# A Novel Adaptive Cluster Based Routing Protocol for Energy-Harvesting Wireless Sensor Networks
###### Abstract
Simultaneously, the triangular fuzzy membership function is applied to choose the node with the higher signal strength and more residual energy as the cluster head. The data packets are aggregated to the chosen cluster head and transmitted to the sink node with the minimum routing overhead. In [28], a particle swarm optimization (PSO) algorithm is adopted in the design of the routing protocol to minimize the intra-cluster distance and thus accomplish the energy-efficient routing protocol. In [29], a fuzzy-enhanced flower pollination algorithm-based, threshold-sensitive, energy-efficient clustering protocol is proposed to optimize the cluster head selection method using the heuristic algorithm. The sensor parameters, including the residual energy, the node centrality, and the distance to BS, are collaboratively considered in the determination of the cluster head node. The existing stable election protocol (SEP) in [30] is optimized to maintain a uniform energy distribution between cluster head nodes and member nodes. Different residual energy thresholds are proposed for the node with different energy states, which can determine the criteria needed to reform the cluster and reselect the CHs. The extra energy consumption in the unnecessary reconstruction of clusters can be restricted. The clustering algorithm and the selection of the cluster head are the crucial aspects of the clustering-based routing protocol. Different clustering methods would directly influence the energy consumption of intra-cluster data transmission. In recent years, the centralized energy-efficient cluster (CEEC) [31], the hybrid unequal clustering layering protocol (HUCL) [32], and the sleep-awake energy-efficient distributed (SEED) [33] are proposed to guarantee a longer MLC. The optimized selection of the cluster head could improve the energy efficiency of data transmission to the base station and the alternative takeover of the cluster head would balance the energy consumption of nodes to prolong the MLC of the network. The routing protocols optimized above have considered the transmission load and the energy consumption; several excellent research achievements have been proposed. However, IoTs (Internet of Things) epitomizes the interconnection of things. Therefore,different from the internet, the information from any terminal is indispensable for IoTs. The death of nodes from one region may lead to the loss of complete control of one type of equipment. Therefore, adaptive networking and distributed data routing would be more suitable for the operation of IoTs. In this paper, an adaptive hierarchical-clustering-based routing protocol is proposed for the WSNs with energy harvesting (EH). Aimed towards the specific problem of EH-WSNs, where parts of nodes are in an inoperative status because of energy harvesting, the proposed HCEH-UC fuses the adaptive node-clustering algorithm and the distributed alternative CHs scheme. The environment adaptive node clustering algorithm can form reasonable node clusters according to the original distribution of nodes, thus reducing the influence of human experience on the number or topology of clustering. According to the remaining energy and the topology of the nodes, the data transmission mode is adaptively regulated in a distributed way through the switch of the energy-harvesting mode and the operation mode. In order to verify the performance of the proposed HCEH-UC protocol, a regular WSNs was constructed and compared with the conventional routing protocols. Simulation results show that the proposed ECEH-UC protocol can accomplish the uninterrupted coverage of target area based on the EH-WSNs through an energy-efficient way. The main contributions of this paper are summarized as follows:  
1. A novel environment-adaptive clustering algorithm for WSNs nodes has been proposed. The clustering termination condition can be adaptively adjusted according to the node deployment and form a suitable node topology.