# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 3 Network Energy Models (Methods)
### Proposed Network Model  
The details of the proposed model are given as follows:  
Step 1: All the SNs are static in the network.  
Step 2: The initial step is to define the network dimensions; the proposed model will have A \(\times\) B dimensions.  
Step 3: The network area will be split into multiple grids or block clusters. In our model, it can be 2 \(\times\) 4 = 8 grid clusters (comprising 100 nodes), 4 \(\times\) 4 = 16 grids clusters (comprising 100 and 200 nodes) and 10 \(\times\) 10 = 100 grids clusters (comprising 625 and 1250 nodes).  
Step 4: A CH for each grid is selected using the concept of trust model [19].  
Step 5: All the SNs in a particular grid sense the environment and transmit the sensed data to the CH.  
Step 6: The path between the CHs towards the sink is generated by considering the paths between adjacent grids in a zigzag fashion, which means that the area of interest is divided into numerous zigzag patterns so that each pattern has line segments and corners and each node is deployed in the corner of the zigzag pattern. The zigzag pattern enables a high coverage efficiency of 91%. In addition, it helps to cover the whole area of interest using the minimum number of nodes, thus generating the minimum coverage redundancy [20]. The score of each route is calculated using Equation (4) [21], where the weight factors are designated to assign the weights to the subcomponents and are configured as \(\alpha+\beta+\gamma+\delta=1\).  
\[Score(P)=\omega\alpha+min.buffer(p)\beta+\bigg{(}\frac{1+maxHC-HC(P)}{maxHC} \bigg{)}\gamma+\bigg{(}1-\frac{no.DelayedPkts}{totalPktsRecv}\bigg{)}\delta \tag{4}\]  
The first component (\(\omega\)) of Equation (4) refers to the energy consumption on a particular path \(P\) and is calculated using Equation (5). \(\omega\) is calculated from the energy consumed on a particular path until a critical point in the network, and afterwards, it calculates the value from the energy reserve along the path. The critical point in the network (\(\Omega\)) is preset and is normally considered as 20% of the initial energy. If the minimum energy on the path \(min.energy(P)\) is less than \(\Omega\), the component \(\omega\) is calculated from  
The second component (_min.buffer(p)_) of Equation (4) represents the minimum buffer of a node along the path \(P\).  
The third component, \(\frac{1+maxHC-HC(P)}{maxHC}\) of Equation (4) estimates the number of hop counts. \(maxHC\) is the maximum hop count allowed in the network, whereas \(HC(P)\) is the hop count of the calculated path.  
Finally, \(1-\frac{no.DelayPkts}{totalPktsRecv}\) is the fourth component that calculates the reliability of the path, that is, the ratio of packets delivered to the BS without delay to the total packets received.  
Step 7: Once the scores are calculated for the current set of solutions, the solution with the best score is saved and the same method is followed for each CH in the network.  
Step 8: Later, the optimization algorithm will process all the iterations and select a final route to transfer data from the CH to the BS.