Adaptive algorithms for sensor activation in renewable energy-based sensor systems
Future sensor networks would comprise of sensing devices with energy harvesting capabilities from renewable energy sources such as solar power. A key research question in such sensor systems is to maximize the asymptotic event detection probability achieved in the system, in the presence of energy constraints and uncertainties. This thesis focuses on the design of adaptive algorithms for sensor activation in the presence of uncertainty in the event phenomena. Ideas from increase/decrease algorithms used in TCP congestion avoidance are applied to design an online and adaptive activation algorithm that varies the subsequent sleep interval according to additive increase and multiplicative decrease based upon the sensor's current energy level. In addition, the proposed algorithm does not depend on global system parameters, or on the degree of event correlations, and hence can easily be deployed in practical scenarios. Through extensive simulations, it is demonstrated that the proposed algorithm not only achieves near-optimal performance, but also exhibits more stability with respect to sensor's energy level and sleep interval variations.