## 1 Introduction  
The use of wireless sensor networks (WSNs) has grown enormously due to the great variety and capability of their modern and real-world applications [1] in areas such as e-health, space and extreme environment applications, traffic control, smart home applications and object tracking [2]. A typical wireless sensor network is made up of some sensor nodes which are distributed in the region of interest (ROI) which are often harsh and inaccessible environments. The sensor nodes collect data from the environment in the proposed application and forward that to a data link using single-hop and multi-hop communication schemes. The sensor nodes are battery powered and it is usually impossible to recharge or replace the battery under harsh conditions. That is the main constraint for designing and employing such sensor networks with limited energy nodes in large-scale applications [3, 4].  
There are some ideas and suggestions for decreasing the energy consumption in different network layers such as using low power sensors and energy management of local processors in physical layer and other methods to improve the lifetime of the whole network instead of a single node, such as MAC protocols in data link layers [5], routing protocols in the network layer [6], the network coding [7, 8, 9] and retaining the network connectivity approaches [10, 11].  
Clustering is one of the most important methods for designing a hierarchical energy efficient sensor network to achieve reliable data transmission, scalability and load-balancing criteria [12]. In clustering schemes, sensor nodes are grouped into virtual groups with a cluster head (CH), and other sensor members. The members measure physical parameters and send them to the cluster head and the cluster head is responsible for forwarding the aggregated data to other cluster heads or to the base station [12, 13].  
Clustering protocols use different parameters such as the energy levels of nodes, the node density and centrality for network virtual portioning and selecting cluster heads [14] and some other ideas for robustness against the network topological changes [15]. As in monitoring applications of WSNs, the data flow direction is from sensor nodes to the BS, therefore every node delivers its generated data to the base station somehow. The routing between CHs for forwarding data can be either direct or multi-hop. The direct routing approach is easy to implement and fast in delivery, so it is used in many cases, but when the size of sensor network grows, this method can be inefficient or useless, so in order to improve the protocol scalability, intercluster routing by multi-hop schemes are employed [15, 16, 17] which are also considered in this paper.  
In multi-hop routing schemes, the cluster heads that are closer to the base station have the responsibility of forwarding data packets of all other cluster nodes to the data link, so an important parameter for clustering and the cluster size is the distance from the base station where the size of clusters closer to the base station is smaller than that for farther clusters. This is an efficient way to avoid hot nodes dying [17, 18] and is considered in this paper. The aim of this paper is to present a new clustering approach which is based on voting and priority ideas and called the DCPVP protocol.  
The remainder of this paper is organized as follows: Section 2 describes the previous related works and explains the LEACH, HEED, WCA, GCMRA and TCAC protocols. Section 3 presents the network and energy models and assumptions. Section 4 describes the proposed protocol in detail and explains the different phases of the DCPVP protocol. A simulation test-bench including the assumptions, parameters and scenarios are presented in Section 5. Finally, Section 6 concludes the paper.