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

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

25. 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