Clustered chain founded on ant colony optimization energy efficient routing scheme for under-water wireless sensor networks

Moussaoui, Djilali; Hadjila, Mourad; Irid, Sidi Mohammed Hadj; Souiki, Sihem

doi:10.11591/ijece.v11i6.pp5197-5205

Cited by 4 publications

(8 citation statements)

References 25 publications

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“…-Most of the FCM-based solutions consider energy efficiency. [101][102][103][105][106][107][108][112][113][114]118,123,124110,111,115 In terms of distribution, previous works 102,103,106 are effective. Previous studies 111,112,118,121 provide load-balancing.…”

Section: Node Clusteringmentioning

confidence: 99%

“…The FCM fitness function was used to select GHs and CHs. Moussaoui et al 115 offered an unequal clustered chain‐based energy‐efficient routing algorithm that combines FCM and ACO to control data transmission in the IoS. The sink divides the sensing area into a number of clusters of different sizes.…”

Section: Solutionsmentioning

confidence: 99%

“…Works in the literature 105,112 are effective for delay optimization. The work described in Moussaoui et al 115 uses unequal clustering in order to alleviate the load of CHs. In terms of security, previous works 109,120,122 provide secure transmission, while the protocol in Salman and Alisa 119 handles CHs failure by considering backup CHs, and the work in Wan et al 104 guarantees outlier diagnosis.…”

Section: Node Clusteringmentioning

confidence: 99%

“…In terms of security, previous works 109,120,122 provide secure transmission, while the protocol in Salman and Alisa 119 handles CHs failure by considering backup CHs, and the work in Wan et al 104 guarantees outlier diagnosis. Works in the literature 105,115,116,119 handle the dynamic aspect of IoT networks. Previous works 103,[106][107][108]114,120,123 perform data aggregation.…”

Section: Node Clusteringmentioning

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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Section: Node Clusteringmentioning

confidence: 99%

Section: Solutionsmentioning

confidence: 99%

Section: Node Clusteringmentioning

confidence: 99%

Section: Node Clusteringmentioning

confidence: 99%

See 2 more Smart Citations

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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“…This technique provides robust clustering while addressing the issue of backward transmission. By including energy-collecting techniques, this protocol can be made much better [15]. According to the node density, Khan et al established a protocol in [16] that can adapt to three different types of networks.…”

Section: Literature Reviewmentioning

confidence: 99%

IEDA-HGEO: Improved Energy Efficient with Clustering-Based Data Aggregation and Transmission Protocol for Underwater Wireless Sensor Networks

et al. 2022

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With the emerging technology in underwater wireless sensor networks (UWSN), many researchers are undergoing this field since it cannot maintain the batteries and recharge them manually. Network duration should be taken into account because they can easily be recharged by a non-conventional resource like solar energy. When coming to the data collection process, clustering is an effective method to construct vitality effective UWSNs. The clustering properties of UWSNs differ from those of terrestrial wireless sensor networks (TWSNs) due to the sparse deployment of nodes as well as the dynamic nature of the channel. This paper proposes improved efficient data aggregation in a Hexagonal grid with energy optimization (IEDA-HGEO) protocol for effective data transmission with an optimal clustering process. It is further compared with ERP2R n energy-efficient routing protocol and EGRC (Energy-efficiency Grid Routing based on 3D Cubes). The three techniques mentioned above are specifically examined for their applicability to underwater communication, and their performance is compared in terms of energy consumption, efficiency, throughput, packet delivery ratio, and delay. The proposed method achieved the following metrics: delay 41%, energy consumption 48%, efficiency 95%, throughput 95%, and PDR 92%.

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Feature selection for sky image classification based on self adaptive ant colony system algorithm

Petwan,

Ku-Mahamud

2023

IJECE

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Statistical-based feature extraction has been typically used to purpose obtaining the important features from the sky image for cloud classification. These features come up with many kinds of noise, redundant and irrelevant features which can influence the classification accuracy and be time consuming. Thus, this paper proposed a new feature selection algorithm to distinguish significant features from the extracted features using an ant colony system (ACS). The informative features are extracted from the sky images using a Gaussian smoothness standard deviation, and then represented in a directed graph. In feature selection phase, the self-adaptive ACS (SAACS) algorithm has been improved by enhancing the exploration mechanism to select only the significant features. Support vector machine, kernel support vector machine, multilayer perceptron, random forest, k-nearest neighbor, and decision tree were used to evaluate the algorithms. Four datasets are used to test the proposed model: Kiel, Singapore whole-sky imaging categories, MGC Diagnostics Corporation, and greatest common divisor. The SAACS algorithm is compared with six bio-inspired benchmark feature selection algorithms. The SAACS algorithm achieved classification accuracy of 95.64% that is superior to all the benchmark feature selection algorithms. Additionally, the Friedman test and Mann-Whitney <em>U</em> test are employed to statistically evaluate the efficiency of the proposed algorithms.

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Clustered chain founded on ant colony optimization energy efficient routing scheme for under-water wireless sensor networks

Cited by 4 publications

References 25 publications

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

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

IEDA-HGEO: Improved Energy Efficient with Clustering-Based Data Aggregation and Transmission Protocol for Underwater Wireless Sensor Networks

Feature selection for sky image classification based on self adaptive ant colony system algorithm

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