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, {em actor-centric} sensor networks. The tiny sensors run on miniature non-rechargeable batteries, are anonymous and are unaware of their location. The sensors differ in their ability to dynamically alter their sleep times. Indeed, the {em periodic} sensors have sleep periods of predefined lengths, established at fabrication time; by contrast, the {em 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 analytical analysis, confirmed by experimental evaluation, show 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.