# Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
## 2 Related Work
### An Overview of Baseline Schemes  
For simplicity, we have provided an overview of our baseline schemes in this section. In [25], the authors exploited ACO for balancing energy consumption among the network nodes. The network is divided into number of regions where ants are deployed to gather data from the desired network field. This protocol presents two functions named Most Energy Efficient Distance (MEED) for ensuring effective energy depletion for maximizing the operational time of the network. ACO is used to find out an optimal distance via pheromone intensity and heuristic desirability that consists of data load and transmission range. The proposed strategy ensures balanced energy consumption resulting in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Technique Used** & **Metrics** & **Parameters** & **Parameters** \\  & & & **Achieved** & **Compromised** \\ \hline HORA [14] & Multi-hoping & Energy and distance & Network lifetime increases & Transmission Delay increases, high energy consumption \\ \hline LBT [15] & Multi hop ring routing & Energy and Distance & Network lifetime maximizes & Energy Hole \\ \hline NEHA [16] & Sleep schedule mode, multi-hoping & Energy and Distance & Network lifetime is maximized & High traffic load on boundary nodes increases energy consumption, E2E delay is increased \\ \hline E-HORM [18] & Sleep schedule mode is adopted, multi-hoping & Energy threshold, distance & Network lifetime maximizes & E2E delay increases \\ \hline IHDHRA [19] & Node replacement & Forward function and energy & Network lifetime is increased & Scalability and Robustness \\ \hline EEDSRS [21] & Data sensing and Data routing, rate allocation & Link Quality & Maximum network lifetime is achieved & MAC layer protocol \\ \hline EEAC [24] & Ant colony optimization & Energy & Network lifetime increases with less energy consumption & Scalability and Robustness \\ \hline ODTS [25] & Ant colony optimization & Distance and Energy & Network lifetime is optimized & Performance degraded in dense area network \\ \hline EEDCP-TB [30] & Data gathering Tree based Routing & TDMA & Network lifetime optimization through energy minimization & E2E delay increases \\ \hline LMDE [27] & Multi hop Routing & Energy & Network performance optimizes & Scalability issue and Data Redundancy \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the State of the Art in WSNs. optimal network lifespan. [12] exploits the ring routing through dividing the network into small sectors for estimating data load at each node. Transmission ranges are adjusted in order to avoid the cyclic selection of the forwarder node. Due to the converge cast nature of the algorithm, data load increases via moving towards the destination resulting in immature end of network. This mechanism adjusts transmission range when nodes in lower coronas' are dead. Due to this factor, as network progresses, transmissions over long distances deplete energy of the node very quickly resulting in undesired end of the network with plenty of nodes alive in the network. A new stable election based routing algorithm (N-SEP) proposed to preserve energy of the network [28]. Authors have exploited heterogeneity in cluster based network for saving energy over long distances. Cluster heads are randomly elected through weighted probability in which every node above certain threshold is illegible to become a cluster head. The probability for the nomination of head node is computed as follows:  
\[Th_{norm}(r)= \begin{cases}\frac{P_{N}(r)}{(1-P_{N}(r))(r\ mod\ \frac{1}{P_{N}(r)})},&\text{if}\quad N\in N _{s}\\ 0,&\text{otherwise}\end{cases}  \tag{1}\]  
\[Th_{norm}(r)= \begin{cases}\frac{P_{A}(r)}{(1-P_{A}(r))(r\ mod\ \frac{1}{P_{A}(r)})},&\text{if}\quad N\in A _{s}\\ 0,&\text{otherwise}\end{cases}  \tag{2}\]  
where \(N_{s}\) and \(A_{s}\) are the sets of normal nodes and forwarder nodes respectively. After the selection of a node as a cluster head, the member nodes are decided based on the minimum distance with respect to the destination.