1. 07/21/2005 Senmetrics1 Xin Liu Computer Science Department University of California, Davis Joint work with P. Mohapatra On the Deployment of Wireless Sensor Networks

2. 07/21/2005 Senmetrics2 Network Deployment Many-to-one communication Data from all nodes directed to a sink node/fusion center Unbalanced traffic load Uneven power consumption Limitations on network lifetime if uniformly distributed “Important” nodes in the route die quickly Capacity bottleneck and Power bottleneck Desire for long-lived sensor networks Linear and planar networks

3. 07/21/2005 Senmetrics3 Many-to-One Communication

4. 07/21/2005 Senmetrics4 Precise deployment With access Expensive nodes Higher layer of a hierarchical structure Random deployment No access Cheap nodes Lower layer of the hierarchy Coverage and connectivity issues Precise vs. Random Deployment

5. 07/21/2005 Senmetrics5 Maximize coverage area Given the desired lifetime and # of node available Maximize the lifetime of the network Given the number of nodes and coverage area Minimize the number of nodes required Given the coverage area and the desired lifetime Consider large networks with long lifetime requirements Objectives

6. 07/21/2005 Senmetrics6 Why linear networks? Applications: Traffic monitoring, border line control, train rail monitoring, etc. Model narrow-and-long networks Great Duck Island deployment Tractability, insights for general cases Highly asymmetric traffic load & location-dependent power consumption Focus on communications What options do we have? Linear Net works

7. 07/21/2005 Senmetrics7 Possible Approaches More energy for nodes with heavier load More nodes in the area closer to the sink Nodes closer to each other Load balancing Deployment involves topology control, routing, power allocation

8. 07/21/2005 Senmetrics8 System Model

9. 07/21/2005 Senmetrics9 Total energy constraint: Energy can be arbitrarily allocated among nodes The network dies when no energy left Thus, i Total Energy Constraint

10. 07/21/2005 Senmetrics10 Problem Formulation Numerical results as benchmark

11. 07/21/2005 Senmetrics11 Arbitrary energy allocation is impractical Performance benchmark More realistic: homogenous individual energy constraint Network lifetime: first node dies Complexity: routing and associated power allocation options Individual power constraint

12. 07/21/2005 Senmetrics12 Homogenous initial energy allocation Observation: longer hops consume more energy “jump” may not be a good idea Observation: we do not want residual energy when the network dies. Power consumption per unit time should be the same for all nodes Consider large T (desired lifetime) A Greedy Algorithm

13. 07/21/2005 Senmetrics13 A Greedy Algorithm Cont’d

14. 07/21/2005 Senmetrics14 Benchmark vs. Greedy

15. 07/21/2005 Senmetrics15 Cont’d

16. 07/21/2005 Senmetrics16 Numerical Result

17. 07/21/2005 Senmetrics17 Individual vs. greedy

18. 07/21/2005 Senmetrics18 Good news: the effect of arbitrary energy allocation is negligible Greedy performs very well Conjecture: greedy is optimal in the case of individual energy constraints Observations

19. 07/21/2005 Senmetrics19 Closed-form for Greedy Lifetime, nodes, and coverage  =4, 19% more node to double lifetime  =4, 138% more node to double coverage

20. 07/21/2005 Senmetrics20 Assume the same communication model Consider receiving power, idling power, etc. Assume negligible sensing/sleep power Assume perfect synchronization These power consumptions will decrease dramatically (hopefully) Transmit at maximum power/rate Keep awaking time as short as possible Other Power Consumption

21. 07/21/2005 Senmetrics21 The other power consumption is well modeled by a power efficiency factor. Pmax: maximum transmission power by the antenna Pa: power consumed by the transmitter other than the power emitted by the antenna Pr: receiving power Transmit at maximum rate, short duration, less energy consumption Other Power Consumption

22. 07/21/2005 Senmetrics22 Decrease in transmission distance does not decrease per-bit energy consumption Nodes very close Limit on modulation and coding A bound on the distance Power Attenuation Model

23. 07/21/2005 Senmetrics23 Performance Evaluation

24. 07/21/2005 Senmetrics24 Non-uniform Data Density Density varies over locations Greedy scheme adapts well

25. 07/21/2005 Senmetrics25 Non-uniform Density Cont’d Greedy scheme performs well in the presence of estimation errors <2% lifetime degradation <1% additional nodes Uniform deployment Lifetime: 35% and 47% Random deployment <1% lifetime

26. 07/21/2005 Senmetrics26 Data within 2-D area is aggregated to a sink node Much more complicated Coverage Potential triangular routes Large search space Heuristic solution based on insights from the linear network Star mode Linear approximation Planar Networks

27. 07/21/2005 Senmetrics27 Conclusions Data back-hauling in a many-to-one network Traffic load vs. communication energy consumption Optimal vs. greedy Lifetime, # of nodes, and network coverage Various issues: Miscellaneous power consumption Minimum distance constraint Non-uniform data density Future work: Planar networks Data compression and aggregation