1 Research Profile Guoliang Xing Assistant Professor Department of Computer Science and Engineering Michigan State University

2 Background Education –Washington University in St. Louis, MO Master of Science in Computer Science, 2003 Doctor of Science in Computer Science, 2006, Advisor: Chenyang Lu –Xi’an JiaoTong University, Xi’an, China Master of Science in Computer Science, 2001 Bachelor of Science in Electrical Engineering, 1998 Work Experience –Assistant Professor, 8/2008 –, Department of Computer Science and Engineering, Michigan State University –Assistant Professor, 8/2006 – 8/2008, Department of Computer Science, City University of Hong Kong –Summer Research Intern, May – July 2004, System Practice Laboratory, Palo Alto Research Center (PARC), Palo Alto, CA

3 Outline Fusion-based network design and analysis –Impact of data fusion on coverage [MobiCom 09, RTSS 09] –Mobility-assisted spatiotemporal detection [ICDCS 08] Interference management and MAC design –Model-driven concurrent MAC [Infocom 08] –Multi-channel Interference measurement & modeling [RTSS09] Proposal ideas

4 Motivation Numerous studies on sensing protocols/analysis –Our earlier work on integrated coverage & connectivity was cited >600 [sensys 03, TOSN 05] Many existing results are based on simplistic models –All 5 related papers since MobiCom 04 used disc model –Ignored sensing uncertainties and collaboration Collaborative signal processing theories –Focused on small-scale networks –Made design & analysis of large-scale networks difficult 4

5 Coverage + Connectivity Select a set of nodes to 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 Sleeping node Communicating nodes Active nodes Sensing range A network with 1-coverage and 1-connectivity

6 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 Transactions on Sensor Networks, Vol. 1 (1), First ACM Conference on Embedded Networked Sensor Systems (SenSys), 2003

7 7 A Reality Check of Disc Model ✘ The (in)famous disc model ✘ Any target within r is detected ✘ Deterministic and independent sensing ✔ Real-world event sensing Probabilistic, no cookie-cutter like “sensing range”! Collaborative sensing is a must r Real Acoustic Vehicle Detection Experiments [Duarte 04]

88 Sensor Measurement Model Reading of sensor i is y i = s i + n i Decayed target energy Noise energy follows normal distribution n i ~ N(μ,σ 2 ) Signal to noise ratio (SNR) is S /σ, 2 ≤ k ≤ 5 Real Acoustic Vehicle Detection Experiments [Duarte 04]

9 N – CDF of Normal distribution s i – Energy reading of sensor i Data Fusion Model Sensors within distance R from target fuse their readings –The sum of readings is compared again a threshold η –R is the fusion range More advanced fusion schemes –Decision fusion, sequential fusion… False alarm rate P F = 1-N(n· η) Detection probability P D = 1 –N(n·η - Σs i ) R 9

10 Outline Fusion-based network design and analysis –Impact of data fusion on coverage [MobiCom 09, RTSS 09] –Mobility-assisted spatiotemporal detection [ICDCS 08] Interference management and MAC design –Model-driven concurrent MAC [Infocom 08] –Multi-channel Interference measurement & modeling [RTSS09] Proposal ideas

11 (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 are (α,β)-covered –Full (α,β)-coverage: any point is (α,β)-covered Random network deployment –Nodes deployed by Poisson process of density ρ 11

12 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 12 grayscale represents P D

13 Network Density for Coverage ρ f and ρ d are densities of random networks under fusion and disc models When k=2 (acoustic signals) Density significantly reduced in data fusion!

14 Trace-driven Simulations Data traces collected from 75 acoustic sensors in vehicle detection experiments [Duarte 04] –α=0.05, β=0.95, deployment region: 1000m x 1000m fusion saves more sensors

15 Mobility-assisted Target Detection First phase – static detection –All sensors send readings to cluster head –Cluster head makes a detection decision, if positive, starts the 2 nd 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 2 nd phase Second phase – movement scheduling –Mobile sensors move toward the possible target according to a movement schedule –Cluster head makes the final detection decision

16 Mobility-assisted Target Detection Two-phase detection –Fusion of static sensors, then move mobile sensors Find detection thresholds & a movement schedule –Minimizes the expected moving distance of sensors –Detection prob. ≥ α, false alarm rate ≤ β, detection delay ≤ T target Example movement schedule ( sensors are assumed to move at steps): M1: t 0 - one step, t 3 - two steps … M2: t 1 - one step, t 2 - one step… M3: t 1 - two steps, t 2 - one step… M1 M2 M3

17 Mobility-assisted Target Detection A two-phase target detection model based on data fusion Optimal sensor movement scheduling –Minimizes expected moving distance –Meets spatiotemporal QoS requirements Simulations based on real data traces of target detection

18 Outline Fusion-based network design and analysis –Impact of data fusion on coverage [MobiCom 09, RTSS 09] –Mobility-assisted spatiotemporal detection [ICDCS 08] Interference management and MAC design –Model-driven concurrent MAC [Infocom 08] –Multi-channel Interference measurement & modeling [RTSS09] Proposal ideas

19 Challenges Low-power wireless networks in critical apps –Sensors Hz in structural monitoring –Deliver critical info to BS in bounded delay Interference in open radio spectrum –Numerous devices in 2.4 GHz: WiFi, cordless phones, bluetooth, ZigBee… –Low throughput and unpredictable comm. delay

20 Enabling Link Concurrency s1s1 r1r1 s2s2 r2r2 +

21 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!

22 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 (%)

23 Model-driven Concurrent MAC Concurrent Transmission Engine Power Control Model Interference Model Handshaking Online Model Estimation Concurrency Check Throughput Prediction Presented at IEEE Infocom 2009 Achieved predictability and high throughput based on power attenuation & interference models Implemented in TinyOS 1.x, evaluated on a 18-mote test-bed Performance gain over TinyOS default MAC is >2X

24 Outline Fusion-based network design and analysis –Impact of data fusion on coverage [MobiCom 09, RTSS 09] –Mobility-assisted spatiotemporal detection [ICDCS 08] Interference management and MAC design –Model-driven concurrent MAC [Infocom 08] –Multi-channel Interference measurement & modeling [RTSS09] Proposal ideas

25 Channels Are Overlapping! signal power ( dbm) Channel X 1 MHz Channel X+1 Channel X-1 Power leakage causes inter-channel Interference Only 3 or 4 channels are orthogonal theoretical channel bandwidth Interference on adjacent channel

26 Multi-channel Interference measurement & modeling Measurement-based interference modeling –Two models for strongly & weakly overlapping channels Lightweight interference measurement algorithm –Exploiting Spectral Power Distribution Throughput capacity of links on overlapping chs Extensions to channel assignment protocols Implemented on 30-node testbed of TelosB nodes

27 Integrated Quality-driven In-Network Processing in Mission-Critical Sensor Networks: Architecture, Models and Algorithms PIs: Hongwei Zhang Wayne State University Guoliang Xing Michigan State University

28 Mission-critical Sensing Applications Resource-constrained sensor nodes Stringent performance requirements –High sensing prob. (e.g. 99%), low false alarm rate (e.g., 1%), short comm. delay seismometers in UCLA campus [Estrin 02] acoustic sensors detecting AAV

29 In-Network Processing (INP) Significantly reduce network bandwidth usage Data-independent INP –Coding, packet packing (aggregating short packets into a long packet)…. –No reliance on data properties, focus on comm. quality Data-dependent INP –Data fusion, inference… –Rely on data characteristics, focus on sensing quality Integrate two INPs for mission-critical sensornets

30 Challenges Two INPs may cause conflicts in system quality of service (e.g. timeliness) –Data fusion needs to aggregate data of same event while packet packing aggregates any packets –Timeliness affects sensing quality (sequential fusion must fuse enough data within certain delay) Lack of network architecture support –Data-independent INPs are in comm. protocols –Data-independent INPs are in app protocols –Deadline of packets may change after data fusion

31 Proposed System Architecture New system architecture for INP integration –Reuse existing INP schemes –Unified abstraction for programming support INP modeling –System performance vs. INP parameters –Time & sensing quality vs. packing/coding/fusing parameters Fusion/packing center placement, fusion/packing rules…

32 Strongly & Weakly Overlapping When two channels are close, RSS grows nearly linearly with sender’s transmit power RSS do not strongly correlate with transmit power

33 Lightweight Measurement Algorithm SS R channel X channel Y Z X,Y (dB) B Y,R (dB) RSS (S X,R Y,P) Spectral power density (SPD) decay Z X,Y Sender’s signal power decay on ch. Y when it transmits on ch. X For any receiver R on channel Y RSS (S X, R Y, P) = P – Z X,Y – B Y,R B Y,R -- intra-channel signal decay Node R doesn’t need to switch channels if Z X,Y and B Y,R are known! signal power (dbm)

34 Conclusions Reveal limitations of current analytical results –Only applicable for slowly decaying signals w high SNRs –Disc model significantly underestimates coverage Provide insights into fusion design of large networks –Data fusion can significantly improve coverage! –Fusion parameters (e.g., fusion range) are critical First step toward bridging the gap bw CSP and performance analysis of sensor networks 34 15th Annual International Conference on Mobile Computing and Networking (MobiCom), 2009

35 Data Transport using Mobiles Base Station 500K bytes 100K bytes 150K bytes 5 mins 10 mins 5 mins Networked Infomechanical Systems UCLA USC

36 Rendezvous-based Data Transport Some nodes serve as “rendezvous points” (RPs) –Other nodes send data to the closest RP –Mobiles visit RPs and transport data to base station Advantages –Combine In-network caching and controlled mobility Mobiles can collect a large volume of data at a time Minimize disruptions due to mobility –Achieve desirable balance between latency and network power consumption Online algorithms for fixed and free mobile trails ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2008 IEEE Real-Time Systems Symposium (RTSS), 2007

37 Research Summary Systems –Wireless interference measurements and modeling –Unified power management architecture for wireless sensor networks –Real-time middleware for networked embedded systems Algorithms, protocols, and analyses –Mobility-assisted data collection and target detection –Holistic radio power management –Data-fusion based network design Publications –7 IEEE/ACM Transactions papers since 2006 –30+ conference/workshop papers –First-tier conference papers: MobiCom (1), MobiHoc (3), RTSS (4), ICDCS (2), INFOCOM (2), SenSys (1), IPSN (3) –The paper "Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks" was ranked the 23rd most cited articles among all papers of Computer Science published in 2003 –Total 780+ citations (Google Scholar, 2009 Jan.)