Hypergraph-Based Joint Channel and Power Resource Allocation for Cross-Cell M2M Communication in IIoT

Zhuansun, Chenlu; Yan, Kedong; Zhang, Gongxuan; Huang, Chanying; Xiao, Shan

doi:10.1109/jiot.2023.3263567

Cited by 4 publications

(2 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the M step, the three parameters are updated based on the previously calculated variables as given by the equation ( 6), (7), and (8).…”

Section: A Clustering Of Active M2m Devicesmentioning

confidence: 99%

“…Many M2M devices necessitate periodic network access to fulfill their data transmissions, often operating at meager data rates. Examples of critical M2M services encompass wireless sensors, environmental monitoring, weather data collection, and vehicular communication [8]- [10]. Moreover, wearable sensors designed for assessing user health are gaining prominence.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Energy-Aware Tailored Resource Management for Cellular-Based Zero-Touch Deterministic Industrial M2M Networks

Prathiba,

Raja,

Saiabirami

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Zero-Touch Deterministic Industrial Machine-to-Machine (ZT-DI-M2M) serves as a customized communication solution designed to meet the specific needs of industrial settings. Though 5G is the promising solution for ZT-DI-M2M, optimized scheduling in 5G remains challenging to design over aspects such as network overload and congestion control, especially in the uplink transmission of ZT-DI-M2M. Effective allocation of resources for Machine-Type Communication (MTC) devices stands out as a pivotal hurdle within the domain of 5G networks. This challenge directly influences the longevity of batteryoperated devices and the Quality of Service (QoS) experienced by applications. This paper aims to develop a Group-Based and Energy Aware (GBEA) resource allocation algorithm for M2M communication in the 5G networks. The GBEA algorithm solves the resource allocation problem by initially clustering the active nodes concerning their delays. Consequently, inter and intra-cluster resource distribution occurs through the Gaussian Mixture Model Expectation Maximization (GMM-EM) algorithm. The GBEA algorithm optimizes the resource allocation of M2M devices by factoring delay, energy, proximity, and fairness into the allocation process. The simulation outcomes unveil the superiority of the GBEA scheduling algorithm over established state-of-the-art resource allocation methods. It showcases remarkable enhancements in terms of throughput, delay sensitivity, and energy efficiency, boasting nearly 1.5-fold, 1.75-fold, and 6.5-fold respective improvements.

show abstract

“…During the M step, the three parameters are updated based on the previously calculated variables as given by the equation ( 6), (7), and (8).…”

Section: A Clustering Of Active M2m Devicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%