Scalable and reliable wireless sensor network systems

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, CO2, 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. Number Crunching Data Storage Mainframe Minicomputer productivity interactive Workstation PC Laptop PDA A new class of computing log (people per computer)‏ streaming information to/from physical world year 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 Intel Developer Forum ExScal Intel Hillsboro Fab Middle America Subduction Experiment

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 Industrial, medical, military Observation based / control based Static / mobile Scales: < 100 to 10000 Application Rising in scale, complexity Performance crucial How to design scalable, reliable WSN applications despite unreliable networks ? Middleware services Network design Network abstraction layer Resource constrained nodes Low bandwidth, fading, interference Harsh, malicious environments Network Unreliable

14. 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 • 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) • Scenario – asset protection • Dense deployment; Resource and bandwidth constrained • Goal: classify and observe tracks of objects • Requirement : low latency (<2 s), high accuracy (> 99%) Aggregator Soldier and vehicle influence fields wrt magnetometer 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] • 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)

15. 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 Distributed vibration control • 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] • 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

16. 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 Distributed tracking – optimal interception • 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 • 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

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. Requirement 1: Find distance sensitivity Network tracking service returns query results in time and work proportional to distance from object Requirement 2: Update distance sensitivity When an object moves, tracking protocol updates the track in time and work proportional to distance moved Informal problem statement

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 trailP • Pointers that lead to current location of P • All tracks rooted at C

25. Trail intuition • If trailP restricted to be a straight line, each move will involve update from C p’ C 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

26. C C C C C C N3 N3 N3 N3 N3 N3 N2 N2 N2 N2 N2 N2 N1 N1 N1 N1 N1 N1 c2 c2 p p p p c2 c2 c3 c3 c3 c1 c3 c1 c2 c2 c3 c1 p c1 p c3 c1 c1 Examples of trailP

27. N3 N2 N1 c2 p’ p c3 c1 Cost for update and find Theorem Cost of updating trailP over a move of distance d is O(d*log(d)) worst case structure: log spiral

28. Algorithm for find • 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 C Theorem N3 Cost of finding P from object Q at point q is O(d) where d is dist(p,q) m N2 q Cost includes reaching trailP, following trailP, returning to q N1 c3 p c2

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. Illustration

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 / 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)

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

42. Thank you Contact Information Vinod Kulathumani Vinod.kulathumani@mail.wvu.edu http://www.csee.wvu.edu/~vkkulathumani