# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 2 Related Work
In such a phase, the assignment of the next-hop forwarding node is also performed through a cost function involving the energy and distance parameters. In [12], a multi-hop clustering routing protocol utilizing a CRCGA is designed to increase energy efficiency and load balancing. In CRCGA, an improved chaotic genetic algorithm is employed to solve the clustering problem, aiming to save energy dissipation and distribute the load among cluster heads in a balanced way. Furthermore, the round time is determined in a new adaptive way to conserve further energy in the network. Ref. [13] introduces a clustering-based mobile routing algorithm for WSNs. A mobile sink divides the network into segments, and the sensor nodes in each segment create a cluster. The mobile sink allocates each cluster's head based on nodes' energy and distance from the cluster center by utilizing greedy and ANN approaches. That is to say, the node with the highest energy and the nearest to the cluster center is assigned as a cluster head. After the clusters are generated, the mobile sink travels around each cluster center on a predetermined route to start the data-transfer process. A genetic algorithm (GA) determines such a route, so the mobile sink travels to all clusters via the shortest route. A meta-heuristic clustering technique (CPMA) is given in [14] to maximize the network's lifetime. The CPMA method used the Harmony Search (HS) algorithm for cluster head selection. The HS elects cluster head nodes using a fitness function that combines consumed energy and the expected ratio of energy distribution to minimize the consumed energy and smooth network energy distribution. ML-TSEP is developed in [15]. The modified threshold value integrated the original LEACH threshold with the sensor node energy, distance to sink node, neighbor node number, and the number of times a node operates as a cluster head to minimize energy consumption and enhance network longevity. The Distance and Energy-Aware Stable Election Routing Protocol (DE-SEP) for a heterogeneous WSN was developed [16]. In DE-SEP, the cluster heads assignment procedure prioritizes the node with the maximum energy that is nearest to the sink node for energy saving and network lifetime improvements. Finally, the DE-SEP computes the optimal number of cluster heads to prevent redundant cluster formation and minimize the consumed energy. Gateway Clustering Energy-Efficient Centroid (GCEEC) routing minimizes energy use and cluster head load [17]. To minimize consumed energy and maximize cluster head coverage, the GCEEC protocol selects and rotates the cluster head near the cluster's energy centroid location. Moreover, it selects a gateway node from each cluster to forward the data towards the sink node, which minimizes cluster heads' load. After the cluster heads and gateways are identified, the data-transfer process starts. In such a process, once the cluster heads have aggregated the data from their members, it is further sent toward the sink node via gateway nodes or directly according to a threshold distance. Enhanced Particle Swarm Optimization-based Clustering Energy Optimization (EPSOCEO) is a technique that was developed for wireless sensor networks [24]. The PSO is adopted to construct clusters within the sink node in a centralized way and assign cluster heads in a distributed manner. In the clustering process, the nodes' energy and distance are considered while determining the clustering fitness function to minimize and balance consumed energy. After clustering, a multi-hop routing strategy is adopted for data transfer to the sink node. The assignment of intermediate cluster heads for data routing depends on a cost function so that the node with a minimum cost is chosen as an intermediate node. Such a function depends on energy and distance factors, where the node with minimum distance and higher energy should be chosen as the next hop.