Management of Positioning Functions in Cellular Networks for Time-Sensitive Transportation Applications

Liu, Qirui; Liu, Rongke; Zhang, Yang; Yuan, Yanli; Wang, Zijie; Thompson, John S.; Ye, Lin; Guizani, Mohsen; Thompson, John

doi:10.1109/tits.2023.3234532

Cited by 12 publications

(3 citation statements)

References 40 publications

(58 reference statements)

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“…In related work, we discussed road-unit-side services based on cooperative edge cloud networks. The network management-enabled networking position enabled services with the mobility suggested in [1]. IoT applications offload workloads for execution and can invoke network-enabled routing services from any location in smart cities.…”

Section: Related Workmentioning

confidence: 99%

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IoT Workload Offloading Efficient Intelligent Transport System in Federated ACNN Integrated Cooperated Edge-Cloud Networks

Lakhan,

Grønli,

Bellavista

et al. 2024

Preprint

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Intelligent transport systems (ITS) provide various cooperative edge cloud services for roadside vehicular applications. These applications offer additional diversity, including ticket validation across transport modes and vehicle and object detection to prevent road collisions. Offloading among cooperative edge and cloud networks plays a key role when these resources constrain devices (e.g., vehicles and mobile) to offload their workloads for execution. ITS used different machine learning and deep learning methods for decision automation. However, the self-autonomous decision-making processes of these techniques require significantly more time and higher accuracy for the aforementioned applications on the road-unit side.Thus, this paper presents the new offloading ITS for IoT vehicles in cooperative edge cloud networks. We present the augmented convolutional neural network (ACNN) that trains the workloads on different edge nodes. The ACNN allows users and machine learning methods to work together, making decisions for offloading and scheduling workload execution. This paper presents an augmented federated learning scheduling scheme (AFLSS). An algorithmic method called AFLSS comprises different sub-schemes that work together in the ITS paradigm for IoT applications in transportation. These sub-schemes include ACNN, offloading, scheduling, and security. Simulation results demonstrate that, in terms of accuracy and total time for the considered problem, the AFLSS outperforms all existing methods.

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Section: Related Workmentioning

confidence: 99%

“…The usage of Internet of Things (IoT) enabled public transport has been increasing daily. [1]. Different modes of public transport, such as the metro, train, and bus, are operated in many metropolises around the world [2].…”

Section: Introductionmentioning

confidence: 99%

IoT Workload Offloading Efficient Intelligent Transport System in Federated ACNN Integrated Cooperated Edge-Cloud Networks

Lakhan,

Grønli,

Bellavista

et al. 2024

Preprint

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“…Furthermore, some studies have also utilized the potential benefits of the UAV to localize the UEs for different use-cases. For instance, in [ 23 ], authors proposed a time difference of arrival-based ranging approach to predict the mobility of the UEs for time-sensitive transportation. In [ 24 ], authors came up with the multiple particle filter-based approach for UE positioning in an ultra-dense environment.…”

Section: Related Workmentioning

confidence: 99%

Q-learning-based UAV-mounted base station positioning in a disaster scenario for connectivity to the users located at unknown positions

Mandloi

Arya

2023

J Supercomput

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Due to its flexibility, cost-effectiveness, and quick deployment abilities, unmanned aerial vehicle-mounted base station (UmBS) deployment is a promising approach for restoring wireless services in areas devastated by natural disasters such as floods, thunderstorms, and tsunami strikes. However, the biggest challenges in the deployment process of UmBS are ground user equipment’s (UE’s) position information, UmBS transmit power optimization, and UE-UmBS association. In this article, we propose Localization of ground UEs and their Association with the UmBS (LUAU), an approach that ensures localization of ground UEs and energy-efficient deployment of UmBSs. Unlike existing studies that proposed their work based on the known UEs positional information, we first propose a three-dimensional range-based localization approach (3D-RBL) to estimate the position information of the ground UEs. Subsequently, an optimization problem is formulated to maximize the UE’s mean data rate by optimizing the UmBS transmit power and deployment locations while taking the interference from the surrounding UmBSs into consideration. To achieve the goal of the optimization problem, we utilize the exploration and exploitation abilities of the Q-learning framework. Simulation results demonstrate that the proposed approach outperforms two benchmark schemes in terms of the UE’s mean data rate and outage percentage.

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Green grant-free power allocation for ultra-dense Internet of Things: A mean-field perspective

Nadif,

Sabir,

Elbiaze

et al. 2024

Journal of Network and Computer Applications

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Management of Positioning Functions in Cellular Networks for Time-Sensitive Transportation Applications

Abstract: Macro BSs V2I link (Uu Interface) Positioning Management Function in C-RAN Road-sides units (RSUs) Small BSs NLoS caused by Blockage Backhaul Strong PRS Near-far problems Weak PRS

Cited by 12 publications

References 40 publications

IoT Workload Offloading Efficient Intelligent Transport System in Federated ACNN Integrated Cooperated Edge-Cloud Networks

IoT Workload Offloading Efficient Intelligent Transport System in Federated ACNN Integrated Cooperated Edge-Cloud Networks

Q-learning-based UAV-mounted base station positioning in a disaster scenario for connectivity to the users located at unknown positions

Green grant-free power allocation for ultra-dense Internet of Things: A mean-field perspective

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