Efficient Location Training Protocols for Heterogeneous Sensor and Actor Networks


In this work, we consider a large-scale geographic area populated by tiny sensors and some more powerful devices called actors, authorized to organize the sensors in their vicinity into short-lived, actor-centric sensor networks. The tiny sensors run on miniature nonrechargeable batteries, are anonymous, and are unaware of their location. The sensors differ in their ability to dynamically alter their sleep times.

Indeed, the periodic sensors have sleep periods of predefined lengths, established at fabrication time; by contrast, the free sensors can dynamically alter their sleep periods, under program control. The main contribution of this work is to propose an energy-efficient location training protocol for heterogeneous actor-centric sensor networks where the sensors acquire coarse-grain location awareness with respect to the actor in their vicinity.


Our theoretical analysis, confirmed by experimental evaluation, shows that the proposed protocol outperforms the best previously known location training protocols in terms of the number of sleep/awake transitions, overall sensor awake time, and energy consumption.





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