IoT Applications for Connected and Autonomous Electric Vehicles

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 1454

Special Issue Editor


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Guest Editor
School of Computer Science and Technology, Auhui University, Hefei 230601, China
Interests: vehicular ad hoc networks; IoT; software-defined networks; cryptography; blockchain; edge computing; AI security
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Special Issue Information

Dear Colleagues,

The automotive industry is at the verge of the connected and autonomous electric vehicles (CAEVs) era. The most representative electric vehicles manufacturers, including Tesla, BYD, and Volkswagen, are heavily investing in CAEV technologies to promote the advancement of CAEVs. Traditional technologies for CAEVs such as vehicular ad-hoc networks (VANETs) and Internet of Vehicles (IoV) focus heavily on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, while placing less emphasis on IoT applications for CAEVs. IoT technology has a significant impact on the automotive industry by enabling various innovations, such as automated driving, smart fleet management, and intelligent transportation infrastructure. By integrating Internet of Things (IoT) technology into CAEVs, a wide range of powerful services can be implemented, including mobility, sensing, computing, traffic control, and energy management. However, many challenges could impede the deployment of IoT applications for CAEVs. These challenges include the design of optimization algorithms, security and privacy threats, trust issues, and so on. Therefore, the objective of this Special Issue is to seek high-quality submissions supporting the deployment of IoT applications for CAEVs. The topics of interest include, but are not limited to:

  • Efficient design of IoT applications for CAEVs;
  • Architecture design for integrating IoT and CAEVs;
  • Efficient management for CAEVs-related IoT application data;
  • Resource allocation in IoT applications for CAEVs;
  • AI or machine-learning technologies in IoT applications for CAEVs;
  • Blockchain technology in IoT applications for CAEVs;
  • Edge or fog computing in IoT applications for CAEVs;
  • Efficient data sharing for communications between IoT devices and CAEVs;
  • Security protocols for securing communications between IoT devices and CAEVs;
  • Security and privacy risk assessment for deploying IoT applications into CAEVs;
  • Design of secure intra-vehicle, inter-vehicle and cross-domain communications for CAEVs;
  • Privacy challenges in the settings of IoT applications for CAEVs;
  • Security verification related to CAEVs;
  • Design of security protocols for CAEVs;
  • Edge computing in IoT applications for CAEVs.

Prof. Dr. Jie Cui
Guest Editor

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Keywords

  • application design
  • architecture
  • data management
  • resource allocation
  • machine learning
  • blockchain
  • security
  • privacy
  • cryptography
  • verification
  • trust
  • in-vehicle networks
  • edge computing

Published Papers (1 paper)

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Research

22 pages, 14897 KiB  
Article
Design and Application of a Resource Allocation Method for CAEVs Internet of Things Based on Swarm Intelligence Computing
by Yibo Han, Zheng Zhang, Pu Han, Bo Yuan, Lu Liu and John Panneerselvam
Electronics 2023, 12(13), 2997; https://doi.org/10.3390/electronics12132997 - 7 Jul 2023
Cited by 1 | Viewed by 751
Abstract
The Internet of Things (IoT) faces significant challenges in the requirements of sensitive task latency, reasonable resource allocation and reliability for resource transactions. This paper introduces a novel method for road resource allocation in the IoT context of connected and autonomous electric vehicles [...] Read more.
The Internet of Things (IoT) faces significant challenges in the requirements of sensitive task latency, reasonable resource allocation and reliability for resource transactions. This paper introduces a novel method for road resource allocation in the IoT context of connected and autonomous electric vehicles (CAEVs). The proposed algorithm leverages the ant colony algorithm (ACA) to effectively allocate and coordinate road resources within groups of CAEVs. By considering the energy consumption and pheromone volatilization, the allocation and coordination process of road resources are optimized. To improve the linear packet loss of RED, we adopt the advanced ACA and CRED in the NS2 platform. The experimental results demonstrate that the proposed method outperforms the RED algorithm in packet loss rate and delay time, significantly enhancing system efficiency and performance. Furthermore, the combination of the CRED algorithm and ant colony algorithm successfully mitigates short-term congestion and identifies optimized paths with minimal delay. Full article
(This article belongs to the Special Issue IoT Applications for Connected and Autonomous Electric Vehicles)
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