Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer

A novel circuit sizing technique with improved accuracy and efficiency is proposed to resolve the sizing issues in the analog circuit design. The grey wolf optimization (GWO) algorithm has the total number of iterations divided equally for exploration and exploitation, overlooking the impact of balance between these two phases, aimed for the convergence at a globally optimal solution. An enhanced version of a typical GWO algorithm termed as enhanced grey wolf optimization (EGWO) algorithm is presented with improved exploration ability and is successfully applied in analog circuit design. A set of 23 classical benchmark functions is evaluated and the outcomes are compared with recent state of the art. A conventional two-stage CMOS operational amplifier circuit realized in UMC 180nm CMOS technology is used as a benchmark to validate the efficiency and accuracy of the proposed optimization technique. A statistical study is also conducted over the final solution to investigate the exploration ability of the algorithm proving it to be one of the robust and reliable techniques.

An efficient EGWO algorithm as feature selection for B-ALL diagnoses and its subtypes classification using peripheral blood smear images

Seyedali Mirjalili et al. (2014) introduced a completely unique metaheuristic technique particularly grey wolf optimization (GWO). This algorithm mimics the social behavior of grey wolves whereas it follows the leadership hierarchy and attacking strategy. The rising issue in wireless sensor network (WSN) is localization problem. The objective of this problem is to search out the geographical position of unknown nodes with the help of anchor nodes in WSN. In this work, GWO algorithm is incorporated to spot the correct position of unknown nodes, so as to handle the node localization problem. The proposed work is implemented using MATLAB 8.2 whereas nodes are deployed in a random location within the desired network area. The parameters like computation time, percentage of localized node, and minimum localization error measures are utilized to analyse the potency of GWO rule with other variants of metaheuristics algorithms such as particle swarm optimization (PSO) and modified bat algorithm (MBA). The observed results convey that the GWO provides promising results compared to the PSO and MBA in terms of the quick convergence rate and success rate.

As an information acquisition and processing method, wireless sensor network is also an important component of the Internet of Things. As a key core technology of wireless sensor networks, localization technology has become an important direction in the current and future research of wireless sensor networks, which is also a key issue related to the real application of wireless sensor networks and the Internet of Things. The research of localization algorithm based on intelligent computing technology is paid more attention. In this paper, a novel intelligent computing method — grey wolf optimization was used to localization in wireless sensor network and proposed a novel localization algorithm. The validity and practicability of the proposed algorithm were verified by simulation experiments. The convergence performance and localization result was discussed and compared by the classical traditional intelligent computing methods—particle swarm optimization algorithm. Moreover, the localization performance under different anchor node proportion and different communication radius were analyzesed in this paper. The simulation results show that the proposed algorithm has higher localization accuracy, and it needs fewer anchor nodes and smaller communication radius to achieve the same accuracy, thus saving cost.

This paper presents a methodology utilizing Coyote Optimization Algorithm (COA) for Global Maximum Power Point Tracking (GMPPT) considering the impact of partial shading on a PV system. COA is utilized to clarify GMPP by comparing all the existing peaks on the PV curve. COA is also employed to control the boost converter. Different shading patterns are applied to a photovoltaic system. Simulations of this process are established utilizing MATLAB software. The suggested algorithm is compared with enhanced Grey Wolf Optimization (E-GWO), Dragonfly Algorithm (DA), Ant Lion Optimization (ALO), and Particle Swarm Optimization (PSO). According to the results, the suggested COA improves the tracking speed and accuracy of MPPT during partial shading conditions. Also, the suggested methodology is providing high efficiency for solar PV systems in any irradiation conditions.

Wireless Sensor Networks are instrumental in various domains, including environmental monitoring, healthcare, and industrial automation. Despite their ubiquity, the energy constraints of sensor nodes remain a pivotal concern. This research delves into energy conservation in WSNs by implementing Adaptive Sleep Scheduling using the Grey Wolf Optimizer (GWO) in the context of heterogeneous networks. Heterogeneous WSNs, characterized by nodes with diverse computational capacities and energy reservoirs, pose unique challenges for optimizing energy efficiency. The conventional static sleep scheduling strategies prove inadequate in accommodating this heterogeneity, often leading to suboptimal energy consumption. In this context, the application of GWO, an optimization algorithm inspired by the social behavior of grey wolves, offers a novel and promising approach to address the energy efficiency challenge. Heterogeneous WSNs necessitate tailored energy management strategies to optimize the utilization of resources. The disparity in the capabilities of sensor nodes, in terms of processing power and energy reserves, demands an adaptive solution for sleep scheduling to minimize energy consumption while maintaining the necessary data collection and transmission capabilities. This research demonstrates the significance of integrating GWO-based adaptive sleep scheduling in heterogeneous WSNs through extensive simulations and in-depth analysis. The proposed approach is compared with the LEACH, E-LEACH, and DEEC. The results show due to the use of GWO in adaptive sleep mechanisms in Heterogenous WSNs nodes die late as compared to other algorithms and energy consumption is also slow. So, in all one can say, GWO is a better approach in terms of energy efficiency and lifetime. quantity demand and investment in research and development, while the other model focuses on a more realistic relationship between the quantity demand and the price.

SummaryA wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters.

Wireless Sensor Network (WSN) is a promising approach that is developed for a wide range of applications due to its low installation cost. However, the nodes in the WSN are susceptible to different security threats, because these nodes are located in hostile or harsh environments. Moreover, an inappropriate selection of routing path affects the data delivery of the WSN. The important goal of this paper is to obtain secure data transmission while minimizing energy consumption. In this paper, Trust Centric - Multiobjective Black Widow Optimization (TC-MBWO) is proposed for selection of Secure Cluster Head (SCH) from the large-scale WSN. Moreover, the secure routing path is generated by using the TC-MBWO, in which the factors considered for the cost function are: residual energy, distance, trust and node degree. Therefore, the secured clustering and routing achieved by using TC-MBWO, provides the resistance against malicious nodes and simultaneously the energy consumption is also minimized by identifying the shortest path. The proposed TC-MBWO method is analyzed in terms of alive nodes, dead nodes, energy consumption, throughput, and network lifetime. Here, the TC-MBWO method is compared with different existing methods such as Low Energy Adaptive Clustering Hierarchy (LEACH), Particle Swarm Optimization - Grey Wolf Optimizer (PSO-GWO), Particle-Water Wave Optimization (P-WWO) and Particle-based Spider Monkey Optimization (P-SMO). The alive nodes of the TC-MBWO are 70 for 2800 rounds which are higher in number when compared to the PSO-GWO, P-WWO and P-SMO.

The emergence of wireless sensor network (WSN) makes the communication between people and things more extensive, and provides a new way for people to obtain information and more accurate real‐time communication. However, the regional distance in WSN is still a big problem. This method can not only optimize the cluster structure, but also reduce the number of member nodes in the cluster, the cluster head receiving energy, and reduce the data transmission distance between nodes. At the same time, the number of packets received by the base station increases, so that more sensing information can be obtained. The simulation results showed that compared with the smart Ethernet protection (SEP) protocol and the low energy adaptive clustering hierarchy (LEACH) protocol, the number of first node deaths (FND) of the grey wolf optimizer (GWO) algorithm was 153 and 233 times different from those of the SEP and LEACH protocols respectively at 100m*100m. In the case of 200m*200m, compared with that of SEP and LEACH, the number of FND of GWO was 152 and 200 times different. Finally, in the case of 300m*300m, compared with that of SEP and LEACH, the number of FND of GWO was 142 and 158 times different. The GWO algorithm has a higher node ARE ratio in a wider region. In the same 1000 rounds, the node ARE of GWO was 61%, while the node ARE of SEP and LEACH protocols were 7% and 3%, respectively. From the above results, it can be found that the GWO algorithm has been greatly improved in terms of network energy efficiency, network lifetime, etc., and can still work normally under larger scale monitoring. The innovation of this article lies in the localization problem of fixed node mobile sensor networks, proposing a mobile sensor network localization method based on mobile error nodes. This can optimize the distance between nodes and improve the positioning accuracy of the algorithm in both distributed and centralized situations. This article is protected by copyright. All rights reserved.

Wireless Sensor Networks (WSN) and other sensing and communication technologies have given man the ability to keep tabs on his surroundings. Distributed sensors in the form of WSNs are used to keep tabs on environmental or physics-related variables. These sensors operate together to send information via the internet to a central location, where it may be used to inform decisions that have real consequences for people's lives. As detecting, processing, and transmitting all take a lot of energy, WSNs are powered by batteries that cannot be replaced during data transmission. Therefore, it is essential to increase the network's durability by reducing the energy consumption of each sensor node. Power consumption problems exist in network's communication routes between the sensor nodes might have disastrous effects on a network that relies on timely data transmission. Since the sensor nodes need electricity to function, losing that power disrupts data transmission and, in most situations, kills the network. A serious issue with WSNs is their rapid loss of energy. Studies have shown that switching the node from active to sleep mode while it is not in use may extend the lifespan of WSNs. Some have argued that a mobile charger should be made available instead of a sensor node with a changeable battery. Even though energy harvesting the practice of drawing power from the surroundings and transforming it into electrical energy has the potential to address this issue, there are situations in which this is not possible. The objective of this study is to design a more energyefficient algorithm for usage by sensor nodes in order to decrease their power consumption. By combining game theory and Grey Wolf Optimisation with the existing Sector Low Energy Adaptive Clustering Hierarchy (LEACH) routing system, a more efficient and effective algorithm was developed called Enhance Game and Grey Wolf Algorithm (EG-GWA).

Wireless Sensor Networks (WSN) play a vital role in IoT, smart agriculture and environmental monitoring, but their performance is severely limited by high energy consumption, inefficient routing and unbalanced clustering. This work introduces a hybrid energy-efficient routing framework that integrates Fuzzy Logic-based Extended Reinforcement Learning (FL-ERL) for adaptive cluster formation, Tent random function-based Bald Eagle Optimization (TBEO) for optimal Cluster Head (CH) selection and a genetic Levy flight-based optimized routing mechanism (GL-ORM) for global inter-cluster routing. The proposed model effectively optimizes node residual energy, node to Base Station (BS) distance and processing delay for a large-scale WSN with 1000 nodes. Simulated results in MATLAB demonstrate significant improvements over state-of-the-art models such as Black Widow Optimization Algorithm (BWOA), Spider Monkey Optimization (SMO), Fish Swarm Optimization (FSO), Grey Wolf Optimization (GWO) and Ant Colony Optimization (ACO). Specifically, the proposed method reduces energy consumption to 6.68 J, achieving 68%–72% reduction compared with GWO (20.12 J) and FSO (17.15 J). The model achieves a 50% higher throughput of 0.95 than ACO and maintains a 95% packet delivery ratio, outperforming existing schemes by 10–30%. These results highlight robustness, scalability and energy efficiency of the proposed framework, which demonstrates its ability to prolong WSN lifetime. This approach provides significance and reliable data delivery.

Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.

A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs are battery-powered, and the energy drainage is a significant issue. The clustering approach holds an imperative part in boosting the lifespan of WSNs. This approach gathers the sensors into clusters and selects the cluster heads (CHs). CHs accumulate the information from the cluster members and transfer the data to the base station (BS). Yet, the most challenging task is to select the optimal CHs and thereby to enhance the network lifetime. This article introduces an optimal cluster head selection framework using hybrid opposition-based learning with the gray wolf optimization algorithm. The hybrid technique dynamically trades off between the exploitation and exploration search strategies in selecting the best CHs. In addition, the four different metrics such as energy consumption, minimal distance, node centrality and node degree are utilized. This proposed selection mechanism enhances the network efficiency by selecting the optimal CHs. In addition, the proposed algorithm is experimented on MATLAB (2018a) and validated by different performance metrics such as energy, alive nodes, BS position, and packet delivery ratio. The obtained results of the proposed algorithm exhibit better outcome in terms of more alive nodes per round, maximum number of packets delivery to the BS, improved residual energy and enhanced lifetime. At last, the proposed algorithm has achieved a better lifetime of ≈20%, ≈30% and ≈45% compared to grey wolf optimization (GWO), Artificial bee colony (ABC) and Low-energy adaptive clustering hierarchy (LEACH) techniques.

A coverage optimization method is suggested to improved node coverage in wireless sensor networks (WSNs), decrease node redundancy, and lengthen network lifetime, this method is based on the adaptive grey wolf arithmetic optimization algorithm The exploitation and exploration are first balanced using a cosine acceleration function. Then, to boost the method's performance, the grey wolf optimizer and arithmetic optimization algorithm are combined. In comparison to the conventional coverage optimization algorithm, our simulation results could improve arithmetic optimization algorithm has higher convergence efficiency and coverage rate, decreases network loss, and significantly lengthens the network life cycle.

Improved DV-HOP localization algorithm based on grey wolf optimization