Moisture Computing-Based Internet of Vehicles (IoV) Architecture for Smart Cities (original) (raw)

Using Vehicles as Fog Infrastructures for Transportation Cyber-Physical Systems (T-CPS)

International Journal of Software Science and Computational Intelligence

The advent of intelligent vehicular applications and IoT technologies gives rise to data-intensive challenges across different architectural layers of an intelligent transportation system (ITS). Without powerful communication and computational infrastructure, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in daily life. The current cloud computing and cellular set-ups are far from perfect because they are highly dependent on, and bear the cost of additional infrastructure deployment. Thus, the geo-distributed ITS components require a paradigm shift from centralized cloud-scale processing to edge centered fog computing (FC) paradigms. FC outspreads the computing facilities into the edge of a network, offering location-awareness, latency-sensitive monitoring, and intelligent control. In this article, the authors identify the mission-critical computing needs of the next generation ITS applications and highlight the scope...

vFog: A Vehicle-Assisted Computing Framework for Delay-Sensitive Applications in Smart Cities

IEEE Access, 2019

The inception of the smart cities concept provides a compelling platform to support innovative applications. It provides distinctive view of cities, where mobile devices, pedestrians, and electronic gadgets can communicate with each other to build an effective urban environment to further improve the living standards. Similarly, the role of the Internet of Things (IoT) and vehicular computing has emerged due to smart cities. This is further complemented by edge and fog computing architectures. The emerging concept of vehicular fog computing has enabled the platform to support delay-sensitive applications and to reduce the workload on the backend networks. Vehicular fog computing is a paradigm that touches the boundaries of thinking vehicles as an infrastructures-as-a-service. The use of vehicles to provide computation on-the-move poses various challenges. The vehicles with onboard computing equipment can facilitate delay-sensitive applications. These vehicles can act as an edge device to reduce the load from a backbone network. However, due to continuous mobility, it is difficult to use traditional frameworks to distribute the computation task among vehicles. In this paper, we propose a framework termed vFog. The vFog is designed to provide computing facilities from nearby fog vehicles. The framework utilizes the onboard computing facility of vehicles without the support of roadside units (RSUs). Moreover, the proposed framework handles churn behavior and supports multi-hop communication to improve the task delivery ratio. The proposed framework allows researchers to benchmark their own task distribution algorithms over the dynamic vehicular networks. INDEX TERMS Vehicular fog computing, tasks scheduling policy, edge devices, multi-vehicle relay.

Vehicular Fog Computing: Challenges Applications and Future Directions

2017

In recent years Vehicular Ad Hoc Networks (VANETs) have received increased attention due to its numerous applications in cooperative collision warning and traffic alert broadcasting. VANETs have been depending on cloud computing for networking, computing and data storage services. Emergence of advanced vehicular applications has led to the increased demand for powerful communication and computation facilities with low latency. With cloud computing unable to satisfy these demands, the focus has shifted to bring computation and communication facilities nearer to the vehicles, leading to the emergence of Vehicular Fog Computing (VFC). VFC installs highly virtualized computing and storage facilities at the proximity of these vehicles. The integration of fog computing into VANETs comes with a number of challenges that range from improved quality of service, security and privacy of data to efficient resource management. This paper presents an overview of this promising technology...

ENHANCED STUDY ON FOG COMPUTING AND IMPLEMENTATION ON VEHICULAR COMMUNICATION

IAEME PUBLICATION, 2021

Internet of things with cloud computing has been around for a while now but there are limitations in using this model such as high latency, security issues with third party involvement and large bandwidth required. These limitations are overruled by an extension of fog computing. In this paper fog computing is elaborated and basic architecture is discussed in detail. In situations where the decision is to be made with no or minimal latency this model outshines a human interacted machine or cloud computing model. In the latter part an application of vehicle to vehicle communication which implements a prototype of fog computing is explained. This will guarantee a safer commute and enables a more robust autonomous vehicle communication.

oneVFC—A Vehicular Fog Computation Platform for Artificial Intelligence in Internet of Vehicles

IEEE Access

We are witnessing the evolution from Internet of Things (IoT) to Internet of Vehicles (IoV). Internet connected vehicles can sense, communicate, analyze and make decisions. Rich vehicle-related data collection allows to apply artificial intelligence (AI) such as machine learning and deep learning (DL) to develop advanced services in Intelligent Transportation Systems (ITS). However, AI/DL-based ITS applications require intensive computation, both for model training and deployment. The exploitation of the huge computational power obtained through aggregation of resources present in individual vehicles and ITS infrastructure brings an efficient solution. In this work, oneVFC, a tangible vehicular fog computing (VFC) platform based on oneM2M is proposed. It benefits from the oneM2M standard to facilitate interoperability as well as hierarchical resource organization. oneVFC manages the distributed resources, orchestrates information flows and computing tasks on vehicle fog nodes and feeds back results to the application users. On a lab scale model consisting of Raspberry Pi modules and laptops, we demonstrate how oneVFC manages the AI-driven applications running on various machines and how it succeeds in significantly reducing application processing time, especially in cases with high workload or with requests arriving at high pace. We also show how oneVFC facilitates the deployment of AI model training in Federated Learning (FL), an advanced privacy preserving and communication saving training approach. Our experiments deployed in an outdoor environment with mobile fog nodes participating in the computation jobs confirm the feasibility of oneVFC for IoV environments whenever the communication links among fog nodes are guaranteed by V2X technology.

An Integrated Framework for Fog Communications and Computing in Internet of Vehicles

EPiC Series in Computing

In this paper, the novel Fog Communications and Computing paradigm is addressed by presenting an integrated system architecture, that is applied to achieve a full con- text awareness for vehicular networks and, consequently, to react on traffic anomalous conditions. In particular, we propose to adopt a specific co-designed approach involving Application and Networks Layers. For the latter one, as no infrastructure usually exists, effective routing protocols are needed to guarantee a certain level of reliability of the in- formation collected from individual vehicles. As a consequence, we investigated classical Epidemic Flooding based, Network Coding inspired and Chord protocols. Besides, we resort to Blockchain principle to design a distributed consensus sensing application. The system has been tested by resorting to OMNeT++ framework for its modularity, high fidelity and flexibility. Performance analysis has been conducted over realistic scenarios in terms of consensus making overh...

Fog computing is a novel computing paradigm

Fog computing is a novel computing paradigm that leverages the benefits of cloud computing while overcoming some of its limitations. It extends cloud computing to the edge of the network, where data is generated and consumed, and enables low-latency, location-aware, and context-aware services. In this paper, we provide an overview of fog computing, its architecture, key enabling technologies, and applications. We also discuss the challenges and open research issues in fog computing, including security, resource management, and scalability. We conclude with a discussion of future research directions in fog computing. Introduction: With the proliferation of smart devices, the amount of data generated at the edge of the network is increasing rapidly. This data is often time-sensitive and requires real-time processing and analysis. Cloud computing has been widely adopted to handle the storage and processing of this data. However, cloud computing has several limitations, such as high latency, limited bandwidth, and lack of mobility support. Fog computing, also known as edge computing, is a new computing paradigm that aims to overcome these limitations by extending cloud computing to the edge of the network. Fog Computing Architecture: Fog computing architecture consists of three layers: the edge layer, the fog layer, and the cloud layer. The edge layer includes smart devices and sensors that generate data. The fog layer includes the fog nodes, which are located closer to the edge than the cloud, and provide computing and storage resources. The cloud layer includes the data centers that provide massive computing and storage resources. Fog computing architecture is a hierarchical model that comprises three layers, namely edge, fog, and cloud layers, as described below: Edge Layer: This layer is the first layer of the fog computing architecture and comprises smart devices and sensors that generate data. The edge devices can include sensors, cameras, and other Internet of Things (IoT) devices. These devices produce a massive amount of data that requires real-time processing and analysis. The edge layer is responsible for collecting, processing, and filtering the data before sending it to the fog layer. Reference: Al-Fuqaha et al. (2015) provide a survey of the enabling technologies, protocols, and applications for IoT. They highlight the importance of edge devices in generating data and collecting sensory information.

FellowMe Cache: Fog Computing approach to enhance (QoE) in Internet of Vehicles

Future Generation Computer Systems, 2020

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

A survey on vehicular cloud computing

Journal of Network and Computer Applications, 2013

Vehicular networking has become a significant research area due to its specific features and applications such as standardization, efficient traffic management, road safety and infotainment. Vehicles are expected to carry relatively more communication systems, on board computing facilities, storage and increased sensing power. Hence, several technologies have been deployed to maintain and promote Intelligent Transportation Systems (ITS). Recently, a number of solutions were proposed to address the challenges and issues of vehicular networks. Vehicular Cloud Computing (VCC) is one of the solutions. VCC is a new hybrid technology that has a remarkable impact on traffic management and road safety by instantly using vehicular resources, such as computing, storage and internet for decision making. This paper presents the state-of-theart survey of vehicular cloud computing. Moreover, we present a taxonomy for vehicular cloud in which special attention has been devoted to the extensive applications, cloud formations, key management, inter cloud communication systems, and broad aspects of privacy and security issues. Through an extensive review of the literature, we design an architecture for VCC, itemize the properties required in vehicular cloud that support this model. We compare this mechanism with normal Cloud Computing (CC) and discuss open research issues and future directions. By reviewing and analyzing literature, we found that VCC is a technologically feasible and economically viable technological shifting paradigm for converging intelligent vehicular networks towards autonomous traffic, vehicle control and perception systems. Systems (ITS) (Al-Sultan et al. , 2013, Hartenstein and. The promise of vehicular networking has led to a fast convergence with ITS and to the advent of Intelligent Vehicular Networks , which are anticipated to transform driving styles by creating a secure, safe and healthy environment that will ultimately encompass our busy city streets and highways. Thus, the intelligent vehicular networks will provide infotainment and will enable a new versatile system that enhances transportation efficiency and safety . Although many efforts have been made to reach these objectives, VANET has several drawbacks, such as the high cost of the service constrained communications due to the high mobility of the vehicle .