scholarly journals Mobile Edge Computing for the Internet of Vehicles

2021 ◽  
pp. 47-64
Author(s):  
Yan Zhang
Keyword(s):  
Edge Computing ◽  
The Internet ◽  
Vehicular Communication ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Delay Constraints ◽  
Communication Environment ◽  
Artificial Intelligence Technology ◽  
Physical Information ◽  
Computing Framework

AbstractThe advancement of cyber physical information has led to the pervasive use of smart vehicles while enabling various types of powerful mobile applications, which usually require high-intensity processing under strict delay constraints. Given their limited on-board computing capabilities, smart vehicles can offload these processing tasks to edge servers for execution. However, a highly dynamic topology, a complex vehicular communication environment, and edge node heterogeneity pose significant challenges in vehicular edge computing management. To address these challenges, in this chapter we investigate the characteristics of edge computing from both the application and service perspectives and introduce a hierarchical edge computing framework. Moreover, we leverage artificial intelligence technology to propose efficient task offloading and resource scheduling schemes.

scholarly journals Moisture Computing-Based Internet of Vehicles (IoV) Architecture for Smart Cities

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3785
Author(s):  
Ali Tufail ◽  
Abdallah Namoun ◽  
Adnan Ahmed Abi Sen ◽  
Ki-Hyung Kim ◽  
Ahmed Alrehaili ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Traffic Monitoring ◽  
Edge Computing ◽  
Small Scale ◽  
The Internet ◽  
Cloud Infrastructure ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Scale Data

Recently, the concept of combining ‘things’ on the Internet to provide various services has gained tremendous momentum. Such a concept has also impacted the automotive industry, giving rise to the Internet of Vehicles (IoV). IoV enables Internet connectivity and communication between smart vehicles and other devices on the network. Shifting the computing towards the edge of the network reduces communication delays and provides various services instantly. However, both distributed (i.e., edge computing) and central computing (i.e., cloud computing) architectures suffer from several inherent issues, such as high latency, high infrastructure cost, and performance degradation. We propose a novel concept of computation, which we call moisture computing (MC) to be deployed slightly away from the edge of the network but below the cloud infrastructure. The MC-based IoV architecture can be used to assist smart vehicles in collaborating to solve traffic monitoring, road safety, and management issues. Moreover, the MC can be used to dispatch emergency and roadside assistance in case of incidents and accidents. In contrast to the cloud which covers a broader area, the MC provides smart vehicles with critical information with fewer delays. We argue that the MC can help reduce infrastructure costs efficiently since it requires a medium-scale data center with moderate resources to cover a wider area compared to small-scale data centers in edge computing and large-scale data centers in cloud computing. We performed mathematical analyses to demonstrate that the MC reduces network delays and enhances the response time in contrast to the edge and cloud infrastructure. Moreover, we present a simulation-based implementation to evaluate the computational performance of the MC. Our simulation results show that the total processing time (computation delay and communication delay) is optimized, and delays are minimized in the MC as apposed to the traditional approaches.

scholarly journals Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities: A Review, Open Issues, and Future Perspectives

Telecom ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 108-140
Author(s):  
Paulo Álvares ◽  
Lion Silva ◽  
Naercio Magaia
Keyword(s):  
Traffic Congestion ◽  
Resource Constraints ◽  
Smart Cities ◽  
The Internet ◽  
Connected Vehicle ◽  
Sensitive Data ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Security Properties ◽  
Vehicle Networks

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

Capsule Network Distributed Learning with Multi-Access Edge Computing for the Internet of Vehicles

2021 ◽  
Vol 59 (8) ◽  
pp. 52-57
Author(s):  
Jie Xu ◽  
F. Richard Yu ◽  
Jingyu Wang ◽  
Qi Qi ◽  
Haifeng Sun ◽  
...  
Keyword(s):  
Distributed Learning ◽  
Edge Computing ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Multi Access

scholarly journals Edge Computing and Blockchain for Quick Fake News Detection in IoV

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4360 ◽  
Author(s):  
Yonggang Xiao ◽  
Yanbing Liu ◽  
Tun Li
Keyword(s):  
Road Safety ◽  
Virtual Machines ◽  
Negative Impact ◽  
Edge Computing ◽  
Fake News ◽  
Network Computing ◽  
Internet Of Vehicles ◽  
Traffic Efficiency ◽  
Computing Framework ◽  
Road Side Units

The dissemination of false messages in Internet of Vehicles (IoV) has a negative impact on road safety and traffic efficiency. Therefore, it is critical to quickly detect fake news considering news timeliness in IoV. We propose a network computing framework Quick Fake News Detection (QcFND) in this paper, which exploits the technologies from Software-Defined Networking (SDN), edge computing, blockchain, and Bayesian networks. QcFND consists of two tiers: edge and vehicles. The edge is composed of Software-Defined Road Side Units (SDRSUs), which is extended from traditional Road Side Units (RSUs) and hosts virtual machines such as SDN controllers and blockchain servers. The SDN controllers help to implement the load balancing on IoV. The blockchain servers accommodate the reports submitted by vehicles and calculate the probability of the presence of a traffic event, providing time-sensitive services to the passing vehicles. Specifically, we exploit Bayesian Network to infer whether to trust the received traffic reports. We test the performance of QcFND with three platforms, i.e., Veins, Hyperledger Fabric, and Netica. Extensive simulations and experiments show that QcFND achieves good performance compared with other solutions.

A Blockchain-based Containerized Edge Computing Platform for the Internet of Vehicles

2020 ◽  
pp. 1-1
Author(s):  
Laizhong Cui ◽  
Ziteng Chen ◽  
Shu Yang ◽  
Zhongxing Ming ◽  
Qi Li ◽  
...  
Keyword(s):  
Edge Computing ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Computing Platform

scholarly journals Decentralized Trusted Data Sharing Management on Internet of Vehicle Edge Computing (IoVEC) Networks Using Consortium Blockchain

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2410
Author(s):  
Muhammad Firdaus ◽  
Sandi Rahmadika ◽  
Kyung-Hyune Rhee
Keyword(s):  
Data Sharing ◽  
Data Storage ◽  
Traffic Safety ◽  
Network Performance ◽  
Intelligent Transportation System ◽  
Edge Computing ◽  
The Internet ◽  
Smart Vehicles ◽  
The Incentive Mechanism ◽  
Trusted Data

The emergence of the Internet of Vehicles (IoV) aims to facilitate the next generation of intelligent transportation system (ITS) applications by combining smart vehicles and the internet to improve traffic safety and efficiency. On the other hand, mobile edge computing (MEC) technology provides enormous storage resources with powerful computing on the edge networks. Hence, the idea of IoV edge computing (IoVEC) networks has grown to be an assuring paradigm with various opportunities to advance massive data storage, data sharing, and computing processing close to vehicles. However, the participant’s vehicle may be unwilling to share their data since the data-sharing system still relies on a centralized server approach with the potential risk of data leakage and privacy security. In addition, vehicles have difficulty evaluating the credibility of the messages they received because of untrusted environments. To address these challenges, we propose consortium blockchain and smart contracts to accomplish a decentralized trusted data sharing management system in IoVEC. This system allows vehicles to validate the credibility of messages from their neighboring by generating a reputation rating. Moreover, the incentive mechanism is utilized to trigger the vehicles to store and share their data honestly; thus, they will obtain certain rewards from the system. Simulation results substantially display an efficient network performance along with forming an appropriate incentive model to reach a decentralized trusted data sharing management of IoVEC networks.

scholarly journals Machine Learning Technologies for Secure Vehicular Communication in Internet of Vehicles: Recent Advances and Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Elmustafa Sayed Ali ◽  
Mohammad Kamrul Hasan ◽  
Rosilah Hassan ◽  
Rashid A. Saeed ◽  
Mona Bakri Hassan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Process ◽  
Channel Modeling ◽  
Learning Technologies ◽  
Edge Computing ◽  
Smart Devices ◽  
Learning Mechanisms ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Surrounding Environment

Recently, interest in Internet of Vehicles’ (IoV) technologies has significantly emerged due to the substantial development in the smart automobile industries. Internet of Vehicles’ technology enables vehicles to communicate with public networks and interact with the surrounding environment. It also allows vehicles to exchange and collect information about other vehicles and roads. IoV is introduced to enhance road users’ experience by reducing road congestion, improving traffic management, and ensuring the road safety. The promised applications of smart vehicles and IoV systems face many challenges, such as big data collection in IoV and distribution to attractive vehicles and humans. Another challenge is achieving fast and efficient communication between many different vehicles and smart devices called Vehicle-to-Everything (V2X). One of the vital questions that the researchers need to address is how to effectively handle the privacy of large groups of data and vehicles in IoV systems. Artificial Intelligence technology offers many smart solutions that may help IoV networks address all these questions and issues. Machine learning (ML) is one of the highest efficient AI tools that have been extensively used to resolve all mentioned problematic issues. For example, ML can be used to avoid road accidents by analyzing the driving behavior and environment by sensing data of the surrounding environment. Machine learning mechanisms are characterized by the time change and are critical to channel modeling in-vehicle network scenarios. This paper aims to provide theoretical foundations for machine learning and the leading models and algorithms to resolve IoV applications’ challenges. This paper has conducted a critical review with analytical modeling for offloading mobile edge-computing decisions based on machine learning and Deep Reinforcement Learning (DRL) approaches for the Internet of Vehicles (IoV). The paper has assumed a Secure IoV edge-computing offloading model with various data processing and traffic flow. The proposed analytical model considers the Markov decision process (MDP) and ML in offloading the decision process of different task flows of the IoV network control cycle. In the paper, we focused on buffer and energy aware in ML-enabled Quality of Experience (QoE) optimization, where many recent related research and methods were analyzed, compared, and discussed. The IoV edge computing and fog-based identity authentication and security mechanism were presented as well. Finally, future directions and potential solutions for secure ML IoV and V2X were highlighted.

scholarly journals A Blockchain-Based Efficient, Secure and Anonymous Conditional Privacy-Preserving and Authentication Scheme for the Internet of Vehicles

2022 ◽  
Vol 12 (1) ◽  
pp. 476
Author(s):  
Kashif Naseer Qureshi ◽  
Luqman Shahzad ◽  
Abdelzahir Abdelmaboud ◽  
Taiseer Abdalla Elfadil Eisa ◽  
Bandar Alamri ◽  
...  
Keyword(s):  
Selection Process ◽  
Data Communication ◽  
Privacy Preserving ◽  
Consensus Algorithm ◽  
Security And Privacy ◽  
The Internet ◽  
Malicious Nodes ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Authentication Mechanism

The rapid advancement in the area of the Internet of Vehicles (IoV) has provided numerous comforts to users due to its capability to support vehicles with wireless data communication. The exchange of information among vehicle nodes is critical due to the rapid and changing topologies, high mobility of nodes, and unpredictable network conditions. Finding a single trusted entity to store and distribute messages among vehicle nodes is also a challenging task. IoV is exposed to various security and privacy threats such as hijacking and unauthorized location tracking of smart vehicles. Traceability is an increasingly important aspect of vehicular communication to detect and penalize malicious nodes. Moreover, achieving both privacy and traceability can also be a challenging task. To address these challenges, this paper presents a blockchain-based efficient, secure, and anonymous conditional privacy-preserving and authentication mechanism for IoV networks. This solution is based on blockchain to allow vehicle nodes with mechanisms to become anonymous and take control of their data during the data communication and voting process. The proposed secure scheme provides conditional privacy to the users and the vehicles. To ensure anonymity, traceability, and unlinkability of data sharing among vehicles, we utilize Hyperledger Fabric to establish the blockchain. The proposed scheme fulfills the requirement to analyze different algorithms and schemes which are adopted for blockchain technology for a decentralized, secure, efficient, private, and traceable system. The proposed scheme examines and evaluates different consensus algorithms used in the blockchain and anonymization techniques to preserve privacy. This study also proposes a reputation-based voting system for Hyperledger Fabric to ensure a secure and reliable leader selection process in its consensus algorithm. The proposed scheme is evaluated with the existing state-of-the-art schemes and achieves better results.

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