A Multi-task Approach For Maximum Survival Ratio Problem In Large-Scale Wireless Rechargeable Sensor Networks

With the breakthrough of electromagnetic power transfer technology, wireless charging has emerged as a hopeful solution for the energy provisioning problem in wireless sensor networks. One of the prominent issues that affect the potential exploitation of this technology is the charging scheduling problem. However, existing works on this topic either focus mainly on using a single mobile charger for the whole network or suffer from several common limitations such as enforcing the chargers to visit all sensors or applying the rigid full-charging scheme. Moreover, they rarely delve into maximizing the survival nodes ratio, which impacts directly on the multi-hop communication of the network. This paper addresses the charging scheduling for multiple mobile chargers without the above limitations. We first formulate a maximum survival ratio problem and prove its NP-hardness. A charging scheme that exploits the advantages of the multifactorial evolutionary algorithm is then proposed to optimize the charging paths of all chargers simultaneously. We finally evaluate the efficacy of the proposed algorithm through extensive simulations. The experimental results demonstrate that our scheduling scheme provides promising outcomes in terms of survival ratio and the traveling energy of chargers.