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Data-Centric Energy Efficient Scheduling for Densely Deployed Sensor Networks

Data-Centric Energy Efficient Scheduling for Densely Deployed Sensor Networks. IEEE Communications Society 2004 Chi Ma, Ming Ma and Yuanyuan Yang. Outline. Introduction Data-Centric Energy Efficient Scheduling Communication-Centric Initialization Phase

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Data-Centric Energy Efficient Scheduling for Densely Deployed Sensor Networks

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  1. Data-Centric Energy Efficient Scheduling for Densely Deployed Sensor Networks IEEE Communications Society 2004 Chi Ma, Ming Ma and Yuanyuan Yang

  2. Outline • Introduction • Data-Centric Energy Efficient Scheduling • Communication-Centric Initialization Phase • Characteristics of the traffic in sensor networks • Data-centric scheduling phase • Power Shutdown Scheme • Performance Comparisons with Existing Protocol • Conclusion

  3. Introduction • Previous work: • Predictive power management strategy • Only highly correlated requests can benefit from it • Markov Chain method based on historic data analysis • [7] point out that this is not suitable for today’s low energy and low computation sensor • Power mode scheduling • Does not distinguish between the routing data and the sensing data

  4. Introduction • All previous work assume that both sensing and routing packets come as homogeneous traffic • They propose DCe2S (Data-centric energy efficient scheduling) to minimizing the power dissipated under heterogeneous packet traffic

  5. Data-Centric Energy Efficient Scheduling • DCe2S protocol consists of two phases: • 1. Communication-centric initialization phase • 2. Data-centric scheduling phase

  6. Communication-Centric Initialization Phase • Determine node’s lengths of sleep according to sensor density (not uniform) • IAR • Energy dissipation • Probability to lose packet • Higher density (after CCI)

  7. Communication-Centric Initialization Phase • :the probability the packet is not lose of node p • :numbers of neighbor of node k • Given , if any routing node i of sending node p has that ,then the can be guaranteed at sending node p IARp IARp IARp P IARp IARp

  8. Characteristics of the traffic in sensor networks • There are two types of data: • Sensing packets • Routing packets • Previously proposed protocols assume that both types of traffic follow are homogeneous Poisson distribution • Apparently, it cannot model real traffic (ex. traffic monitoring) • Even the sensing traffic is homogeneous, the routing traffic cannot not be homogeneous

  9. Characteristics of the traffic in sensor networks Sensing traffic Path length

  10. Characteristics of the traffic in sensor networks • There are k path • Traffic out of Di : • t : latency for each node • Consider traffic from A • Consider traffic from both A and C

  11. Characteristics of the traffic in sensor networks • When sensing traffic is heterogeneous Poisson traffic • Suppose A has sensing rate of • When ,the case is equivalent to A broadcasts packets at homogeneous rate , and A` broadcasts after t1 • And is the same

  12. Data-Centric Scheduling Algorithm • Use exponentially weighted average time to combine and to obtain • is a threshold means a sudden change • A sliding window with size W is used to cache the recent packet arrival intervals

  13. //exponentially weighted //average of the window

  14. Power Shutdown Scheme • DCS algorithm uses the shut-down scheme in [8] • The shut-down latency for turning on/off : • Sensing unit 30ms • Transmitter 5ms • Receiver 5ms

  15. Power Shutdown Scheme

  16. Power Shutdown Scheme Derive a set of sleep time threshold{Tth,k} if ti<Tth,k will result net energy loss next event

  17. Performance Comparisons with Existing Protocol • The Time Out Protocol • Node switches to sleeping blindly for a time period of Tout • The Greedy Protocol • Without any power control protocol • The Power Mode Scheduling Protocol (PMS)

  18. Power dissipation (homogeneous) 100ms 500ms

  19. Packet Loss Rate (homogeneous) 61.4% better than Greedy 31% better than PMS Unstable because of predict

  20. Heterogeneous traffic • 1000 packets • First phase: 200 packets • Second phase: 400 packets • Third phase: 400 packets • Packet Lost Rate

  21. Power dissipation (heterogeneous)

  22. Packet Loss Rate (heterogeneous) Events are not uniformly distributed

  23. Conclusions • They first prove the routing traffic is heterogeneous with Poisson sensing traffic • Then proposed a well defined power model to extend the lifetime without compromising their performance • Presented DCe2S in this paper and try to achieve maximum lifetime

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