Abstract
The application of industrial wireless sensor networks (IWSNs) frequently appears in modern industry, and it is usually to deploy a large quantity of sensor nodes in the monitoring area. This way of deployment improves the robustness of the IWSNs but introduces many redundant nodes, thereby increasing unnecessary overhead. The purpose of this paper is to increase the lifetime of IWSNs without changing the physical facilities and ensuring the coverage of sensors as much as possible. Therefore, we propose a quantum clone grey wolf optimization (QCGWO) algorithm, design a sensor duty cycle model (SDCM) based on real factory conditions, and use the QCGWO to optimize the SDCM. Specifically, QCGWO combines the concept of quantum computing and the clone operation for avoiding the algorithm from falling into a local optimum. Subsequently, we compare the proposed algorithm with the genetic algorithm (GA) and simulated annealing (SA) algorithm. The experimental results suggest that the lifetime of the IWSNs based on QCGWO is longer than that of GA and SA, and the convergence speed of QCGWO is also faster than that of GA and SA. In comparison with the traditional IWSN working mode, our model and algorithm can effectively prolong the lifetime of IWSNs, thus greatly reducing the maintenance cost without replacing sensor nodes in actual industrial production.
Highlights
As industrial wireless sensor networks (IWSNs) have more and more applications in the factories, the way to prolong the lifetime of IWSNs without changing the physical facilities has become a hot issue [1, 2]
The quantum clone gray wolf optimization (QCGWO) method we propose on solving the sensor duty cycle problem will take a series of experiments, and QCGWO has been compared with genetic algorithm (GA) and simulated annealing (SA) for proving its effectiveness
We modeled the industrial sensor network in the real factory, proposed a quantum clone gray wolf optimization (QCGWO) algorithm, designed the sensor duty cycle model from a different perspective compared with the previous works, and proposed a concept of measurable sensor lifetime
Summary
As industrial wireless sensor networks (IWSNs) have more and more applications in the factories, the way to prolong the lifetime of IWSNs without changing the physical facilities has become a hot issue [1, 2]. The use of an artificial intelligence algorithm to optimize the established model can effectively prolong the lifetime of the IWSNs, thereby reducing the maintenance cost of the IWSNs and increasing the benefit of the factory [4, 5]. We investigate the IWSNs frequently used in factories, such as chemical sensors that monitor the content of harmful gases, pressure sensors in industrial production, and ultrasonic sensors in the field of industrial automation. We find that these IWSNs are basically placed by using the traditional wide-spreading method and periodically control some sensors to enter the sleep state for saving energy [6]. This approach has two disadvantages, one is that it cannot meet the requirements of full coverage, another is it cannot minimize the energy consumption of the sensors
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