Fuzzy Clustering Algorithm for Enhancing Reliability and Network Lifetime of Wireless Sensor Networks

Lata, Sonam; Mehfuz, Shabana; Urooj, Shabana; Alrowais, Fadwa

doi:10.1109/access.2020.2985495

Cited by 106 publications

(70 citation statements)

References 16 publications

(18 reference statements)

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“…Sonam Lata (2020) [16] has proposed a new centralized fuzzy based clustering algorithm based on three parameters like centrality, concentration, and energy level. Fuzzy logic technique elects a vice cluster head.…”

Section: Literature Surveymentioning

confidence: 99%

FOC-MOP – Fuzzy Optimal Clustering based Multi-Objective Parameter Route Selection for Energy Efficiency

SREED

Venkateswarlu

2021

Preprint

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Recently, the research area interest towards the development of wireless sensor network (WSN) has increased. However, WSNs have one of significant issues as improving an energy-efficient routing protocol. A WSN has a crucial problem of energy consumption that effects the network lifetime as sensor nodes have a limitation of power. To overcome these limitations, it’s required to improve energy-efficient communication protocols for WSNs. Different types of techniques have considered by various research communities for providing energy-efficient solutions for WSNs. The energy consumption reduces using the clustering as an efficient data collection method and the collected data forward to a cluster-head which belong to the nodes in clustered networks. The information transmits to BS (base station) either in an uncompressed or compressed manner after collecting all data by a cluster-head from all member nodes. Based on other cluster-heads, the data transmit in a multi-hop network. Due to the heavy inter-cluster relay, earlier death happens to the cluster-heads (CHs) that locates very closely to the sink. Therefore, a fuzzy optimal CH selection algorithm has proposed to select the optimal CHs to improve the lifetime. Based on different parameters like cluster load, communication cost, neighbour density, node degree, inter and intra cluster distance, and node energy, the proposed algorithm of CH selection chooses the CHs. To determine an optimal route for transmitting the data from CH to sink, the modified Emperor Penguin Optimization (EPO) uses after selecting the CH. The proposed technique implements and compares with other earlier methods in terms of packet delivery ratio, lifetime, energy consumption, end to end delay and throughput. The proposed approach shows best performance than the other methods based on the simulation results.

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Section: Literature Surveymentioning

confidence: 99%

FOC-MOP – Fuzzy Optimal Clustering based Multi-Objective Parameter Route Selection for Energy Efficiency

SREED

Venkateswarlu

2021

Preprint

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“…Beyond that, some works adopted sensor clustering method to extend the network lifetime [10], [14][15][16][17][18][19]. Clustering of nodes is the most common technology for energy aware routing in WSN, and the most popular clustering protocol in WSN is low energy adaptive clustering hierarchy (LEACH) based on adaptive clustering technology, which is widely used in various fields [14].…”

Section: Related Workmentioning

confidence: 99%

Prolonging Network Lifetime by Optimizing Actuators Deployment with Probabilistic Mutation Multi-layer Particle Swarm Optimization

2021

KSII TIIS

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In wireless sensor and actuator networks (WSANs), the network lifetime is an important criterion to measure the performance of the WSAN system. Generally, the network lifetime is mainly affected by the energy of sensors. However, the energy of sensors is limited, and the batteries of sensors cannot be replaced and charged. So, it is crucial to make energy consumption efficient. WSAN introduces multiple actuators that can be regarded as multiple collectors to gather data from their respective surrounding sensors. But how to deploy actuators to reduce the energy consumption of sensors and increase the manageability of the network is an important challenge. This research optimizes actuators deployment by a proposed probabilistic mutation multi-layer particle swarm optimization algorithm to maximize the coverage of actuators to sensors and reduce the energy consumption of sensors. Simulation results show that this method is effective for improving the coverage rate and reducing the energy consumption.

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“…Clustering sensor nodes based on computational intelligence is another active research trend [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. Compared with the non-computational intelligence methods, technologies of computational intelligence has the characteristics of self-learning, self-organization, and self-adaptive, which enables them to provide more effective solutions for sensor node clustering.…”

Section: Related Workmentioning

confidence: 99%

“…The EECPK-means method can balance the number of sensor nodes between clusters. To maximize the lifetime of the network, Lata et al [ 35 ] proposed a LEACH-Fuzzy clustering method which realized CH selection and cluster formation based on fuzzy logic. To effectively cluster the sensor nodes and select the optimal CH for each cluster, Fei et al [ 36 ] proposed a hybrid clustering method based on fuzzy c means (FCM) and moth-flame optimization method (MFO) to improve the network quality (FCMMFO).…”

Section: Related Workmentioning

confidence: 99%

Energy-Balanced Cluster-Routing Protocol Based on Particle Swarm Optimization with Five Mutation Operators for Wireless Sensor Networks

Han

Byun

Zhang

2020

Sensors

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Prolonging the network lifetime is one of the fundamental requirements in wireless sensor networks (WSNs). Sensor node clustering is a very popular energy conservation strategy in WSNs, allowing to achieve energy efficiency, low latency, and scalability. According to this strategy, sensor nodes are grouped into several clusters, and one sensor node in each cluster is assigned to be a cluster head (CH). The responsibility of each CH is to aggregate data from the other sensor nodes within its cluster and send these data to the sink. However, the distribution of sensor nodes in the sensing region is often non-uniform, which may lead to an unbalanced number of sensor nodes between clusters and thus unbalanced energy consumption between CHs. This, in turn, may result in a reduced network lifetime. Furthermore, a different number of clusters lead to a different quality of service of a WSN system. To address the problems of unbalanced number of sensor nodes between clusters and selecting an optimal number of clusters, this study proposes an energy-balanced cluster-routing protocol (EBCRP) based on particle swarm optimization (PSO) with five mutation operators for WSNs. The five mutation operators are specially proposed to improve the performance of PSO in optimizing sensor node clustering. A rotation CH selection scheme based on the highest residual energy is used to dynamically select a CH for each cluster in each round. Simulation results show that the proposed EBCRP method performs well in balancing energy consumption and prolonging the network lifetime.

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Fuzzy Clustering Algorithm for Enhancing Reliability and Network Lifetime of Wireless Sensor Networks

Cited by 106 publications

References 16 publications

FOC-MOP – Fuzzy Optimal Clustering based Multi-Objective Parameter Route Selection for Energy Efficiency

FOC-MOP – Fuzzy Optimal Clustering based Multi-Objective Parameter Route Selection for Energy Efficiency

Prolonging Network Lifetime by Optimizing Actuators Deployment with Probabilistic Mutation Multi-layer Particle Swarm Optimization

Energy-Balanced Cluster-Routing Protocol Based on Particle Swarm Optimization with Five Mutation Operators for Wireless Sensor Networks

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