NEEF: A Novel Energy Efficient Fuzzy Logic based Clustering Protocol for Wireless Sensor Network

Dwivedi, Anshu Kumar; Sharma, Anuj

doi:10.12694/scpe.v21i3.1749

Cited by 14 publications

(9 citation statements)

References 1 publication

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“…Dwivedi and Sharma 96 proposed a novel energy‐efficient clustering protocol based on fuzzy logic called NEEF. Two fuzzy fitness values, FF1 and FF2, are used in CHs selection and cluster formation, respectively.…”

Section: Solutionsmentioning

confidence: 99%

Machine learning‐based clustering protocols for Internet of Things networks: An overview

Merah

Aliouat

Harbi

et al. 2023

Int J Communication

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Summary The Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML‐based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology‐centered ML‐based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML‐based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.

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Section: Solutionsmentioning

confidence: 99%

Machine learning‐based clustering protocols for Internet of Things networks: An overview

Merah

Aliouat

Harbi

et al. 2023

Int J Communication

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“…LEASE 26 protocol is proposed as an enhancement to LEACH protocol which significantly increases the lifetime of WSN. CHEF 27,28 protocol works as LEACH protocol, as it also forms the cluster in every round. Using the energy and local farness as the variables, it uses the fuzzy output to select the CH.…”

Section: Relevant Workmentioning

confidence: 99%

EETSP: Energy‐efficient two‐stage routing protocol for wireless sensor network‐assisted Internet of Things

Dwivedi¹,

Mehra

Pal

et al. 2021

Int J Communication

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Summary Wireless sensor network (WSN) is a key technology that can support Industrial Revolution 4.0. The sensor nodes (SNs) are the important component of WSN. Recharge or repair of batteries of SNs is impossible, and thus, energy is the most valuable resource of WSN. Over the years, many methods have been proposed to conserve the precious energy resource of WSN. The most important concern in WSN is how much of the energy of the SNs can be saved, which can contribute to the better network life of WSN. WSN‐assisted IoT output is reduced when a wide region of the network is deployed. Energy‐efficient routing is one of the ways to reduce power consumption in WSNs. Therefore, a novel energy‐efficient two‐stage routing protocol (EETSP) has been proposed to reduce the energy used in WSN and extend the stability period as well as the network lifetime of WSN. Through this protocol, the number of packets communicated to the base station (BS) is significantly improved, and the cluster heads (CHs) and secondary cluster heads (SCHs) reduce the energy utilization in the network. SCH works as a backup CH. EETSP consists of two stages. The selection of CH and SCH is made in the primary stage, based on the input parameters. Intercluster routing and intracluster routing are performed in the second stage. The improvements of EETSP are examined against LEACH and Z‐SEP, and obtained results indicate that the EETSP is better in terms of stability period, throughput and network lifetime.

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“…Sensor nodes are expected to last until their energy drains and they are automatically disposable 5 . Consequently, it is vigorous for managing the energy incisively to spread the existence of the sensors for the extent of particular mission 6 . Failures in sensor networks continue to increase due to energy depletion 7 .…”

Section: Introductionmentioning

confidence: 99%

“…5 Consequently, it is vigorous for managing the energy incisively to spread the existence of the sensors for the extent of particular mission. 6 Failures in sensor networks continue to increase due to energy depletion. 7 As a result, a certain parts of the network become energy embarrassed and stop functioning after sometime.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Elman spike neural network for cluster head based energy aware routing in WSN

Meenakshi¹,

Karunkuzhali²

2022

Trans Emerging Tel Tech

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An enhanced Elman spike neural network (EESNN) optimized with hybrid wild horse optimization and chameleon swarm algorithm is proposed in this manuscript for multi‐objective cluster head selection and energy aware routing in wireless sensor network (CH‐EESNN‐Hyb‐WH‐CSOA‐WSN). Initially, EESNN is used to intelligent selection of cluster head. Then, the optimal cluster head utilized the selected data transferring process with the consideration of multi‐objective fitness function based EESNN. Here, some of the multi‐objective fitness function factors are considered, like energy, delay, throughput, distance between the nodes, traffic rate and cluster density. The hybrid wild horse optimization and chameleon swarm algorithm (Hyb‐WH‐CSOA) is taken into account for the optimal route path selection with minimal delay. The proposed CH‐EESNN‐Hyb‐WH‐CSOA‐WSN method is activated in network simulator 3 (NS‐3) tool. The performance of the proposed method is examined under certain performance metrics, like count of alive nodes, drop, network lifetime, delay, throughput, energy consumption, and packet delivery ratio. Finally, the proposed method attains 98.78%, 97.21%, 99.61% lower delay, 98.78%, 99.21%, 96.78% higher delivery ratio, and 99.57%, 98.67%, 98.88% lower packet drop compared with the existing methods, like optimal secure cluster head placement through source coding techniques in wireless sensor networks (CHP‐HMC‐WSN), optimal placement of single cluster head in wireless sensor networks via clustering (CHP‐K‐Means C‐PSO‐WSN) and hybrid firefly approach along particle swarm optimization for energy efficient optimum cluster head selection in wireless sensor networks (CHP‐HFAPSO‐WSN) respectively.

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NEEF: A Novel Energy Efficient Fuzzy Logic based Clustering Protocol for Wireless Sensor Network

Cited by 14 publications

References 1 publication

Machine learning‐based clustering protocols for Internet of Things networks: An overview

Machine learning‐based clustering protocols for Internet of Things networks: An overview

EETSP: Energy‐efficient two‐stage routing protocol for wireless sensor network‐assisted Internet of Things

Enhanced Elman spike neural network for cluster head based energy aware routing in WSN

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