Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

Optimized cluster head selection using krill herd algorithm for wireless sensor network

Wireless Sensor Network (WSNs) can perform transmission within themselves and examination is performed based on their range of frequency. It is quite difficult to recharge devises under adverse conditions. The main limitations are area of coverage, network’s lifetime and aggregating and scheduling. If the lifetime of a network should be prolonged, then it can become a success along with reliability of the data transferred, conservation of sensor and scalability. Through many research works, this challenge can be overcome which are being proposed and the network’s lifespan improved which can preserve the sensor’s energy. By schemes of clustering, a low overhead is provided and the resources are efficiently allocated thus increasing the ultimate consumption of energy and reducing interfaces within the sensor nodes. Challenges such as node deployment and energy-aware clustering can be considered as issues of optimization with regards to WSNs, along with data collection. An optimal solution can be gotten through evolutionary and SI algorithm, pertaining to Non-deterministic Polynomial (NP)-complete along with a number of techniques. In this work, Krill Herd Algorithm based clustering is proposed

Learning automata and sigma imperialist competitive algorithm for optimization of single and multi-objective functions

Evolutionary Algorithms (EA) consist of several heuristics which are able to solve optimisation tasks by imitating some aspects of natural evolution. Two widely-used EAs, namely Harmony Search (HS) and Imperialist Competitive Algorithm (ICA), are considered for improving single objective EA and Multi Objective EA (MOEA), respectively. HS is popular because of its speed and ICA has the ability for escaping local optima, which is an important criterion for a MOEA. In contrast, both algorithms have suffered some shortages. The HS algorithm could be trapped in local optima if its parameters are not tuned properly. This shortage causes low convergence rate and high computational time. In ICA, there is big obstacle that impedes ICA from becoming MOEA. ICA cannot be matched with crowded distance method which produces qualitative value for MOEAs, while ICA needs quantitative value to determine power of each solution. This research proposes a learnable EA, named learning automata harmony search (LAHS). The EA employs a learning automata (LA) based approach to ensure that HS parameters are learnable. This research also proposes a new MOEA based on ICA and Sigma method, named Sigma Imperialist Competitive Algorithm (SICA). Sigma method provides a mechanism to measure the solutions power based on their quantity value. The proposed LAHS and SICA algorithms are tested on wellknown single objective and multi objective benchmark, respectively. Both LAHS and MOICA show improvements in convergence rate and computational time in comparison to the well-known single EAs and MOEAs

Analytical Report on Metaheuristic and Non-Metaheuristic Algorithms for Clustering in Wireless Networks

This analytical report delves into the comprehensive evaluation of both metaheuristic and non-metaheuristic algorithms utilized for clustering in wireless networks. Clustering techniques play a pivotal role in enhancing the efficiency and performance of wireless networks by organizing nodes into meaningful groups. Metaheuristic algorithms, inspired by natural processes, offer innovative solutions to complex optimization problems, while non-metaheuristic algorithms rely on traditional mathematical principles. This report systematically compares and contrasts the efficacy of various algorithms, considering key metrics such as convergence speed, scalability, robustness, and adaptability to dynamic network conditions. By scrutinizing both categories of algorithms, this report aims to provide a holistic understanding of their respective advantages, limitations, and applicability in wireless network clustering scenarios. The insights derived from this analysis can guide network engineers, researchers, and practitioners in selecting the most suitable algorithms based on specific network requirements, ultimately contributing to the advancement of wireless network clustering techniques

A Pre-defined Scheme for Optimum Energy Consumption in Wireless Sensor Network

One of the fundamental issues that needs a serious attention in Wireless Sensor Network (WSN) is node deployment. A proper node deployment scheme can help extending the lifetime of WSN’s by reducing the energy consumption. In this paper, we implement Improved Harmony Search (IHS) algorithm with several modifications in determining the best position for each sensor node in order to obtain an optimum energy consumption. The number range in selecting the random number to determine a new harmony is expended. Based on the experiment, the result obtained gives lower energy consumption rate. To enhance the energy performance, we test the best sink node’s position to obtain the best position for sink node

Analysis on Energy and Coverage Issues in Wireless Sensor Network

Two major fundamental issues in Wireless Sensor Network (WSN) are energy efficiency and coverage. Energy efficiency is the result of controlling and maintaining the energy usage. A method is considered as energy efficient if it can provide more services with the same amount of energy input, while coverage efficiency is measured by how long and how well a sensor monitors the subjected area. Hence, to obtain an energy and coverage efficiency, maximizing the coverage by reducing the energy consumption needs to be achieved. Our paper presents the potential of Derivative Harmon Search Algorithm (DHSA) in a connected WSN to achieve deployment of node that can cover optimal area and at the same time give low energy consumption

Lightning search algorithm: a comprehensive survey

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA’s applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime