In the research of heterogeneous wireless sensor networks, clustering is one of the most commonly used energy-saving methods. However, existing clustering methods face challenges when applied to heterogeneous wireless sensor networks, such as energy balance, node heterogeneity, algorithm efficiency, and more. Among these challenges, a well-designed clustering approach can lead to extended node lifetimes. Efficient selection of cluster heads is crucial for achieving optimal clustering. In this paper, we propose an Enhanced Pelican Optimization Algorithm for Cluster Head Selection (EPOA-CHS) to address these issues and enhance cluster head selection for optimal clustering. This method combines the Levy flight process with the traditional POA algorithm, which not only improves the optimization level of the algorithm, but also ensures the selection of the optimal cluster head. The logistic-sine chaotic mapping method is used in the population initialization, and the appropriate cluster head is selected through the new fitness function. Finally, we utilized MATLAB to simulate 100 sensor nodes within a configured area of 100 × 100 m${}^2$. These nodes were categorized into four heterogeneous scenarios: $m=0,\alpha =0$, $m=0.1,\alpha =2$, $m=0.2,\alpha =3$, and $m=0.3,\alpha =1.5$. We conducted verification for four aspects: total residual energy, network survival time, number of surviving nodes, and network throughput, across all protocols. Extensive experimental research ultimately indicates that the EPOA-CHS method outperforms the SEP, DEEC, Z-SEP, and PSO-ECSM protocols in these aspects.

A collection of smaller, less expensive sensor nodes called wireless sensor networks (WSNs) use their sensing range to gather environmental data. Data are sent in a multi-hop manner from the sensing node to the base station (BS). The bulk of these sensor nodes run on batteries, which makes replacement and maintenance somewhat difficult. Preserving the network’s energy efficiency is essential to its longevity. In this study, we propose an energy-efficient multi-hop routing protocol called ESO-GJO, which combines the enhanced Snake Optimizer (SO) and Golden Jackal Optimization (GJO). The ESO-GJO method first applies the traditional SO algorithm and then integrates the Brownian motion function in the exploitation stage. The process then integrates multiple parameters, including the energy consumption of the cluster head (CH), node degree of CH, and distance between node and BS to create a fitness function that is used to choose a group of appropriate CHs. Lastly, a multi-hop routing path between CH and BS is created using the GJO optimization technique. According to simulation results, the suggested scheme outperforms LSA, LEACH-IACA, and LEACH-ANT in terms of lowering network energy consumption and extending network lifetime.

Clustering-based routing techniques are key to significantly extending the lifetime of wireless sensor networks (WSNs). However, these approaches often do not address the common hotspot issue in multi-hop WSNs. To overcome this challenge and enhance network lifespan, this study presents FQ-UCR, a hybrid approach that merges unequal clustering based on fuzzy logic (FL) with routing optimized through Q-learning. In FQ-UCR, a tentative CH employs a fuzzy inference system (FIS) to compute its probability of being selected as the final CH. By using the Q-learning algorithm, the best forwarding cluster head (CH) is chosen to construct the data transmission route between the CHs and the base station (BS). The approach is extensively evaluated and compared with protocols like EEUC and CHEF. Simulation results demonstrate that FQ-UCR improves energy efficiency across all nodes, significantly extends network lifetime, and effectively alleviates the hotspot issue.