Efficient Bulk Transport and Mobility Control in Wireless Sensor Networks

1. Efficient Bulk Transport and Mobility Control in Wireless Sensor Networks Guoliang Xing Department of Computer Science City University of Hong Kong

2. Outline • Motivation • Model-driven medium access control • Mobility-assisted spatiotemporal detection • Other projects • Spatiotemporal query service for mobile users • Relationship bw coverage and connectivity • Future work

3. Sensing Applications • Redwood Ecophysiology, Occidental, CA [Culler05] • "40-50 nodes per tree, 5 min sample interval, temp, light, humidity…" • Structural health monitoring [Szewczyk04] • "sample vibration at 100Hz, 30 min/day”, 100 G data gathered per year • Volcano monitoring, [Tungurahua 2007] • "16 nodes, sample at 100Hz, 19 days“, most data processed locally, 107 M data gathered

4. Promise of Sensor Networks "Dense monitoring and analysis of complex phenomena over large regions of space for long periods" ----- David Culler "Dense monitoring and analysis of complex phenomena over largeregions of space for long periods" ----- David Culler

5. Application Characteristics • Data-intensive • Long-lived • 1 month ~ years on limited power supplies • Delay-tolerant • "sample accelerometers at 100Hz and report data hourly"

6. Outline • Motivation • Model-driven medium access control • Establish empirical power control and interference models • Enable concurrent transmissions of multiple nodes • Mobility-assisted spatiotemporal detection • Other projects • Spatiotemporal query service for mobile users • Relationship bw coverage and connectivity • Future work

7. Signal-to-interference-noise ratio (SINR) model Received Signal Strength (RSS) > b Noise +å Interference Understanding Interference • Traditional MACs try to completely avoid interference • Back-off and channel reservation • Only one link within the interference range can transmit s1 s2 r1 r2 +

8. Improve Throughput by Concurrency • Enable concurrency by controlling senders' power s1 s2 r1 r2 +

9. Received Signal Strength (RSS) > b Noise +å Interference Existing Models • How does radio power decay? • Classical exponential decay model: • How does packet reception relate to SINR? • Classical deterministic SINR model: RSS = P / distα log(RSS) = log(P)- α log(dist)

10. Model Estimation Experiments • 18 Tmotes with Chipcon 2420 radio • 32 tunable power leves, -25 – 0 dBm • One sender, multiple receivers at different positions • Four environments • Office, corridor, parking lot, grass field • Over two weeks of experiments

11. Received Signal Strength • Near-linear RSSdBm vs. transmission power level • Non-linear RSSdBm vs. log(dist), different from the classical model! • Observable difference in hourly measurements Received Signal Strength (dBm) Received Signal Strength (dBm) Transmission Power Level Transmission Power Level

12. Pair-wise Power Control Model • Received signal strength (RSS) at r when s transmits with power Ps is given by RSSr(s) = a x Ps+ b • a and b are interpolated using multiple measurements • a is estimated once • b is updated periodically s Ps r RSSr(s)

13. Packet Reception Ratio vs. SINR • Classical model doesn't capture the gray region 0~3 dB is "gray region" Packet Reception Ratio (%) parking lot, no interferer office, no interferer office, one interferer Received Signal Strength (RSS) > b Noise +å Interference

14. Probabilistic SINR Model • PRR(SINRi ) (1≤ i ≤ m) 100 80 60 Packet Reception Ratio (%) 40 20 0 1 2 3 4 SINR (dB)

15. Online Model Estimation • Each node bcasts N beacons at K power levels • Each node estimates neighbors' power control models and its own interference model • Nodes exchange their model parameters s2 s1 model parameters r2 r1

16. System Components Power Control Model Currency Check Concurrent Transmission Engine handshaking Online Model Estimation Interference Model Throughput Prediction Throughput Prediction

17. Throughput Prediction • s tries to transmit to r • s overhearsm packets (belonging to K links) • s finds power P that maximizes • If negative, abort, otherwise transit a block of B packets RSS model PRR model RSSr(P) Σ PRRv( SNRv ) – |K| SNRr = Interferencer+Noiser assuming 100% PRR for all active links obtained from handshaking

18. Performance Evaluation • Implemented in TinyOS 1.x • 16 Tmotes deployed in a 25x24 ft office • 8 senders and 8 receivers

19. Sample Experimental Results Improve throughput linearly w num of senders system throughput (Kbps) system throughput (Kbps) Number of Senders Time (second)

20. Summary of Contributions • Established power control and interference models using experimental data • Developed a model-driven MAC protocol for concurrent bulk-data transmissions • Evaluated system performance on an 18-node test-bed • Improved network capacity by ~3 times

21. Outline • Motivation • Model-driven medium access control • Mobility-assisted spatiotemporal detection • Other projects • Spatiotemporal query service for mobile users • Relationship bw coverage and connectivity • Future work

22. Mission-critical Target Detection • Stringent Spatiotemporal QoS requirements • High detection probability, e.g., 90% • Low false alarm rate, e.g., 5% • Bounded detection delay, e.g., 20s • Network and environmental dynamics • Death of nodes (battery depletions, attacks…) • Changing noise levels and target profiles

23. State of the Art • Over-provisioning of sensing capability • Careful advance network planning • Dense node deployment • Incremental redeployment • Issues • High deployment costs and long redeployment periods • Fails to adapt to network and environmental changes

24. Mobility-assisted Target Detection • Mobile sensors collaborate with static sensors in target detection • Achieve higher signal-to-noise ratios by moving closer to possible targets • Reconfigure sensor coverage dynamically target

25. Mobile Sensor Platforms • Limitations • Low movement speed (0.1~1 m/s) • High power consumption (~60 W for PackBot) PackBot @ iRobot.com Koala @ NASA Robomote @ USC

26. Overview of Our Approach • Data-fusion-based detection model for collaboration between mobile and static sensors • Optimal sensor movement scheduling algorithm • Minimizes the moving distance of sensors • Meets spatiotemporal QoS requirements • High detection probability, low false alarm rate, and bounded detection delay • Simulations based on real data traces of target detection

27. Signal and Noise Models • Target energy decays quadratically with distance • Noise energy follows the Normal distribution • Sensor reading = decayed target energy + noise energy Plotted based on real acoustic sensor data traces in military vehicle detection

28. Fusion-based Detection Model noise energy distribution sensor reading distribution sensor reading distribution • Sensors send readings to the cluster head • Cluster head sums up all readings and compare against a threshold energy detection threshold false alarm rate detection probability

29. Problem Formulation • Mobile sensors are assumed to move synchronously at equal-distant steps • Find two detection thresholds and a movement schedule • Minimizes the moving distance of mobile sensors • Detection probability ≥ α, false alarm rate ≤β, detection delay ≤ T Example movement schedule: M1: t0 - one step, t3 - two steps … M2: t1 -one step, t2 -one step… M3: t1 -two steps, t2 -one step… target

30. A Two-phase Detection Scheme • First phase – static detection • All sensors send readings to cluster head • Cluster head makes a detection decision, if positive, starts the 2nd phase • Second phase – movement scheduling • Mobile sensors move toward the possible target according to a movement schedule • Cluster head makes the final detection decision • First phase – static detection • All sensors send readings to cluster head • Cluster head makes a detection decision, if positive, starts the 2nd phase • Second phase – movement scheduling • Mobile sensors move toward the possible target according to a movement schedule • Cluster head makes the final detection decision

31. Optimal Movement Schedule • Theorem I: max sum of readings in the 2nd phase leads to min total moving distance • Optimal schedule maximizes the sum of energy readings

32. Examples of Optimal Schedules • Assume that all sensors can move one step every second, and detection delay is T seconds • Case 1: only one step allowed • Opt schedule: move B/C one step at time zero • Case 2: two steps allowed • Schedule I: move B and C one step at time zero • Schedule II: move A two steps at time zero sample T-1 second sample T-2 second All move combinations must be considered to find the optimal schedule! B A C

33. Finding the Optimal Schedule • Theorem II: if a sensor moves in the 2nd phase, it moves continuously before a stop • Num of move combinations is limited • Dynamic programming algorithm

34. Simulations • Public dataset of detecting military vehicles [Duarte04] • Sensors are randomly deployed in a 50×50m2 field

35. Performance Results Total 6 sensors are deployed MD-random1: randomly choose one sensor and moves to the target MD-random2: randomly move the next step from an arbitrary sensor

36. Outline • Motivation • Model-driven medium access control • Rendezvous-based bulk-data transport • Mobility-assisted spatiotemporal detection • Other projects • Spatiotemporal query service for mobile users • Relationship bw coverage and connectivity • Future work

37. Spatiotemporal Query • Spatial constraint • All and only the sensors within 100m should respond • Temporal constraints • Data are no older than 1s, and must be delivered within 2s “report the temperature within 100m in every 2s; data can be at most 1s old.”

38. Implementation • Implemented MobiQuery on Mica2 motes • Acroname PPRK robot carrying Stargate was used to emulate the user • Demonstrated at Sensys 04 International Conference on Distributed Computing Systems (ICDCS), 2005 International Symposium on Information Processing in Sensor Networks (IPSN) 2005

39. Coverage + Connectivity • Backbone nodes must achieve: • K-coverage: every point is monitored by at least K active sensors • N-connectivity: network is still connected if N-1 active nodes fail Active nodes Sensing range Sleeping node Communicating nodes A network with 1-coverage and 1-connectivity

40. Connectivity vs. Coverage: Analytical Results • Network connectivity does not guarantee coverage • Connectivity only concerns with node locations • Coverage concerns with all locations in a region • If Rc/Rs 2 • K-coverage  K-connectivity • Implication: given requirements of K-coverage and N-connectivity, only needs to satisfy max(K, N)-coverage • Solution: Coverage Configuration Protocol (CCP) • If Rc/Rs< 2 • CCP + connectivity mountainous protocols ACM Transactions on Sensor Networks, Vol. 1 (1), 2005. First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003

41. Acknowledgements • Collaborators • Jianping Wang, Xiaohua Jia, Weijia Jia, Minming Li, Hing Cheung So (CityU of HK) • Chenyang Lu, Robert Pless (Washing Univ.), Gang Zhou (College of William and Mary), Benyuan Liu (Univ. of Mass, Lowell) • Students and associates • PhDs: Hongbo Luo (Ph.D, joint supervision with Dr. Xiaohua Jia), Rui Tan, Zhaohui Yuan, Shucheng Liu, • Masters: Mo Sha • Research Staff: Tian Wang, Shaoliang Peng (joint supervision with Prof. Weijia Jia)

42. Research Summary • Systems • C-MAC: concurrent model-driven MAC [TR08] • UPMA: unified power management architecture [IPSN 07] • MobiQuery: spatiotemporal query service for mobile users [ICDCS 05, IPSN 05] • nORB: light-weight real-time middleware for networked embedded systems [RTAS 04] • Algorithms, protocols, and analyses • Mobility-assisted spatiotemporal detection [ICDCS 08,IWQoS 08] • Rendezvous-based data transport [MobiHoc 08, RTSS 07] • Minimum power configuration [MSWiM 07, MobiHoc 05, TOSN 3(2)] • Integrated coverage and connectivity configuration [TOSN 1(1), SenSys 03] • Impact of sensing coverage on geographic routing [TPDS 17(4), MobiHoc 04] • Real-time power-aware routing in sensor networks [IWQoS 06] • Data fusion for target detection [IPSN 04]

43. Relevant Publications ACM/IEEE Transaction Papers: • Rendezvous Planning in Wireless Sensor Networks with Mobile Elements, G. Xing, T. Wang, Z. Xie and W. Jia, IEEE Transactions on Mobile Computing (TMC), accepted for publication, to appear. • Minimum Power Configuration for Wireless Communication in Sensor Networks, G. Xing C. Lu, Y. Zhang, Q. Huang, R. Pless, ACM Transactions on Sensor Networks, Vol 3(2), 2007 • Integrated Coverage and Connectivity Configuration for Energy Conservation in Sensor Networks, G. Xing; X. Wang; Y. Zhang; C. Lu; R. Pless; C. D. Gill, ACM Transactions on Sensor Networks, Vol. 1 (1), 2005 • Impact of Sensing Coverage on Greedy Geographic Routing Algorithms, G. Xing; C. Lu; R. Pless; Q. Huang. IEEE Transactions on Parallel and Distributed Systems (TPDS),17(4), 2006 Conference Papers: • Rendezvous Design Algorithms for Wireless Sensor Networks with a Mobile Base Station, G. Xing, T. Wang, W. Jia, M. Li, the 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), May 26-30, 2008, Hong Kong, acceptance ratio 44/300=14.6%. • Mobility-assisted Spatiotemporal Detection in Wireless Sensor Networks, G. Xing, J. Wang, K. Shen, Q. Huang, X. Jia and H. So, the 28th International Conference on Distributed Computing Systems (ICDCS), Beijing, China, Jun 17-20, 2008, acceptance ratio 102/638=16%. • Rendezvous Planning in Mobility-assisted Wireless Sensor Networks, Guoliang Xing, Tian Wang, Zhihui Xie and Weijia Jia, The 28th IEEE Real-Time Systems Symposium (RTSS), December 3-6, 2007, Tucson, Arizona, USA. • Minimum Power Configuration in Wireless Sensor Networks, G. Xing; C. Lu; Y. Zhang; Q. Huang; R. Pless, The Sixth ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc),2005,acceptance ratio: 40/281=14% • On Greedy Geographic Routing Algorithms in Sensing-Covered Networks, G. Xing; C. Lu; R. Pless; Q. Huang. The Fifth ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), May, 2004, Tokyo, Japan, acceptance ratio: 24/275=9% • Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks, X. Wang; G. Xing; Y. Zhang; C. Lu; R. Pless; C. D. Gill, First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003, acceptance ratio: 24/135=17.8%

44. Concurrency Check • Overhear m packets (say, belonging to K links) • For each of link uv, predict the PRR if s transmits with min power PRRv(SNRv) • If the PRR of any link would drop below α (i.e., 20%), fails stored in data packet compute PRRv(SNRv)from v's interference model RSSv(Pu) SNRv = RSSv(Psmin) + Ir+Nr compute from v's RSS model RSSv(Psmin) = av Psmin + bv

45. Mobile Platforms • Rreplenishable power supplies and large storage capacity • Low movement speed (0.1~2 m/s) • It takes a mobile of 0.5 m/s > 4 hours to visit 200 nodes in a 500×500 m2 field XYZ @ Yale Networked Infomechanical Systems (NIMS) @ UCLA Robomote @ USC