# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### TPSO-CR  
[PERSON] & [PERSON] [59], proposed a two-tier PSO protocol for the Two-tier Particle Swarm Optimization for Clustering and Routing protocol (TPSO-CR) in WSNs. The proposed algorithm used a clustering algorithm which finds the optimal set of CHs in order to efficiently use the energy, and maximize the transmission reliability and the network coverage. Meanwhile, its routing algorithm is developed with a novel particle encoding scheme and fitness function which finds the optimal routing tree that connects these CHs to the BS. TPSO-CR is similar to PSO-HC in the sense that it is centralized and based on the information received by the BS. After the computation of the average energy level of nodes by the BS, only nodes with an energy level above the average are eligible to be CCHs for each round to ensure that only nodes with sufficient energy are selected as CHs. Next, the BS runs the first-tier algorithm (clustering algorithm) which find the best K-CHs. The second-tier algorithm (routing algorithm) is performed after the first-tier and is responsible for constructing the optimal routing tree. In order to evaluate TPSO-CR, the authors have used two different scenarios WSN#1 and WSN#2 which use, respectively, homogeneous and heterogeneous nodes. The PSO-based approach is compared with the well-known protocols LEACH, EHE-LEACH, EEHC, LEACH-C, PSO-C, and GA-C. The results have shown that TPSO-CR has a smaller number of non-clustered nodes than other protocols on both scenarios WSN#1 and WSN#2. The results have also revealed that TPSO-CR has the highest throughput, its energy consumption is less than energy consumption of LEACH, EEHC, EHE-LEACH but is slightly similar to the consumed energy of protocols LEACH-C, PSO-C, and GA-C.  
PSO-ECHS  
[PERSON] et al. [60] proposed an energy efficient CH selection algorithm based on PSO called PSO-ECHS. It consists in two phases: the CH election and the cluster formation. The CH election phase is based on PSO and considers the residual energy of nodes and their distance with the BS. Initially, nodes send their location and residual energy to the BS which executes the PSO algorithm. For the CH selection in PSO-ECHS, a particle represents optimal positions of the CHs by representing the coordinates of the sensor nodes to be selected as CH. Contrary to algorithms where non-CH sensor nodes simply join a cluster by considering only the distance with the CH, in PSO-ECHS, the non-CH sensor nodes join a CH depending on the weight function called CH-Weight which consider the distance, the remaining energy, and the node degree of CHs. The evaluation of PSO-ECHS consists of three different scenarios on the position of the BS: at the first scenario, the BS is located in the center of the study field; the second scenario considers the BS located at the top right corner of the field; and the last scenario assumes the BS is located outside the field. Experiments are conducted on these scenarios by varying the number of nodes on four networks (300, 400, 500, and 700). The proposed algorithm is compared to LEACH, E-LEACH, LEACH-C, PSO-C, and the Least Distance Clustering algorithm (LDC) [61]. The results show that PSO-ECHS outperforms the others algorithms in terms of the energy consumption and thereafter has the best network lifetime. The results have also shown that PSO-ECHS had the highest packets received by the BS in the different scenarios with network which consist of 300, 400, 500, or 700 nodes.