1. Department of Computer Science City University of Hong Kong Department of Computer Science City University of Hong Kong 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 2 Agenda Introduction Objective Related Work System Model Methodology Performance Analysis Conclusion

3. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 4 Objective To propose an approach for monitoring residual energy information continuously in the WSN Scalability Accuracy Maximized lifetime & Minimized message cost

5. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 6 System Model Base Station m Communication Range R m

7. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 9 Topology Discovery (cont.) Base Station Topology Discovery Request

10. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 10 Topology Discovery (cont.) Base Station Topology Discovery Request

11. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 11 Topology Discovery (cont.) Base Station Topology Discovery Request

12. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 12 Topology Discovery (cont.) Base Station Topology Discovery Request Become Black after an interval And broadcast the request again

13. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 14 Topology Discovery (cont.) Figure.2. Monitoring Tree

15. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 15 Residual Energy Monitoring Abstracted Representation of Energy Graph Structure of the message Structure of the polygon information Sender IDReceiver IDEnergy RangePolygon InformationPart 1Part 2Part 3Part 4 Outside contour Hole 1 Hole 2

16. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 16 Residual Energy Monitoring (cont.) Determining the Local Energy Graph 1) Divide sensors according to energy range 2) Get the convex contour for each energy range 3) Perform Boolean Computing on the set of polygons

17. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 18 Topology Maintenance Node Selection Criteria Residual energy Proximity to lower energy range X Y After Selection Initial State X Y

19. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 21 Cost ratio CREM vs. Centralized Collection

22. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 22 Fidelity vs. Network Size

23. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 23 CREM Fidelity vs. Monitoring Cycles

24. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 24 Residual reachable nodes vs. Monitoring Cycle

25. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 25 Methods Comparison: Fidelity vs. Monitoring Cycles

26. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 26 Message Cost: CREM vs. static clustering

27. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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. Department of Computer Science City University of Hong Kong Continuous Residual Energy Monitoring in Wireless Sensor Networks 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.