Optimal Energy Consumption Tasks Scheduling Strategy for MultiRadio WSNs Multiradio technology is regarded as a promising way to improve the performance of Wireless Sensor Networks (WSNs) and has attracted much attention of researchers. It is very important to reduce energy consumption and to prolong the lifetime of MultiRadio WSNs (MRWSNs), since the node is generally batteryoperated in MRWSN environments. In this paper, two typical types of energy consumption process, the transmitting energy consumption and idle listening energy consumption, are analyzed firstly. Based on the above analysis, the energy consumption model of multiradio nodes is built, and then it is considered as the optimization objective for the minimization energy consumption of multiradio nodes. Furthermore, the heuristic optimal energy consumption task scheduling strategy based on the Particle Swarm Optimization (PSO) algorithm is proposed, and then the detailed steps of the proposed strategy are presented. Finally, the effectiveness and performance of the proposed strategy are evaluated through practical experiments and simulations. Evaluation results show that the proposed strategy outperforms some other algorithms, in terms of energy consumption, network lifetime, and tasks extensibility.

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.