Fuzzy‐Based Optimized Cluster Head Selection With Energy Prediction for Unequal Clustering in Wireless Sensor Networks

The sensor network consists of several low‐powered electronic sensing devices that are strategically deployed in a predefined area to gather important information. The sensors have low computing capacity, constrained battery energy, and limited storage. Therefore, the sensed data should be communicated to the sink efficiently for further processing. In this work, a clustering‐based multihop data transmission method is proposed using fuzzy logic. The proposed method, termed fuzzy‐based cluster head (CH) selection for unequal clustering (FSUC), utilizes four sensor factors: residual energy, distance to the sink node, node density, and the average distance of neighboring nodes to select optimal CHs. The FSUC selects CHs locally through a distributed competition process, enhancing the scalability of the sensor network. The comparative analysis of FSUC is performed with six existing models that involve LEACH, EEUC, FL‐SEP, MOFCA, FMCR‐CT, and FLPSOC in three different scenarios. The performance evaluation metrics are the number of alive nodes, total energy of alive nodes, and energy consumption in each round. The performance is also evaluated for the metrics occurrence of first node dead, half node dead, and last node dead. The energy consumption prediction across all compared models is conducted using exponential weighted mean (EWM), autoregressive integrated moving average (ARIMA), and linear regression (LR). Furthermore, statistical validation through t ‐test is employed to examine the reliability of the proposed model. The computational complexity of the model is also analyzed along with the complexity–performance curve to assess its practical feasibility.