An Unequal Clustering Algorithm for Wireless Sensor Networks Based on Interval Type-2 TSK Fuzzy Logic Theory

Tao, Yang; Zhang, Jinming; Yang, Liu

doi:10.1109/access.2020.3034607

Cited by 18 publications

(18 citation statements)

References 33 publications

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“…The existing algorithms [11][12][13][14][15][16][17][18][19] perform the clustering process based on the distance between the nodes and the following drawbacks have been identified from the state of art clustering methodologies.…”

Section: Drawbacks Of the Existing Methodologiesmentioning

confidence: 99%

“…The proposed algorithm enhanced the lifetime (LT) of the network by 25% when compared to the state of art algorithms. Tao et al had proposed an unequal clustering algorithm that relies on type-2 TSK fuzzy logic algorithm (UCT2TSK) [17]. The outputs of the proposed algorithm are acquired by optimizing the cluster head and by determining the cluster dimensions.…”

Section: Literature Reviewmentioning

confidence: 99%

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Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Manikandan¹,

Sakthivel²,

Vivekanandhan³

2023

Intelligent Automation &Amp; Soft Computing

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Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network. This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms. The efficiency of the sensor node is energy bounded, acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors. Network management plays a significant role in wireless sensor networks, which was obsessed with the factors like the reliability of the network, resource management, energy-efficient routing, and scalability of services. The topology of the wireless sensor networks acts driven factor for network efficiency which can be effectively maintained by performing the clustering process effectively. More solutions and clustering algorithms have been offered by various researchers, but the concern of reduced efficiency in the routing process and network management still exists. This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks. The memetic algorithm employs a local searching methodology to mitigate the premature convergence, while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain (i.e.,) best cluster head in the wireless sensor networks. The proposed hybrid algorithm is compared with the state of art clustering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time, energy consumption, throughput, etc.

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Section: Drawbacks Of the Existing Methodologiesmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Manikandan¹,

Sakthivel²,

Vivekanandhan³

2023

Intelligent Automation &Amp; Soft Computing

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“…Modern optimization approaches are based on biological, molecular, insect swarm, and neurobiological systems features and behavior [1]. With the advancement of information technology, a large number of optimization problems develop in a variety of disciplines like Internet routing, economics, and engineering design, which can be solved using a variety of ways such as, Fuzzy Logic [2], [3]. Sine Cosine Algorithm(SCA) [4].…”

Section: Introductionmentioning

confidence: 99%

Solving Suggested Problems using Grey Wolf Optimization

Issa¹,

Alaa²,

Abed³

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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In this research, the Grey Wolf Optimizer (GWO) algorithm was applied, which is a type of metaheuristic optimization algorithms that is important for identifying problems and finding ways to improve them. In 2014, Seyedali Mirjalili came up with this algorithm. Its social hierarchy is modeled after that of grey wolves in nature. These wolves live in groups consisting of( 5 -12) individuals. Wolves are divided into four levels, alpha represents the first level it is accountable for the manufacture of important resolutions for the peak like hunting, bedtime, wake up, etc. As for the second level in the hierarchy, it represents the beta wolf and is the advisor of the alpha. Beta can be the leader after the death of one of the alpha wolves. The third level represents delta, which follows the commands of alpha and beta. it is the dominant omega. omega represents the last level, which obeys all other wolves. Furthermore, the chief algorithm stages such as chasing, searching the prey, encircle and attack the prey were applied. GWO algorithm was tested on three benchmarks test functions using MATLAB R2014a. the results were confirmed by comparing GWO through another intelligent swarm algorithm like Particle Swarm Optimization (PSO) algorithm. results showed the superiority GWO in achieving better results and high convergence speed.

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“…Hence, the issue of energy hole is likely to happen. To solve problem of hot spot, unequal clustering algorithms are typically introduced in which the network is separated into clusters with unequal sizes [7].Single hop consumes more energy for transmission of data to far distance so multi hop communication is used. In multi hop communication through intermediate relay nodes source node transfers data to base station [8].Fuzzy Logic is based on human reasoning and heuristic knowledge, is one of the best problemsolving allowing a time-consuming latency and extends lifespan of the network.In this cluster formation each non-CH nodes calculates a chance value for being linked to each CH by applying FL, using residual energy and closeness to CH as fuzzy inputs [9].Fuzzy Logic is one such computational intelligence used for selecting CH nodes in the WSN.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Based Zone Head Selection Algorithm for Wireless Sensor Networks

Garg¹,

Kaur²

2021

JUSST

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Hot-spots are a problem that comes in the cluster-based routing protocol that employs multi-hop communication due to this problem the energy among the sensor nodes is not balanced. The hot-spots issue requires high overhead and is prone to connectivity issues in the sensor network this can be only possible because of unequal clustering. In this method, we have to act on all the nodes of the sensor network for communication. This process consumes high system energy if the numbers of nodes are very high. To offer guaranteed connectivity, decrease high usage and complexity, a fuzzy logic-based zone divisional method has been proposed in this paper. Use fuzzy logic to create clusters and assign nodes to them to decrease the consumption of energy and the age of the network prolongation. The simulation and results section shows the outperformance of the proposed protocol, where the (LEACH) low-energy adaptive clustering hierarchy, (EAUCF) energy-aware unequal clustering fuzzy,(EAMMH) energy-aware multi-hop multi-path hierarchical, and (TTDFP) two-tier distributed fuzzy logic-based protocol for efficient data aggregation in multi-hop wireless sensor networks algorithms. The proposed algorithm has better results in terms of energy consumption minimization, and prolongation of the network lifetime.

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An Unequal Clustering Algorithm for Wireless Sensor Networks Based on Interval Type-2 TSK Fuzzy Logic Theory

Cited by 18 publications

References 33 publications

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

Solving Suggested Problems using Grey Wolf Optimization

Fuzzy Logic Based Zone Head Selection Algorithm for Wireless Sensor Networks

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