An energy-efficient fuzzy-based scheme for unequal multihop clustering in wireless sensor networks

Phoemphon, Songyut; So-In, Chakchai; Aimtongkham, Phet; Nguyen, Tri Gia

doi:10.1007/s12652-020-02090-z

Cited by 27 publications

(20 citation statements)

References 32 publications

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“…Energy consumption for large-scale sensor fields is efficiently controlled using a fuzzy-based clustering [24]. Authors have used fuzzy logic in 4 stages: communication radius estimation, CHs selection, cluster formation, and relay node selection.…”

Section: Related Workmentioning

confidence: 99%

HFLBSC: Heuristic and Fuzzy Based Load Balanced, Scalable Clustering Algorithm for Wireless Sensor Network

Maratha

Gupta

2022

Wireless Pers Commun

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Section: Related Workmentioning

confidence: 99%

HFLBSC: Heuristic and Fuzzy Based Load Balanced, Scalable Clustering Algorithm for Wireless Sensor Network

Maratha

Gupta

2022

Wireless Pers Commun

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“…To enhance the performance of the MCFL method, an energy-efficient fuzzy logic for unequal clustering (EEFUC) protocol is proposed to enhance the MCFL protocol. This protocol was proposed by [ 19 ] to reduce the energy consumption in the network by multi-hop clustering using the fuzzy logic method.…”

Section: Related Workmentioning

confidence: 99%

“…However, the clustering protocols have some problems, such as transmission data to the sink, sometimes increasing energy consumption in WSNs [ 18 ] because the farthest distance CH will consume more energy to send data to BS. Multi-hop communication is usually adopted to save energy [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Wireless Sensor Network with an Unequal Clustering Protocol Based on a Balanced Energy Method (EEUCB)

Jasim

Idris

Azzuhri

et al. 2021

Sensors

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A hot spot problem is a problem where cluster nodes near to the base station (BS) tend to drain their energy much faster than other nodes due to the need to perform more communication. Unequal clustering methods such as unequal clustering routing (UDCH) and energy-efficient fuzzy logic for unequal clustering (EEFUC) have been proposed to address this problem. However, these methods only concentrate on utilizing residual energy and the distance of sensor nodes to the base station, while limited attention is given to enhancing the data transmission process. Therefore, this paper proposes an energy-efficient unequal clustering scheme based on a balanced energy method (EEUCB) that utilizes minimum and maximum distance to reduce energy wastage. Apart from that, the proposed EEUCB also utilizes the maximum capacity of node energy and double cluster head technique with a sleep-awake mechanism. Furthermore, EEUCB has devised a clustering rotation strategy based on two sub-phases, namely intra- and inter-clustering techniques, that considers the average energy threshold, average distance threshold, and BS layering node. The performance of the proposed EEUCB protocol is then compared with various prior techniques. From the result, it can be observed that the proposed EEUCB protocol shows lifetime improvements of 57.75%, 19.63%, 14.7%, and 13.06% against low-energy adaptive clustering hierarchy (LEACH), factor-based LEACH FLEACH, EEFUC, and UDCH, respectively.

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“…The authors in [25], proposed a bacterial foraging optimization (BFO)-fuzzy method for multipath routing. The work in [21] adopted fuzzy logic system for unequal multihop clustering to extend lifetime of the network. The work in [15], uses fuzzy approach for unequal clustering.…”

Section: Related Workmentioning

confidence: 99%

FLAG: Fuzzy Logic Augmented Game Theoretic Hybrid Hierarchical Clustering Algorithm for Wireless Sensor Networks

Naik

2021

Preprint

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Stability of the Wireless Sensor Network(WSN) is the most critical factor in real-time and data-sensitive applications like military and surveillance systems. Many energy optimization techniques and algorithms have been proposed to extend the stability of a wireless sensor network. Clustering is a well regarded method in the research communities among them. Hence, this paper presents hybrid hierarchical artificial intelligence based clustering techniques, named FLAG and I-FLAG. The first phase of these algorithms use game theoretic technique to elect suitable cluster heads(CHs) and later phase of the algorithms use fuzzy inference system to select appropriate super cluster heads(SCHs) among CHs. The I-FLAG is an improved version of FLAG where additional parameters like energy and distance are considered to elect CHs. Simulations are performed to check superiority of the proposed algorithms over the existing protocols like LEACH, CHEF, and CROSS. Simulation results show that the average stability period of WSN is better in FLAG and I-FLAG compared to other protocols, and so is the throughput of WSN during the stability period.

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An energy-efficient fuzzy-based scheme for unequal multihop clustering in wireless sensor networks

Cited by 27 publications

References 32 publications

HFLBSC: Heuristic and Fuzzy Based Load Balanced, Scalable Clustering Algorithm for Wireless Sensor Network

HFLBSC: Heuristic and Fuzzy Based Load Balanced, Scalable Clustering Algorithm for Wireless Sensor Network

Energy-Efficient Wireless Sensor Network with an Unequal Clustering Protocol Based on a Balanced Energy Method (EEUCB)

FLAG: Fuzzy Logic Augmented Game Theoretic Hybrid Hierarchical Clustering Algorithm for Wireless Sensor Networks

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