Performance analysis of an adaptive algorithm for sensor activation in renewable energy based sensor systems
Linear increase-decrease algorithms have applicability in various fields of research. For instance, transmission control protocol (TCP) congestion control mechanism employs an additive increase and multiplicative decrease (AIMD) algorithm to vary the congestion window size dynamically at the sender. Recently, an adaptive algorithm for sensor activation in renewable energy based systems was proposed. This activation algorithm is designed in such a way that the sensor dynamically computes its sleep interval according to additive increase and multiplicative decrease, based on its current energy level. The objective is to maximize the asymptotic event detection probability achieved in the system in the presence of uncertainties and energy constraints. This thesis provides a simple, but accurate model to compute the performance of the algorithm for a single sensor scenario. By means of the proposed model, the performance of the algorithm is evaluated and is validated with that of the results obtained from simulations. A Markov Chain is used to analyze the system for a single sensor scenario. Furthermore, the AIMD based algorithm is extended towards a distributed implementation in a network with multiple sensors and multiple event processes. Through extensive simulations, it is shown that the proposed algorithm performs better than other algorithms in this scenario. In addition, the proposed algorithm is completely localized, which makes it extremely suitable for distributed deployment.