## 2 Related Work  
This section provides a brief overview of existing energy-efficient techniques in WSNs. Different techniques have been carried out to design feasible protocols that improve energy efficiency in WSNs. In WSNs, scalability, reliability and predictability are considered to be major problems in high density networks. Clustering is considered to be the best solution for improving scalability. Energy efficiency is crucial to prolong the network lifetime of WSNs. Moreover, the selection of the MAC protocol is considered important for predictable channel access and reliability [3]. Furthermore, data aggregation improves the reliability and scalability for high density networks. In this paper, we are concerned about cluster formation, CH selection schemes, MAC and data aggregation in CHs. In clustering, the nodes are grouped into different clusters, and each cluster contains one CH that forwards the data to the sink node or to the nearest CH. Many clustering algorithms are proposed to increase network lifetime among the nodes in the WSNs. Some of the well-known clustering algorithms are low-energy adaptive clustering hierarchy (LEACH) [4], energy efficient clustering scheme (EECS) [2], energy-efficient unequal clustering (EEUC) [5], _etc_. In LEACH [4], the CHs are elected based on the minimum communication distance to the maximum number of one-hop neighbors. Another feature is that CH will be rotated among the members of the cluster. The CH re-election and cluster formation happens every round. Unfortunately, this will lead to additional delay and energy usage. HEED [6] selects CHs based on the remaining energy of sensor nodes. All other sensor nodes join the CH based on the minimum communication cost between CM and CH. In the second round, the election of CHs is based on two parameters: communication cost between CMs and remaining energy. However, the hot spot issue appears in areas that are close to the sink, as nodes in such areas need to relay incoming traffic from other parts of the network. The energy of these nodes gets depleted early as compared to other nodes. The authors of [2] proposed EECS, which selects the formation of the cluster based on the weighted parameter to minimize the energy used for communication to the sink node. This is achieved by allocating clusters who are far away from the sink with a small number of CMs. Simulation results show that EECS performs better compared to LEACH in terms of energy efficiency. However, additional overheads are required for having information about other CHs and the sink node. Furthermore, it is not suitable for high density networks where nodes are scattered non-uniformly. In EEUC [5], the cluster sizes with a longer distance from the sink node are larger compared to the cluster sizes near the sink node. This was proposed to save energy in communication between CMs and CHs. However, extra overheads are caused due to the additional data aggregation in all nodes, which degrades the efficiency of the network in a multi-hop environment. Furthermore, the energy of CH closer to the sink node tends to deplete quickly, because they forward more data traffic compared to other sensor nodes. The trade-off between the distance to the sink from the source and the cluster size should be studied analytically before setting up the network hierarchy. The multi-hop routing protocol with unequal clustering (MRPUC) [7] was proposed to alleviate the hot spot problem. A sensor node that has the maximum remaining energy is elected as the CH. The CHs closer to the sink node have smaller cluster sizes to reduce the energy for heavy inter-cluster transmissions. The operation of MRPUC consists of three phases: cluster creation, routing between CHs and the sink node and packet transmission. However, inter-cluster multi-hop routing formation may cause an additional overhead. In [8], the CH is elected based on the value assigned by the weighted factor to the CMs in the cluster. A new cluster formation is started when the value of the original CH drops below a certain threshold.