Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks
Xiaowei Chen, Lei Yu, Tian Wang, Anfeng Liu, Xiaofeng Wu, Benhong Zhang, Zhiguo Lv, Zeyu Sun
Abstract
Artificial intelligence-empowered path selection plays an important role in wireless sensor networks (WSNs), because it can exceed the cognitive performance of humans and determine multiple aspects of the network performance. Ant colony optimization (ACO) is an effective intelligence algorithm which succeeds in addressing several issues of WSNs, including data transmission, node deployment, etc. There exist several ACO-based transmission strategies for WSNs, but the summary and comparison of such works are very limited. This paper provides a comprehensive overview of ACO-based transmission strategies for static and mobile WSNs. First, we provide a classification of existing ACO-based transmission methods, which distinguishes itself from other works in network types. Second, the highly typical ACO-based transmission strategies for WSNs are illustrated and discussed. Finally, we summarize the paper and present several open issues concerning the design of such networks. This survey contributes to system design guidance and network performance improvement.