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Decentralized Reactive Clustering in Sensor Networks Yingyue Xu April 26, 2015.

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Presentation on theme: "Decentralized Reactive Clustering in Sensor Networks Yingyue Xu April 26, 2015."— Presentation transcript:

1 Decentralized Reactive Clustering in Sensor Networks Yingyue Xu April 26, 2015

2 2 Wireless Sensor Networks Consists of battery operated sensor nodes Deployed randomly on the field in large numbers Resource constrained sensor nodes – power, computation, communication No networking infrastructure

3 3 MOTE Two Board Sandwich –CPU/Radio board –Sensor Board: temperature, light Size –Mote: 1  1 in –Pocket PC: 5.2  3.1 in CPU –Mote: 4 MHz, 8 bit –Pocket PC: 133 MHz, 32 bit Memory –Mote: 512 B RAM; 8K ROM –Pocket PC: 32 MB RAM; 16 MB ROM Radio –900 Hz, 19.2 kbps –Bluetooth: kbps (symmetric) Lifetime (Power) Mote: 3-65 days Pocket PC: 8 hrs Cost Mote: $100 Pocket PC: $400

4 4 Motivation: CSIP in sensor networks Collaborative Signal and Information Processing (CSIP) Each node senses the event individually, need to collaborate with other nodes to exchange information and get final and accurate results Challenges –Energy Efficiency –Scalability –Reliability –Real-time Performance How?

5 5 Architectural overview of the CSIP system Wireless Communication Routing Layer Clustering & Distributed Computing Paradigm Collaborative Information processing

6 6 Computing paradigms in WSN - Client/Server based paradigm Client/Server paradigm Centralized processing Powerful central server Not energy efficient Network traffic – high Difficult to reconfigure

7 7 Computing paradigms in WSN - Mobile agent based paradigm Agent travels to each node No centralized node Reduced network traffic Scalable network Adaptive to dynamic WSN environment like node failures, etc.

8 8 Using Network Simulator 2 ( ns -2) Randomly deployed in a 10m by 10m area MAC layer protocol: Routing protocol: DSDV Metrics: - Execution Time - Energy Consumption 8 experiments are designed Results: When the number of nodes is large, the mobile agent paradigm performs better; But when the number is small, the client/server paradigm is better Performance Evaluation

9 9 Hybrid Computing Paradigms Scheme A: mobile agent paradigm within clusters and client/server paradigm between cluster heads Scheme B: client/server paradigm within clusters and mobile agent paradigm between cluster heads

10 10 Clustering Partition the network into clusters –Achieving scalability –Easer to management –Increase lifetime and energy efficiency Nodes communicate with clusterheads, clusterheads communicate with processing center

11 11 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 Two level Clustering Algorithm –Estrin’s method –Not event driven, Proactive

12 12 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

13 13 Benefits of DRC Reactive clustering - driven by events Localized protocol Energy-efficient Less transmission power to reach cluster head

14 14 Benefits of DRC event (A) A predefined clustering(B) Clustering after DRC

15 15 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

16 16 Desirable Features of DRC 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

17 17 Outline of DRC 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

18 18 Outline of DRC Intra-cluster Data Processing Phase Cluster Head to Processing Center Phase

19 19 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

20 20 Simulation of DRC In Java 3 Classes –Simu.java (main class) –Node.java (node object) –Message.java (message object) Simple communication model –No routing, transmission error

21 21 States of Nodes Sleep Idle Undecide Cluster Head Member

22 22 Flow chart Parameters initialize Generate nodes Nodes deployment Calculate distance Run protocol (DRC, LEACH or Fix) Change target position Display results

23 23 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

24 24 Node deployment Random Grid

25 25 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

26 26 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

27 27 State Machines (A) Operation of fix clustering(B) Operation of LEACH and DRC

28 28 Output results Each node’s ID, status, energy and clusterID Total energy consumption Lifetime of the network

29 29 Number of Nodes DRC consumes less energy and has longer lifetime than LEACH and predefined clustering (A) Energy Consumption(B) Network Lifetime

30 30 Target Speed DRC performs better than LEACH and predefined clustering (A) Energy Consumption(B) Network Lifetime

31 31 Signal Range More nodes will wake up as signal range increases, thus consume more energy (A) Energy Consumption(B) Network Lifetime

32 32 Number of Events More events will cause more nodes to wake up and thus consume more energy (A) Energy Consumption(B) Network Lifetime

33 33 Future Directions Implement DRC into UCB’s Mote nodes –Energy consumption –Network lifetime Setup a testbed of Motes and WINSNG nodes Do experiments and measure results –Energy consumption –Network lifetime


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