Elite Immune Ant Colony OptimizationBased Task Allocation for Maximizing Task Execution Efficiency in Agricultural Wireless Sensor Networks The research of agricultural wireless sensor networks (AWSNs) plays an important role in the field of facility agricultural technology. The temperature and humidity nodes in AWSNs are so tiny that they are limited on computation, network management, information collection, and storage size. Under this condition, task allocation plays a key role in improving the performance of AWSNs to reduce energy consumption and computational constraints. However, the optimization of task allocation is a nonlinearly constrained optimization problem whose complexity increases when constraints such as limited computing capabilities and power are undertaken. In this paper, an elite immune ant colony optimization (EIACO) is proposed to deal with the problem of task allocation optimization, which is motivated by immune theory and elite optimization theory. The EIACO uses ant colony optimization (ACO) to combine the clone operator and elite operator together for the optimization of task allocation. The performances of EIACO with different numbers of temperature and humidity sensor nodes and tasks have been compared by both genetic algorithm (GA) and simulated annealing (SA) algorithm. Simulation results show that the proposed EIACO has a better task execution efficiency and higher convergence speed than GA and SA. Furthermore, the convergence speed of EIACO is faster than GA and SA. Therefore, the whole system efficiency can be improved by the proposed algorithm.

The path planning scheme for joint charging and data collection in WRSNs: A multiobjective optimization method Abstract   Considering the limited energy of the mobile wireless charging equipment (WCE) in wireless rechargeable sensor networks (WRSNs), strategies for energy replenishment and data collection are proposed. A novel path planning model for the mobile WCE based on multiobjective optimization is constructed to both replenish energy and collect data, as well as to maximize the total energy utility of the mobile WCE and minimize the average delay of data transmission. An algorithm of multiobjective ant colony optimization (ESMOAC) based on the elitist strategy is proposed to determine the Pareto set, so that the state transition strategy and the pheromone updating strategy improve. How the parameter settings of the ant colony algorithm affect the proposed algorithm is analyzed. The results of 50 groups of numerical simulation experiments show that the average of the Pareto set of the ESMOAC algorithm is 27.8% higher than that of the NSGAII algorithm.