Enhanced Fuzzy Logic Zone Stable Election Protocol for Cluster Head Election (E-FLZSEPFCH) and Multipath Routing in wireless sensor networks

Ali, Asad; Ali, Amir; Masud, Farhan; Bashir, Muhammad Khawar; Zahid, Amjad Hussain; Mustafa, Ghulam; Ali, Zakir

doi:10.1016/j.asej.2023.102356

Cited by 18 publications

(9 citation statements)

References 81 publications

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“…Machine learning is increasingly being utilized to divide the network into clusters, from which CHs are selected depending on predefined criteria. This can be accomplished by employing algorithms like k-means [8,9] and fuzzy c-means [10], which are increasingly being employed in WSNs, IoT, and crowd-sensing applications. To deal with uncertainty in WSNs [11], fuzzy logicbased clustering algorithms were applied.…”

Section: IImentioning

confidence: 99%

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Performance of ML-Based Unsupervised Clustering Algorithms for WSN Node Clustering

Manoranjani

2024

IJRASET

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Wireless Sensor Networks are generally deployed in dynamically changing environment. When compared to common wired network nodes, WSN nodes must do more work. Since WSN devices are battery powered, so power management is a challenge. Clustering is one solution that has been proposed to alleviate the issue of limited power. Clustering is the most important method stabilizes the lifetime of the network. It entails the aggregation of sensor nodes into clusters and cluster head is picked out from all the clusters. Clustering is implemented in wireless sensor networks through the Machine Learning techniques. In wireless sensor networks, machine learning algorithms have an important role in cluster head formation and maintain the stability of the nodes in the cluster. Machine Learning approaches used in wireless sensor networks can be classified as Supervised learning, Unsupervised learning, and Reinforcement learning. Among these learning techniques, unsupervised learning deals with different clustering algorithms such as k-means, K-medoids, Fuzzy C-means, hierarichalbased, and SOM. This paper evaluates the performance of the variants of k-Means (kM) and Fuzzy C-means (FCM) algorithms in terms of the clustering and accuracy. This paper imparts performance analysis of different clustering algorithms in machine learning applied for wireless sensor networks. From the analysis, the Fuzzy C-Means algorithm found to be more suitable for node clustering in WSN

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Section: IImentioning

confidence: 99%

“…To deal with uncertainty in WSNs [11], fuzzy logicbased clustering algorithms were applied. The authors [10] proposed fuzzy logic-based data processing and grouping for WSNs. This technique considers the energy level, bandwidth, and connection efficiency of each node.…”

Section: IImentioning

confidence: 99%

Performance of ML-Based Unsupervised Clustering Algorithms for WSN Node Clustering

Manoranjani

2024

IJRASET

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“…Yinan et al [25] reiterated the importance of energy-efficient routing in IoT and introduced a hybrid DAI-SOM method to optimize energy consumption and intra-cluster communication. Finally, Ali et al [26] underscored the significance of energy-efficient routing in WSNs supporting IoT applications and introduced a multipath routing approach that outperformed existing methods in terms of efficiency and resilience. The state-of-the-art work highlights the importance of efficient routing, load balancing, congestion control, trust management, and service recommendation in next-gen wireless networks, IoT, and SIoT, revealing the need for further research and development in these areas to overcome challenges and enhance the performance of these networks.…”

Section: Introductionmentioning

confidence: 99%

Enhancing routing efficiency in social internet of things: R-OPTICS and vEBT based congestion free model

Vinod,

Prakash

et al. 2024

IJECE

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The emergence of the social internet of things (SIoT) network has brought forth distinctive challenges, including node mobility and varying densities, leading to congestion and hampered network efficiency. To overcome these issues, a congestion-free routing model for SIoT is proposed. This model combines the relationship-ordering points to identify the clustering structure (R-OPTICS) algorithm for intelligent node clustering based on relationships and ordering,along with a van emde boas tree (vEBT) for efficient path selection. R-OPTICS enables effective network management by clustering nodes appropriately. The model’s performance is evaluated using metrics such as Rand-Index (1.5765),Davies-Bouldin (-0.4305), and Silhouette Coefficient (1.71685) to assess average goodness values. vEBT identifies optimal paths between clusters, facilitating smart routing decisions. The primary objective of the model is to enhance network efficiency and alleviate congestion by intelligently routing data between clusters. Through extensive simulations, the proposed model outperforms existing routing methods, resulting in improved efficiency and congestion reduction. This congestion-free routing model presents a promising solution to address the unique challenges of SIoT networks, ensuring optimal performance and effective resource management.

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“…Existing research in WSNs has made several persistent limitations, motivating the need for further exploration and innovation in heterogeneous IoT networks for specific applications. Primary limitations include limited battery life (Ali et al 2024), scalability challenges (Bilal et al 2022), limited energy resources (Kumar Sharma et al 2023), and unequal clustering (Lahmar et al 2024). In light of these limitations, there is a compelling need to explore alternative approaches to WSN design and implementation.…”

Section: Introductionmentioning

confidence: 99%

Combined localization and clustering approach for reduced energy presumption in heterogeneous IoT

et al. 2024

Phys. Scr.

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The field of H-IoT is emerging with enormous potential to empower various technologies. Smart cities and advanced manufacturing are a few of the fields where H-IoT is currently used. The issue with H-IoT is that it uses a lot of energy when transmitting data, which makes it difficult to scale. To overcome such issues, a hybrid approach of CrayFish Optimization (CFO) with FCM and Restricted Boltzmann Machine (RBM) with Soft Sign Activation (SSA) has been proposed. Initially, Node initialization lays the foundation by configuring individual sensor nodes for network participation. After initialization, Fuzzy C Means clustering optimizes data aggregation by categorizing nodes into clusters based on similarity. Gathering Neighbor Node Traffic Data (NNTD) provides insights into communication patterns. Based on the threshold of NNTD node localization is done that enhances network accuracy by pinpointing sensor node locations. Integration of CFO into clustering with localization further improves cluster head selection for optimal data routing. Classification through the RBM with SSA function enhances anomaly detection with data analysis for optimizing energy utilization in heterogeneous IoT environments. The “combined CFO-FCM and SSA-RBM” has been implemented in MATLAB and achieved an accuracy of 94.50 %. As a result, the overall performance of the system is improved.

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Enhanced Fuzzy Logic Zone Stable Election Protocol for Cluster Head Election (E-FLZSEPFCH) and Multipath Routing in wireless sensor networks

Cited by 18 publications

References 81 publications

Performance of ML-Based Unsupervised Clustering Algorithms for WSN Node Clustering

Performance of ML-Based Unsupervised Clustering Algorithms for WSN Node Clustering

Enhancing routing efficiency in social internet of things: R-OPTICS and vEBT based congestion free model

Combined localization and clustering approach for reduced energy presumption in heterogeneous IoT

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