Comparison Studies of Hierarchical Cluster-Based Routing Protocols in Wireless Sensor Networks

Nam, Deok Hee

doi:10.29007/stzv

Cited by 7 publications

(4 citation statements)

References 24 publications

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“…Although a large number of approaches exploit benefit of clustering for the most recent MANET contexts because of its advantages resulting in high level network managing [14] it provides several issues to be addressed; for example, hierarchical strategies could involve significant routing overhead [15] while deterministic approaches could lead to scalability troubles and use of specific cluster shapes [16,17]. Advancements in deep learning technologies, specifically Graph Neural Networks (GNN), offer a robust alternative to traditional clustering techniques.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Clustering Beamforming Approach for Future 6G Mobile Ad Hoc Networks

Inzillo¹,

Garompolo²

2023

Preprint

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The rapid advancement of wireless technologies and the increasing demand for seamless, high-speed connectivity has spurred the need for efficient routing protocols in 6G Mobile Ad Hoc Networks (MANETs). This paper proposes a modern approach, the Deep Learning Clustering Beamforming Massive MIMO Routing Protocol (DLCB), that is designed to achieve high level throughput by exploiting beamforming advantages and for enhancing communication efficiency for future next generation 6G MANETs. The DLCB algorithm establishes robust and efficient routes for data transmission by adopting a clustering strategy based on deep learning allowing to select cluster heads by taking advantage of Graph Neural Networks (GNN) features and creating effective cluster paths. Furthermore, this approach integrates IEEE 802.11ay and Massive MIMO technologies to exploit their benefits in terms of high-speed data transmission, improved network capacity, and better signal quality. DLCB aims to facilitate seamless data transmission in dynamic network environments, paving the way for enhanced network performance in 6G MANETs. Experimental evaluations and simulations are conducted to validate the performance of the proposed approach through the use of Omnet++ Network Simulator.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Clustering Beamforming Approach for Future 6G Mobile Ad Hoc Networks

Inzillo¹,

Garompolo²

2023

Preprint

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“…The generic ideas of hierarchical routing have been integrated with several methodologies to save energy in WSNs. In the structure of a hierarchical method, nodes with high energy are only considered for processing and transmit information [3]. On the other hand, nodes with low energy are used to collect information in regions near the target.…”

Section: Introductionmentioning

confidence: 99%

Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme

et al. 2021

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Multiple applications of sensor devices in the form of a Wireless Sensor Network (WSN), such as those represented by the Internet of Things and monitoring dangerous geographical spaces, have attracted the attention by several scientific communities. Despite their interesting properties, sensors present an adverse characteristic: they manage very limited energy. Under such conditions, saving energy represents one of the most important concepts in designing effective protocols for WSNs. The objective of a protocol is to increase the network lifetime through the reduction of energy consumed by each sensor. In this paper, a robust clustering routing protocol for WSNs is introduced. The scheme uses the Locust Search (LS-II) method to determine the number of cluster heads and to identify the optimal cluster heads. Once the cluster heads are recognized, the other sensor elements are assigned to their nearest corresponding cluster head. Numerical simulations exhibit competitive results and demonstrate that the proposed protocol allows for the minimization of the energy consumption, extending the network lifetime in comparison with other popular clustering routing protocols.

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“…Therefore optimizing sensors energy consumption is an important and challenging issue for large scale sensor network. Hierarchical cluster based routing methods have advantages related to energy efficiency and scalability [12]. Hierarchical cluster based routing methods have been proven an efficient way to increase overall network lifetime and reduce energy consumption of sensor node within clusters by performing information aggregation [13].…”

Section: Introductionmentioning

confidence: 99%

Improved energy efficient hierarchical cluster tree based routing to prolong network lifetime

S¹

2021

ITII

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Wireless sensor networks (WSN) play a key role in enabling wireless communication technology among several nodes to remotely communicate and exchange information. WSN consists of tiny sensor nodes equipped with battery, scattered in an area to gather information around an environment and send to data collection node known as sink or base station (BS). WSN have been widely used in various applications like agriculture, fire detection, health care and military and has become imperative necessity for future revolutionary area like UAV (unmanned aerial vehicles), IoT (Internet of things) and smart cities which employs large scale sensor nodes. However sensor nodes are limited to battery, memory, low computational power, resource and bandwidth. Continues sensing of events, makes node to drain its battery faster and goes dead fast. For resource constrained WSN, hierarchical cluster based approaches are considered as energy efficient and improves network performance for large scale WSN. Minimizing energy consumption and extending network lifetime are major challenging issues of WSN, clustering methods with optimized routing have offered solution to optimize energy utilization. To balance energy consumption and improve network lifetime many existing hierarchical clustering approaches have been proposed, however existing method does not consider rotation of cluster head (CH) and considers cluster head selection based on residual energy and distance parameter. In this paper we propose an improved energy efficient cluster tree (IEECT) based routing to improve energy efficiency of hierarchical cluster. IEECT considers modification of existing LEACH (Low energy adaptive clustering hierarchy) protocol to improved energy efficient LEACH by considering energy parameters like residual node energy and average network energy. IEECT accounts optimal number of cluster head (CH) and selection of CH is done using threshold value among sensor nodes. Proposed IEECT combines tree based routing and data aggregation scheme to maintain desirable quality of service. Simulation experiments are carried out by using network simulator. Performance of IEECT is evaluated in terms of PDR, delay, energy consumption, network lifetime and overhead.

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Comparison Studies of Hierarchical Cluster-Based Routing Protocols in Wireless Sensor Networks

Cited by 7 publications

References 24 publications

A Deep Learning Clustering Beamforming Approach for Future 6G Mobile Ad Hoc Networks

A Deep Learning Clustering Beamforming Approach for Future 6G Mobile Ad Hoc Networks

Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme

Improved energy efficient hierarchical cluster tree based routing to prolong network lifetime

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