Low Energy and Location Based Clustering Protocol for Wireless Sensor Network

Kassan, Sara; Lorenz, Pascal; Gaber, Jaafar

doi:10.1109/icc.2018.8422179

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…In Fig. 7, we compare the energy consumed by the network for 7000 rounds by comparing our proposed approach with other protocols from the literature: the LEACH clustering protocol (Xiangning and Yulin, 2007) and a clustering based protocol (Kassan et al, 2018). The results show that these Leach protocol consumes all its energy after 2000 rounds.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…where p is the cluster head probability in the network, r is the current round of election and G is the set of nodes that were not cluster head in the last round. In this paper, we use the clustering approach based on LEACH protocol with strategy based on location and residual energy of a sensor node to select the CHs (Kassan et al, 2018).…”

Section: Distributed Clusteringmentioning

confidence: 99%

“…It concerns the formation of the clusters and their heads for each round using sensor location and individual energy consumption (Kassan et al, 2018). Two CHs cannot be in the same cluster.…”

Section: Set-up Phasementioning

confidence: 99%

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Game theory based distributed clustering approach to maximize wireless sensors network lifetime

Kassan

Gaber

Lorenz

2018

Journal of Network and Computer Applications

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One of the most significant difficulty in Wireless Sensors Network (WSN) is the development of an effective topology control method that can support the quality of the network, respect the limited memory and at the same time increase the lifetime of the network. This paper introduces a new approach by mixing a non-cooperative Game Theory technique with a decentralized clustering algorithm to address the problem of maximizing the network lifetime. More precisely, this approach uses Game Theory techniques to control the activities of a sensor node and its neighbors to limit the number of the forwarding messages and to maximize the lifetime of the sensor's battery. In other words, the approach will decrease the energy consumed by the WSN by decreasing the number of forwarded packets and improve the network lifetime by harvesting energy from the environment. The simulations results show that the performances in terms of energy saving and increasing the number of data packets received by base station outperforms those with distributed based clustering algorithms without GT, such as low energy and location based clustering LELC and LEACH algorithms. 2. Related work 2.1. Clustering

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Distributed Clusteringmentioning

confidence: 99%

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Game theory based distributed clustering approach to maximize wireless sensors network lifetime

Kassan

Gaber

Lorenz

2018

Journal of Network and Computer Applications

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“…During the first stage, non-clustered nodes generate NS using physical parameters. During the second stage, non-clustered nodes elect CHs using the residual energy metric (M.5) to extent network lifetime and coverage [157,158] in order to enhance cluster stability and network scalability. Consider an out-of-range node that declares itself as a temporary CH.…”

Section: Reducing Ch Election Time Based On Energy Consumptionmentioning

confidence: 99%

Survey and taxonomy of clustering algorithms in 5G

Khan

Yau

Noor

et al. 2020

Journal of Network and Computer Applications

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The large-scale deployment of fifth generation (5G) is expected to produce a massive amount of data with high variability due to ultra-densification and the rapid increase in a heterogeneous range of applications and services (e.g., virtual reality, augmented reality, and driver-less vehicles), and network devices (e.g., smart gadgets and sensors). Clustering organizes network topology by segregating nodes with similar interests or behaviors in a network into logical groups in order to achieve network-level and cluster-level enhancements, particularly cluster stability, load balancing, social awareness, fairness, and quality of service. Clustering has been investigated to support mobile user equipment (UE) in access networks, whereby UEs form clusters themselves and may connect to BSs. In this paper, we present a comprehensive survey of the research work of clustering schemes proposed for various scenarios in 5G networks and highlight various aspects of clustering schemes, including objectives, challenges, metrics, characteristics, performance measures. Furthermore, we present open issues of clustering in 5G.

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“…The formation of clusters and assigning special responsibilities to cluster heads can significantly contribute to overall network scalability, energy efficiency and lifetime [8]. An adaptive approach for clustering in WSN is proposed in [9]. CHs selection is based on sensor node location and its current residual energy.…”

Section: Energy Routing Protocolsmentioning

confidence: 99%