Weight Based Hierarchical Clustering Algorithm for Mobile Ad hoc Networks

Sahana, Sudipta; Saha, Soumyabrata; DasGupta, Suparna

doi:10.1016/j.proeng.2012.06.137

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…On the contrary, it requires throughput and DL for further improvement. Sahana et al [15] proposed a weight-based hierarchical clustering based on the combined weight that comprises node's degree, communication area and node's mobility. Although CH is the highest weighted node, the network performance lacks efficiency.…”

Section: Literature Surveymentioning

confidence: 99%

Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Rahul¹,

Kanthimathi²,

Kaarthick³

et al. 2023

Computer Systems Science and Engineering

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Recently, the fundamental problem with Hybrid Mobile Ad-hoc Networks (H-MANETs) is to find a suitable and secure way of balancing the load through Internet gateways. Moreover, the selection of the gateway and overload of the network results in packet loss and Delay (DL). For optimal performance, it is important to load balance between different gateways. As a result, a stable load balancing procedure is implemented, which selects gateways based on Fuzzy Logic (FL) and increases the efficiency of the network. In this case, since gateways are selected based on the number of nodes, the Energy Consumption (EC) was high. This paper presents a novel Node Quality-based Clustering Algorithm (NQCA) based on Fuzzy-Genetic for Cluster Head and Gateway Selection (FGCHGS). This algorithm combines NQCA with the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm divides the network into clusters based upon node priority, transmission range, and neighbour fidelity. In addition, the simulation results tend to evaluate the performance effectiveness of the FFFCHGS algorithm in terms of EC, packet loss rate (PLR), etc.

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Section: Literature Surveymentioning

confidence: 99%

Fuzzy Fruit Fly Optimized Node Quality-Based Clustering Algorithm for Network Load Balancing

Rahul¹,

Kanthimathi²,

Kaarthick³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

show abstract

“…On the contrary, throughput and DL were required for further improvement. Sahana et al [15] proposed a weight-based hierarchical clustering on the basis of the combined weight that comprises node's degree, communication area and node's mobility. Though the highest weighted node was selected as CH, the network performance was not efficiently analyzed.…”

Section: Literature Surveymentioning

confidence: 99%

Quality Based Clustering of Node using Fuzzy-Fruit Fly Optimization for Cluster Head and Gateway Selection in Healthcare Application

Rahul¹,

Kaarthick²

2021

Preprint

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Big data recently has gained tremendous importance in the way information is being disseminated. Transaction based data, unstructured data streaming to and fro from social media, increasing amounts of sensor and machine-to-machine data and many such examples rely on big data in conjunction with cloud computing. It is desirable to create wireless networks on-the-fly as per the demand or a given situation. In such a scenario reliable transmission of big data over mobile Ad-Hoc networks plays a key role in military healthcare applications. Limitations like congestion, Delay, Energy Consumption and Packet Loss Rate constraint pose a challenge for such systems. The most essential problem of Hybrid Mobile Ad-hoc Networks (H-MANET) is to select a suitable and secure path that balances the load through the Internet gateways. Also, the selection of gateway and overload through the network may cause packet losses and Delay (DL). Therefore, load-balancing between different gateways is required for achieving better performance. As a result, steady load balancing technique was employed that selects the gateways based on the Fuzzy Logic (FL) system and enhances the network efficiency. However, the Energy Consumption (EC) was high since gateways were selected directly from the number of nodes. Hence in this article, a novel Node Quality-based Clustering Algorithm (NQCA) using Fuzzy-Genetic for Cluster Head and Gateway Selection (FGCHGS) is proposed. In this algorithm, NQCA is performed based on the Improved Weighted Clustering Algorithm (IWCA). The NQCA algorithm separates the total network into number of clusters and the Cluster Head (CH) for each cluster is elected on the basis of the node priority, transmission range and node neighborhood fidelity. Moreover, the clustering quality is estimated according to the different parameters like node degree, EC, DL, etc, which are also utilized for estimating the combined weight value by using the FL system. Then, the combined weight values are optimized by using Genetic Algorithm (GA) to pick the most optimal weight value that selects both optimal CH and gateway. Conversely, the convergence time of GA and the error due to parameter tuning during optimization are high. Hence, a NQCA using Fuzzy-Fruit Fly optimization for Cluster Head and Gateway Selection (FFFCHGS) is proposed. In this algorithm, improved Fruit Fly (FF) algorithm is proposed instead of GA to select the most optimal CH and gateway. Finally, a performance effectiveness of the FFFCHGS algorithm is evaluated through the simulation outcomes in terms of EC, Packet Loss Rate (PLR), etc.

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“…= Optimal number of primary node, Ns = Number of nodes distributed randomly in a AxA region _fsd2 = amplifier energy (10* 0.00000000001) _mpd4 = amplifier energy (multi-path) (0.0013*0.00000000001) d = distance between transmitting node and base station d to BS = average distance between nodes and base station (50m) The value for both amplifier energy are taken from [17][18][19]. For MAP algorithm, the total number of sensor nodes at tier one are 25 nodes and for tier two are 75 nodes.…”

Section: Primary Node Selectionmentioning

confidence: 99%