A Novel Chaotic Quantum Grey Wolf Algorithm for Optimizing the Task Allocation of Water Quality Monitoring Sensor Networks

Water quality monitoring sensor networks (WQMSNs) are crucial technological applications in environmental management, facilitating real-time monitoring and data collection of water quality. However, allocating tasks among sensor nodes in complex aquatic environments poses significant challenges to network efficiency. To tackle the task allocation problem in WQMSNs and enhance network performance, this paper introduces a novel Chaotic Quantum Grey Wolf Algorithm (CQGWA). This algorithm incorporates a new chaotic operator and a quantum operator to optimize the task allocation of sensor nodes. The chaotic operators introduce randomness, helping to avoid local optima and enhancing the global search capability. Meanwhile, the quantum operators utilize quantum bit representation and quantum rotation gates to refresh the evolutionary search. They select predefined candidate solutions from the candidate states and seek the globally optimal predicted solution for a given objective function. Extensive simulation experiments were conducted to validate the effectiveness of CQGWA. The results demonstrate that compared to the traditional Grey Wolf Algorithm (GWA) and Genetic Algorithm (GA), the task allocation efficiency in WQMSNs optimized using CQGWA increases by at least 8.46%. This significantly improves the effectiveness of water quality monitoring and enhances the network's performance and reliability.