile network PeopleNET Cellphones integrated with psi-mote Buddy messaging, elevator status

Vehicle classification Los Alamos National Labs [2007] Seismic + Acoustic sensors

Trail: network service for tracking

Motivating scenario

Mobile Objects tracked by network of static sensors over a large area Network runs a tracking service Application (residing on mobile objects) issues query of the

form “Find object X” to the tracking service

Motivation for Trail

Queries answered by one (or more) central nodes not scalable Depletes energy Increases latency

One way to make queries local Publish object state everywhere But upon every move, global update needed

Global update for every object move not scalable

We need to publish object information systematically

Informal problem statement

Network tracking service returns query results in time and work proportional to distance from object

Requirement 1: Find distance sensitivity

When an object moves, tracking protocol updates the track in time and work proportional to distance moved

Requirement 2: Update distance sensitivity

Trail tracking structure

Trail protocol based on geometric ideas Properties proved on continuous 2-d plane Then implemented on discrete plane

Model 2-d real bounded plane, C denotes center of this plane Cost measured in Euclidean distance

One track maintained for each object Let P be object being tracked located at point p Tracking data structure for P denoted as trailP

Pointers that lead to current location of P

All tracks rooted at C

Trail intuition

If trailP restricted to be a straight line, each move will involve update from C

C

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p

Instead, trailP marked with vertices on-the-fly Vertices serve as anchor points for update Distance between vertices increases exponentially moving

towards C Anchor updated depending on distance moved After sufficiently large distance, update from C

Examples of trailPC

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Cost for update and find

Cost of updating trailP over a move of distance d is O(d*log(d))

Theorem N3

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worst case structure: log spiral

Algorithm for find

Cost of finding P from object Q at point q is O(d) where d is dist(p,q)

Theorem

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m

Draw successive circles of radii 20, 21, 22 .. 2(log dist(C,q)) Until trailP is intersected

Or reach C

Follow trailP to reach current location of P

Cost includes reaching trailP, following trailP, returning to q

Fault-tolerance and adaptivity of Trail

Fault-tolerance Nodes may fail after creating trail or old trails may not be deleted

Self-stabilizing actions using heartbeats along trail structure

Tolerating failures during update and find Route around failures using a method such as left hand rule in GPSR

As size of holes increases, update and find cost proportionally increase Trail supports graceful degradation

Adaptivity (Trail yields family of protocols) Can be tuned based on update and query frequency When query frequency higher, publish structure increases and find

increasingly straight Extreme case – find is a straight line to C and publish in circles

Performance evaluation

Experimental evaluation (Kansei testbed at OSU) Used to demonstrate PE tracking application for NEST DARPA project

Intruder tracks collected from Richmond Field Station [140m X 60m]

Tracks injected into Kansei testbed nodes to emulate motion of

evaders 15 X 7 node network, 3 ft spacing

3 pursuer 3 evader scenario

Study effect of interference on scaling in Objects [2 - 10] Query frequency [0.25 – 1 Hz]

Simulations [JProwler] 8100 nodes (90 by 90)

Up to 50 objects (uniformly separated and collocated)

Garcia Robots as Pursuers

Summary of Trail features

Trail – a distance sensitive network service Assumes no hierarchical partitioning of network O(d) find time, cost for object distance d away O(d*log(d)) update time, cost for distance d moved Fault-tolerant

Self-stabilizing, graceful degradation

When many objects come close together, network interference can cause delay Synchronized push version? Distance sensitive snapshot service

Distance sensitive snapshot service

A brief overview

Informal problem statement

Given N nodes, with bounded memory, in f dimensions each can sense m-bit information at any time each can communicate at W bits per second

Deliver a global snapshot at each node (can be relaxed to a subset) that uniformly has distance sensitive latency (and distance sensitive

resolution, and distance sensitive rate) State of nearby nodes is fresher State of nearby nodes more precise State of nearby nodes refreshed more often

periodically, as fast as possible (can be relaxed to lower rate)

Illustration

Illustration

Results

Maximum staleness in state of a node i received by a

snapshot at node j is O(log(n) * m * d) where d = dist(i, j)

Resolution of state of a node i in a snapshot received at node

j is Ω(1 / d2) where d = dist(i, j)

Communication cost to deliver a snapshot of one sample

from each node to all nodes is on average O(N * log(n) * m)

Conclusions

Research focus Reliable network services for WSN applications

Applications for classification, tracking, distributed control

Network services tested in actual field deployments

Key role in integrating complete WSN systems ExScal, Line in the Sand, Kansei, Catch Me If You Can

Facility monitoring at Los Alamos National Labs

Provided deep insight into real problems in wireless and sensor

networks

Future research interests

WSNs combined with mobility, actuation

Mobile heterogeneous wireless networks

Convergence of mobile devices with sensors Urban surveillance, online health monitoring, disaster relief, mobile

gaming, vehicular networks

Realization of ubiquitous systems

Research questions Low power self – localization of mobile units

Scenarios: low cost indoor tracking, security, identity management

Reliable, secure information management Protect against eavesdropping, jamming

Provide reliable content delivery

Architecture Composing applications across heterogeneous networks [MODUS 2008]

Convergence / inter-operability with Internet, cellular networks

Wireless sensor networks for control

WSNs suited for control applications Wireless feature: industrial control and process control applications

Large scale feature: control of distributed parameter systems, power grids

Challenges / research questions Performance

How to guarantee reliability / low latency and meet wire-line standards?

How to secure the network against jamming?

Architecture Underlying network independent of control system / application ?

Theory Joint stabilization of control application and network layer

Cross cutting research

Network protocolsNetwork architecture

•Reliable•Secure

Information processing Control systems

Computer vision (urban surveillance)

Wireless communication technology

MEMS / sensor fabrication

Database systemsData Mining

Thank you

Contact Information

Vinod Kulathumani

Vinod.kulathumani@mail.wvu.edu

http://www.csee.wvu.edu/~vkkulathumani