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RUGGeD: R o U ting on fin G erprint G ra D ients in Sensor Networks

This paper explores the use of natural information gradients in sensor networks to efficiently locate physical events. The proposed algorithm is distributed, robust, and capable of finding multiple sources.

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RUGGeD: R o U ting on fin G erprint G ra D ients in Sensor Networks

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  1. RUGGeD: RoUting on finGerprint GraDients in Sensor Networks Jabed Faruque, Ahmed Helmy Wireless Networking Laboratory Department of Electrical Engineering University of Southern California faruque@usc.edu, helmy@usc.edu URL: http://nile.usc.edu, http://ceng.usc.edu/~helmy ICPS 2004 1

  2. Introduction • Sensor networks consist of sensor nodes with • Limited Energy source • Sensor devices • Short range radio • On-board processing capability Mica2 mote and sensor board • Use of Sensor networks is tightly coupled with physical phenomena • May be most widely used for habitat and environment monitoring (e.g. temperature, humidity) • For unattended and fine grained monitoring of natural phenomena • Self configuration capability • Also others e.g., for defense purpose … ICPS 2004 2

  3. Motivation • Every physical event produces a fingerprint in the environment, e.g., • Fire event increases temperature • Nuclear leakage causes radiation • Many physical phenomena follow diffusion law • f(d) 1/d, where • d = distance from the source,  = diffusion parameter, depends on the type of effect (e.g. for temperature  ~ 1, light ~ 2) ICPS 2004 3

  4. Example (of diffusion): Isoseismal (intensity) maps (North Palm Springs earthquake of July 8, 1986 ) Ref.: Southern California Earthquake Center. (http://www.scec.org) ICPS 2004 4

  5. Why Using Natural Information Gradient is Important? • This natural information gradient isFREE • Routing protocols can use it to forward query packet(greedily) • - Locate event(s); e.g., fire, nuclear leakage. • Can be extended for other notions of gradients • - Example: Time gradients can be used for mobile target tracking • Existing approaches – flooding, expanding ring search, random-walk, etc. do not utilize this information gradient ICPS 2004 5

  6. Challenges • In real life, sensors are unable to detect or measure the event’s effect below certain threshold. So, diffusion curve has finite tail • - Lack of sensitivity of sensordevice(s) • Erroneous reading of malfunctioning sensors • - Due to calibration errors or obstacle- Cause local maxima or minima • Environmental noise ICPS 2004 6

  7. Environment Model • Event’s effect follows the diffusion law • Discontinuity exists in the diffusion curve with finite tail • Environmental noise Objective • Design an efficient algorithm to locate source(s) in sensor networks, exploiting natural information gradients i.e., the diffusion pattern of the event’s effect • - Gradient based- Fully distributed- Robust to node or sensor failure or malfunction- Capable of finding multiple sources ICPS 2004 7

  8. Related Work [1,2,3] • Traditional routing protocols for sensor networks are based on Flooding (directed-diffusion) or Random-walk (Rumor- routing, ACQUIRE, etc.) • - Flooding based methods cause huge energy overhead • - Random-walk increases latency and failure probability • - Do not utilizes the natural information gradient • Existing Information driven protocols [4,5] use single path approaches with/without look-ahead parameter • - Use a proactive phase to prepare information repository • Cause significant overhead at low query rate • - Unable to handle local maxima or minima • - Unable to find multiple sources • - Robustness depends on the proactive phase and the look- ahead parameter ICPS 2004 8

  9. Protocol A node can exist in one of two modes/states - flat-region mode - gradient-region mode A node forwards the query to neighbors with its information level To forward the query, each node uses following algorithm: 1. Information gradient region follows greedy approach - Forwards the query to the neighbors if the information level about the event improves 2. Unsmooth gradient region use probabilistic forward based on Simulated Annealing - Probabilistic function is fp(x) = 1/xa, where x = hop count in the information gradient region and ‘a’ depends on the diffusion parameter () 3. Use flooding for the flat (i.e., zero) information region - Decrease latency to reach gradient information region - Handles query in the absence of events Query ID prevents looping Once query is resolved, a node uses the reverse path to reply ICPS 2004 9

  10. E Q’ Q’ Q’ Q’ Q’ Q’ Q’ Q’ Q’ ng ng ng ng ng Q ng ng ng np np np Mn np Mx np np np np E Q • All neighbors (np) of Mx have less information, so they forward the query to their neighbors probabilistically • All neighbors (ng) of Mn have more information, so they forward the query to their neighbors ICPS 2004 10

  11. Simulation Model • Two different sensor network layouts 1. 100 X 100 regular grid of 10000 nodes. Event located at (74,49) 2. 15 X 6 grid of 90 nodes in 225 x 375 m2 sensor field with 50m communication radius. Grid points are perturbed by Gaussian noise (0,25) • Diffusion parameter set to 0.8 • Two regions exist in each layout - Flat or zero information region - Gradient information region • Malfunctioning nodes are uniformly distributed in both region • Environmental noise is present in the gradient information region • Malfunctioning nodes have arbitrary readings - For global maxima search, protocol uses a filter to prohibit replies from nodes having arbitrary high value ICPS 2004 11

  12. Performance Metrics • Reachability i.e., success probability- Probability that the query will reach the source • Overhead in terms of average energy dissipation - Number of transmissions required to forward the query and to get the reply from the source • For multiple events detection, ratio of sources found to actual number of sources Query Types • Single-value query- Search for a specific value and have a single response • Global Maxima search (only sensor layout 1 is used) - Search for the maximum value of information in the system - Intermediate nodes suppress non-promising replies • Multiple Events detection (only sensor layout 1 is used) - Search for multiple events of the same type ICPS 2004 12

  13. Single-value query- effect of flat information region nodes(3% environmental noise and 15% malfunctioning nodes) - With increase of flat region - Flooding overhead becomes dominantincreasing energy consumption - Malfunctioning nodes cause query to switch to gradient mode erroneously - Decrease in ‘a’ creates more paths, increasing reachability and energy consumption ICPS 2004 13

  14. Single-value query- effect of the malfunctioning nodes(3% environmental noise and 36% flat information region nodes) • With increase of malfunctioning nodes the protocol switches from the flat region mode to the gradient region mode rapidly - Reduces flooding overhead - Increases failure rate ICPS 2004 14

  15. Single-value query- route a query around the sensors hole(3% environmental noise and 20% malfunctioning nodes) • For smaller value of ‘a’ (e.g., a ~0.65), reachability is above 98% even at the presence of 55% flat information region • For the probabilistic function fp(x) = 1/xa, a <  is recommended, but close to gives optimal trade-off between reachability and overhead ICPS 2004 15

  16. Global Maxima Search-effect of flat information region nodes(3% environmental noise and 15% malfunctioning nodes) (without Filter) (with Filter) • Average energy dissipation reduces significantly due to use of the simple filter ICPS 2004 16

  17. Multiple Events Detection-effect of flat information region nodes(3% environmental noise and 15% malfunctioning nodes) • With the increase of number of sources, some plateaux regions are created in the resultant gradient information region that require more probabilistic forwarding • - for five or more sources, a ~ 0.35 is a good setting in the simulated scenario ICPS 2004 17

  18. Conclusion • Developed a multiple-path exploration protocol to discover events in sensor networks efficiently • The protocol is fully reactive, effectively exploits the natural information gradients and controls the instantiation of multiple paths probabilistically • The performance of the probabilistic function is closely tied to the diffusion parameter • Three different problems were studied • Single-value, Global maximum, Multiple events • Obtained high success rate to route around the sensors hole, with proper setting of the probability function parameters • More efficient than existing approaches ICPS 2004 18

  19. On-going and Future work • Establish analytical relationship between diffusion pattern and the probabilistic forwarding function • Develop protocol for target tracking and target counting using the multiple path exploration mechanisms ICPS 2004 19

  20. Backup Slides

  21. Environment Model • f(di) = f*(di) ± fEN(f*(di)), • fEN(f(*di))  fmax - f*(di) • where, • di = distance of the location from peak information point (i.e., the event) • f(di) = gradient information of the location with environmental noise, • fmax = peak information, • f*(di) = gradient information without environmental noise. • The proportional constant is considered 0.03 to model the environmental for our protocol, i.e., 3% environmental noise is considered

  22. Filtering of Malfunctioning Nodes • Let distance of sensors S1 and S2 from the event’s location are d and d+1 hops with readings R1 and R2 In our simulations  = 0.8 We use the filter

  23. Reply Suppression Mechanism Intermediate nodes suppress the non-promising replies

  24. References [1] C. Intanagonwiwat, R. Govindan and D. Estrin, ``Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” MobiCom 2000. [2] D. Braginsky and D. Estrin, ``Rumor Routing Algorithm for Sensor Networks", WSNA 2002. [3] N. Sadagopan, B. Krishnamachari, and A. Helmy, ``Active Query Forwarding in Sensor Networks (ACQUIRE)", SNPA 2003. [4] M. Chu, H. Haussecker, and F. Zhao, ``Scalable Information-Driven Sensor Querying and Routing for ad hoc Heterogeneous Sensor Networks", Int'l J. High Performance Computing Applications, 16(3):90-110, Fall 2002. [5] J. Liu, F. Zhao, and D. Petrovic, ``Information-Directed Routing in Ad Hoc Sensor Networks", WSNA 2003. ICPS 2004 20

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