A Framework for Energy-Efficient Clustering With Utilizing Wireless Energy Balancer

Abstract

Clustering has been accepted as one of the most efficient techniques for conserving the energy of wireless sensor networks (WSNs). In cluster-based WSN, the cluster heads (CH) consume more energy compared with normal nodes, which results in rising their energy consumption and reducing the network lifetime. There are several energy-efficient routing schemes which have been presented to reduce the energy consumption of CHs. In such schemes, further clustering of CHs will result in reducing the energy consumption of CHs. However, this rule has not been considered in previous related works. Likewise, unbalanced energy consumption of CHs is one the main design challenges which leads to increase the wasted energy and premature network death. A number of recent studies have sought to improve the balanced energy via utilizing devices equipped with large and expensive energy harvesters, which leads to extra cost Therefore, a feasible solution is needed to achieve balanced energy consumption of CHs. Consequently, to overcome the aforementioned limitations, we proposed a framework for energy-efficient clustering by utilizing a wireless energy balancer. Firstly, an n-level clustering is presented which results in fully leveraging and reducing the energy consumption of CHs. Secondly, an energy balancer is utilized in order to reduce the wasted energy to the fullest possible extent by zeroing the variance between the remaining energy of CHs. The performance of the proposed scheme has been compared against CMS2TO and DGOB mechanisms. Simulation results show that the proposed scheme achieved improved performance in terms of network lifetime by 20%, total energy consumption by 52%, network overhead by 20%, computation time by 46%, and wasted energy by 86% compared to existing schemes. In conclusion, the proposed framework proves to be a viable solution for enhancing the network lifetime and energy efficiency.

Mixed Linear Programming for Charging Vehicle Scheduling in Large-Scale Rechargeable WSNs

“…However, the performance is improved but failed to identify the optimal number of CVs to control the cost. A clustering algorithm is also used in multiple CV scheduling in [34] using a statistical approach and heuristic formulae. This approach efficiently identifies the APs in the networks and controls the scheduling, but it does not provide the optimal number of CVs.…”

Multi‐objective‐derived energy efficient routing in wireless sensor networks using hybrid African vultures‐cuckoo search optimization

“…If the given condition Q 1 ≥ rndq 1 ð Þis satisfied, then the vulture position is updated by Equation ( 5); otherwise, the position is updated by Equation ( 6). If jGj < 1 ð Þ, then the solution is updated using Equation (7). Finally, the best solution is obtained.…”

“…In the exploitation phase at the second stage, motion vultures are attracted by other eagles through the food sources and violent and siege attacks to detect the food. The second exploitation stage is expressed in Equation (7).…”

“…Determine the best fitness values for each individual. Here, the attained solution is updated by Equation (7); otherwise, the solution is updated by Equation (9). Moreover, the standard deviation and also the mean value is evaluated.…”

“…Clustering approaches develop the Cluster Head (CH) with the help of probability value, which can communicate with different nodes present at the short range. 7 So, the Multi-Hop (MH) routing protocol supports routing inside the network, which is far away from the communication range and suffers from energy factors. Hence, the delay got reduced, but the consumption of energy got maximized, and thus, a huge amount of energy got saved by routing.…”