scholarly journals Computational Intelligence based Clustering Algorithms for Wireless Sensor Networks: Trends and Possible Solutions

2021 ◽  
Vol 12 (2) ◽  
pp. 2992-3002
Author(s):  
Nitin Mittal
Keyword(s):  
Computational Intelligence ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Low Energy ◽  
Wireless Sensor ◽  
Clustering Methods ◽  
Radio Communication ◽  
Adaptive Clustering ◽  
Ch Selection ◽  
Selection Of

A wireless sensor network (WSN) is a state-of-the-art technology for radio communication. A WSN includes several sensors that are arbitrarily distributed in a particular region to detect and track physical characteristics that are hard for humans to observe, like temperature, humidity, and pressure. Because of the nature of WSNs, many issues may arise, including information routing, power consumption, clustering, and cluster head (CH) selection.  Although there are still some difficulties in the WSN, owing to its versatility and robustness, it has gained considerable attention among scientists and technologists despite the shortcomings. Various protocols were designed to solve these problems. Low energy adaptive clustering hierarchy (LEACH) is one of the significant hierarchical protocols used to reduce energy consumption in WSNs. This article provides an extensive analysis of LEACH-variant clustering protocols for WSNs. Recent research on Machine Learning, Computational Intelligence, and WSNs has highlighted the optimized WSN clustering algorithms. However, the selection of a suitable paradigm for a clustering solution continues an issue owing to the diversity of WSN applications. In this paper, a comprehensive review of suggested optimized clustering alternatives has been conducted and a comparison of these optimized clustering methods has been suggested based on various performance parameters. The centralized clustering approaches based on the Swarm Intelligence paradigm are observed to be more suitable for the applications in which low energy is required, high information delivery rate, or elevated scalability than algorithms that are based on the other paradigms described.

Analysis of Binary and Discrete Grey Wolf Optimization Algorithms Applied for Enhancing Performance of Energy Efficient Low Energy Adaptive Clustering Hierarchy

2020 ◽  
Vol 17 (9) ◽  
pp. 3850-3859
Author(s):  
G. Devika ◽  
D. Ramesh ◽  
Asha Gowda Karegowda
Keyword(s):  
Life Span ◽  
Computational Intelligence ◽  
Energy Efficient ◽  
Cluster Head ◽  
Relay Node ◽  
Low Energy ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Adaptive Clustering ◽  
Selection Of

Wireless sensor networks (WSN) are a yield of advancement in information technology and the requirement of large-scale communication infrastructures. Routing of data via selected paths is a critical task in WSN as process need to be carried on under resource constraint situations. This route identification problem can be better handled by employing appropriate heuristic bio-inspired computational intelligence optimization method. The most frequently applied routing is hierarchical routing algorithm is Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm which has limitations in identifying energy efficient inter and intra route communication, identification of number of cluster head (CH), an eminent node to communicate to CH and Base Station (BS), selection of CH, and computing residual energy level, etc. Hence, researchers are focusing on boosting the capability of LEACH clustering algorithm by applying heuristic bio-inspired computational intelligence optimization methods. The proposed work is an attempt in this direction through applying heuristic bio-inspired Grey Wolf Optimization algorithm (GWO) for improving the performance of LEACH algorithm. In this paper, focus is given to increase the overall network time by adapting two modifications to conventional algorithms (i) selection of vice cluster head (VCH) in addition to CH (VCH node will replace the CH when CH when CH node goes down due to unexpected reasons as sensor node work under critical and uninterruptable environments and (ii) selection of intra and inter relay nodes (intra relay node will enhance the life span during CH data gathering and inter relay node will further enhance the life span of CH by acting as a mediator between CH an BS). The Spyder-py3 tool is used to simulate the proposed algorithms, LEACH Binary Grey Wolf search based Optimization (LEACH-BGWO) and LEACH Discrete Grey Wolf search based Optimization (LEACH-DGWO) protocols. The proposed work is compared with cluster based LEACH algorithm, chain based power-efficient gathering in sensor information systems (PEGASIS) algorithm, bio-inspired GWO and Genetic Algorithm data Aggregation (GADA) LEACH protocols. The results prove that both proposed algorithms outperformed other conventional algorithms in terms of prolonged network lifespan and increased throughput. Among proposed algorithms LEACH-BGWO outperformed LEACH-DGWO

scholarly journals Consumption of Energy and Routing Protocols in Wireless Sensor Network

2016 ◽  
Vol 8 (3) ◽  
pp. 76 ◽  
Author(s):  
Ridha Azizi
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocols ◽  
Cluster Head ◽  
Low Energy ◽  
Wireless Sensor ◽  
Simulation Tool ◽  
New Approach ◽  
Adaptive Clustering

Extend the life of a wireless sensor network (WSN) is a fundamental challenge, as they have a limited supply. Multiple protocols and approaches have been proposed to minimize power consumption. Routing protocols and especially the hierarchical approach is one of the techniques used to minimize energy consumption and to improve the duration of network life. In this paper we propose a new approach to transfer and select the CH (Cluster Head). ART-LEACH (Advanced Routing Transfer- Low-Energy Adaptive Clustering Hierarchy) is a self-organizing protocol based on clustering. Our approach is to use energy more evenly the selected nodes as CH. We evaluated the performance of LEACH (Low-Energy Adaptive Clustering Hierarchy) and IB-LEACH (Improved and Balanced Low Energy Adaptive Clustering Hierarchy) protocol with the proposed new approach using MATLAB as a simulation tool. The simulation results showed that our proposal provides a reduction in energy consumption and increase the duration of network life.

Research on Low Power Routing Protocol for WSNs

2014 ◽  
Vol 610 ◽  
pp. 927-932
Author(s):  
Ahmed Rouaba ◽  
Nouamane Soualmi ◽  
He Zun Wen
Keyword(s):  
Energy Source ◽  
Routing Protocol ◽  
Routing Protocols ◽  
Communication Systems ◽  
Cluster Head ◽  
Life Time ◽  
Low Energy ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Save Energy

A wireless sensor network (WSN) consists of large number of autonomous sensors nodes; these nodes communicate with each other in dispersed manner to observe the environment. WSNs become one of the most important researches in modern communication systems. The energy source of nodes is limited and practically it is impossible to change or charge the battery. In order to save energy and increases the life time of battery in WSNs. Many energy routing protocols using the clustering were proposed in the literature. Low Energy Adaptive Clustering Hierarchy (LEACH) is the most famous routing protocol. In this paper we propose a new algorithm to choose the cluster head which has the highest energy. We shared the network to four regions, between them 90° for each part we find the powerful sensor between the sensors groups, and this last will be the cluster head of this round. Each sensor sends its data to the nearest cluster head and this last will send it to the sink. The same work for five and six clusters heads with sink in the center and in the corner (100, 0) is done.

scholarly journals Optimal Firefly based Balanced Energy Efficient Multi-hop (BEEM) Algorithm in Wireless Sensor Network

2020 ◽  
pp. 213-220
Author(s):  
Gaurav Kumar ◽  
Harjit Pal Singh
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Life Time ◽  
Wireless Sensor ◽  
Algorithm Performance ◽  
Selection Of

Life time of sensor network is very crucial and hot topic of research in wireless sensor network (WSN) from past to future. It is crucial due to system recharging and replacing the sensors are difficult and costly affair. Clustering provides some solution to extend the network lifetime. Existing clustering algorithms, such as LEACH and other heterogeneous routing protocol, can significantly minimize the power consumption on each sensor and prolong the network lifetime but not consideration of coverage network area. Balanced Energy Efficient Multi-hop (BEEM) algorithm has implemented to simulated WSN network and the selection of the cluster head on the basis of firefly (FF) optimization algorithm. Performance of the proposed hybrid Algorithm is well suited in terms of energy consumptions, stability period, network lifetime, throughput, Alive & Dead Nodes & other parameters. Proposed algorithm has showed improved result in energy consumption with firefly-BEEM over the existing BEEM.

scholarly journals Increasing life-time of wireless sensor network using energy-efficient and fault tolerance algorithms

2021 ◽  
Vol 23 (2) ◽  
pp. 1093
Author(s):  
Sama Hussam Sabah ◽  
Muayad Sadik Croock
Keyword(s):  
Fault Tolerance ◽  
Energy Efficient ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Life Time ◽  
Research Area ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Available Energy ◽  
Leach Protocol

Energy-efficiency ofwireless sensor networks (WSN) becomes an essential issue in the research area. This is because of the energy constraints in WSN that depend on a battery, which is difficult to replace or recharge; therefore, multiple clustering algorithms were proposed to achieve efficiency in using the available energy as much as possible. This paper proposed energy-efficient and fault-tolerance algorithms that enhance thelow energy adaptive clustering hierarchy (LEACH) protocol by three algorithms. The first focuses on selecting the best cluster head and the second focuses on minimizing the required nodes within the same cluster. Simultaneously, the third fault tolerance algorithm uses software engineering techniques like sleep schedules to increase network lifetime as much as possible. The testing results of the proposed algorithms prove the claim of enhancing the lifetime of WSN. In order to check improvement of lifetime of WSN we have compered the results of the proposed algorithms with standered algorthim. The results show prove the claim of enhancing the life-time of WSN, where the total lifetime of WSN rise from about 550 rounds to reach 4100 when utilized self-checking process and rised up to 5200 after enhance minimum distans.

A Clustering Protocol for Maximum Coverage in Large-Scale Wireless Sensor Networks

2015 ◽  
Vol 11 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Wassim Jerbi ◽  
Abderrahmen Guermazi ◽  
Hafedh Trabelsi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Large Scale ◽  
Cluster Head ◽  
Low Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Basic Algorithm ◽  
Adaptive Clustering

The optimum use of coverage in wireless sensor networks (WSNs) is very important. The hierarchical routing protocol LEACH (Low Energy Adaptive Clustering Hierarchy) is referred to as the basic algorithm of distributed clustering protocols. LEACH allows clusters formation. Each cluster has a leader called Cluster Head (CH). The selection of CHs is made with a probabilistic calculation. It is supposed that each non-CH node join a cluster and becomes a cluster member. Nevertheless, some CHs can be concentrated in a specific part of the network. Thus several sensor nodes cannot reach any CH. As a result, the remaining part of the controlled field will not be covered; some sensor nodes will be outside the network. To solve this problem, the authors propose O-LEACH (Orphan Low Energy Adaptive Clustering Hierarchy), a routing protocol that takes into account the orphan nodes. O-LEACH presents two scenarios, a gateway and sub cluster that allow the joining of orphan nodes.

New Routing Technique to Enhance Energy Efficiency and Maximize Lifetime of the Network in WSNs

2020 ◽  
Vol 9 (2) ◽  
pp. 81-93
Author(s):  
Nezha El Idrissi ◽  
Abdellah Najid ◽  
Hassan El Alami
Keyword(s):  
Energy Efficiency ◽  
Energy Conservation ◽  
Network Lifetime ◽  
Clustering Algorithms ◽  
Cluster Head ◽  
Residual Energy ◽  
Base Station ◽  
Wireless Sensor ◽  
Save Energy ◽  
Selection Of

Energy conservation plays a role important in wireless sensor network (WSN) design. The technique of clustering is one of the approaches to save energy of WSNs; several protocols based on the clustering technique are proposed in the context of energy conservation and maximization of network lifetime. This article proposed a new routing technique to maximize the network lifetime and enhance energy efficiency in WSNs, namely optimal selection of cluster head in the grid (OSCH-Gi). This technique divides the network area into a clustered grid. Each clustered grid has a cluster head (CH) it has been chosen based on the residual energy and distance to the base station of each node. Simulation results indicate that the proposed technique is more effective than other clustering algorithms in terms of the network lifetime and energy consumption.

scholarly journals An Energy-Balanced Routing Protocol for a Wireless Sensor Network

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Lin Li ◽  
Donghui Li
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocol ◽  
Cluster Head ◽  
Low Energy ◽  
Fuzzy Rules ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Adaptive Clustering ◽  
Energy Balanced Routing

The wireless sensor network is an intelligent self-organizing network which consists of many sensor nodes deployed in the monitoring area. The greatest challenge of designing a wireless sensor network is to balance the energy consumption and prolong the lifetime of the network, seeing that the nodes can be powered only by batteries in most conditions. An energy-balanced routing protocol (EBRP) for wireless sensor networks is proposed in this paper. In EBRP, we divide the network into several clusters by using K-means++ algorithm and select the cluster head by using the fuzzy logical system (FLS). Since the previous researches did not demonstrate how to get the fuzzy rules for different networks, we propose a genetic algorithm (GA) to obtain the fuzzy rules. We code the rules as a chromosome, and the lifetime of the network is treated as a fit function. Then, through the selection, crossover, and mutation of each generation, the best offspring can be decoded as the best rule for each network model. Through the simulation, comparing with the existing routing protocols such as low-energy adaptive clustering hierarchy (LEACH), low-energy adaptive clustering hierarchy-centralized (LEACH-C), and stable election protocol (SEP), the EBRP prolongs the network lifetime (first node dies) by 57%, 63%, and 63%, respectively.

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