As a result, despite traditional routing that minimizes latency, most WSN routing methods aim to make the most of the network node's limited resources. The CH threshold function is updated to resize the clusters, and the network region partition technique is optimized by considering residual energy and node distance. By comparing the results of the designed PMR-GWO with the results of other current cluster head selection algorithms, the superiority of the proposed PMR-GWO is demonstrated.
By comparing the results of the designed PMR-GWO with the results of other current cluster head selection algorithms, the superiority of the proposed PMR-GWO is demonstrated. Within each loop of the network, the number of CHs was varied according to the percentage of live nodes. 2019) proposed fuzzy logic. The sensors provided the position and initial charge to the BS, which collected and stored the knowledge in the first phase of the CH setup.
The initial strength of the nodes was provided to the BS, and the location of each node was preset. The main objectives of the planned research are to extend the network lifetime sequence, reduce life force utilization and increase network performance. This study proposed a new computational framework, PMR-GWO's optimization capability, to certify the selection of the optimum CHs for WSN.
The main goal of PMR-GWO was to determine CHs for extended lifespan.
Probabilistic Multi-Tiered Grey Wolf Optimizer-Based Routing for Sustainable Sensor Networks
Level
The communication, reception, residual energy and data collection energy were separated from the node energy, which were mixed into the reserve energy (spent vitality and residual when the node changed to CH). The node hibernation strategy is divided into two parts: (a) selecting nodes at different locations to be idle on an arbitrary basis, and (b) identifying nodes at different locations to be idle based on CH expansion. The node hibernation process on this pool of nodes causes the CMs with the least energy and extended maximum range to become idle and eventually choose the CH as the cluster, which reduces the energy consumption per CH and thus improves the bandwidth utilization.
Instead of minimizing each fitness function independently, it is preferable to minimize the sum of all of them, as stated in Eq. The energy model was similar to the model given in Elshrkawey et al. 2018) that took into account the energy required to transmit a k-bit signal over a radius of d, as formulated in equation 12:. For level 1, the optimal value of CHs (K1) was: all nodes of the First Phase (m) in the network model were in the region that was close to the BS.
Where is the gap in CH to BS, and the data accumulation is as expected in Equation 14:. The CH selection variables were directly linked to the modeling approach of the probabilistic threshold strategy. It was calculated using a regular distribution with a mean of 0 and a variance of 1. The must be different, the node will choose a fresh one.
The CH selection was improved by the additional residual energy of the node and the number of living neighbors. The node index was integrated with the threshold equation, which was derived from Equation 20 by embracing the above metrics: The BS chose K1 nodes as in Equation 18 and designated them as CHs for Tier 1 through a centralized process.
A node's eligibility for CH was determined by its distance from the BS, remaining energy and energy utilization ratio. The adaptation function includes the residual energy and relative centrality of the node, its distance from the BS, the node degree and the node dormancy. A cluster node communicates with CHs in a one-hop network, and the MGWO communication path is established between CHs to prevent long-distance transmission.
PMR-GWO Input
Each node determines which layer it belongs to depending on its position and distance from BS. Probabilistic multi-layer GWO-based selection of CHs, depending on the appropriate fitness function based on coverage area and CH balancing factor, is implemented to identify the most dominant member of the group as CH and subordinates to CH as backup CH. After the CH is determined, all connected nodes select the nearest CH to form the cluster.
The CH is reselected based on its remaining energy and round-robin time to establish and maintain the new route. A fitness function determined the CH selection; the fitness function played a critical role in the MGWO algorithm's search-for-prey process. The node's properties, consisting of residual energy (Er) and the population of neighbors, were entered into this formula.
The initial cluster population, defined as the best possible cluster set in this study, was referred to as the initial cluster set, and the objective role estimate for the current optimal cluster set was determined. MGWO generated a new cluster set by changing each cluster in the existing optimal set arbitrarily, and a multiplicity of the new clusters created a new cluster set. The objective role estimate for the new cluster set was then determined as in Equation 22. 22) Where, ฯ, ยต, ฯ, and ฯ are the arbitrary statistics in [0,1], is the checklist of sensors adjacent to a specific and is the total communication distance from CH to BS.
The efficient competitor was the one with the maximum that the node with the highest residual energy and adequate adjacent nodes would be declared as a CH. Then, the choice was finalized; CHs would send one with CH _ID and CH string from BS. Probabilistic GWO - Default preference of CHs on nodes ๐๐ (๐ถ๐ถ๐ถ๐ถ = ๐ถ๐ถ๐ถ๐ถ๐ถ๐ถ1, ๐ถ๐ถ ๐ถ๐ถ๐ถ๐๐). ๐ถ๐ถ๐ธ๐ธ๐ป๐ป๐ป๐ป๐ป๐ป_๐๐๐๐๐๐ with CH _ID and CH string from BS.
The PMR-GWO pseudocode was defined as Algorithm 1. N = number of living nodes K = number of desired clusters. Where ๐๐1, ฯ, ยต, ฯ and ฯ are the random statistics in [0,1], ๐๐(๐ถ๐ถ๐ถ๐ถ๐ถ๐ถ๐ถ๐ถ) is the checklist of sensors adjacent to a specific ๐ถ๐ถ๐ถ๐ถ๐ถ๐ถ, and ๐ ๐๐ก๐ก๐ก๐ก๐ก๐ก๐ก๐ก is the total communication distance from CH to BS. The effective contender was the one with the maximum ๐๐; the node with the top remaining energy and enough adjacent nodes would announce itself as a CH.
PMR-GWO Input
The PMR-GWO method was compared with the P-SEP, L-DDRI, Novel-LEACH-POS, hetDEEC-3 and DBSCDS-GWO algorithms under similar study environments. The network lifetime of PMR-GWO was extended as the dead node count approached 50 percent compared to P-SEP, L-DDRI, Novel-LEACH-POS, hetDEEC-3, and DBSCDS-GWO. Likewise, imitation consequences amid HND and the sum of rounds for several conventions are revealed for P-SEP, L-DDRI, Novel-LEACH-POS, hetDEEC-3, DBSCDS-GWO and the proposed PMR-GWO method with fluctuating preliminary energies in figure 7.
The network lifetime of PMR-GWO was extended when the death node count approached 50 percent, relative to P-SEP, L-DDRI, Novel-LEACH-POS, hetDEEC-3 and DBSCDS-GWO. Likewise, simulation results amid HND and the sum of rounds for numerous conventions for P-SEP, L-DDRI, Novel-LEACH-POS, hetDEEC-3, DBSCDS-GWO and the proposed PMR-GWO method with varying preliminary energy revealed in Figure 7. The PMR-GWO algorithm required extra rounds for the LND compared to the other methods.
The PMR-GWO method used a suitable CH selection process and the nodes had an extended lifetime. The PMR-GWO method used a proper CH selection process and the nodes had an extended lifetime. Figure 9-10 displays the personnel count of the P-SEP, L-DDRI, Novel-LEACH-POS, hetDEEC-3, DBSCDS-GWO, and PMR-GWO protocols per round for each group.
Compared to existing techniques, the CH variance in the proposed PMR-GWO provided better results as seen in the graph. The PMR-GWO technique required determining the optimal CH number based on the WSN's energy consumption per transmission, which reduced cluster number unpredictability. The importance of stabilizing the CH number in PMR-GWO in relation to nodule death could not be overestimated.
The maximum number of CHs for P-SEP, L-DDRI, Novel-LEACH-POS, hetDEEC-3, DBSCDS-GWO and PMR-GWO are shown in Table 3. Comparative estimation of energy consumption for PMR-GWO and paths of other protocols are shown in Figure 11. The PMR-GWO approach, as shown in Figure 11, used the least amount of energy possible.
According to the simulation results, the service life of the PMR-GWO network was extended while energy consumption was reduced. As shown by the simulation results, the proposed PMR-GWO method was computationally competent in stabilizing energy.
