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Research Profile of My Group

Research Profile of My Group. Guoliang Xing Department of Computer Science City University of Hong Kong. Facts of My Group. Members Three PhD students CityU, CityU-USTC, CityU-WuhanU One Master student Two research assistants (joint supervision) Part of CityU wireless group

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Research Profile of My Group

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  1. Research Profile of My Group Guoliang Xing Department of Computer ScienceCity University of Hong Kong

  2. Facts of My Group • Members • Three PhD students • CityU, CityU-USTC, CityU-WuhanU • One Master student • Two research assistants (joint supervision) • Part of CityU wireless group • 6 faculty members • more than 20 research staff/students • ~3 million HK$ government funding in 2007-08

  3. Research Directions • Controlled mobility • Data fusion based target detection • Power management • Sensing coverage

  4. 2007-08 Conference Publications • Controlled mobility • Rendezvous Design Algorithms for Wireless Sensor Networks with a Mobile Base Station, G. Xing, T. Wang, W. Jia, M. Li, MobiHoc 2008, 44/300=14.6%. • Rendezvous Planning in Mobility-assisted Wireless Sensor Networks,G. Xing, T. Wang, Z. Xie and W. Jia;RTSS 2007, 44/171=25.7%. • Data fusion based target detection • Mobility-assisted Spatiotemporal Detection in Wireless Sensor Networks, G. Xing; J. Wang; K. Shen; Q. Huang; H. So; X. Jia, ICDCS 2008, 102/638=16%. • Collaborative Target Detection in Wireless Sensor Networks with Reactive Mobility, R. Tan, G. Xing, J. Wang and H. So, IWQoS 2008 • Power management • Link Layer Support for Unified Radio Power Management in Wireless Sensor Networks. K. Klues, G. Xing and C. Lu,IPSN 200738/170=22.3%. • Dynamic Multi-resolution Data Dissemination in Storage-centric Wireless Sensor Networks. H. Luo, G. Xing, M. Li, and X. Jia, MSWiM 2007, 41/161=24.8%.

  5. Earlier Work on Sensor Networks ACM/IEEE Transactions Papers • 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, extended MobiHoc 2005 paper • 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, extended MobiHoc 2004 paper • 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, extended SenSys 2003 paper, one of the most widely cited work on the coverage problem of sensor networks, total number of citations is 358 in Google Scholar.

  6. Focus of this Talk • Controlled mobility • Rendezvous Planning in Mobility-assisted Wireless Sensor Networks,G. Xing, T. Wang, Z. Xie and W. Jia;RTSS 2007, 44/171=25.7%. • Power management • Link Layer Support for Unified Radio Power Management in Wireless Sensor Networks. K. Klues, G. Xing and C. Lu,IPSN 200738/170=22.3%. • Sensing Coverage • 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, extended SenSys 2003 paper

  7. Motivations • Sensor nets face the fundamental performance bottleneck • Many applications are data-intensive • Multi-hop wireless relays are power-consuming • A tension exists between the sheer amount of data generated and limited power supply • Controlled mobility is a promising solution • Number of related papers increases significantly in last 3 years: MobiSys, MobiHoc, MobiCom, IPSN

  8. Mobile Sensor Platforms • Low movement speed (0.1~2 m/s) • Increased latency of data collection • Reduced network capacity XYZ @ Yale http://www.eng.yale.edu/enalab/XYZ/ Robomote @ USC [Dantu05robomote] Networked Infomechanical Systems (NIMS) @ CENS, UCLA

  9. A Data Collection Tour Base Station 1 minute 150K bytes 50K bytes 2 minute 1 minute 1 minute 100K bytes 100K bytes 200K bytes • Analogy • What's the most reliable way of sending 1000 G bytes of data from Hong Kong to Suzhou?

  10. Static vs. Mobile

  11. Basic idea • Some nodes serve as “rendezvous points” (RPs) • Other nodes send their data to the closest RP • Mobiles visit RPs and transport data to base station • Advantages • In-network caching + controlled mobility • Mobiles can collect a large volume of data at a time • Minimize disruptions due to mobility • Mobiles contact static nodes at RPs at scheduled time

  12. An Example mobile node The field is 500 × 500 m2 The mobile moves at 0.5 m/s It takes ~20 minutes to visit six randomly distributed RPs It takes > 4 hours to visit 200 randomly distributed nodes. rendezvous point source node

  13. The Rendezvous Planning Problem • Choose RPs s.t. mobile nodes can visit all RPs within data collection deadline • Total network energy of transmitting data from sources to RPs is minimized • Joint optimization of positions of RPs, motion paths of mobile, and routing paths of data

  14. Assumptions • Only one mobile is available • Mobile moves at a constant speed v • Mobile picks up data at locations of nodes • Data collection deadline is D • User requirement: “report every 10 minutes and the data is sampled every 10 seconds” • Recharging period: e.g., Robomotes powered by 2 AA batteries recharge every ~30 minutes

  15. Data Aggregation • Data from different sources can be aggregated • Reduces the amount of network traffic • "what's the lowest temperature of this region"? • Without aggregation • Optimal routing tree is the shortest path tree • With aggregation • Optimal routing tree is the minimum spanning/Steiner tree

  16. Geometric Network Model • Transmission energy is proportional to distance • Base station, source nodes and branch nodes are connected with straight lines a multi-hop route is approximated by a straight line Rendezvous points Non-source nodes a branch node lies on two or more source-to-root routes Source nodes Branch nodes approximated data route real data route Source nodes

  17. Problem Formulation • Given a tree T(V,E) rooted at B and sources {si}, find RPs, {Ri}, and a tour no longer than L=vD thatvisits {B}U{Ri}, and • The problem is NP-hard (reduction from the Traveling Salesman Problem) dT(si,Ri)– the on-tree distance between si and Ri

  18. Rendezvous Planning under Limited Mobility • The mobile only moves along routing tree • Simplifies motion control and improves reliability XYZ @ Yale

  19. An Optimal Algorithm • Sort edges in the descending order of the number of sources in descendents • Choose a subset of (partial) edges from the sorted list whose length is L/2 • The mobile tour is the pre-order traversal of the chosen edges

  20. A Heuristic for Unlimited Mobility • Add virtual nodes s.t. each edge is no longer than L0 • In each iteration, choose the RP candidate with the max utilitydefined by c(x) • Terminate if no more RPs can be chosen or all sources become RPs the decreased length of data routes the increased length of the mobile node tour TSP(W) computes the distance to visit nodes in W using a Traveling Salesman Problem solver

  21. Í Rendezvous Planning w Aggregation Given a base station B, and sources {si}, find trees Ti(Vi, Ei), {B}U{si}UVi, and a tour visiting the roots of Ti such that 1) the tour is no longer than L; 2) the totallength of edges of Tiis minimized B s6 R4 R1 s5 R3 s1 R2 s4 s2 A special case when L=0, the opt solution is Steiner minimum tree that connects {B} U {si} s3

  22. An Approx. Algorithm • Find an approx. Steiner min tree of {B}U{si} • Depth-first traverse the tree until covers L/2 length

  23. Approx. Ratio • The approximation ratio of the algorithm is α+β(2α-1)/2(1-β) • α is the best approximation ratio of the Steiner Minimum Tree problem • β = L/SMT({B} U {si}) • Assume L << SMT({B} U {si})

  24. Focus of this Talk • Controlled mobility • Rendezvous Planning in Mobility-assisted Wireless Sensor Networks,G. Xing, T. Wang, Z. Xie and W. Jia;RTSS 2007, 44/171=25.7%. • Power management • Link Layer Support for Unified Radio Power Management in Wireless Sensor Networks. K. Klues, G. Xing and C. Lu,IPSN 200738/170=22.3%. • Sensing Coverage • 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, extended SenSys 2003 paper

  25. Problem • Communication power cost is high Explosion in the development of various radio power management protocols • Protocols make different assumptions No single protocol is suited to the needs of every application • Existing radio stack architectures are monolithic Hard to develop new protocols or tune existing ones to specificapplication requirements

  26. Power Management Interfaces Backoff Control Interfaces Send/Receive Interfaces Radio Component Send/Receive Buffers MAC Radio State Machine Radio Power Management Clear Channel Assessment Backoff Controller Traditional Core Radio Functionality Incoming and Outgoing data buffers State machine Integrated Radio Power Management CCA Functionality Real Implementations do not separate these functional components so nicely

  27. Solution: UPMA • Unified Radio Power Management Architecture • Monolithic --> Composable radio stack architecture • Pluggable power management policies • Separation of power management features • Cross layer in nature

  28. Unified Power Management Architecture interfaces of sleep schedulers Protocol 1 Protocol 2 Protocol 0 Protocol 3 … SyncSleep AsyncSleep Other Interface … parameters specified by upper-level protocols OnTime Mode Param 0 OffTime Preamble Param 1 DutyCycling Table LPL Table Other Table Power Management Abstraction • Consistency check • Aggregation Power Manager sleep scheduling protocols … Others Sync Scheduler Async Listening MAC ChannelMonitor PreambleLength On/Off interfaces with MAC PHY

  29. Implementation • Implemented UPMA in TinyOS 2.0 for both Mica2 and Telosb motes • Developed interfaces with different types of MAC • CSMA based: S-MAC [Ye et al. 04], B-MAC [Polastre et al. 04] • TDMA based: TRAMA [Rajendran et al. 05] • Hybrid: 802.15.4, Z-MAC [Rhee et al. 05] • Separated sleep scheduling modules from B-MAC • Implemented two new sleep schedulers on top of B-MAC

  30. Focus of this Talk • Controlled mobility • Rendezvous Planning in Mobility-assisted Wireless Sensor Networks,G. Xing, T. Wang, Z. Xie and W. Jia;RTSS 2007, 44/171=25.7%. • Power management • Link Layer Support for Unified Radio Power Management in Wireless Sensor Networks. K. Klues, G. Xing and C. Lu,IPSN 200738/170=22.3%. • Sensing Coverage • 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, extended SenSys 2003 paper

  31. Power Management under Performance Constraints • Performance constraints • “Any target within the region must be detected”  K-coverage: every point is monitored by at least K active sensors • “Report the target to the base station within 30 sec”  N-connectivity: network is still connected if N-1 active nodes fail Routing performance: route length can be predicted • Focus on fundamental relations between the constraints base station

  32. 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 + SPAN [chen et al. 01]

  33. Greedy Forwarding with Coverage • Always forward to the neighbor closest to destination • Simple, local decision based on neighbor locations • Fail when a node can’t find a neighbor better than itself • Always succeed with coverage when Rc/Rs > 2 • Hop count from u and v is shortest Euclidean distance to destination Rc A destination B

  34. Bounded Voronoi Greedy Forwarding (BVGF) • A neighbor is a candidate only if the line joining source and destination intersects its Voronoi region • Greedy: choose the candidate closest to destination x and y are candidates Rc x y u z v not a candidate

  35. Analytical Results Dilation result of four-hop analysis GF bound is high when Rc/Rs 2 Both performs well for high Rc/Rs BVGF bound result of one-hop analysis result of two-hop analysis Dilation =

  36. Thanks!

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