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Optimal Data Compression and Forwarding in Wireless Sensor Networks Bulent Tavli, Mehmet Kayaalp, Ibrahim E. Bagci TOBB University of Economics and Technology.

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Presentation on theme: "Optimal Data Compression and Forwarding in Wireless Sensor Networks Bulent Tavli, Mehmet Kayaalp, Ibrahim E. Bagci TOBB University of Economics and Technology."— Presentation transcript:

1 Optimal Data Compression and Forwarding in Wireless Sensor Networks Bulent Tavli, Mehmet Kayaalp, Ibrahim E. Bagci TOBB University of Economics and Technology Ankara, Turkey

2 Goals Maintain balanced energy consumption among sensors Increase network lifetime Focus on whole network rather than individual nodes Exploit data compression Explore different strategies for mitigating sensor network hotspots

3 Transmission Scheduling … In a many-to-one (converge-cast) multi-hop wireless sensor network, how should we schedule transmissions so as to balance energy usage and maximize lifetime?

4 Direct Transmission … High energy drain in the furthest nodes

5 Next Hop … High energy drain in the closest nodes

6 Split Transmissions … Will a scheme like this help?

7 Problem Definition Given Sensor locations Power model Traffic generation pattern Initial energy distribution Goal Determine optimal flow pattern to maximize network lifetime Solution Linear programming

8 Models Power model: Compression model:

9 Linear Program for flow balancing

10 Linear Program for flow balancing and data compression

11 Strategies NCFB (No Compression and Flow Balancing) Only flow balancing MCFB (Mandatory Compression and Flow Balancing) All nodes compress all of their data Flow balancing OCFB (Optimal Compression and Flow Balancing) Nodes compress their data and balance the flow on the network jointly

12

13 Example 1: All nodes compress P cp = , node-separation = 15m 123456

14 Example 2: No compression at all P cp = 10 , node-separation = 15m 123456

15 Example 3: Some compression P cp = 10 , node-separation = 25m 123456

16 Example 4: All nodes compress P cp = 10 , node-separation = 80m 123456

17 Conclusions Data compression is becoming an integral part of in-network data processing Allocate energy budget on compression and forwarding optimally Linear Programming Avoid data compression Small network & high compression energy Partial data compression Large network & high compression energy Small network & low compression energy Compress most of the data Large network & low compression energy For all parameter space jointly optimizing data compression and load balancing results in maximal network lifetime

18 Q&A


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