Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks

1. 2004 IEEE International Conference on Mobile Data Management Yingqi Xu, Julian Winter, Wang-Chien Lee Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks

2. Contents • The direction of designing energy-aware object tracking algorithms • A prediction-based energy saving scheme. • An extensive performance evaluation • A sensor node: a logical representation of a set of sensor nodes which collaboratively decide the properties of a moving object. A sensing leader/cluster head in a multi-level sensor network.

3. Impacting factors Factors impacting the energy consumptions. • Number of moving objects • Reporting frequency • Data precision • Sensor sampling frequency • Higher object speed  higher sampling frequency • Object moving speed • Location models

4. Object Track problem • Requirements: A sensor network with S sensor nodes is equipped to track O moving objects. Each sampling duration takes X seconds. The application requires the sensor nodes to report the objects location (represented by Sensor ID) every T seconds. • Problem Definition: Given the requirements for the object tracking application, develop energy saving schemes which minimize overall energy consumption of the OTSN under an acceptable missing rate.

5. Basic scheme • Naïve: all the sensor nodes stays in active mode to monitor their detection areas all the time. • Scheduled Monitoring (SM): all the S nodes will be activated for X second then go to sleep for (T − X) seconds. • Continuous Monitoring (CM): only the sensor node who has the object in its detection area will be activated (stay awake).

6. Solution space of energy saving schemes

7. Prediction-based Energy Saving Scheme • How to reduce the missing of objects? • Prediction, Wake up • How to re-locate the missing object? • Recovery

8. Prediction model • Object movement usually remains constant for a certain period of time. • Direction, speed • Heuristics INSTANT • Assume the moving objects stay in the current speed and direction. • Heuristics AVERAGE • The average of the object movement history. • Comm overhead (the size of history) • Heuristics EXP AVG • Assign weights to the historical stages • Compress the history info, reduce the comm overhead

9. Wake up mechanisms • Do not expect 100% prediction accuracy. • To accommodate the prediction errors, a set of nodes are woken up. • Heuristic DESTINATION • Only inform the destination node • Heuristic ROUTE • Also include the nodes on the route • Heuristic ALL NBR • Dest + Route + neighbors

11. Recovery mechanism • All neighbors • Flooding recovery

12. Simulation • Total energy consumption • Radio, sensing, computing … • Missing rate • Failing to report on time

13. Workload

14. Pause time The frequency a moving object changes its state in terms of speed and direction.

15. Moving speed

16. Sampling frequency

17. Q & A