AN APPROACH FOR TRACKING WILDLIFE USING WIRELESS SENSOR NETWORKS

Transcription

1 AN APPROACH FOR TRACKING WILDLIFE USING WIRELESS SENSOR NETWORKS Francine Lalooses* Hengky Susanto* Chorng Hwa Chang* * Tufts University MITRE Corporation Approved for Public Release; Distribution Unlimited. Case Number

2 Outline Overview Introduction Tracking moving target Related work Sensor network for habitat monitoring ZebraNet Great Duck Island Distributed Predictive Tracking Algorithm Problem with Target Tracking Tracking Failure Recovery Quandary of Recovering from Failure Future Work

3 Introduction: Wireless Sensor Networks Consist of sensors and base stations Purpose of tracking in sensor networks is a necessity for many applications Including computer vision, tactical battlefield surveillance, perimeter security, emergency response, animal tracking

4 Analogy Illustration

5 Purpose of Tracking For wildlife habitat monitoring Sensors only monitor land targets (animals) Animals are tagged Sensors purposely placed at certain locations Saves time finding target in the region Better understanding of region/animal relationship

6 Tracking Moving Target Sensors: Hibernate to conserve energy Awaken when target detected entering the monitored area Sensors record information of the target Relay information between sensors and follows target movement

7 Tracking Moving Target in Wildlife Awake Hibernating

8 ZebraNet Track animals long term and over long distances GPS enabled All nodes mobile Zebras are tagged with RFID Peer-to-peer routing and data storage

9 Great Duck Island Biologists put sensor devices on Maine's Great Duck Island In underground nests 4-inch stilts placed just outside their burrows Record data about the birds Sensors relay information to a gateway node (the base station) Gateway node transmits information to a laptop then to satellite dish ultimately, to an Intel Research lab at Berkeley California

10 Great Duck Island

11 Distributed Predictive Tracking No central point Cluster based architecture Assumptions: Randomly distributed sensors Default to normal beam Hibernation mode Predictive mechanism Wakeup all sensors for recovery A Protocol for Tracking Mobile Targets using Sensor Networks, RPI

12 Outline Overview Introduction Tracking moving target Related work Sensor network for habitat monitoring ZebraNet Great Duck Island Distributed Predictive Tracking Algorithm Problem with Target Tracking Tracking Failure Recovery Quandary of Recovering from Failure Future Work

13 Problem Tracking Moving Target What if... in Wildlife

14 Lost Target Conditions Network failure: When node that is currently monitoring target fails to wake the next node Prediction failure: Failure predicting where the target is heading Multiple sensors interaction: Failure to recognize multiple targets at once Hardware malfunction: Hardware may malfunction or the battery is weakening

15 Outline Overview Introduction Tracking moving target Related work Sensor network for habitat monitoring ZebraNet Great Duck Island Distributed Predictive Tracking Algorithm Problem with Target Tracking Tracking Failure Recovery Quandary of Recovering from Failure Future Work

16 Approach in Tracking Failure Recovery Goal Finds the monitored target after failure occurs Purpose: Conserve energy Quick and efficient method to recover from failure

17 How Does The Algorithm Work? Analogy of lost tourist in Marrakesh Keeping track the traveler Lost the traveler in Hotel Kenzi Farah Where to find the traveler Check the popular places around the hotel Casino, Jemaa El Fna, Mosque, Koutoubia Establish the search region Perform search for the lost traveler

18 Why This Approach Studies show: Animals tend to move in certain patterns Along river Tree Animals are sensitive to temperature and chemical elements

19 Algorithm Summary Establish search region To limit the search area Take advantage of hierarchical cluster Finding popular place surrounding where the animal last seen Perform search algorithm

20 Establish Search Region Computes the diameter of search region Uses approximate velocity of animal Radius d = time t * V Uses hierarchical cluster to find popular place in that region Place that is often visited by animals such as {River, Trees, etc}

21 Hierarchical Cluster Nodes form clusters after deployment Each cluster select a leader or a Cluster Head (CH) Only CH communicate to each other Reduces traffic Eliminates network collisions

22 Hierarchical Cluster Cluster based algorithm Hierarchical approach CH CH CH d d Master CH First level CH Variables: Distance d = Velocity * time

23 Search Algorithm x = CH of the target s last seen position y = Identified CH popular place Computes distance y to x y X y y y

24 Broadcast and Activate Nodes X = the object s last seen position Broadcast from X Activate sensors Establish minimized region X

25 Minimizing Search Region Calculation based on maximum hop and popularity Lost region 2h 2d 1h 4h Variables: h = CH hop count

26 Understanding Necessary Conditions Coverage area Sensor placement and terrain Sensor limitation Sensor reliability Multiple sensor interaction and identification

27 Oops!! What if the animal is not in the desired search region? Awake all nodes in the initial search region

28 Outline Overview Introduction Tracking moving target Related work Sensor network for habitat monitoring ZebraNet Great Duck Island Distributed Predictive Tracking Algorithm Problem with Target Tracking Tracking Failure Recovery Quandary of Recovering from Failure Future Work

29 Quandary of Recovering from Failure Long description to describe a single target Due to limited information from sensor itself Takes large amount of bandwidth Requires processing power Requires storage space in sensor Not energy efficient Moving target s speed Difficult to estimate the speed accurately

30 Outline Overview Introduction Tracking moving target Related work Sensor network for habitat monitoring ZebraNet Great Duck Island Distributed Predictive Tracking Algorithm Problem with Target Tracking Tracking Failure Recovery Quandary of Recovering from Failure Future Work

31 Future Work More in depth understanding on Relation between animal s behavior and accuracy of establishing search region Performance analysis evaluation Network traffic analysis during recovery process

32 In Progress Minimizing target description More efficient description to describe the target Improving the test bed in our sensor lab

33 Thank You Merci Shukran