# ERCP: Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks
## 2 Related Work  
This section discusses different clustering strategies developed for maximizing the WSN's lifetime. To our knowledge, most of them are mainly focused on an energy-efficient scheme in which the optimal set of cluster head nodes is determined by employing the nodes' energy to achieve a balance in energy consumption and optimize the network's lifetime [3; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. However, from a reliability viewpoint, such algorithms cannot have any reaction to the dynamic nature of wireless communication links. Therefore, in this section, we first explain some relevant studies that were developed for energy efficiency and network lifetime optimization [3; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24]. Finally, we present a discussion of the differences from our proposal. LEACH is the classical clustering technique for WSNs [18]. In every round, cluster head nodes are randomly selected to optimize the network's lifetime. Hence, all nodes have the same opportunity to be the cluster head. Meanwhile, as the assignment of cluster head nodes is performed randomly, it is possible to choose the nodes with low energy as cluster head nodes, resulting in an unbalance in energy consumption which ultimately degrades the network's lifetime. Therefore, several types of research have been developed over time that aim to enhance LEACH performance [19; 20; 21; 22; 23]. In [19; 20; 21; 22; 23], the authors improve the basic LEACH protocol by considering the energy factor in deciding cluster head nodes. FR-LEACH is presented in [3]. It developed an adaptive fuzzy-based technique in which the decision to select cluster head nodes was made by modifying the basic LEACH threshold by considering both nodes' residual energy and optimum cluster number. Through the initial phase, the energy is at its maximum level; thus, there is a greater cluster number during this period. However, energy has to be depleted with time, and the cluster number also decreases. That is to say, using the fuzzy rule, this algorithm tracks the variation in the network's energy in each round, which helps to maintain a balance in energy consumption that optimizes the network's lifetime. Distance-aware residual energy-efficient SEP for WSN (DARE-SEP) is presented in [9]. The DARE-SEP locates the cluster head nodes by combining the energy of the sensor nodes and the Euclidean distance to the sink node. The energy factor balances the energy consumption of sensor nodes, maximizing network longevity. Finally, the distance factor assists in the preservation of data transmission energy to the sink node. The hybridization of the meta-heuristic method for the dynamic cluster-based routing protocol (HMBCR) in WSNs is reported in [10]. In HMBCR, BSO-LD clustering assigns cluster head nodes, and WWO-HC routing determines optimum routing routes. The distance to neighbors, energy, distance to sink nodes, and network load determine cluster head nodes in BSO-LD. Cluster heads are selected based on the remaining node energy to balance sensor node energies. Distance factors reduce the routing path communication distance to conserve energy and increase the network life. The network load manages the cluster head load. After selecting clusters, WWO-HC assigns the appropriate data transfer path to the sink node. The WWO-HC produces a fitness function with two parameters, energy and distance. The energy parameter helps select high-energy nodes as the next hop, while the distance parameter minimizes energy use. In [11], an energy-aware cluster-based routing protocol (ECRP) is proposed for WSNs. The energy-aware clustering and routing problem design are addressed through the ECRP protocol introduced in [11]. Initially, the ECRP adapts the same threshold of the basic LEACH in the cluster head assignment, and then the assignment is performed through a cost function based on two main factors, energy and distance, to minimize and balance energy dissipation. After the clustering, the data-forwarding phase begins.