Abstract
Aiming at the coverage problem of self-organizing wireless sensor networks, a target coverage method for wireless sensor networks based on Quantum Ant Colony Evolutionary Algorithm (QACEA) is put forward. This method introduces quantum state vector into the coding of ant colony algorithm, and realizes the dynamic adjustment of ant colony through quantum rotation port. The simulation results show that the quantum ant colony evolutionary algorithm proposed in this paper can effectively improve the target coverage of wireless sensor networks, and has obvious advantages compared with the other two methods in detecting the number of targets and the convergence speed. Based on the above findings, it is concluded that the algorithm proposed plays an essential role in the improvement of target coverage and it can be widely used in the similar fields, which has great and significant practical value.
Highlights
With the development of embedded computing technology, the wireless sensor network combining sensor technology, computer network technology and distributed formula signal processing technology has become a new research hotspot
The search driven by the quantum rotation port can transform the global search into the local search so that the equalization of rough search and fine search can be improved to avoid the stagnation of evolution and improve the convergence rate of the algorithm, that is, with the increase of the number of the sensor nodes, the performance advantage of the quantum ant colony evolutionary algorithm is more obvious compared with the other two algorithms
How to improve the target coverage ratio of the monitored area has become a key issue in the research of self-organizing wireless sensor networks with limited number of sensors and monitoring radius
Summary
With the development of embedded computing technology, the wireless sensor network combining sensor technology, computer network technology and distributed formula signal processing technology has become a new research hotspot. In these studies, a class of self-organizing wireless sensor networks with self-organizing ability has received more and more attention. Based on the graph theory, when the scale of the problem is small, the optimal solution can be obtained quickly. With the increase of sensor nodes and the number of monitored objects, the complexity of the problem grows exponentially, and the algorithm based on graph theory can't get the feasible solution in effective time. Akbarzadeh et al [3]
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