Continuous Residual Energy Monitoring i n Wireless Sensor Network s

1. Continuous Residual Energy Monitoring in Wireless Sensor Networks Song Han and Edward Chan Department of Computer Science, City University of Hong Kong 83 Tat Chee Avenue, Kowloon, HONG KONG

2. Agenda • Introduction • Objective • Related Work • System Model • Methodology • Performance Analysis • Conclusion

3. Introduction • Features of Wireless Sensor Network (WSN) • Large scale • Static nodes • Limited resources • Residual Energy Monitoring (REM) • Get WSN’ s energy information • Maintain the WSN active • Accurate vs. Approximate monitoring

4. Objective • To propose an approach for monitoring residual energy information continuously in the WSN • Scalability • Accuracy • Maximized lifetime & Minimized message cost

5. Related Work • Energy Consumption Prediction by Nath et al. • Energy dissipation model • Probabilistic prediction scheme • Residual Energy Scan by Zhao et al. • Notion of energy map • In-network aggregation • Abstract representation of energy graph

6. System Model Base Station m Communication Range R m

7. Methodology • Topology Discovery • Divide the WSN into several clusters • Construct a monitoring tree • Residual Energy Monitoring • Abstracted Representation of Energy Graph • Determining the Local Energy Graph • In-Network Aggregation of Energy Graphs • Topology Maintenance

8. Topology Discovery • Step 1: A “Topology Discovery Request” is initiated from the base station and propagates through controlled flooding. • Step 2: WSN is divided into clusters based on TopDisc algorithm by Nath et al. • A simple greedy log (n)-approximation algorithm. • Communication range is reduced to R/2.

9. Topology Discovery (cont.) Base Station Topology Discovery Request

10. Topology Discovery (cont.) Base Station Topology Discovery Request

11. Topology Discovery (cont.) Base Station Topology Discovery Request

12. Topology Discovery (cont.) Base Station Topology Discovery Request Become Black after an interval And broadcast the request again

13. Topology Discovery (cont.) • At the end of this phase, Monitoring tree is constructed (Figure.2) • Consists of black nodes and grey nodes: • Black node: Cluster head • Grey node: Bridge between two heads

14. Cluster Boundary Ordinary Node Delivery Node Cluster Head Topology Discovery (cont.) Figure.2. Monitoring Tree

15. Part 1 Part 2 Part 3 Part 4 Outside contour Hole 1 Hole 2 Sender ID Receiver ID Energy Range Polygon Information Residual Energy Monitoring Abstracted Representation of Energy Graph • Structure of the message • Structure of the polygon information

16. Residual Energy Monitoring (cont.) Determining the Local Energy Graph • Divide sensors according to energy range • Get the convex contour for each energy range • Perform Boolean Computing on the set of polygons

17. Residual Energy Monitoring (cont.) In-Network Energy Graph Aggregation • Scheme: • Forward energy information along the monitoring tree from leaf to root. • Non-leaf node merges two polygons if they are in the same energy range and adjacent physically. • Vertex number and communication cost are reduced.

18. X Y X Y Topology Maintenance Node Selection Criteria • Residual energy • Proximity to lower energy range Initial State After Selection

19. Topology Maintenance (cont.) Static topology maintenance schema • Parent cluster selects a new head; • Child cluster selects new head and deliver node; • Both new head nodes broadcast the change in their clusters.

20. Performance Analysis Performance Metrics: • Residual reachable nodes • Fidelity • Total Message Cost Methods to compare: • Continuous Residual Energy Monitoring (CREM) • Centralized Collection • Static Clustering

21. Cost ratioCREM vs.Centralized Collection

22. Fidelity vs. Network Size

23. CREMFidelity vs. Monitoring Cycles

24. Residual reachable nodes vs. Monitoring Cycle

25. Methods Comparison:Fidelity vs.Monitoring Cycles

26. Message Cost: CREM vs. static clustering

27. Conclusion In this paper, we proposed a hierarchical structure for energy monitoring, in the monitoring process, we use the in-network graph aggregation and node selection schema to reduce the message cost, expand the lifetime of the WSN and at the same time, we maintain the accuracy of the result energy graph.

28. References • [1] A. F. Mini, Badri Nath and Antonio A. F. Loureiro, “Prediction-based Approaches to Construct the Energy Map for Wireless Sensor Networks”, Proc. 21st Brasilian Symposium on Computer Networks, Natal, RN, Brazil, May 19-23, 2003. • [2] J. Zhao, R. Govindan, and D. Estrin, “Computing aggregates for monitoring wireless sensor networks”, Technical Report 02-773, USC, September 2003. • [3] B. Deb, S. Bhatangar, and B. Nath, “A Topology Discovery Algorithm for Sensor Networks with Applications to Network Management”, Proc. IEEE CAS Workshop on Wireless Communications and Networking, Pasadena, USA, Sept. 2002. • [4] Michael V. Leonov, Alexey G. Nikitin, “An Efficient Algorithm for a Closed Set of Boolean Operations on Polygonal Regions in the Plane”, Preprint 46, Novosibirsk, A. P. Ershov Institute of Informatics Systems, 1997.