Cloud assisted Internet of things intelligent transportation system and the traffic control system in the smart city

Liu, Chenchen; Li, Ké

doi:10.1080/23307706.2021.2024460

Cited by 77 publications

(27 citation statements)

References 29 publications

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“…Integrated with three networks: intra-vehicle network, inter-vehicle network, and onboard mobile Internet, the CV, also known as the "Internet of Vehicles" (IoV), is intrinsically a large distributed communication system for V2X interaction, where X involves other CVs, individuals, and networks [82]. As a profound scenario for the application of the Internet of Things (IoT) in the intelligent transportation system (ITS), the CVs enable a multifunctional network that supports intelligent transportation management, intelligent dynamical information services, and intelligent driving control [83]. The information exchange process follows specific communication protocol standards such as the IEEE 802.11p WAVE standard or cellular data protocols [84].…”

Section: Technologiesmentioning

confidence: 99%

Trends and Emerging Technologies for the Development of Electric Vehicles

Liu

Lau

et al. 2022

Energies

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In response to severe environmental and energy crises, the world is increasingly focusing on electric vehicles (EVs) and related emerging technologies. Emerging technologies for EVs have great potential to accelerate the development of smart and sustainable transportation and help build future smart cities. This paper reviews new trends and emerging EV technologies, including wireless charging, smart power distribution, vehicle-to-home (V2H) and vehicle-to-grid (V2G) systems, connected vehicles, and autonomous driving. The opportunities, challenges, and prospects for emerging EV technologies are systematically discussed. The successful commercialization development cases of emerging EV technologies worldwide are provided. This review serves as a reference and guide for future technological development and commercialization of EVs and offers perspectives and recommendations on future smart transportation.

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Section: Technologiesmentioning

confidence: 99%

Trends and Emerging Technologies for the Development of Electric Vehicles

Liu

Lau

et al. 2022

Energies

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“…Examples of the IoT applications under the category of smart health [52] - [55] include the monitoring of the organs or health conditions of a patient, remote surgery, diagnosing a patient's health condition(s), authentication of patients, and making real-time information about a patient's health condition available to the appropriate remote healthcare centers [56], [57]. Examples of the IoT applications under the class of smart city include intelligent transportation [58] - [62], assisted driving [63] and [64], passenger services, logistic services [65] and [66], car parking and counting [67] - [70], fleet management [71] - [73], emergency reporting services [74] - [76], and intelligent traffic control [77] - [79]. For instance, in smart water quality sensing and water supply applications, IoT technology could assist to efficiently monitor changes in water quality, control the distribution of clean water for various consumption uses, guarantee the safety of the public health since it helps to increase access to clean water, and prevents the distribution of unclean water to the public.…”

Section: A Benefits Of Iot In Real-life Applicationsmentioning

confidence: 99%

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Olatinwo

Joubert

2022

IEEE Access

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In this study, we conducted a bibliometric analysis and comprehensive review of the studies published between the period of 2012 and 2022 on resource management in the internet of things (IoT) networks using the Scopus database to determine the current state of research and gain insight into the research challenges and opportunities in the field. The bibliometric analysis technique was employed to bibliometrically analyze the published studies that were collected from the Scopus database and this helped to discover the majority of research subjects in the field of resource management in IoT networks. Following this, we conducted a comprehensive review of the relevant studies to provide an insight into the recent progress and the research gaps in the field. According to the results of our bibliometric analysis and the comprehensive review, we discovered that resource management problems in IoT networks is still a growing challenge as a result of the limited available resources for operating IoT networks. Consequently, the resource management problem is a critical research area due to the advantages of IoT in terms of collecting vital data that could be used in analyzing and predicting human behavior as well as environmental conditions. Also, the results of our bibliometric analysis and comprehensive review further revealed that research on the use of artificial techniques, such as optimization approaches and game theory approaches, for resource management are common while research on the use of the modern artificial intelligence technique, like deep learning approaches, is less common. Therefore, this study aims to fill the research gap in the area of resource management in IoT networks by introducing the use of deep learning approaches. Deep learning is a powerful artificial intelligence method that is advantageous for obtaining low-complexity resource allocation solutions in a near real-time. Also, various open research issues that are associated with the use of deep learning approaches are highlighted as future research directions to enable the development of novel deep learning models for IoT networks.

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“…A traffic optimization model is proposed 2 which takes both the time cost requirement for vehicle passages and the traffic load imbalance in road networks into consideration. The cloud‐assisted internet of things ITSs is proposed 3 to overcome the problems in the traffic management. The system integrates the traffic flow data, the traffic direction and signal control data to realize the intelligent traffic control.…”

Section: Introductionmentioning

confidence: 99%

Data‐driven resilient platooning control for vehicular platooning systems with denial‐of‐service attacks

Yue

Che

2022

Intl J Robust & Nonlinear

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This article addresses the data‐driven resilient platooning control problem for nonlinear vehicular platooning systems with denial‐of‐service attacks. First, the dynamic linearization technique is used for transforming the nonlinear vehicular platooning systems into an equivalent linear data model with robustness. Then, an observer is designed to present the estimation of the pseudo‐partial derivative parameter and a novel model‐free adaptive platooning control (MFAPC) framework is presented by defining a novel vehicular platooning system output. Based on this framework, a novel MFAPC algorithm is designed with an attack compensation mechanism to achieve vehicular platooning control objectives. At last, the effectiveness of the MFAPC algorithm is proved by an example with comparisons.

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Cloud assisted Internet of things intelligent transportation system and the traffic control system in the smart city

Cited by 77 publications

References 29 publications

Trends and Emerging Technologies for the Development of Electric Vehicles

Trends and Emerging Technologies for the Development of Electric Vehicles

Deep Learning for Resource Management in Internet of Things Networks: A Bibliometric Analysis and Comprehensive Review

Data‐driven resilient platooning control for vehicular platooning systems with denial‐of‐service attacks

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