An Elite Clone Parallel Genetic Algorithm for Trust Monitoring Assignment Problem in Adaptive Wireless Sensor Networks

Adaptive wireless sensor networks (AWSNs) consist of sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. AWSNs have applications in the fields of transportation, environmental monitoring, homelands security, automated assistance for the elderly monitors, industrial process control, military purposes and many other areas. The trust monitoring assignment scheme is usually designed for AWSNs to enhance the monitored rate. However, the trust monitoring assignment problem can be formulated as a nonlinear mixed integer optimization problem. In this paper, we propose an elite clone parallel genetic algorithm (ECPGA) based trust monitoring assignment approach for maximizing the monitored rate. We formulate our objective function to optimize the monitored rate under multiple constraints. The proposed algorithm combines the advantages of high efficiency of elite selection and the global search ability of the clone mutation. An analysis is given to show the correctness of ECPGA, and simulations are conducted to demonstrate the performance improvement of ECPGA against simulated annealing (SA) and ant colony optimization (ACO). Although sub-optimal for AWSNs systems, simulation results demonstrate that the proposed ECPGA algorithm can reach higher monitoring rate compared to the SA and ACO approach.