Accuracy-aware aquatic diffusion process profiling using robotic sensor networks
Proceedings of the 11th international conference on Information Processing in Sensor Networks
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This paper concerns the use of large-scale wireless sensor networks to detect and locate leaks of specified gases in the presence of time-varying advection (air currents) and diffusion. We show that when leaks are rare but constant for long periods, Kalman filtering combined with binary hypothesis testing provides an effective alternative to full-scale hypothesis testing covering all possible combinations of leaks and leak intensities. To reduce energy consumption and use of communication bandwidth, a two-tiered strategy is proposed in which a reduced number of sensors (Tier 1) provides coarse-grid sensing. When a leak is detected by the Tier 1 strategy, fine-grained grids of sensors (Tier 2) are activated around the vicinities of the detected leak areas to provide more precise detection and localization. Energy consumption is further reduced by applying an information versus energy-based dynamic sensor selection technique. Details of a laboratory implementation are presented and simulation results illustrate the approach and demonstrate its effectiveness.