Machine learning-based radio access technology selection in the Internet of moving things

Sánchez-Iborra, Ramón; Bernal-Escobedo, Luis; Santa, José

doi:10.23919/jcc.2021.07.002

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…However, are not efficient when dataset exhibit imbalanced behavior. In Iborra et al [20], showed existing model fails to address QoS and energy efficiency factor together. They modelled a supervised-based ML based method for selecting efficient network to meet application real-time prerequisite.…”

Section: Figure 1 Architecture Of Heterogeneous Wireless Networkmentioning

confidence: 99%

Resource optimization-based network selection model for heterogeneous wireless networks

Nayakwadi¹,

Fatima²

2023

IJ-AI

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<p class="Abstract"><span lang="EN-US">The internet of things (IoT) environment prerequisite seamless connectivity for meeting real-time application requirements; thus, required efficient resource management techniques. Heterogeneous wireless networks (HWNs) have been emphasized for providing seamless connectivity with high quality of service (QoS) performance to provision IoT applications. However, the existing resource allocation scheme suffers from interference and fails to provide a quality experience for low-priority users. As a result, induce bandwidth wastage and increase handover failure. In addressing the research issues this paper presented the resource-optimized network selection (RONS) method for HWNs. The RONS method employs better load balancing to reduce handover failure and maximizes resource utilization through dynamic slot optimization. The RONS method assures tradeoffs between high performance to high priority users and quality of experience (QoE) for low priority users. The experiment outcome shows the RONS achieves very good performance in terms of throughput, packet loss, and handover failures in comparison with existing resource selection methods.</span></p>

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Section: Figure 1 Architecture Of Heterogeneous Wireless Networkmentioning

confidence: 99%

Resource optimization-based network selection model for heterogeneous wireless networks

Nayakwadi¹,

Fatima²

2023

IJ-AI

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“…Sanchez-Iborra et al [43] considers the incorporation of new environmentally friendly mobile devices, such as motorcycles or bicycles, into collaborative intelligent transportation systems, and a variety of communication techniques-for instance, vehicular Wi-Fi, lowpower WAN, and cellular networks-are already available. These communication methods, however, are not fully covered and do not meet the demands of energy consumption and quality of service (QoS).…”

Section: Publicationmentioning

confidence: 99%

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

Danba

Bao

Han³

et al. 2022

Sensors

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Internet of Vehicles (IoV) technology has been attracting great interest from both academia and industry due to its huge potential impact on improving driving experiences and enabling better transportation systems. While a large number of interesting IoV applications are expected, it is more challenging to design an efficient IoV system compared with conventional Internet of Things (IoT) applications due to the mobility of vehicles and complex road conditions. We discuss existing studies about enabling collaborative intelligence in IoV systems by focusing on collaborative communications, collaborative computing, and collaborative machine learning approaches. Based on comparison and discussion about the advantages and disadvantages of recent studies, we point out open research issues and future research directions.

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“…Besides, NB-IoT can also be integrated into the architecture by external providers as to compare different Low Power Wide Area Network (LPWAN) solutions [15]. This unique mix of radio access technologies has enabled the development of solutions like the on-device smart selection of access network [12].…”

Section: Technical Descriptionmentioning

confidence: 99%

GAIA 5G: A Multi-access Smart-Campus Architecture

Gallego-Madrid

Bernal-Escobedo²,

Asensio³

et al. 2022

Internet of Things

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Smart-campuses are an emerging ecosystem that permit to enhance the performance and efficiency of academic facilities. Besides, they are also adopted as research, development, and testing platforms for the integration of novel management and governance mechanisms in complex ICT infrastructures. In this line, they are considered as small smart-city-like scenarios which can be used as a playground prior to large-scale deployments. This work presents the GAIA 5G smart-campus, located in the Espinardo campus of the University of Murcia (Spain). In the first place, its technological architecture is presented, detailing the multi-access platform that provides 5G, Internet of Things (IoT), and vehicular communications connectivity. Also, the virtualized computation environment is described. Thanks to these two pillars, GAIA 5G has the potential to host diverse use cases in multiple verticals, such as 5G connectivity, Network Function Virtualization (NFV) management and orchestration, or cybersecurity, which are also described. As discussed along the paper, GAIA 5G is an operative smart-campus infrastructure ready to support state-of-the-art research and accommodate novel 5Gand-beyond (B5G) test cases.

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Machine learning-based radio access technology selection in the Internet of moving things

Cited by 6 publications

References 11 publications

Resource optimization-based network selection model for heterogeneous wireless networks

Resource optimization-based network selection model for heterogeneous wireless networks

Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues

GAIA 5G: A Multi-access Smart-Campus Architecture

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