UNIVERSITY OF SOUTHERN CALIFORNIA RUGGeD: RoUting on finGerprint GraDients in Sensor Networks Jabed Faruque, Ahmed Helmy Wireless Networking Laboratory Department of Electrical Engineering University of Southern California URL: ICPS

UNIVERSITY OF SOUTHERN CALIFORNIA Introduction 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 … Sensor networks consist of sensor nodes with -Limited Energy source -Sensor devices -Short range radio -On-board processing capability ICPS Mica2 mote and sensor board

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

UNIVERSITY OF SOUTHERN CALIFORNIA ICPS Example (of diffusion) : (North Palm Springs earthquake of July 8, 1986 ) Example (of diffusion) : Isoseismal (intensity) maps (North Palm Springs earthquake of July 8, 1986 ) Ref.: Southern California Earthquake Center. (

UNIVERSITY OF SOUTHERN CALIFORNIA Why Using Natural Information Gradient is Important? ICPS This natural information gradient is FREE This natural information gradient is FREE Routing protocols can use it to forward query packet (greedily) 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 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 Existing approaches – flooding, expanding ring search, random-walk, etc. do not utilize this information gradient

UNIVERSITY OF SOUTHERN CALIFORNIA 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 sensor device(s) -Erroneous reading of malfunctioning sensors - Due to calibration errors or obstacle - Cause local maxima or minima -Environmental noise ICPS

UNIVERSITY OF SOUTHERN CALIFORNIA 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 Environment Model Event’s effect follows the diffusion law Event’s effect follows the diffusion law Discontinuity exists in the diffusion curve with finite tail Discontinuity exists in the diffusion curve with finite tail Environmental noise Environmental noise ICPS

UNIVERSITY OF SOUTHERN CALIFORNIA Related Work [1,2,3] Traditional routing protocols for sensor networks are based on Flooding (directed-diffusion) or Random-walk (Rumor- routing, ACQUIRE, etc.) 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 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

UNIVERSITY OF SOUTHERN CALIFORNIA 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 1. Information gradient region follows greedy approach - Forwards the query to the neighbors if the information level about the event improves - 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 2. Unsmooth gradient region use probabilistic forward based on Simulated Annealing - Probabilistic function is f p (x) = 1/x a, where x = hop count in the information gradient region and ‘a’ depends on the diffusion parameter  - Probabilistic function is f p (x) = 1/x a, 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 3. Use flooding for the flat (i.e., zero) information region - Decrease latency to reach gradient information region - Decrease latency to reach gradient information region - Handles query in the absence of events - Handles query in the absence of events  Query ID prevents looping  Once query is resolved, a node uses the reverse path to reply ICPS

UNIVERSITY OF SOUTHERN CALIFORNIA E Q Q’Q’Q’ Q’Q’Q’ Q’Q’Q’ E Q MnMn ngng ngng ngng ngng ngng ngng ngng ngng MxMx npnp npnp npnp npnp npnp npnp npnp npnp All neighbors (n g ) of M n have more information, so they forward the query to their neighbors All neighbors (n g ) of M n have more information, so they forward the query to their neighbors All neighbors (n p ) of M x have less information, so they forward the query to their neighbors probabilistically All neighbors (n p ) of M x have less information, so they forward the query to their neighbors probabilistically ICPS

UNIVERSITY OF SOUTHERN CALIFORNIA Simulation Model Two different sensor network layouts X 100 regular grid of nodes. Event located at (74,49) X 6 grid of 90 nodes in 225 x 375 m 2 sensor field with 50m communication radius. Grid points are perturbed by Gaussian noise (0,25) Two different sensor network layouts X 100 regular grid of nodes. Event located at (74,49) X 6 grid of 90 nodes in 225 x 375 m 2 sensor field with 50m communication radius. Grid points are perturbed by Gaussian noise (0,25) Diffusion parameter  set to 0.8 Diffusion parameter  set to 0.8 Two regions exist in each layout - Flat or zero information region - Gradient information region Two regions exist in each layout - Flat or zero information region - Gradient information region Malfunctioning nodes are uniformly distributed in both region Malfunctioning nodes are uniformly distributed in both region Environmental noise is present in the gradient information 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

UNIVERSITY OF SOUTHERN CALIFORNIA Query Types Single-value query - Search for a specific value and have a single response 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 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 Multiple Events detection (only sensor layout 1 is used) - Search for multiple events of the same type ICPS Performance Metrics Reachability i.e., success probability - Probability that the query will reach the source 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 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 For multiple events detection, ratio of sources found to actual number of sources

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

UNIVERSITY OF SOUTHERN CALIFORNIA ICPS 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 Single-value query- effect of the malfunctioning nodes (3% environmental noise and 36% flat information region nodes)

UNIVERSITY OF SOUTHERN CALIFORNIA 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 ICPS For the probabilistic function f p (x) = 1/x a, a <  is recommended, but close to  gives optimal trade-off between reachability and overhead For the probabilistic function f p (x) = 1/x a, a <  is recommended, but close to  gives optimal trade-off between reachability and overhead

UNIVERSITY OF SOUTHERN CALIFORNIA 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

UNIVERSITY OF SOUTHERN CALIFORNIA 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

UNIVERSITY OF SOUTHERN CALIFORNIA Developed a multiple-path exploration protocol to discover events in sensor networks efficiently 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 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 The performance of the probabilistic function is closely tied to the diffusion parameter Three different problems were studied Three different problems were studied Single-value, Global maximum, Multiple events Single-value, Global maximum, Multiple events Obtained high success rate to route around the sensors hole, with proper setting of the probability function parameters Obtained high success rate to route around the sensors hole, with proper setting of the probability function parameters More efficient than existing approaches More efficient than existing approaches Conclusion ICPS

UNIVERSITY OF SOUTHERN CALIFORNIA On-going and Future work Establish analytical relationship between diffusion pattern and the probabilistic forwarding function 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 Develop protocol for target tracking and target counting using the multiple path exploration mechanisms ICPS

UNIVERSITY OF SOUTHERN CALIFORNIA Backup Slides

UNIVERSITY OF SOUTHERN CALIFORNIA 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

UNIVERSITY OF SOUTHERN CALIFORNIA 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

UNIVERSITY OF SOUTHERN CALIFORNIA Reply Suppression Mechanism Intermediate nodes suppress the non-promising replies

UNIVERSITY OF SOUTHERN CALIFORNIA

UNIVERSITY OF SOUTHERN CALIFORNIA References [1] C. Intanagonwiwat, R. Govindan and D. Estrin, ``Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” MobiCom [2] D. Braginsky and D. Estrin, ``Rumor Routing Algorithm for Sensor Networks", WSNA [3] N. Sadagopan, B. Krishnamachari, and A. Helmy, ``Active Query Forwarding in Sensor Networks (ACQUIRE)", SNPA [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 [5] J. Liu, F. Zhao, and D. Petrovic, ``Information-Directed Routing in Ad Hoc Sensor Networks", WSNA ICPS