Data Fusion Improves the Coverage of Sensor Networks Guoliang Xing Assistant Professor Department of Computer Science and Engineering Michigan State University

Outline Background Problem definition –Coverage of large-scale sensor networks Scaling laws of coverage Other projects –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration 2

Mission-critical Sensing Applications Large-scale network deployments –OSU ExScal project: 1450 nodes deployed in a 1260X288 m 2 region Resource-constrained sensor nodes –Limited sensing performance Stringent performance requirements –High sensing probability, e.g., 90%, low false alarm rate, e.g., 5%, bounded delay, e.g., 20s 3

Fundamental requirement of critical apps –How well is a region monitored by sensors? Coverage of static targets –How likely is a target detected? Sensing Coverage 4

Network Density for Achieving Coverage How many sensors are needed to achieve full or instant coverage of a geographic region? –Any static target can be detected at a high prob. Significance of reducing network density –Reduce deployment cost –Prolong lifetime by putting redundant sensors to sleep 5

State of the Art K-coverage –Any physical point in a large region must be detected by at least K sensors Coverage of mobile targets –Any target must be detected within certain delay Barrier coverage –All crossing paths through a belt region must be k-covered Most previous results are based on simplistic models –All 5 papers on the coverage problem published at MobiCom since 2004 assumed the disc model 6

Single-Coverage under Disc Model Deterministic deployment –Optimal pattern is hexagon Random deployment –Sensors deployed by a Poisson point process of density ρ –The coverage (fraction of points covered by at least one sensor): deterministic deployment random deployment 7 [ Liu 2004 ]

8 Sensing Model ✘ The (in)famous disc model ✘ Sensor can detect any target within range r ✔ Real-world sensor detection There is no cookie-cutter “sensing range”! r Acoustic Vehicle Tracking Data in DARPA SensIT Experiments [Duarte 04]

Contributions Introduce probabilistic and collaborative sensing models in the analysis of coverage –Data fusion: sensors combine data for better inferences Derive scaling laws of network density vs. coverage –Coverage of both static and moving targets Compare the performance of disc and fusion models –Data fusion can significantly improve coverage! 9

Outline Background Problem definition –Coverage of large-scale sensor networks Scaling laws of coverage Other projects –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration Personal perspectives on research 10

11 Sensor Measurement Model Sensor reading y i = s i + n i Decayed target energy s i = S · w(x i ) Noise energy follows normal distribution n i ~ N(μ,σ 2 ) Acoustic Vehicle Tracking Data in DARPA SensIT Experiments [Duarte 04], 2≤ k ≤ 5

Single-sensor Detection Model Sensor reading y i H 0 – target is absent H 1 – target is present sensor reading distribution detection threshold noise energy distribution false alarm rate detection probability t energy Q(·) – complementary CDF of the std normal distribution false alarm rate: detection probability: probability 12

χ n – CDF of Chi-square distribution w(x i ) – Energy reading of sensor x i from target Data Fusion Model Sensors within distance R from target fuse their readings –R is the fusion range The sum of readings is compared again a threshold η False alarm rate P F = 1-χ n (n· η) Detection probability P D = 1 –χ n (n·η - Σ w(x i )) R 13

Outline Background Problem definition –Coverage of large-scale sensor networks Scaling laws of coverage –Coverage of static targets Other projects –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration Personal perspectives on research 14

(α,β)-coverage A physical point p is (α,β)-covered if –The system false alarm rate P F ≤ α –For target at p, the detection prob. P D ≥ β (α,β)-coverage is the fraction of points in a region that is (α,β)-covered –Full (0.01, 0.95)-coverage: system false alarm rate is no greater than 1%, and the prob. of detecting any target in the region is no lower than 95% 15

Extending the Disc Model Classical disc model is deterministic Extends disc model to stochastic detection –Choose sensing range r such that if any point is covered by at least one sensor, the region is (α,β)-covered Previous results based on disc model can be extended to (α,β)-coverage δ-- signal to noise ratio S/σ 16

Disc and Fusion Coverage Coverage under the disc model –Sensors independently detect targets within sensing range r Coverage under the fusion model –Sensors collaborate to detect targets within fusion range R 17

(α,β)-coverage under Fusion Model The (α,β)-coverage of a random network is given by F(p) – set of sensors within fusion range of point p N(p) – # of sensors in F(P) optimal fusion range 18

Network Density for Full Coverage ρ f and ρ d are densities of random networks under fusion and disc models Sensing range is a constant Opt fusion range grows with network density  ρ f <ρ d when high coverage is required 19

20 Network Density w Opt Fusion Range When fusion range is optimized with respect to network density When k=2 (acoustic signals) Data fusion significantly reduces network density, 2≤ k ≤ 5

Network Density vs. SNR For any fixed fusion range The advantage of fusion decreases with SNR 21

22 Trace-driven Simulations Data traces collected from 75 acoustic nodes in vehicle detection experiments from DARPA SensIT project –α=0.5, β=0.95, deployment region: 1000m x 1000m

Simulation on Synthetic Data k=2, target position is localized as the geometric center of fusing nodes 23

Conclusions Bridge the gap between data fusion theories and performance analysis of sensor networks Derive scaling laws of coverage vs. network density –Data fusion can significantly improve coverage! Help to understand the limitation of current analytical results based on ideal sensing models Provide guidelines for the design of data fusion algorithms for large-scale sensor networks 24

Outline Background Problem definition –Coverage of large-scale sensor networks Scaling laws of coverage –Coverage of static targets Other projects –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration Personal perspectives on research 25

Improve Throughput by Concurrency Enable concurrency by controlling senders' power s1s1 r1r1 s2s2 r2r2 +

Received Signal Strength Transmission Power Level Received Signal Strength (dBm) 18 Tmotes with Chipcon 2420 radio Near-linear RSS dBm vs. transmission power level Non-linear RSS dBm vs. log(dist), different from the classical model! 27

Packet Reception Ratio vs. SINR Classical model doesn't capture the gray region office, no interfererparking lot, no interfereroffice, 1 interferer 0~3 dB is "gray region" Packet Reception Ratio (%) 28

C-MAC Components Concurrent Transmission Engine Power Control Model Interference Model Handshaking Online Model Estimation Currency Check Throughput Prediction Throughput Prediction Presented at IEEE Infocom 2009 Implemented in TinyOS 1.x, evaluated on a 18-mote test-bed Performance gain over TinyOS default MAC is >2X 29

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

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

Deterministic Coverage + Connectivity Select a set of nodes to achieve –K-coverage: every point is monitored by at least K sensors –N-connectivity: network is still connected if N-1 nodes fail Sleeping node Communicating nodes Active nodes Sensing range A network with 1-coverage and 1-connectivity 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 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 + connectivity mountainous protocols ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003 ACM Transactions on Sensor Networks, Vol. 1 (1), 2005 (~600 citations on Google Scholar) 33

Research Summary Data fusion in sensor networks –Coverage [MobiCom 09]; deployment[RTSS 08]; mobility [ICDCS 08] MAC protocol design and architecture –C-MAC: concurrent model-driven MAC [Infocom08] –UPMA: unified power management architecture [IPSN 07] Sensornet/real-time middleware –MobiQuery: spatiotemporal query service for mobile users [ICDCS 05, IPSN 05] –nORB: light-weight real-time middleware for networked embedded systems [RTAS 04] Controlled mobility –Mobility-assisted spatiotemporal detection [ICDCS 08,IWQoS 08] –Rendezvous-based data transport [MobiHoc 08, RTSS 07] Power management –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]

Acknowledgement Students –Rui Tan, Mo Sha Collaborators –Benyuan Liu, Jianping Wang…..

Michigan State University First land-grant institution –Founded in 1855, prototype for 69 land-grant institutions established under the Morrill Act of 1862 One of America's Public Ivy universities Big ten conferences –University of Illinois, Indiana University, University of Iowa, University of Michigan, University of Minnesota, Northwestern University, Ohio State University, Pennsylvania State University, Purdue University, University of Wisconsin Single largest campus, 8 th largest university in the US with 46,648 students and 2,954 faculty members Rankings of 2008 –80 th worldwide, Shanghai Jiao Tong University’s Institute of Higher Education –71th in US, U.S. News & World Report

Computer Science & People –27 tenure-stream faculty –Each year awards approximately 100 BS, 40 MS, and 10 PhD degrees in Computer Science Research –9 research laboratories, with annual research expenditures exceeding $3.5 million Rankings –15 th graduate program in US, a recent article of Comm. of ACM –Top 100, Shanghai Jiao Tong University’s Institute of Higher Education

My Group Research –Sensor networks Data fusion, power management, voice streaming, controlled mobility –Low-power wireless networks MAC, Interference management –Cyber-physical systems Students –Supervise 6 PhDs (CityU and MSU), 2 MS –Co-supervise 4 PhDs (CAS, CWM, UTK, MSU)

Ranking of Journals* Tier 1 –IEEE/ACM Trans. on Networking (TON) Tier 1.5 –ACM Trans. on Sensor Networks (TOSN) –IEEE Trans. on Mobile Computing (TMC) –IEEE Trans. on Computers (TC) –IEEE Trans. on Parallel and Distributed Systems (TPDS) *The ranking is only applicable to Sensor Network research

Ranking of Conferences* Tier 0: SIGCOMM, MobiCom (~10%)[1] Tier 1: MobiHoc (10~15%)[3], SenSys[1], MobiSys (15~18%), Tier 1.5: Infocom[1], ICDCS (~18%) [3], RTSS[4] (20~25%), ICNP, PerCom (10-15%) Tier 2:IPSN[3], IWQoS, [1] MSWiM (20~25%) [1], MASS[2], DCOSS (~25%) Tier 4: Globecom, WCNC, ICC …..(>30%) *Partially borrowed from Prof. Dong Xuan ’ s talk *The ranking is only applicable to Sensor Network research, and may significantly change for other fields