Tracking Mobile Sensor Nodes in Wildlife Francine Lalooses Hengky Susanto EE194-Professor Chang

Outline Recap Tracking with Binary Sensor Network Distributed Predictive Tracking Algorithm Our Tracking Approach Future Work References

Recap Sensors only monitor land animals Animals are tagged Sensors placed at certain location Better understanding of region/animal relationship Not specific to animal size or velocity

Tracking with Binary Sensor Network Assumptions: Sensors have only one bit of information Sensors broadcast bit to base station (BS) Proximity sensor requires one more bit One bit of information gives accurate predictions about direction of motion Approaching (+) Moving away (-) Simple broadcast protocol Tracking a Moving Object with a Binary Sensor Network, Dartmouth College.

Binary Sensor Network Geometry Future position lies: Inside plus sensor overlap (+) Outside minus sensor (-) + + -

Tracking with a Proximity Bit Simulation results Estimated trajectory (star – dashed line) Actual trajectory (triangle – line) Plus sensors (squares) Minus sensors (circles) Object gets in range at time 3

Binary Sensor Summary Advantages: Trajectory prediction error is low Broadcasting single bits over network feasible BS computation is fast Disadvantages: Only tracks one animal at a time No consideration for energy efficiency No failure recovery model

What if... Tracking Algorithm: Low Duty Cycle

Distributed Predictive Tracking Algorithm No central point Cluster based architecture Assumptions: Randomly distributed sensors Default to normal beam Hibernation mode Predictive mechanism Cluster head activates appropriate sensors before target arrives A Protocol for Tracking Mobile Targets using Sensor Networks, RPI

DPT: Target Descriptor Formulation Target descriptor (TD) consists of: Target’s identity Unique Created when target first detected Target’s present location Sensor triplet triangulation Target’s next predicted location Alerts CHs most likely approached Linear predictor Time stamp Time TD created

DPT: Sensor Selection Algorithm Prediction: When CH i predicts target location, downstream CH i+1 receives message CH i+1 has information of all sensors in cluster Selection: CH i+1 locally decides sensor-triplet to sense target and sends wake-up message Each sensor sends location message to CH i+1 CH i+1 formulates TD i+1 CH CH i+1 CH i+2 CH = cluster headTD = target description downstream upstream

DPT: Failure Recovery Failure: If upstream CH does not receive confirmation from downstream CH, assumes downstream CH is not available and target lost Target changes direction or speed and moves away from predicted location Recovery: Wake up all sensors within area Calculated from target’s previous actual location

DPT: Energy Considerations Sensor-hibernation method Most sensors stay in hibernation mode Only chosen sensors become active Energy for obtaining TD of one location Keep p miss small to minimize energy consumed for recovery Energy consumed in failure recovery Failures cause extra communication between clusters and sensors E total = (1 - p miss ) E success + p miss E failure E failure = E success + 3E HB P HB + (1-P HB )(3E HB +C)

Our Tracking Approach Nature Cluster based algorithm Hierarchical approach Variables: distance = E[velocity running ] * time CH d d First level CH Master CH

Our Tracking Algorithm Calculation based on maximum hop and popularity Variables: h = CH hop count 2d 1h 4h 2h Lost region

Our Intuition Accuracy finding lost target improves over time More information = better search boundary Error handling wakes up all nodes in region of diameter 2*d Advantages over sweeping across region: More energy efficient Less network traffic

Sweeping Across the Region Perform increment layer outward from last seen position until lost target is found or reaches border layer Only notifies their neighbor at outer layer When successful, the founder takes over target When target is not found, border sensors report to node in charge Awake all nodes in region and flood network Running time is O(n) Sensor node layers Example of sweeping:

Future Work Failure and recovery algorithm Further develop our algorithm Compare DPT with our algorithm Performance Energy efficiency Error handling

References A Protocol for Tracking Mobile Targets using Sensor Networks. H. Yang and B. Sikdar. RPI. Tracking a Moving Object with a Binary Sensor Network. J. Aslam, Z. Butler, F. Constantin, V. Crespi, G. Cybenko, D. Rus. Dartmouth College, CSU Los Angeles. Rumor Routing Algorithm for Sensor Networks. D. Braginsky, D. Estrin. UCLA. The ACQUIRE Mechanism for Efficient Querying in sensor Networks. N Sadagopan, A Helmy. USC. Distribute Online localization in Sensor Networks Using a Moving Target. A Galstyan, K Lerman, S Pattem. USC. Distributed Target Classification and Tracking in Sensor Networks. R. R. Brooks, P Ramanathan, A Sayeed. Penn State University and University of Wisconsin. Detecting Moving Radioactive Source Using Sensor Networks. D Stephens, A Peurrung. Pacific National Laboratory.

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