A quantum-inspired evolutionary clustering algorithm for the lifetime problem of wireless sensor network

One of the solutions to the lifetime problem of a wireless sensor network (WSN) is to select a sensor as the cluster head (CH) to reduce the transmission cost of the other sensors in a cluster. However, the high computation load will quickly run out of its energy. The most well-known method for selecting the CHs of a WSN is the so-called low energy adaptive clustering hierarchy (LEACH), but it is far from optimal in terms of the energy consumed. On the other hand, some recent studies showed that the quantum-inspired evolutionary algorithm (QEA) can provide a better result than rule-based and metaheuristic algorithms. This paper is, therefore, aimed at applying QEA to the lifetime problem of a WSN. Simulation results show that the proposed algorithm can provide a better result than LEACH and genetic algorithm in terms of the overall energy consumed, especially for complex and large lifetime problems.