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Abstract

The spreading of Sixth Generation (6G) communication within the Internet of Things (IoT) is one of the recent innovations in networking services. As the number of devices becomes increasingly connected through extensive communication network connectivity, energy efficiency has become a significant concern with the widespread use of massive IoT devices. While general lack of energy optimization can lead to several issues, including data loss, link failure, and high-power utilization, directly affect the communication quality of the network. To address these challenges, it is crucial to develop a model that combines effective optimization and resource allocation, based on both Quality of Service (QoS) and energy efficiency. In this study, we propose an Energy with Capacity-Awareness Effective Satellite Communication (ECESC) approach for 6G in an IoT environment. The network environment comprises Base Stations (BS) and User Equipment (UEs), with devices equipped with data transmission models, energy consumption models, and QoS-based service parameters. Our proposed technique is to enhance resource utilization and optimization among devices using the Superior Hidden Markov Model (SHMM) and Improved Neighborhood Field Optimization (INFO) Algorithm, significantly increasing energy and network utility. The ECESC approach was implemented using NS2 and compared with EERS-IoT, SECA-IoT, and DATS-IoT. We evaluated results using varying number of nodes and speeds. The performance metrics include end-to-end delay, throughput, packet delivery ratio, energy efficiency, packet loss, and routing overhead. The experimental results demonstrate that our proposed method achieves significantly improved outcomes in terms of packet delivery, throughput, and energy efficiency compared to earlier studies.

Keywords

Energy efficiency, Internet of Things, improved neighborhood field optimization algorithm, satellite communication, superior hidden Markov model, 6G technology

Article Type

Article

First Page

335

Last Page

355

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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