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 –6 faculty members –more than 20 research staff/students –~3 million HK$ government funding in

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

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 /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%.

Earlier Work on Sensor Networks ACM/IEEE Transactions Papers 1.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 2.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 3.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.

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 /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

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

Mobile Sensor Platforms Low movement speed (0.1~2 m/s) –Increased latency of data collection –Reduced network capacity Networked Infomechanical Systems CENS, UCLA USC [Dantu05robomote] Yale enalab/XYZ/

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

Static vs. Mobile All-static networks Mobility-assisted Networks DelayLowHigh Energy Consumption High nonreplenishable High replenishable BandwidthMediumMedium to high

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

mobile node rendezvous point An Example source node The field is 500 × 500 m 2 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.

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

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

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

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 Source nodes approximated data route real data route Non-source nodes Branch nodes Rendezvous points a branch node lies on two or more source- to-root routes

Problem Formulation Given a tree T(V,E) rooted at B and sources {s i }, find RPs, {R i }, and a tour no longer than L=vD that visits {B}U{R i }, and The problem is NP-hard (reduction from the Traveling Salesman Problem) d T (s i,R i ) – the on-tree distance between s i and R i

Rendezvous Planning under Limited Mobility The mobile only moves along routing tree –Simplifies motion control and improves reliability Yale

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

A Heuristic for Unlimited Mobility Add virtual nodes s.t. each edge is no longer than L 0 In each iteration, choose the RP candidate with the max utility defined 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

Rendezvous Planning w Aggregation Given a base station B, and sources {s i }, find trees T i (V i, E i ), {B} U {s i } U V i, and a tour visiting the roots of T i such that 1) the tour is no longer than L; 2) the total length of edges of T i is minimized R1R1 s1s1 s5s5 s4s4 B s2s2 s3s3 R2R2 R3R3 R4R4 s6s6  A special case when L=0, the opt solution is Steiner minimum tree that connects {B} U {si}

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

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 {s i }) –Assume L << SMT({B} U {s i })

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 /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

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

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

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

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

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: , Z-MAC [Rhee et al. 05] Separated sleep scheduling modules from B-MAC Implemented two new sleep schedulers on top of B-MAC

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 /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

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

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 R c / R s  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 R c / R s < 2 –CCP + SPAN [chen et al. 01]

Greedy Forwarding with Coverage A destination shortest Euclidean distance to destination B 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 R c /R s > 2 –Hop count from u and v is RcRc

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 u v x and y are candidates not a candidate xy z RcRc

BVGF bound Analytical Results GF bound is high when R c /R s  2 Both performs well for high R c /R s result of one-hop analysis result of two-hop analysis result of four-hop analysis Dilation Dilation =

Thanks!