Abstract
The limited lifespan of the traditional Wireless Sensor Networks (WSNs) has always restricted the broad application and development of WSNs. The current studies have shown that the wireless power transmission technology can effectively prolong the lifetime of WSNs. In most present studies on charging schedules, the sensor nodes will be charged once they have energy consumption, which will cause higher cost and lower networks utility. It is assumed in this paper that the sensor nodes in Wireless Rechargeable Sensor Networks (WRSNs) will be charged only after its energy is lower than a certain value. Each node has a charging time window and is charged within its respective time window. In large-scale wireless sensor networks, single mobile charger (MC) is difficult to ensure that all sensor nodes work properly. Therefore, it is propoesd in this paper that the multiple MCs which are used to replenish energy for the sensor nodes. When the average energy of all the sensor nodes falls below the upper energy threshold, each MC begins to charge the sensor nodes. The genetic algorithm has a great advantage in solving optimization problems. However, it could easily lead to inadequate search. Therefore, the genetic algorithm is improved by 2-opt strategy. And then multi-MC charging schedule algorithm with time windows based on genetic algorithm is proposed and simulated. The simulation results show that the algorithm designed in this paper can timely replenish energy for each sensor node and minimize the total charging cost.
Highlights
Wireless Sensor Networks (WSNs) consist of various distributed sensor nodes (SNs) that collect useful information from their ambiance
This paper studies the problem of multi-mobile charger (MC) charging schedule for Wireless Rechargeable Sensor Networks (WRSNs) with time windows
When the average energy of the sensor networks is lower than a certain value, the MC begins to charge the sensor nodes
Summary
Wireless Sensor Networks (WSNs) consist of various distributed sensor nodes (SNs) that collect useful information from their ambiance. In order to prolong the lifetime of the networks, scholars have carried out a large number of studies, including the rational deployment of sensor nodes and obtaining energy from the surrounding environment. They can not solve the problem from the root, and the lifetime of networks is still a bottleneck that limits the widespread application of WSNs. For example, the method of replacing batteries can prolong the lifetime of the sensor nodes [1]. In large-scale wireless sensor networks, these energy-limited nodes may be deployed in remote areas, even in hostile environments, so it is difficult to maintain once deployed. It is not convenient to replenish energy by replacing the battery of the sensor nodes, so energy harvesting technologies have been proposed
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