ACO-Based Dynamic Decision Making for Connected Vehicles in IoT System

Bui, Khac-Hoai Nam; Jung, Jason J.

doi:10.1109/tii.2019.2906886

Cited by 61 publications

(18 citation statements)

References 30 publications

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“…The V2X information exchange is carried out through correspondence protocols and data standards, such as the Standard IEEE 802.11p WAVE and cellular networks. CVs can support intelligent traffic management, intelligent vehicle control, and intelligent dynamic information services, as a typical application of Internet of Things (IoT) technology in intelligent transportation systems (ITS) [75,76]. CVs are connected to the internet by cellular technologies (such as HSDPA, GSM, and LTE telecommunications), 5G networks, Bluetooth, and other short-distance communications technologies [77], as shown in Figure 7.…”

Section: Connected Vehiclesmentioning

confidence: 99%

Commercialization of Electric Vehicles in Hong Kong

Lau

Liu

et al. 2022

Energies

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Road vehicles are responsible for air pollution in Hong Kong, and electric vehicles (EVs) are a promising alternative to internal combustion engine vehicles as the city is transitioning to clean energy. In this work, EV adoption in Hong Kong is investigated and analyzed, including the global EV markets, present EV status in Hong Kong, local challenges facing EV development, suggestions for EV promotion in Hong Kong, emerging technologies, and decommissioning of batteries and EVs. The challenges of EVs include insufficient charging infrastructures, inadequate management of public charging facilities, difficulties in EV repair and maintenance, “dead mileage” during charging, unacceptable long charging times, and limited commercial EV models. Strategies such as providing incentives and bonuses for commercial EVs, offering high-power quick-charging facilities, actively developing commercial EVs, installing more charging infrastructures for private EVs, building connections among stakeholders, encouraging the participation of the private sector to promote fee-based services, and supporting the development of innovative technologies should be implemented to promote EVs in Hong Kong. Emerging technologies for EVs such as wireless charging, smart power distribution, vehicle-to-grid and vehicle-to-home systems, connected vehicles, and self-driving are discussed. Eco-friendly decommissioning of EV batteries can be realized by recycling and second-life applications. This paper serves as a reference and guide for the sustainable and smart evolution of the transportation sector in Hong Kong and other global large cities.

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Section: Connected Vehiclesmentioning

confidence: 99%

Commercialization of Electric Vehicles in Hong Kong

Lau

Liu

et al. 2022

Energies

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“…Considering an IoT connected vehicle environment, this work shows that smart vehicles can make dynamic and adaptive decisions. Here, the authors show how IoT based communication is effectively utilized for traffic intensity calculations and subsequent utilization of IoT data for dynamic decision making using swarm intelligence [23].…”

Section: Iot Data Analytics For Smart Vehiclesmentioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

181

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The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

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“…It refers to the process of realizing objectives via multiple steps and suitable decisions in a series of scenarios, which can be regarded as a multi-step sequential decision problem [8]. To overcome the problem of decentralized management of connected vehicles in a distributed intelligent transportation system, reference [19] designed an ant colony optimization algorithm that is based on swarm intelligence (SI) for dynamic decisionmaking of networked vehicles, which enables vehicles to automatically and adaptively identify the best path to the destination. In [20], an intelligent resource management strategy for joint communication mode selection, resource block allocation and power control in D2D-V2V communication vehicle networks is proposed.…”

Section: Internet Access and Communication And Other Technologiesmentioning

confidence: 99%

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

Alfarraj

Tolba

2020

IEEE Access

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With the proliferation of mobile devices and a wealth of rich application services, the Internet of vehicles (IoV) has struggled to handle computationally intensive and delay-sensitive computing tasks. To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing. Compared with remote clouds, mobile edge clouds are located at the edge of the network. Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay. In addition, mobile devices are increasingly moving toward intelligence. To satisfy the service experience and service quality requirements of mobile users, the vehicle Internet is transforming into the intelligent vehicle Internet. Artificial intelligence (AI) technology can adapt to rapidly changing dynamic environments to provide multiple task requirements for resource allocation, computational task scheduling, and vehicle trajectory prediction. On this basis, combined with MEC technology and AI technology, computing and storage resources are placed on the edge of the network to provide real-time data processing while providing more efficient and intelligent services. This article introduces IoV from three aspects, namely, MEC, AI and the advantages of combining the two, and analyzes the corresponding architecture and implementation technology. The application of MEC and AI in IoV is analyzed and compared with current approaches. Finally, several promising future directions in the field of IoV are discussed. INDEX TERMS Internet of Vehicles (IoV), mobile edge computing (MEC), artificial intelligence (AI).

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ACO-Based Dynamic Decision Making for Connected Vehicles in IoT System

Cited by 61 publications

References 30 publications

Commercialization of Electric Vehicles in Hong Kong

Commercialization of Electric Vehicles in Hong Kong

Machine learning and data analytics for the IoT

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

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