Abstract
Network performance optimization has always been one of the important research subjects in mobile wireless sensor networks. With the expansion of the application field of MWSNs and the complexity of the working environment, traditional network performance optimization algorithms have become difficult to meet people’s requirements due to their own limitations. The traditional swarm intelligence algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many novel swarm intelligence optimization algorithms, which have strong applicability and achieved good experimental results in solving complex practical problems. These algorithms, like their natural systems of inspiration, show the desirable properties of being adaptive, scalable, and robust. Therefore, the swarm intelligent algorithms (PSO, ACO, ASFA, ABC, SFLA) are widely used in the performance optimization of mobile wireless sensor networks due to its cluster intelligence and biological preference characteristics. In this paper, the main contributions is to comprehensively analyze and summarize the current swarm intelligence optimization algorithm and key technologies of mobile wireless sensor networks, as well as the application of swarm intelligence algorithm in MWSNs. Then, the concept, classification and architecture of Internet of things and MWSNs are described in detail. Meanwhile, the latest research results of the swarm intelligence algorithms in performance optimization of MWSNs are systematically described. The problems and solutions in the performance optimization process of MWSNs are summarized, and the performance of the algorithms in the performance optimization of MWSNs is compared and analyzed. Finally, combined with the current research status in this field, the issues that need to be paid attention to in the research of swarm intelligence algorithm optimization for MWSNs are put forward, and the development trend and prospect of this research direction in the future are prospected.
Highlights
As an extension and extension of the Internet, the Internet of Things (IoT) is another major change in the field of information technology
With the continuous development and maturity of swarm intelligence optimization algorithm and artificial intelligence theory, the swarm intelligent optimization algorithm has been widely used in the coverage optimization strategy, location algorithm, dynamic deployment of the network, node scheduling, data fusion, reliability and other aspects of mobile wireless sensor networks
CONTRIBUTION In this paper, aiming at the performance optimization of the key technologies of Mobile wireless sensor networks (MWSNs), first, we provide a review of the key technology of MWSNs and a detailed classification of the swarm intelligence algorithms based on various metrics
Summary
As an extension and extension of the Internet, the Internet of Things (IoT) is another major change in the field of information technology. As one of the key technologies of the industrial Internet of Things, the wireless communication technology provides a solution for building an information-based, intelligent production management and control network [13]. Some related work has been done addressing the various issues individually (e.g., energy efficiency, data fusion, QoS, reliability and security) and they tend to overlook the whole scenario of collective optimization approach which encompasses these two or three MWSNs issues Applying these definitions to MWSNs, the swarm intelligence optimization algorithms are important in MWSNs applications for the following main reasons [28]:.
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