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 –Coverage of static targets –Coverage of moving targets Other projects –Sensor placement for fusion-based detection –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? Coverage of mobile targets –How quickly can the network detect a target? 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. –Any moving target can be detected almost instantly 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 coverage vs. network density –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 –Coverage of static targets –Coverage of moving targets Other projects –Sensor placement for fusion-based detection –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration 10

11 Sensor Measurement Model Sensor reading y i = s i + n i Decayed target energy s i = S · w(x i ) Noise strength 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: 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 –Coverage of moving targets Other projects –Sensor placement for fusion-based detection –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration 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

Outline Background Problem definition –Coverage of large-scale sensor networks Scaling laws of coverage –Coverage of static targets –Coverage of moving targets Other projects –Sensor placement for fusion-based detection –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration 24

25 Mobile Target Detection Sensors perform detection every T seconds –Max. detection delay is one or multiple Ts detection under disc model detection under fusion model

26 α-delay The avg. number of detection periods before a target is detected subject to the max system false alarm rate of α –Trade-off exists bw false alarm rate and detection delay false alarm!

27 Network Density for Instant Coverage ρ f /ρ d vs. Sensing and fusion ranges ρ f /ρ d vs. SNR ρ f <ρ d for small false alarm rates ρ f <ρ d for low SNRs

28 Implications of Results Limitations of the disc model –Significantly overestimates network density –Only suitable for high-SNR apps Design of data fusion algorithms –Fusion range is critical –Higher performance gain for Low-SNR apps 

Outline Background Problem definition –Coverage of large-scale sensor networks Scaling laws of coverage –Coverage of static targets –Coverage of moving targets Other projects –Sensor placement for fusion-based detection –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration 29

30 Sensor Placement for Detection A set of surveillance spots to be monitored. Cluster head compares average reading to threshold η Minimize # of sensors to achieve (α, β)-coverage –A non-convex optimization problem with high complexity Developed a globally optimal algorithm and two efficient heuristics surveillance spots fusion radius Presented at IEEE Real-time Systems Symposium (RTSS) 2008

Outline Background Problem definition –Coverage of large-scale sensor networks Scaling laws of coverage –Coverage of static targets –Coverage of moving targets Other projects –Sensor placement for fusion-based detection –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration 31

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! 33

Packet Reception Ratio vs. SINR Classical model doesn't capture the gray region office, no interfererparking lot, no interfereroffice, 1 interferer  Noise +  Interference Received Signal Strength (RSS) 0~3 dB is "gray region" Packet Reception Ratio (%) 34

C-MAC Components Concurrent Transmission Engine Power Control Model Interference Model Handshaking Online Model Estimation Currency Check Throughput Prediction Throughput Prediction To be 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 35

Outline Background Problem definition –Coverage of large-scale sensor networks Scaling laws of coverage –Coverage of static targets –Coverage of moving targets Other projects –Sensor placement for fusion-based detection –Model-driven concurrent medium access control –Integrated coverage and connectivity configuration 36

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 37

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) 38

Conclusions Bridge the gap between data fusion theories and performance analysis of sensor networks Derive scaling laws of coverage vs. network density –Coverage of both static and moving targets –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 39

References Integrated Coverage and Connectivity Configuration for Energy Conservation in Sensor Networks, Guoliang Xing; Xiaorui Wang; Yuanfang Zhang; Chenyang Lu; Robert Pless; Christopher D. Gill, ACM Transactions on Sensor Networks, Vol. 1 (1) Fast Sensor Placement Algorithms for Fusion-based Target Detection, Zhaohui Yuan, Rui Tan, Guoliang Xing, Chenyang Lu, Yixin Chen, Jianping Wang, the 29th IEEE Real- Time Systems Symposium (RTSS), Nov Dec. 3, 2008 Collaborative Target Detection in Wireless Sensor Networks with Reactive Mobility, Rui Tan, Guoliang Xing, Jianping Wang and Hing Cheung So, the 16th International Workshop on Quality of Service (IWQoS), 2008, Mobility-assisted Spatiotemporal Detection in Wireless Sensor Networks, Guoliang Xing, Jianping Wang, Ke Shen, Qingfeng Huang, Xiaohua Jia and Hing Cheung So, the 28th International Conference on Distributed Computing Systems (ICDCS), 2008 Impact of Data Fusion on Intrusion Detection in Large-scale Wireless Sensor Networks, Rui Tan, Guoliang Xing, Benyuan Liu, Jianping Wang, Tech. Report, MSU-CSE Data Fusion Improves the Coverage of Wireless Sensor Networks, Rui Tan, Guoliang Xing, Benyuan Liu, Jianping Wang, Xiaohua Jia, Chih-Wei Yi, Tech. Report, MSU-CSE