Smart Load-Based Resource Optimization Model to Enhance the Performance of Device-to-Device Communication in 5G-WPAN

Logeshwaran, J.; Kiruthiga, T.; Kannadasan, Raju; Vijayaraja, Loganathan; Alqahtani, Ali; Alqahtani, Nayef; Alsulami, Abdulaziz A.

doi:10.3390/electronics12081821

Cited by 34 publications

(4 citation statements)

References 30 publications

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“…It provides a transparent and efficient way to collect and manage data from IoT devices, ensuring data authenticity and integrity while promoting energy efficiency through smart contract-based incentives. Logeshwaran et al [44] have discussed in the context of 5G wireless personal area networks (WPANs), a smart load-based resource optimization model is proposed to improve the performance of device-to-device (D2D) communication. This model incorporates techniques such as user grouping and resource allocation to efficiently manage the network's resources, reducing interference and enhancing the overall network capacity and quality of service.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

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This paper presents an analysis of the performance of the Energy Aware Scheduling Algorithm (EASA) in a 5G green communication system. 5G green communication systems rely on EASA to manage resource sharing. The aim of the proposed model is to improve the efficiency and energy consumption of resource sharing in 5G green communication systems. The main objective is to address the challenges of achieving optimal resource utilization and minimizing energy consumption in these systems. To achieve this goal, the study proposes a novel energy‐aware scheduling model that takes into consideration the specific characteristics of 5G green communication systems. This model incorporates intelligent techniques for optimizing resource allocation and scheduling decisions, while also considering energy consumption constraints. The methodology used involves a combination of mathematical analysis and simulation studies. The mathematical analysis is used to formulate the optimization problem and design the scheduling model, while the simulations are used to evaluate its performance in various scenarios. The proposed EASM reached a 91.58% false discovery rate, a 64.33% false omission rate, a 90.62% prevalence threshold, and a 91.23% critical success index. The results demonstrate the effectiveness of the proposed model in terms of reducing energy consumption while maintaining a high level of resource utilization.

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Section: Literature Reviewmentioning

confidence: 99%

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Sangeetha,

Logeshwaran,

Faheem

et al. 2024

The Journal of Engineering

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“…Problems on the brink of detection can be detected early in the process. Logeshwaran and Karthick [8] discussed that in global and local networks, information is spread by various technologies. It is only after that data transfer that a connection is made between the first two 5G communication channels.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of user applications and the nature of operational interruption, the word "active," do not match the expected service level the user receives. It is recommended to use analytical solutions for decisionmaking support and adequate response to emerging problems [8,9]. It is possible to believe that these factors are governed by network and system administration service utilization and quality [10].…”

Section: Introductionmentioning

confidence: 99%

The Smart Network Management Automation Algorithm for Administration of Reliable 5G Communication Networks

Ramesh

Logeshwaran²,

Kumar

2023

Wireless Communications and Mobile Computing

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Smart network management automation (SNMA) is a technology that enables the automation of network management tasks. It provides a platform for automating network management, allowing administrators to manage their networks more reliably and efficiently. It provides the tools to monitor and manage network performance, identify and troubleshoot problems, and automate the deployment of new services and applications. SNMA also allows for scalability to meet the needs of any organization, regardless of size or complexity. With SNMA, network administrators can quickly and accurately identify problems, increase network uptime, and reduce time spent on manual tasks. The SNMA provides the ability to adjust configurations quickly and respond to changes in the network environment. It can help reduce costs and improve the efficiency of network operations. This paper proposed an intelligent network management automation algorithm for network administration and management in 5G communication networks. The proposed algorithm achieved 91.82% of remote network administration, 95.25% of global network administration, 96.59% of urban network administration, and 95.07% of local network administration. It creates a single submission to the network to make configuration changes, register network resources, manage users’ IP addresses, and filter packets to ensure information security and other tasks.

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“…Finally, IWSNs must be reliable and secure. IWSNs must have robust communication protocols and authentication to ensure data integrity and prevent unauthorized access and tampering [11]. Additionally, IWSNs must have reliable network infrastructure components such as routers, firewalls, and other measures to provide the reliability and security necessary for an industrial application [12].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

Bagwari,

Logeshwaran,

Usha

et al. 2023

IEEE Access

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Industrial Wireless Sensor Networks (WSNs) are becoming increasingly popular due to their enhanced scalability and low cost of deployment. However, they also present new challenges, such as energy optimization and network maintenance, which industrial users must address. In order to meet the challenges, Machine Learning techniques have been used to create an enhanced energy optimization model for Industrial WSNs. This model utilizes knowledge-based learning to identify and optimize the energy consumption of the nodes, allowing Industrial WSNs to consume the least amount of energy for the given tasks. In addition, the model also evaluates the effectiveness of feedback control schemes and predicts the best possible outcomes for its application in Industrial WSNs to ensure higher efficiency and longer network lifetime. The model also enables the exploration of potential trade-offs between power consumption and communication performance to ensure a better energy-efficient solution. The proposed EEOM obtained 64.72% transmission energy consumption, 35.28% transmission energy saving, 67.27% received energy consumption, 32.73% received energy storage, 52.16% idle-mode energy consumption, 47.84% idle-mode energy storage, 66.31% sleep-mode energy consumption, and 33.69% sleep-mode energy storage. It also obtained 90.44% prevalence threshold, 90.33% critical success index, 93.93% Delta-P, 90.06% MCC and 92.17% FMI rates. It also provides the ability to identify the best selection of nodes and paths for data transmission to reduce network traffic. When applied in conjunction with manual intervention, these automated knowledge-based techniques will make Industrial WSNs more reliable, efficient, and energy-cost effective.

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Smart Load-Based Resource Optimization Model to Enhance the Performance of Device-to-Device Communication in 5G-WPAN

Cited by 34 publications

References 30 publications

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

Smart performance optimization of energy‐aware scheduling model for resource sharing in 5G green communication systems

The Smart Network Management Automation Algorithm for Administration of Reliable 5G Communication Networks

An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning

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