Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks

Chen, Xiaowei; Yu, Lei; Wang, Tian; Liu, Anfeng; Wu, Xiaofeng; Zhang, Benhong; Lv, Zhiguo; Sun, Zeyu

doi:10.1109/access.2020.2984329

Cited by 20 publications

(9 citation statements)

References 113 publications

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“…Other sophisticated optimization strategies were explored together with the needs of different optimization approaches such as mathematical programming and heuristics/meta-heuristics based optimization. A survey of ACO techniques for static and mobile WSNs is proposed in [37].…”

Section: Related Workmentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

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The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in numerous sectors such as manufacturing, smart cities, transportation and Internet of Things by providing pervasive real-time applications. In this survey, we analyze the existing research trends with respect to Artificial Intelligence (AI) methods in WSN and the possible use of these methods for WSN enhancement. The main goal of data collection, aggregation and dissemination algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. In this paper, we highlight data collection, aggregation and dissemination challenges in WSN and present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. We compare and contrast different algorithms on the basis of optimization criteria, simulation/real deployment, centralized/distributed kind, mobility and performance parameters. We conclude with possible future research directions. This would guide the reader towards an understanding of up-to-date applications of AI methods with respect to data collection, aggregation and dissemination challenges in WSN. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for the research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new possibilities for future studies.

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Section: Related Workmentioning

confidence: 99%

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

et al. 2022

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“…The improvement of dissimilarity calculation makes the optimal path and dissimilarity path more in line with that of the traveler's [20]. Vehicle evacuation based on a static network can complete the optimal allocation of evacuation vehicles from a macro perspective, but the dynamic and unpredictable characteristics of the evacuation process make it impossible to implement this evacuation plan accurately, and the effect of the evacuation model is not ideal [21,22].…”

Section: Related Workmentioning

confidence: 99%

Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

Zhang

Sun

Tsai

2021

Wireless Communications and Mobile Computing

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In order to improve the congestion of the evacuation plan and further improve the evacuation efficiency, this paper proposes the priority Pareto partial order relation and the vector pheromone routing method based on the priority Pareto partial order relation. Numerical experiments show that compared with the hierarchical multiobjective evacuation path optimization algorithm based on the hierarchical network, the fragmented multiobjective evacuation path optimization algorithm proposed in this paper effectively improves the evacuation efficiency of the evacuation plan and the convergence of the noninferior plan set. However, the congestion condition of the noninferior evacuation plan obtained by the fragmented multiobjective evacuation route optimization algorithm is worse than the congestion condition of the noninferior evacuation plan obtained by the hierarchical multiobjective evacuation route optimization algorithm. The multiple factors that affect the routing process considered in the probability transfer function used in the traditional ant colony algorithm routing method must be independent of each other. However, in actual route selection, multiple factors that affect route selection are not necessarily independent of each other. In order to fully consider the various factors that affect the routing, this paper adopts the vector pheromone routing method based on the traditional Pareto partial order relationship instead of the traditional ant colony algorithm. The model mainly improves the original pheromone distribution and volatilization coefficient of the ant colony, speeds up the convergence speed and accuracy of the algorithm, and obtains ideal candidate solutions. The method is applied to the location of sports facilities and has achieved good results. The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large-scale space.

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“…In recent years, the research on the optimization of inspection path planning of IWSNs has attracted a large number of scholars [16,17]. In IWSNs, an effective inspection program can obtain lower inspection time and lower cost and improve industrial production efficiency [18,19]. There are also many optimization algorithms for path planning, which are basically divided into heuristic algorithms and nonheuristic algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Xiao

Liu

et al. 2021

Journal of Sensors

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Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N-P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

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Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks

Cited by 20 publications

References 113 publications

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review

Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

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