# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 2 Related Work
It aims to optimize routing paths and conserve node energy, thus providing a longer network lifetime. RCBRP has two main algorithms: a cluster-based energy efficiency algorithm and another to obtain the distances and amount of energy required. In this way, they ensure balanced path loads on the network devices and achieve a more functional period. In 2023, [PERSON] et al. developed energy-efficient cluster routing protocols [19]. This is a Honey Badger-based algorithm that selects CHs using a chance value. The chances of the nodes take into account their distance from the base station, their residual energy, the proximity of neighboring nodes, their degrees, and their centrality. Then, a Fuzzy Firebug Swarm optimization algorithm is used to give the optimal path between the CHs and their BS. Later in 2022, [PERSON] et al. proposed a Fuzzy Independent Circular Zones Protocol called FICZP [20]. It selects the cluster head based on the lifetime of neighbors and the remaining energy of the nodes as the most essential parameters, and a second parameter considers the lifetime as a member, which takes into account the average energy consumed in transmitting packets from the nodes to their neighbors. Then, a fuzzy system combines them and gives a cost value for each linked node, allowing the optimal path to the base station to be selected. In 2024, [PERSON] presented fuzzy logic-based clustering and quantum annealing algorithm-based routing to enhance WSNs' lifespans [21]. They suggested using the residual energy, the number of neighbors, the distance to the BS, and the nodes' centrality emphasized in a fuzzy inference system to obtain suitable CHs. Also, they implemented an energy threshold to neglect nodes with insufficient levels of energy, which gives more chances to nodes that are highly preferred and have a long lifetime period. To address the challenge of selecting the optimal cluster head (CH), [PERSON] presented a new clustering model based on a novel hybrid algorithm combining the dragonfly and firefly algorithms. The model optimizes CH selection based on nodes' energy, delay, distance, and security [22]. Finally, [PERSON] and [PERSON] proposed a new Region-Based Cluster Head Selection (RBCHS) protocol [23]. It optimizes energy in heterogeneous sensor networks. RBCHS divides the network area into multiple zones. It uses static clustering to ensure area coverage. It employs a hybrid routing scheme for data transmission to the BS. RBCHS selects CHs based on their minimum distance to the BS, their residual energy (RE), and their number of neighboring nodes. To summarize, the assumption that \"clustering improves network lifetime and conserves energy\" is always used to develop the most efficient routing protocols in WSN and IoT networks. Moreover, most of the parameters, such as node residual energy, number of neighbors, centrality, and distance to the BS, are often chosen to select CHs and control node lifetime. Clustering is the main approach used to enhance WSN performance. It begins with the LEACH protocol [11], which is still considered the foundation of many routing protocols, especially those focused on extending network lifespan. LEACH organizes the WSN into multiple clusters, each composed of several nodes that connect directly to one another. It employs a random-based CH selection process. CHs define their neighbors within a range of 20 m. These neighbors then send the data they collect directly to their CHs. Afterward, the CHs aggregate the data and send them in a single hop towards the BS. Most of the work in the domain of enhancing WSN lifetime involves cluster-based protocols. This section aims to present the most recent and high-quality work on cluster-based and LEACH successor protocols. In 2021, another study presented an improved version of the LEACH protocol [12]. This protocol aims to extend the WSN's lifetime by considering the clusters' density. They consider that clusters of different densities unbalance the workload of cluster leaders, leading to exhaustion of their energy.