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Scalable and reliable wireless sensor network systems Vinod Kulathumani Dept. of Computer Science and Electrical Engineering West Virginia University CS/EE.

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Presentation on theme: "Scalable and reliable wireless sensor network systems Vinod Kulathumani Dept. of Computer Science and Electrical Engineering West Virginia University CS/EE."— Presentation transcript:

1 Scalable and reliable wireless sensor network systems Vinod Kulathumani Dept. of Computer Science and Electrical Engineering West Virginia University CS/EE 796 Graduate seminar series

2 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

3 Networked embedded systems What if embedded processors were connected ? Not wired but wireless Enter Wireless Sensor Networks - Really a network of embedded systems

4 Enabling technology Micro-sensors (MEMS, Materials, Circuits)‏  acceleration, vibration, gyroscope, tilt, motion  magnetic, heat, pressure, temp, light, moisture, humidity, barometric  chemical (CO, CO 2, radon), biological, micro-radar  actuators (mirrors, motors, smart surfaces, micro-robots)‏ Communication  short range, low bit-rate, CMOS radios

5 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

6 A new class of computing year log (people per computer)‏ streaming information to/from physical world Number Crunching Data Storage productivity interactive Mainframe Minicomputer WorkstationPCLaptop PDA Slide courtesy: Murat Demirbas

7 Application areas Science: oceanography, seismology Engineering: industrial automation, structural monitoring Daily life: health care, disaster recovery

8 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

9 Trends Increasing in scale Increasing in complexity Middle America Subduction Experiment ExScal Intel Developer Forum Intel Hillsboro Fab

10 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

11 My research focus

12 Interests Distributed systems / networking  Fault-tolerance  Self-healing systems  Scalability Sensor networks pose plenty of problems in these areas !

13 Research challenge Application Network Resource constrained nodes Low bandwidth, fading, interference Harsh, malicious environments Network abstraction layer Middleware 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 Unreliable

14 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 ( 99%)

15 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]

16 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

17 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

18 Systems built ExScal (Extreme Scaling Experiment)  Goal: classify between person, soldier, SUV and ATV and track  Deployment area: 1,260m x 288m  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

19 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

20 Trail: network service for tracking

21 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

22 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

23 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

24 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 trail P Pointers that lead to current location of P All tracks rooted at C

25 Trail intuition If trail P restricted to be a straight line, each move will involve update from C C p’ p Instead, trail P 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

26 Examples of trail P C N3N3 N2N2 p N1N1 c3c3 c2c2 c1c1 N3N3 N2N2 p N1N1 c3c3 c2c2 c1c1 C N3N3 N2N2 p N1N1 c3c3 c2c2 c1c1 C C N3N3 N2N2 p N1N1 c3c3 c2c2 c1c1 N3N3 N2N2 N1N1 c3c3 c2c2 c1c1 p C C N3N3 N2N2 p N1N1 c3c3 c2c2 c1c1

27 Cost for update and find Cost of updating trail P over a move of distance d is O(d*log(d)) Theorem N3N3 N2N2 p’ N1N1 c3c3 c2c2 c1c1 p worst case structure: log spiral

28 Algorithm for find Cost of finding P from object Q at point q is O(d) where d is dist(p,q) Theorem C p c2c2 N3N3 N2N2 N1N1 c3c3 q m Draw successive circles of radii 2 0, 2 1, (log dist(C,q))  Until trail P is intersected  Or reach C Follow trail P to reach current location of P Cost includes reaching trail P, following trail P, returning to q

29 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

30 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

31 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

32 Distance sensitive snapshot service A brief overview

33 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)

34 Illustration

35

36 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 / d 2 ) 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)

37 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

38 Future research interests WSNs combined with mobility, actuation

39 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

40 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

41 Cross cutting research Network protocols Network architecture Reliable Secure Information processingControl systems Computer vision (urban surveillance) Wireless communication technology MEMS / sensor fabrication Database systems Data Mining

42 Thank you Contact Information Vinod Kulathumani


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