Energy Efficient Data Gathering in Wireless Sensor Networks Using Rough Fuzzy C-Means and ACO

Mondal, Sanjoy; Ghosh, Swaroop; Dutta, Pratik

doi:10.1007/978-981-10-3953-9_16

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…For resolving unbalanced clustering, load, frequent CH switching, and communication failure, a novel DLB-fACO clustering protocol is investigated in this research. The proposed protocol provided degree-load analysis-based clustering T A B L E 2 Fuzzy rules for optimized route selection analysis are provided against LEACH, 13 M-LEACH, 47 LEACH-C, 13 LEACH-CC, 48 Fuzzy-PSO, 49 Fuzzy-ACO, 50 Fuzzy-Cuckoo, 51 and FMCB-ER 52 protocols. The analysis results are provided in terms of dead nodes, alive nodes, roundbased packet communication, residual energy of nodes, and the average energy of nodes.…”

Section: Resultsmentioning

confidence: 99%

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

Juneja

2021

Int J Communication

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A sensor network is a situation-based critical network defined under certain restrictions and constraints. The clustered architecture is defined over these networks to utilize the criticality vectors and to improve network communication. In this paper, a degree load-balanced and fuzzy-ACO (DLB-fACO) protocol is proposed for optimizing the clustered architecture. In this architecture, the load analysis is performed while forming the clusters. The cluster head selection is performed based on energy, cluster density, and probability vector. The node degree-and energy-balanced analysis is performed for identifying the cluster members. Once the clusters are formed, the ACO approach is applied to perform the cluster-based hierarchical routing. Communication is performed at two levels. In the first level, node-to-cluster head communication is performed by considering energy and degree consideration. In the second level, the fuzzy-integrated ACO method is applied for inter-cluster route formation. The route optimization is here performed under fuzzy-based energy, degree, and distance parameters. These fuzzy parameters are evaluated within the ACO algorithm for generating the optimized route. The proposed loadbalanced protocol is analyzed against the LEACH, LEACH-C, LEACH-CC, M-LEACH, Fuzzy-PSO, Fuzzy-ACO, Fuzzy-Cuckoo, and FMCB-ER protocols.The experimentation results confirm the significant gain in network lifetime and packet communication. The cluster count, clustering switching, and communication failure are also reduced in comparison with existing conventional and optimized protocols.

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Section: Resultsmentioning

confidence: 99%

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

Juneja

2021

Int J Communication

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“…Finally, an improved FCM clustering method is proposed to group nodes into the defined number of clusters. Mondal et al 118 proposed an energy‐efficient load‐balanced data gathering method called RFCM‐ACO based on rough FCM (RFCM) and ant colony optimization (ACO). The network is divided into clusters by RFCM followed by ACO‐based lower and upper chain formation.…”

Section: Solutionsmentioning

confidence: 99%

“…‐ Most of the FCM‐based solutions consider energy efficiency 101‐103,105‐108,112‐114,118,123,124 110,111,115 . In terms of distribution, previous works 102,103,106 are effective.…”

Section: Solutionsmentioning

confidence: 99%

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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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“…WSN is a vital part of IoT, and the investigation of efficient data gathering techniques is essential in order to further minimize energy consumption [30]. This technique to eliminate unnecessary information and to find only the appropriate data for transmission is called data aggregation.…”

Section: Data Aggregationmentioning

confidence: 99%

Leader Election in the Internet of Things: Challenges and Opportunities

Rahman

2019

Preprint

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The different functions of a leader node in Ad hoc networks, particularly in Wireless Sensor Networks (WSNs) and Internet of Things (IoT) include generation of keys for encryption/decryption, finding a node with minimum energy or node located in an extreme side of the network. One essential application of IoT is to monitor any dangerous, sensitive or non-accessible site. Such type of application requires the election of a leader located on the extreme left of the network. Indeed, one can then use any leader election (LE) algorithm to find this node, and then start the process of finding its boundary nodes. However, the chosen algorithm must be fault-tolerant and robust since the detection of a node failure under such circumstances becomes impossible. This paper surveys the most significant work performed in this direction, highlights the challenges and proposes possible extensions.

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Energy Efficient Data Gathering in Wireless Sensor Networks Using Rough Fuzzy C-Means and ACO

Cited by 13 publications

References 16 publications

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

Design of a novel degree load‐balanced and fuzzy ant colony optimization protocol for optimizing the clustering architecture in WSN

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

Leader Election in the Internet of Things: Challenges and Opportunities

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