Periodic Charging And Data Collection For Wireless Charging Vehicle In Wireless Rechargeable Sensor Network

In a Wireless Rechargeable Sensor Network(WRSN),an active charging equipment is used to charging sensor nodes and collecting data for Wireless Sensor Network(WSN),which prolongs the lifespan of the network and relief the load of the network due to multi-hop data transmission.The researches on WRSN have a promising prospective both in science and applications for Internet of Things(Io T).In this thesis,we use Wireless Charging Vehicle(WCV)to replenish energy and collect data for sensor nodes in WSN.Based on the periodic charging planning,we distribute the docking time of WCV over sensor nodes and add waiting time for WCV staying at sensor nodes to utilize the time with further improving the data collection efficiency.Then,we construct a WCV periodic charging planning model with a waiting time sequence and analyze how to connect the initial charging period with an ordinary charging period for a sensor node.To solve this problem,we propose the Mixed Variable Optimization Algorithm Based on Fireworks Algorithm(MVOA-FA).Through simulations,we demonstrate the proposed MVOA-FA is feasible.Compared with Time Sequence Distribution Based on Power Algorithm(TSDPA)and Charging Planning Algorithm Based on the Shortest Path(CPA-SP),the proposed MVOA-FA is averagely 26.4% higher than TSDPA and 40.6% higher than CPA-SP.With the expanding of the scale of a network,the service of a single WCV over a large-scale network prolongs the charging time too long.A single WCV for a periodic charging planning with limited traveling energy and finite charging power cannot satisfy the requirements from a large-scale network.To solve this problem,we propose the quantity of WCVs evaluation algorithm based on power and temporal and spatial factors,a heuristic algorithm to decide the required number of WCVs in a given network.To maximize the data ratio in a charging cycle,we propose the discrete fireworks algorithm based on sequential group encoding.We improve the fast grouping crossover.Through simulations,we demonstrate the feasibility of the proposed algorithm.Compared with partial greedy algorithms,Districtdivision-based Charging Planning(DDCP)and Nearest Job Next with Preemption(NJNP),Multi-WCVs Period Charging Planning(MPCP)in this thesis has 1.71 times and 2.83 times superiority.