A Novel Sensor Task Allocation Method Based on Quantum Elite Shuffled Frog Leaping Algorithm in IWSNs

Abstract

Maximizing the efficiency of sensor task allocation has long been a question of great interest in industrial wireless sensor networks (IWSNs). In IWSNs, different tasks performed by different sensors produce varied benefit values. The purpose of this paper is to obtain the optimal task allocation scheme of IWSNs. Therefore, we design a sensor task allocation model, and propose a quantum elite shuffled frog leaping algorithm (QES-FLA) for optimizing the task allocation in IWSNs. The proposed algorithm combines the quantum operator and elite operator to achieve better performance. By using the concept of quantum probability amplitude and quantum revolving gate, the algorithm can search the solution space in parallel, thus enhancing the efficiency of solving the task allocation problem in IWSNs. In addition, the elite operator keeps the optimal individual in the population, which ensures the performance of the algorithm. Subsequently, the proposed algorithm is compared with two other popular heuristic algorithms to make the conclusion more convincing. According to the simulation results, the algorithm we proposed has higher task benefits and better performance, thus it successfully solves the sensor task allocation problem in IWSNs.