Fuzzy-based Clustering of Wireless Sensor Networks for Multiple Mobile Agent Itinerary Planning

Mobile agent (MA) technology exhibits remarkable efficiency when integrated into Wireless Sensor Networks (WSNs) for information processing tasks. MAs reduce network overhead by executing processing code locally on nodes and selectively transmitting significant data to designated remote sensor nodes, thereby enhancing data fusion and acquisition while minimizing energy depletion. However, in large-scale networks, relying on a single MA leads to significant delays, necessitating the use of multiple MAs to operate asynchronously and minimize latency. The challenge lies in effectively grouping nodes to ensure MAs reach their intended destinations. To address this challenge, this paper introduces a novel approach, the Adaptive FCM Clustering Algorithm (AFCM), a fuzzy-based clustering algorithm designed for addressing network partitioning challenges in Multiple Mobile Agent Itinerary Planning (MIP). A systematic analysis of the existing literature examines various MIP algorithms, emphasizing their strengths and uncovering potential research gaps. AFCM is specifically developed to create disjoint and load-balanced partitions tailored for multi-mobile agent itinerary planning. A Methodical analysis with three traditional clustering algorithms is conducted. The correctness of the Adaptive Fuzzy C-Means (AFCM) algorithm is demonstrated through a detailed manual application on a wireless network comprising 15 nodes.