## 2 Literature Survey
Their proposed design involves assistant CHs with Load Balancing Threshold and Role Transfer Threshold techniques. On approaching the first threshold level, a node having the highest energy level in the cluster called assistant cluster head is selected to share the load of the CH. The CH uses this node as its forwarding node rather than directly sending the CH data to the next cluster. An assistant CH either sends this received data directly to the BS or to the next assistant CH of an adjacent cluster. Using the dynamic power adjustment technique, energy utilization in data transmission is saved since the assistant CH is far nearer to the CH compared to the CH of the next cluster. Another idea for introducing assistant clusters was introduced by [PERSON] [17] for power mitigation. The authors name these assistant clusters as partaker nodes. These special nodes assist the CH in the routine job of data collection. Instead of having the CH collect data solely from all sensors in the cluster, a certain number of partaker nodes participate in the data collection. They help collect the raw data, and perform initial data aggregation and necessary processing before transferring data to the CH. With partakers, a portion of power that would have been consumed by CHs is handled by the partakers. Another proposed mechanism; Energy Aware Distributed Unequal Clustering (EADUC) by [PERSON] _et al._[18] is an energy-aware routing algorithm for cluster-based wireless sensor networks. They introduced unequal size clusters to remedy the hot spot issue that results in better network lifetime. The designing of the clustering topology comprises a neighbor node information collection phase, cluster head competition phase and cluster formation phase. These constituents make up the setup phase. Each one is given a specific duration, _i.e._, \(T_{1}\), \(T_{2}\) and \(T_{3}\). Next to it is the data transmission phase. To start the cluster formation process, the BS broadcasts a signal at a certain power level. Each node can compute its approximate distance to the BS based on the received signal strength. In the first phase, each node broadcasts a _Node_Msg_ message within its radio range \(r\) having node id and its residual energy \(E_{r}\). Based on the collected information, each node calculates the average residual energy of its neighbor nodes. The next calculation is of the waiting time \(t\) using the following Equation (1):  
\[t= \begin{cases}\dfrac{E_{a}}{E_{r}}\ T_{2}V_{r},E_{r}\geq\ E_{a}\\ \ T_{2}V_{r},\ E_{r}<\ E_{a}\end{cases}  \tag{1}\]  
where \(V_{r}\) is a real value randomly distributed in [0.9,1] which is introduced to reduce the probability that two nodes send _Head_Msg_s at the same time. Each node waits for this calculated time prior to broadcasting a _Head_Msg_ message. To start the cluster head selection competition phase, each node compares its average residual energy to its neighbor's calculated average residual energy and decide whether to be a cluster head or not. After waiting for the calculated time \(t\) the decision of the nodes to be cluster heads is broadcast within their calculated radio range. If a node does not receive any _Head_Msg_ message until the expiry of its time \(t\) then it broadcasts the _Head_Msg_ within radio range \(R_{C_{i}}\) to advertise that it will be a cluster head. The competition radius \(R_{C}\) determines the size of the cluster that is based on the proximity to the BS, and each node calculates its own value for \(R_{C}\) using the following Equation (2):  
\[R_{C}=\ \left[1-\alpha\ \dfrac{d_{max}-d(s_{t},BS)}{d_{max}-\ d_{min}}-\ \beta\left(1-\dfrac{E_{r}}{E_{max}}\right)\right]R_{max} \tag{2}\]  
where \(d_{max}\) and \(d_{min}\) are the maximum and minimum distance from the nodes in the network to the BS, \(d(s_{t},BS)\) is the distance from node \(s_{t}\) to the BS, \(\alpha\) and \(\beta\) are weighted factors having values in [0,1], and \(R_{C}^{0}\) is the maximum value of the competition radius.