On the Deployment of Wireless Sensor Networks

1. On the Deployment of Wireless Sensor Networks Xin Liu Computer Science Department University of California, Davis Joint work with P. Mohapatra Senmetrics

2. 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 Senmetrics

3. Many-to-One Communication Senmetrics

4. Precise vs. RandomDeployment • 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 Senmetrics

5. Objectives • 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 Senmetrics

6. Linear Networks • 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? Senmetrics

7. 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 Senmetrics

8. System Model Senmetrics

9. i Total Energy Constraint • Total energy constraint: • Energy can be arbitrarily allocated among nodes • The network dies when no energy left • Thus, Senmetrics

10. Problem Formulation Numerical results as benchmark Senmetrics

11. Individual power constraint • 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 Senmetrics

12. A Greedy Algorithm • 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) Senmetrics

13. A Greedy Algorithm Cont’d Senmetrics

14. Benchmark vs. Greedy Senmetrics

15. Cont’d Senmetrics

16. Numerical Result Senmetrics

17. Individual vs. greedy Senmetrics

18. Observations • 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 Senmetrics

19. Closed-form for Greedy • Lifetime, nodes, and coverage • =4, 19% more node to double lifetime • =4, 138% more node to double coverage Senmetrics

20. Other Power Consumption • 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 Senmetrics

21. Other Power Consumption • 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 Senmetrics

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

23. Performance Evaluation Senmetrics

24. Non-uniform Data Density • Density varies over locations • Greedy scheme adapts well Senmetrics

25. 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 Senmetrics

26. Planar Networks • 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 Senmetrics

27. 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 Senmetrics