1. Simulation of DeReClus Yingyue Xu September 6, 2003

2. 2 Introduction DeReClus - Decentralized Reactive Clustering A clustering protocol that partition the whole sensor network into clusters –Achieving scalability –Easer to management

3. 3 State of Arts and Problems in Sensor Networks SPAN, GAF –Need to know position of the node in advance LEACH –Random rotation of cluster head –Need time synchronized in advance Estrin’s method –Not event driven, Proactive

4. 4 Problems of Existing Clustering Protocols Proactive clustering results in –unnecessary radio transmission –large transmission power to reach cluster head, lack of intermediate nodes acting as routing nodes, –energy inefficiency

5. 5 Benefits of DeReClus Reactive clustering - driven by events Localized protocol Energy-efficient Less transmission power to reach cluster head

6. 6 Benefits of DeReClus event (A) A predefined clustering(B) Clustering after DeReClus

7. 7 Message Format TYPE: the type of message which can be REQUEST, REPLY, JOIN, JOIN- FORWARD and END. Power Level: the transmission power the node currently uses. Destination ID: the destination node identification and we use all 1's as the broadcast address. Source ID: the address of current node. Cluster ID: the cluster head address of the cluster the current node belongs to and we use 0 if the node is unclustered. Energy: the remaining energy of the node. Signal Energy: the signal energy sensed by the current node emitted by the potential target. Messages are exchanged only locally

8. 8 Desirable Features of DeReClus Reactive clustering driven by the events Uses power control technique to minimize the transmission power A localized clustering protocol that simple local node behavior achieves a desired global objective

9. 9 Outline of DeReClus Post-deployment Phase Cluster Forming Phase –Wait for a time –Broadcast REQUEST –Increase transmission power and rebroadcast if receive no message in a certain time –4 scenarios in determine the clusterhead –A timer in cluster head determines the end of this phase

10. 10 Outline of DeReClus Intra-cluster Data Processing Phase Cluster Head to Processing Center Phase

11. 11 Different Scenarios Node A, B unclustered Choose one with higher energy as cluster head Node A clustered, node B unclustered B join the cluster A belongs to Node A unclustered, node B clustered A join the cluster B belongs to Node A, B clustered B discard the message

12. 12 Simulation of DeReClus In Java 3 Classes –Simu.java (main class) –Node.java (node object) –Message.java (message object) Simple communication model –No routing, transmission error

13. 13 States of Nodes Sleep Idle Undecide Cluster Head Member

14. 14 Flow chart Parameter initialize Generate nodes Nodes deployment Calculate distance DeReClusLEACHFix Clustering Change target position Display results

15. 15 Parameters in Simulation 30 by 30 area Transmit power level: 8 Initial energy: 36 joules Data size: 8000 bits Mobile agent size: 800 bits Message size: 152 bits

16. 16 Node deployment Random Grid

17. 17 DeReClus Simulation Outer loop changes the simulation time Inner loop changes to every node Change target position Calculate the distance to the target Determine to wake up or not undecidesleepOther states Receive message requestreply send joinResend if failure Continue sleepReceive message

18. 18 LEACH Generate random number Calculate threshold Determine clusterhead Use client/server to transfer data Divide the network into random number of clusters Use client/server paradigm in each cluster Nodes are driven by events and go back to sleep after sending data Change target position

19. 19 Fix clustering Determine cluster head Member sends data to cluster head Change target position Divide the network into 4 fixed clusters Use client/server paradigm in each cluster Use highest level of transmission power Nodes are driven by events and go back to sleep after sending data

20. 20 Output results Each node’s ID, status, energy and clusterID Total energy consumption Lifetime of the network

21. 21 Simulation Result DeReClus consumes less energy and has longer lifetime than LEACH and predefined clustering (A) Energy Consumption(B) Network Lifetime

22. 22 Future simulations Simulate dynamic mobile agent path finding Model node accuracy rate Comparison of three methods –Random selection: mobile agent random select nexthop –Greedy method: mobile agent select the nexthop that has the lowest cost and highest accuracy rate –Optimize method: mobile agent get an optimize path before sent out, need global information