Energy Efficient and Secured Clustering Algorithm using Fuzzy Logic with K-means Method in MANET

Prasad, Yogendra; Balakrishna, R.

doi:10.17485/ijst/2019/v12i19/144195

Cited by 4 publications

(6 citation statements)

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“…The basic Lowest ID approach was used to decrease the re-clustering frequency and packet delivery delay. The authors of [49] proposed a new fuzzy-based integrated clustering method for MANET. The suggested clustering technique used fuzzy logic to recognize CHs and establish a borderline between them in order to produce a fair CH distribution based on a guaranteed zone.…”

Section: Related Workmentioning

confidence: 99%

SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles

et al. 2023

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The Internet of Vehicles (IoV) has evolved from the classic Vehicular Ad-hoc NETworks (VANETs) as a result of the emergence of the Internet of Things (IoT). IoV is used for communication among vehicles in real-time with their drivers, other vehicles, pedestrians, fleet management systems, and roadside infrastructure. High vehicular speeds and frequent network topology changes make vehicle communication extremely difficult on the IoV network. More constraints are imposed on IoV communication performance in a huge network environment due to the difficult road conditions and the enormous quantity of vehicles. A promising approach to improve the IoV communication performance is through vehicle clustering. Minimizing the number of clusters and identifying a reliable Cluster head (CH) are some of the most challenging tasks. In this paper, we propose a Swarm optimization-based and mobility-aware clustering method termed SOMACA. SOMACA consists of two phases clustering phase and the routing phase. During the clustering phase, we combine mobility measures and cluster distance to generate the minimum number of clusters having stable CHs and employ the Sparrow Search algorithm (SSA) for CH selection. The routing phase consists of two steps (1) Route Formation and (2) Route Upkeep. The main target for route formation step is to build a secure routing path between IoV nodes and base station (BS) by establishing an optimal list of links that are ordered from high to low, and in each round, it selects the best one. Moreover, the Upkeep step aims to update and maintain the existing connection. The performance of SOMACA is assessed using simulation experiments with various metrics including average cluster lifetime, transmission range, and network grid size. The simulation results show that SOMACA reduces the average number of clusters by 42%, 48%, 47%, 9%, 22%,31%, 16%, and 43% less than CAVDO, GOA, GWOCNET, MFCA-IOV, MOGA-AWCP, HHOCNET, AMONE, and p-WOA algorithms respectively when the transmission ranges vary between 200 and 500. Moreover, SOMACA increases the average network lifetime by 11%, 12%, 9%, 6%, 7%, 9%, 3%, and 13% longer than the mentioned above compared algorithms, respectively.INDEX TERMS Clustering, the Internet of Things, the Internet of Vehicles, swarm intelligence, sparrow search algorithm, routing data, VANET.The associate editor coordinating the review of this manuscript and approving it for publication was Chan Hwang See.

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Section: Related Workmentioning

confidence: 99%

SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles

et al. 2023

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“…In general, when differential equations are solved as difference equations, conditions on the step size of differentiation are important in guaranteeing the stability of the solutions of the difference equations. In difference equation ( 18), a positive constant b determines the amount of state quantity exchanged between adjacent nodes at one discrete-time step, which affects the stability of the solution of difference equation (18). Generally speaking, b must be less than a certain value to obtain a stable solution for difference equation (18).…”

Section: Stability Condition For Autonomous Decentralized Spectral Al...mentioning

confidence: 99%

“…In difference equation ( 18), a positive constant b determines the amount of state quantity exchanged between adjacent nodes at one discrete-time step, which affects the stability of the solution of difference equation (18). Generally speaking, b must be less than a certain value to obtain a stable solution for difference equation (18). On the other hand, if b is too small, the amount of state quantity exchanged becomes small, and convergence of the solution slows dramatically.…”

Section: Stability Condition For Autonomous Decentralized Spectral Al...mentioning

confidence: 99%

“…On the other hand, in dynamic network environments, clustering using non-local information is complicated by changes in the network structure created by the movement of user nodes. Attempts have been made to apply a clustering algorithm based on the k-means method, which requires global network information, to dynamic environments [17], [18]. In addition, there is a method based on node information in a range that is several hops away from each node [19].…”

Section: Introduction a Backgroundmentioning

confidence: 99%

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Autonomous Decentralized Spectral Clustering for Hierarchical Routing of Multi-Hop Wireless Networks

2023

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In multi-hop wireless networks (MWNs), hierarchical structures are important to achieve scalable routing control, as well as clustering algorithms for creating such structures and so have been extensively studied. Due to the constraints of MWNs as distributed systems, these clustering algorithms must be implemented in an autonomous and decentralized manner. In particular, ensuring transparency to network topology and node mobility is important. Spectral clustering is a technique for appropriately creating clusters regardless of the network topology. However, since this technique requires information on the entire network, it is difficult to implement it autonomously and in a decentralized manner. Therefore, autonomous and decentralized spectral clustering has only succeeded in simple grid networks. This paper proposes a spectral clustering algorithm that can be implemented in an autonomous and decentralized manner for any network topology. In this method, a certain spatial structure is generated in an autonomous and decentralized manner by using differential equation-based temporal evolution equations; clusters are then formed using the spatial structure yielded by the temporal evolution equations. We demonstrate that this method can realize spectral clustering in an autonomous and decentralized manner in a static network model and also can realize a stable cluster structure that responds to node movement in a dynamic network model. INDEX TERMSAutonomous decentralized clustering, spectral clustering, multi-hop wireless network, hierarchical routing, spectral graph theory.

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“…In Prasad et al, 45 the authors presented a new fuzzy based incorporated bunching algorithm (FL-ECC/D) for vitality productive steering conventions in MANET. The proposed bunching scheme utilizes fuzzy rationale to elect CHs and introduces a partition separate between them for a balanced CHs dispersion based on a secured zone.…”

Section: Mobile Ad Hoc Network Clustering Algorithmsmentioning

confidence: 99%

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

Senouci

Harous

Aliouat

2020

Int J Communication

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Summary Vehicular ad hoc networks (VANETs) have recently attracted considerable attention owing to their wide range of applications. However, there are several challenges, such as mobility, routing, scalability, quality of services, and security. Clustering is an important control mechanism in high‐mobility networks and has been verified to be a promising approach in VANETs as well, as it ensures a basic level of network performance. Accordingly, several clustering algorithms have been proposed for these networks, and different protocols typically focus on various performance metrics. In this study, we provide a thorough review of clustering algorithms in VANETs. First, we present background material regarding the clustering process. Secondly, we propose a new taxonomy that categorizes clustering algorithms in VANETs based on different design aspects and provides a description of the algorithms in each category. Thirdly, an analysis of the algorithms in each category is provided according to various comparison metrics. Fourthly, we highlight the main challenges for each category and discuss some open research issues. Finally, we provide a general comparison of different clustering algorithms according to selected key parameters. Thus, this study provides a more thorough understanding of VANET clustering algorithms and the research trends in this area.

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Energy Efficient and Secured Clustering Algorithm using Fuzzy Logic with K-means Method in MANET

Cited by 4 publications

References 0 publications

SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles

SOMACA: A New Swarm Optimization-Based and Mobility-Aware Clustering Approach for the Internet of Vehicles

Autonomous Decentralized Spectral Clustering for Hierarchical Routing of Multi-Hop Wireless Networks

Survey on vehicular ad hoc networks clustering algorithms: Overview, taxonomy, challenges, and open research issues

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