AIMD Rule-Based Duty Cycle Scheduling in Wireless Sensor Networks Using Quartile-Directed Adaptive Genetic Algorithm

Abstract

Wireless sensor networks (WSNs) are attractive, in large part because they do not require wired infrastructure. However, this very characteristic renders them energy-constrained. Duty cycle scheduling (DCS) is thought to contribute to the energy efficiency of sensing. We develop a novel paradigm for modeling WSNs and a DCS strategy based on the additive increase/multiplicative decrease (AIMD) rule. The scheduling problem is framed as an optimization problem with the optimization objectives of lowering energy consumption and enhancing detection ability. The classic GA converges slowly in late evolution phases for using fixed crossover and mutation probability. An adaptable genetic algorithm (AGA) eliminates the above concern by allowing modifying crossover and mutation probability as per fitness values, but, unfortunately, it often fails to select the best individuals. We suggest a quartile-directed adaptive genetic algorithm (Q-AGA) in which individuals with fitness above the first quantile of the last generation were selected. The interquartile range (IQR) is utilized to weight crossover and mutation probability. If IQR is larger, the fitness distribution is more concentrated, and then, we can diminish the crossover and mutation probability, allowing favorable traits to be retained to the greatest extent feasible. Simulation results show that Q-AGA has a faster convergence speed and a stronger global search ability. Furthermore, we change the hypothesis of the system model by considering that the two objectives can be weighted, thereby broadening the application scenarios.