UNIVERSITY OF SOUTHERN CALIFORNIA RUGGeD: RoUting on finGerprint GraDients in Sensor Networks Jabed Faruque, Ahmed Helmy Wireless Networking Laboratory

UNIVERSITY OFSOUTHERN CALIFORNIA

RUGGeD: RUGGeD: RRooUUting on ting on finfinGGerprint erprint GGraraDDients in ients in

Sensor NetworksSensor Networks

Jabed FaruqueJabed Faruque, Ahmed , Ahmed HelmyHelmy

Wireless Networking LaboratoryWireless Networking Laboratory

Department of Electrical EngineeringDepartment of Electrical Engineering

University of Southern CaliforniaUniversity of Southern California

[email protected], [email protected]@usc.edu, [email protected]

URL: http://nile.usc.edu, http://ceng.usc.edu/~helmyURL: http://nile.usc.edu, http://ceng.usc.edu/~helmy

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IntroductionIntroduction

Use of Sensor networks is tightly coupled with physical phenomenaUse of Sensor networks is tightly coupled with physical phenomena

-May be most widely used for habitat and environment monitoring May be most widely used for habitat and environment monitoring (e.g. temperature, humidity) (e.g. temperature, humidity)

-For unattended and fine grained monitoring of natural phenomena For unattended and fine grained monitoring of natural phenomena

-Self configuration capabilitySelf configuration capability

-Also others e.g., for defense purpose …Also others e.g., for defense purpose …

Sensor networks consist of sensor nodes withSensor networks consist of sensor nodes with

-Limited Energy sourceLimited Energy source

-Sensor devicesSensor devices

-Short range radioShort range radio

-On-board processing capabilityOn-board processing capability

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Mica2 mote and sensor boardMica2 mote and sensor board


Many physical phenomena follow diffusion law Many physical phenomena follow diffusion law

f(d)f(d) 1/d 1/d, where , where

d = distance from the source, d = distance from the source, = diffusion parameter, depends on the type of effect = diffusion parameter, depends on the type of effect ((e.g. for temperature ~ 1, light ~ 2))

Every physical event produces a fingerprint in the environment, Every physical event produces a fingerprint in the environment, e.g.,e.g.,

-Fire event increases temperatureFire event increases temperature

-Nuclear leakage causes radiationNuclear leakage causes radiation

MotivationMotivation

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ExampleExample (of diffusion) (of diffusion): : Isoseismal (intensity) maps (North Palm Springs earthquake of July 8, 1986 )(North Palm Springs earthquake of July 8, 1986 )

Ref.: Southern California Earthquake Center. (http://www.scec.org)


Why Using Natural Information Why Using Natural Information Gradient is Important?Gradient is Important?

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• This natural information gradient isThis natural information gradient is FREEFREE

• Routing protocols can use it to forward query packetRouting protocols can use it to forward query packet ((greedilygreedily))

- Locate event(s); e.g., fire, nuclear leakage.- Locate event(s); e.g., fire, nuclear leakage.

• Can be extended for other notions of gradientsCan be extended for other notions of gradients

- Example: Time gradients can be used for mobile target tracking- Example: Time gradients can be used for mobile target tracking

• Existing approaches – Existing approaches – flooding, expanding ring search, flooding, expanding ring search, random-walk random-walk, etc. do not utilize this information gradient, etc. do not utilize this information gradient


Challenges Challenges -In real life, sensors are unable to detect or measure the event’s In real life, sensors are unable to detect or measure the event’s effect below certain threshold. So, diffusion curve has finite tail effect below certain threshold. So, diffusion curve has finite tail

- - Lack of sensitivity of sensorLack of sensitivity of sensor device(s)device(s)

-Erroneous reading of malfunctioning sensors Erroneous reading of malfunctioning sensors

- Due to calibration errors or obstacle- Due to calibration errors or obstacle- Cause local maxima or minima- Cause local maxima or minima

-Environmental noiseEnvironmental noise

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ObjectiveObjectiveDesign an efficient algorithm to locate source(s) in Design an efficient algorithm to locate source(s) in sensor networks, exploiting natural information gradients sensor networks, exploiting natural information gradients i.e., the diffusion pattern of the event’s effecti.e., the diffusion pattern of the event’s effect

- Gradient based- Gradient based- Fully distributed- Fully distributed- Robust to node or sensor failure or malfunction- Robust to node or sensor failure or malfunction- Capable of finding multiple sources- Capable of finding multiple sources

Environment ModelEnvironment Model• Event’s effect follows the diffusion lawEvent’s effect follows the diffusion law

• Discontinuity exists in the diffusion curve with finite tailDiscontinuity exists in the diffusion curve with finite tail

• Environmental noiseEnvironmental noise

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Related Work Related Work [1,2,3][1,2,3]

• Traditional routing protocols for sensor networks are based Traditional routing protocols for sensor networks are based on on FloodingFlooding (directed-diffusion) or (directed-diffusion) or Random-walkRandom-walk (Rumor- (Rumor- routing, ACQUIRE, etc.) routing, ACQUIRE, etc.)

- Flooding based methods cause huge energy overhead- Flooding based methods cause huge energy overhead- Random-walk increases latency and failure probability- Random-walk increases latency and failure probability- Do not utilizes the natural information gradient- Do not utilizes the natural information gradient

• Existing Information driven protocols [4,5] use Existing Information driven protocols [4,5] use single pathsingle path approaches with/without approaches with/without look-aheadlook-ahead parameter parameter

- Use a proactive phase to prepare information repository- Use a proactive phase to prepare information repository Cause significant overhead at low query rateCause significant overhead at low query rate

- Unable to handle local maxima or minima- Unable to handle local maxima or minima- Unable to find multiple sources- Unable to find multiple sources- Robustness depends on the proactive phase and the look- - Robustness depends on the proactive phase and the look- ahead parameter ahead parameter

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ProtocolProtocol A node can exist in one of two modes/statesA node can exist in one of two modes/states

- flat-region mode- flat-region mode- gradient-region mode - gradient-region mode

A node forwards the query to neighbors with its information level A node forwards the query to neighbors with its information level To forward the query, each node uses following algorithm:To forward the query, each node uses following algorithm: 11. Information gradient region follows . Information gradient region follows greedy approachgreedy approach

- Forwards the query to the neighbors if the information level about the - Forwards the query to the neighbors if the information level about the event improves event improves

22. Unsmooth gradient region use probabilistic forward based . Unsmooth gradient region use probabilistic forward based on on Simulated AnnealingSimulated Annealing

- Probabilistic function is - Probabilistic function is ffpp(x) = 1/x(x) = 1/xaa, where x = hop count in the information , where x = hop count in the information

gradient region and ‘ gradient region and ‘a’a’ depends on the diffusion parameter depends on the diffusion parameter ( )

33. Use flooding for the flat (i.e., zero) information region. 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 Query ID prevents looping Once query is resolved, a node uses the Once query is resolved, a node uses the reverse path reverse path to replyto replyICPS 2004 9


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• All neighbors (All neighbors (nngg) of M) of Mnn have more information, so they forward the have more information, so they forward the

query to their neighbors query to their neighbors

• All neighbors (All neighbors (nnpp) of M) of Mxx have less information, so they forward the have less information, so they forward the

query to their neighbors query to their neighbors probabilisticallyprobabilistically

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Simulation ModelSimulation Model• Two different sensor network layoutsTwo different sensor network layouts 1. 100 X 100 regular grid of 10000 nodes. Event located at (74,49) 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 m 2. 15 X 6 grid of 90 nodes in 225 x 375 m22 sensor field with 50m sensor field with 50m communication radius. Grid points are perturbed by Gaussian noise (0,25) communication radius. Grid points are perturbed by Gaussian noise (0,25)

• Diffusion parameter Diffusion parameter set to set to 0.80.8

• Two regions exist in each layoutTwo regions exist in each layout - Flat or zero information region - Flat or zero information region - Gradient information region - Gradient information region

• Malfunctioning nodes are uniformly Malfunctioning nodes are uniformly distributed in both region distributed in both region

• Environmental noise is present in the Environmental noise is present in the gradient information region gradient information region

• Malfunctioning nodes have arbitrary readingsMalfunctioning nodes have arbitrary readings - For global maxima search, protocol uses a filter to prohibit replies - For global maxima search, protocol uses a filter to prohibit replies from nodes having arbitrary high value from nodes having arbitrary high value

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Query TypesQuery Types• Single-value querySingle-value query

- Search for a specific value and have a single response- Search for a specific value and have a single response

• Global Maxima search Global Maxima search (only sensor layout 1 is used)(only sensor layout 1 is used)- Search for the maximum value of information in the system- Search for the maximum value of information in the system- Intermediate nodes suppress non-promising replies- Intermediate nodes suppress non-promising replies

• Multiple Events detection Multiple Events detection (only sensor layout 1 is used)(only sensor layout 1 is used)- Search for multiple events of the same type- Search for multiple events of the same type

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Performance MetricsPerformance Metrics• Reachability i.e., success probabilityReachability i.e., success probability

- Probability that the query will reach the source- Probability that the query will reach the source

• Overhead in terms of average energy dissipation Overhead in terms of average energy dissipation - Number of transmissions required to forward the query and to get the reply - Number of transmissions required to forward the query and to get the reply

from the source from the source

• For multiple events detection, ratio of sources found to actual For multiple events detection, ratio of sources found to actual number of sources number of sources


Single-value query- Single-value query- effect of flat information effect of flat information region nodesregion nodes(3% environmental noise and 15% malfunctioning nodes)(3% environmental noise and 15% malfunctioning nodes)

- With increase of flat region- With increase of flat region

- - Flooding overhead becomes dominantFlooding overhead becomes dominant increasing energy consumptionincreasing energy consumption

- Malfunctioning nodes cause query to switch to gradient mode erroneously- Malfunctioning nodes cause query to switch to gradient mode erroneously

- Decrease in ‘- Decrease in ‘a’ a’ creates more paths, increasing reachability and energy consumptioncreates more paths, increasing reachability and energy consumptionICPS 2004 13


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- With increase of malfunctioning nodes the protocol switches from the flat region With increase of malfunctioning nodes the protocol switches from the flat region mode to the gradient region mode rapidlymode to the gradient region mode rapidly

- Reduces flooding overhead- Reduces flooding overhead- Increases failure rate- Increases failure rate

Single-value query- Single-value query- effect of the malfunctioning effect of the malfunctioning nodesnodes(3% environmental noise and 36% flat information region nodes)(3% environmental noise and 36% flat information region nodes)


Single-value query- Single-value query- route a query around the route a query around the sensors holesensors hole(3% environmental noise and 20% malfunctioning nodes)(3% environmental noise and 20% malfunctioning nodes)

- For smaller value of ‘For smaller value of ‘aa’ (e.g., ’ (e.g., aa ~0.65), reachability is above 98% even at the presence ~0.65), reachability is above 98% even at the presence of 55% flat information regionof 55% flat information region

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• For the probabilistic function fFor the probabilistic function fpp(x) = 1/x(x) = 1/xaa, , a a < < is recommended, but is recommended, but

close to close to gives optimal trade-off between reachability and overheadgives optimal trade-off between reachability and overhead


Global Maxima Search-Global Maxima Search-effect of flat information effect of flat information region nodesregion nodes(3% environmental noise and 15% malfunctioning nodes)(3% environmental noise and 15% malfunctioning nodes)

((without without FilterFilter)) ((withwith Filter Filter))

- Average energy dissipation reduces significantly due to use of the simple filterAverage energy dissipation reduces significantly due to use of the simple filter

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Multiple Events Detection-Multiple Events Detection-effect of flat information effect of flat information

region nodesregion nodes

(3% environmental noise and 15% malfunctioning nodes)(3% environmental noise and 15% malfunctioning nodes)

- With the increase of number of sources, some plateaux regions are created in the With the increase of number of sources, some plateaux regions are created in the resultant gradient information region that require more probabilistic forwarding 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- for five or more sources, a ~ 0.35 is a good setting in the simulated scenario

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• Developed a multiple-path exploration protocol to Developed a multiple-path exploration protocol to discover events in sensor networks efficientlydiscover events in sensor networks efficiently• The protocol is fully reactive, effectively exploits the The protocol is fully reactive, effectively exploits the natural information gradientnatural information gradients and controls the s and controls the instantiation of multiple paths probabilisticallyinstantiation of multiple paths probabilistically• The performance of the probabilistic function is closely The performance of the probabilistic function is closely tied to the diffusion parametertied to the diffusion parameter

• Three different problems were studiedThree different problems were studied• Single-value, Global maximum, Multiple eventsSingle-value, Global maximum, Multiple events

• Obtained high success rate to route around the Obtained high success rate to route around the sensors hole, with proper setting of the probability sensors hole, with proper setting of the probability function parametersfunction parameters• More efficient than existing approachesMore efficient than existing approaches

ConclusionConclusion

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On-going and Future On-going and Future workwork• Establish analytical relationship between Establish analytical relationship between diffusion pattern and the probabilistic diffusion pattern and the probabilistic forwarding function forwarding function

• Develop protocol for target tracking and target Develop protocol for target tracking and target counting using the multiple path exploration counting using the multiple path exploration mechanisms mechanisms

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Backup Slides


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


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


Reply Suppression Mechanism

Intermediate nodes suppress the non-promising replies



ReferencesReferences

[1][1] C. Intanagonwiwat, R. Govindan and D. Estrin, ``Directed Diffusion: C. Intanagonwiwat, R. Govindan and D. Estrin, ``Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor A Scalable and Robust Communication Paradigm for Sensor Networks,” MobiCom 2000.Networks,” MobiCom 2000.

[2][2] D. Braginsky and D. Estrin, ``Rumor Routing Algorithm for Sensor D. Braginsky and D. Estrin, ``Rumor Routing Algorithm for Sensor Networks", WSNA 2002.Networks", WSNA 2002.

[3][3] N. Sadagopan, B. Krishnamachari, and A. Helmy, `Àctive Query N. Sadagopan, B. Krishnamachari, and A. Helmy, `Àctive Query Forwarding in Sensor Networks (ACQUIRE)", SNPA 2003.Forwarding in Sensor Networks (ACQUIRE)", SNPA 2003.

[4][4] M. Chu, H. Haussecker, and F. Zhao, ``Scalable Information-Driven M. Chu, H. Haussecker, and F. Zhao, ``Scalable Information-Driven Sensor Querying and Routing for ad hoc Heterogeneous Sensor Sensor Querying and Routing for ad hoc Heterogeneous Sensor Networks", Int'l J. High Performance Computing Applications, Networks", Int'l J. High Performance Computing Applications, 16(3):90-110, Fall 2002.16(3):90-110, Fall 2002.

[5][5] J. Liu, F. Zhao, and D. Petrovic, `Ìnformation-Directed Routing in J. Liu, F. Zhao, and D. Petrovic, `Ìnformation-Directed Routing in Ad Hoc Sensor Networks", WSNA 2003.Ad Hoc Sensor Networks", WSNA 2003.

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UNIVERSITY OF SOUTHERN CALIFORNIA RUGGeD: RoUting on finGerprint GraDients in Sensor Networks Jabed Faruque, Ahmed Helmy Wireless Networking Laboratory