HDAMMF: Hierarchical Data Aggregation Method using Mobile sink and Fuzzy logic in Wireless Sensor networks

Nodes in wireless sensor networks (WSNs) have limited energy reserves. A primary goal is to collect data efficiently while minimizing energy use. Clustering is an approach that can help reduce energy consumption in these networks, but data transmission to a stationary sink can cause energy holes. Using a mobile sink helps mitigate this issue, enhancing network performance. This paper presents a hierarchical cluster data aggregation method based on fuzzy logic and a mobile sink. This method consists of two phases: the clustering phase based on fuzzy logic and the data aggregation phase. The clustering phase includes two steps: selecting cluster heads and forming clusters. First, the fuzzy inference system calculates the probability of each node becoming a cluster head. Nodes with the highest scores, based on residual energy, node degree, and centrality, are chosen as cluster heads, while those with the second-highest scores are selected as backup cluster heads. In the second step, clusters are formed around the selected cluster heads. In the data aggregation phase, the cluster heads collect data from their cluster members and transfer it to either a mobile sink or the base station. Cluster heads within a direct region (with a maximum distance of rrr from the BS) send their data directly to the BS. Data from other cluster heads is routed to the BS through a mobile sink. This method ensures efficient data transmission and energy usage, enhancing overall network performance. The HDAMMF method significantly outperformed previous methods in various aspects, including the mean amount of energy used, latency, packet delivery rate, and network longevity, according to a comparison of their respective performances.