Load‐Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Srivastava, Ankita; Mishra, Pramod Kumar

doi:10.1155/2023/4343404

Cited by 9 publications

(1 citation statement)

References 65 publications

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“…We have considered Multi-Attribute Decision-Making (MADM) methods for CH's selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs [50,51].…”

Section: Related Worksupporting

confidence: 61%

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Ahmad,

Hussain,

Shah

et al. 2024

CMC

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Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost. One of them is a sensor network with embedded sensors working as the primary nodes, termed Wireless Sensor Networks (WSNs), in which numerous sensors are connected to at least one Base Station (BS). These sensors gather information from the environment and transmit it to a BS or gathering location. WSNs have several challenges, including throughput, energy usage, and network lifetime concerns. Different strategies have been applied to get over these restrictions. Clustering may, therefore, be thought of as the best way to solve such issues. Consequently, it is crucial to analyze effective Cluster Head (CH) selection to maximize efficiency throughput, extend the network lifetime, and minimize energy consumption. This paper proposed an Accelerated Particle Swarm Optimization (APSO) algorithm based on the Low Energy Adaptive Clustering Hierarchy (LEACH), Neighboring Based Energy Efficient Routing (NBEER), Cooperative Energy Efficient Routing (CEER), and Cooperative Relay Neighboring Based Energy Efficient Routing (CR-NBEER) techniques. With the help of APSO in the implementation of the WSN, the main methodology of this article has taken place. The simulation findings in this study demonstrated that the suggested approach uses less energy, with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH, 1.003 to 0.0521 for 10 CH, and 0.1734 to 0.0911 for 15 CH. The sending packets ratio was also raised for all three CH selection scenarios, increasing from 659 to 1730. The number of dead nodes likewise dropped for the given combination, falling between 71 and 66. The network lifetime was deemed to have risen based on the results found. A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol. Similar to underwater, WSN can make use of the proposed protocol. The overall results have been evaluated and compared with the existing approaches of sensor networks.

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Section: Related Worksupporting

confidence: 61%

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Ahmad,

Hussain,

Shah

et al. 2024

CMC

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Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

Srivastava,

Mishra

2023

Concurrency and Computation

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SummarySensors plays an important role in day‐to‐day life as they sense and transfer data on the cloud and servers. These sensors have limited battery power due to which their optimized use is preferred with efficient energy consumption. As sensors were deployed almost everywhere under‐earth, underwater, electronic devices, and mobiles thus their enhanced performance is really important. In WSN, clustering is considered to be an essential approach that gives various advantages such as efficient energy, stability period, network lifetime, less delay, and scalability but has an issue of hot‐spot or energy‐hole problems. For this Un‐Equal Clustering is proposed where the size of clusters varies directly to the distance of the B.S. (Base Station). The main objective of Un‐Equal clustering is energy consumption, hot‐spot problem, and load balance among the cluster heads. In this paper, we have proposed an improved fuzzy‐based multi‐attributes un‐equal clustering to overcome the problem of hot‐spot problem. Earlier, researchers considered two attributes for selecting cluster heads (CHs), but later some of the MADM approaches were used that considered multiple attributes for optimal cluster head selection. In this paper, we have applied the fuzzy‐based TOPSIS method for optimized Un‐Equal clustering where the optimal node deployments with load balance among sensor nodes having optimal coverage and connectivity is considered. The results of the proposed method validate that it is one of the effective ways for selecting optimal cluster heads using fuzzy‐based multi‐attributes. Fuzzy‐based TOPSIS for cluster heads selection method shows 37% enhanced network performance with other compared existing algorithms. The performance of the proposed method is evaluated in terms of network stability, packet delivery, energy consumption, and network lifetime and F‐MAUC (Fuzzy based multi‐attributes un‐equal clustering) outperforms all other compared algorithms.

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Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization

Sennan,

Ramasubbareddy,

Dhanaraj

et al. 2024

Telecommun Syst

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Load‐Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Cited by 9 publications

References 65 publications

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization

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