Fogbanks: Future Dynamic Vehicular Fog Banks for Processing, Sensing and Storage in 6G
A. A. Alahmadi, M. O. I. Musa, T. E. H. El-Gorashi, J. M. H. Elmirghani, S. Grant-Muller, D. Hutchison, A. Mauthe, M. Dianati, C. Maple, L. Lefevre, A. Lason
Fixed edge processing has become a key feature of 5G networks, while playing a key role in reducing latency, improving energy efficiency and introducing flexible compute resource utilization on-demand with added cost savings. Autonomous vehicles are expected to possess significantly more on-board processing capabilities and with improved connectivity. Vehicles continue to be used for a fraction of the day, and as such there is a potential to increase processing capacity by utilizing these resources while vehicles are in short-term and long-term car parks, in roads and at road intersections. Such car parks and road segments can be transformed, through 6G networks, into vehicular fog clusters, or Fogbanks, that can provide processing, storage and sensing capabilities, making use of underutilized vehicular resources. We introduce the Fogbanks concept, outline current research efforts underway in vehicular clouds, and suggest promising directions for 6G in a world where autonomous driving will become commonplace. Moreover, we study the processing allocation problem in cloud-based Fogbank architecture. We solve this problem using Mixed Integer Programming (MILP) to minimize the total power consumption of the proposed architecture, taking into account two allocation strategies, single allocation of tasks and distributed allocation. Finally, we describe additional future directions needed to establish reliability, security, virtualisation, energy efficiency, business models and standardization.
The rapid development of vehicles on-board units and the proliferation of autonomous vehicles in modern cities create a potential for a new fog computing paradigm, referred to as vehicular fog computing (VFC). In this paper, we propose an architecture that integrates a vehicular fog (VF) composed of vehicles clustered in a parking lot with a fixed fog node at the access network and the central cloud. We investigate the problem of energy efficient software matching in the VF considering different approaches to deploy software packages in vehicles.
Vehicular congestion is directly impacting the efficiency of the transport sector. A wireless sensor network for vehicular clients is used in Internet of Vehicles based solutions for traffic management applications. It was found that vehicular congestion detection by using Internet of Vehicles based connected vehicles technology are practically feasible for congestion handling. It was found that by using Fog Computing based principles in the vehicular wireless sensor network, communication in the system can be improved to support larger number of nodes without impacting performance. In this pa…
This paper investigates distributed processing in Vehicular Edge Cloud (VECs), where a group of vehicles in a car park, at a charging station or at a road traffic intersection, cluster and form a temporary vehicular cloud by combining their computational resources in the cluster. We investigated the problem of energy efficient processing task allocation in VEC by developing a Mixed Integer Linear Programming (MILP) model to minimize power consumption by optimizing the allocation of different processing tasks to the available network resources, cloud resources, fog resources and vehicular proce…
To enlarge the perception range and reliability of individual autonomous vehicles, cooperative perception has been received much attention. However, considering the high volume of shared messages, limited bandwidth and computation resources in vehicular networks become bottlenecks. In this paper, we investigate how to balance the volume of shared messages and constrained resources in fog-based vehicular networks. To this end, we first characterize sum satisfaction of cooperative perception taking account of its spatial-temporal value and latency performance. Next, the sensing block message, co…
Along with the development of Internet of Things (IoT) and the rise of fog computing, more new joint technologies have been proposed. Vehicular Ad-hoc Networks (VANET) are one of the emergent technologies that come with a very promising role, where the spare processing capabilities of vehicles can be exploited. In this paper, we propose a fog architecture to provide services for end users based on a cluster of static vehicles in a parking lot referred to as a vehicular fog. The proposed vehicular fog architecture is connected to the central data center through an optical infrastructure. As the…
Vehicular fog computing (VFC) has been envisioned as a promising paradigm for enabling a variety of emerging intelligent transportation systems (ITS). However, due to inevitable as well as non-negligible issues in wireless communication, including transmission latency and packet loss, it is still challenging in implementing safety-critical applications, such as real-time collision warning in vehicular networks. In this paper, we present a vehicular fog computing architecture, aiming at supporting effective and real-time collision warning by offloading computation and communication overheads to…
Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. The rogue nodes in VANETs broadcast malicious information leading to potential hazards, including the collision of vehicles. Previous researchers used either cryptography, trust values, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose fog-based rogue nodes detection (F-RouND), a fog computing scheme, which dynamically creates a fog utilizing the on-board units (OBUs) of all vehicles in the region fo…
Vehicular users are expected to consume large amounts of data, for both entertainment and navigation purposes. This will put a strain on cellular networks, which will be able to cope with such a load only if proper caching is in place, this in turn begs the question of which caching architecture is the best-suited to deal with vehicular content consumption. In this paper, we leverage a large-scale, crowd-collected trace to (i) characterize the vehicular traffic demand, in terms of overall magnitude and content breakup, (ii) assess how different caching approaches perform against such a real-wo…
Future vehicles will have rich computing resources to support autonomous driving and be connected by wireless technologies. Vehicular fog networks (VeFN) have thus emerged to enable computing resource sharing via computation task offloading, providing wide range of fog applications. However, the high mobility of vehicles makes it hard to guarantee the delay that accounts for both communication and computation throughout the whole task offloading procedure. In this article, we first review the state-of-the-art of task offloading in VeFN, and argue that mobility is not only an obstacle for timel…
In recent years, the number of IoT devices has been growing fast which leads to a challenging task for managing, storing, analyzing, and making decisions about raw data from different IoT devices, especially for delay-sensitive applications. In a vehicular network (VANET) environment, the dynamic nature of vehicles makes the current open research issues even more challenging due to the frequent topology changes that can lead to disconnections between vehicles. To this end, a number of research works have been proposed in the context of cloud and fog computing over the 5G infrastructure. On the…
Advances in connected vehicles based on Vehicular Ad-hoc Networks (VANETs) in recent years have gained significant attention in Intelligent Transport Systems (ITS) in terms of disseminating messages in an efficient manner. VANET uses Dedicated Short Range Communication (DSRC) for disseminating messages between vehicles and between infrastructures. Though DSRC based communications are viable, it is still challenging to disseminate messages in a timely manner when vehicles are not in the transmission range of each other. Furthermore, DSRC communication channels are heavily congested when the veh…
Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynam…
Vehicular fog computing (VFC) is envisioned as a promising solution to process the explosive tasks in autonomous vehicular networks. In the VFC system, task offloading is the key technique to process the computation-intensive tasks efficiently. In the task offloading, the task is transmitted to the VFC system according to the 802.11p standard and processed by the computation resources in the VFC system. The delay of task offloading, consisting of the transmission delay and computing delay, is extremely critical especially for some delay-sensitive applications. Furthermore, the long-term reward…
With the increasing development of advanced communication technologies, vehicles are becoming smarter and more connected. Due to the tremendous growth of various vehicular applications, a huge amount of data is generated through advanced on-board devices and is deemed critical to improve driving safety and enhance vehicular services. However, cloud based models often fall short in applications where latency and mobility are critical. In order to fully realize the potential of vehicular networks, the challenges of efficient communication and computation need to be addressed. In this direction, …
Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked rec…
The emergence of in-vehicle entertainment systems and self-driving vehicles, and the latter's need for high-resolution, up-to-date maps, will bring a further increase in the amount of data vehicles consume. Considering how difficult WiFi offloading in vehicular environments is, the bulk of this additional load will be served by cellular networks. Cellular networks, in turn, will resort to caching at the network edge in order to reduce the strain on their core network, an approach also known as mobile edge computing, or fog computing. In this work, we exploit a real-world, large-scale trace com…
Through connecting intelligent vehicles as well as the roadside infrastructure, the perception range of vehicles can be significantly extended, and hidden objects at blind spots can be efficiently detected and avoided. To realize this, accurate road map data must be downloaded in real time to these intelligent vehicles for navigation and localization purposes. Besides, the cloud must be updated with dynamic changes that happened in the road network. These involve the transmissions of high-definition 3D road map data for accurately representing the physical environments. In this work, we propos…
With the emerging of the fifth generation (5G) mobile communication systems and software defined networks, not only the performance of vehicular networks could be improved but also new applications of vehicular networks are required by future vehicles, e.g., pilotless vehicles. To meet requirements from intelligent transportation systems, a new vehicular network architecture integrated with 5G mobile communication technologies and software defined network is proposed in this paper. Moreover, fog cells have been proposed to flexibly cover vehicles and avoid frequently handover between vehicles …
Vehicular Edge Clouds (VECs) is a new distributed processing paradigm that exploits the revolution in the processing capabilities of vehicles to offer energy efficient services and improved QoS. In this paper we tackle the problem of processing allocation in a cloud-fog-VEC architecture by developing a joint optimization Mixed Integer Linear Programming (MILP) model to minimize power consumption, propagation delay, and queuing delay. The results show that while VEC processing can reduce the power consumption and propagation delay, VEC processing can increase the queuing delay because of the lo…
LLM-Empowered Cooperative Content Caching in Vehicular Fog Caching-Assisted Platoon Networks
School of Internet of Things Engineering, Jiangnan University（江南大学物联网工程学院）；School of Information Engineering, Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications, Nanchang University（江西省级先进信号处理与智能通信重点实验室，南昌大学信息工程学院）；Department of Electronic Engineering, State Key Laboratory of Space Network and Communications, and the Beijing National Research Center for Information Science and Technology, Tsinghua University（电子工程系，空间网络与通信国家重点实验室，信息科学与技术国家研究中心，清华大学）；Department of Computer Science, Brunel University（计算机科学系，布鲁内尔大学）；State Key Laboratory of ISN and the School of Telecommunications Engineering, Xidian University（信息与通信国家重点实验室，西安电子科技大学电信工程学院）
Autonomous driving is expected to provide a range of far-reaching economic, environmental and safety benefits. In this study, we propose a fog computing based framework to assist autonomous driving. Our framework relies on overhead views from cameras and data streams from vehicle sensors to create a network of distributed digital twins, called an edge twin, on fog machines. The edge twin will be continuously updated with the locations of both autonomous and human-piloted vehicles on the road segments. The vehicle locations will be harvested from overhead cameras as well as location feeds from …
The increasing availability of on-board processing units in vehicles has led to a new promising mobile edge computing (MEC) concept which integrates desirable features of clouds and VANETs under the concept of vehicular clouds (VC). In this paper we propose an architecture that integrates VC with metro fog nodes and the central cloud to ensure service continuity. We tackle the problem of energy efficient resource allocation in this architecture by developing a Mixed Integer Linear Programming (MILP) model to minimize power consumption by optimizing the assignment of different tasks to the avai…
With the rapid growth of Vehicular Ad-Hoc Networks (VANETs), huge amounts of road condition data are constantly being generated and sent to the cloud for processing. However, this introduces a significant load on the network bandwidth causing delay in the network and for a time-critical application like VANET such delay may have severe impact on real-time traffic management. This delay maybe reduced by offloading some computational tasks to devices as close as possible to the vehicles. Further, security and privacy of vehicles is another important concern in such applications. Thus, this paper…
Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes; however, these methods suffer from high processing delay, overhead, and False-Positive Rate (FPR). We propose herein Greenshield's traffic model-based fog computing scheme called Fog-based Rogue Nodes Detection (F-RouND), which dynamically utilizes the On-Board Units (OBUs) of all vehicles in the region for rogue node detection. We aim to reduce the data…
The increasing use of Internet of Things (IoT) devices generates a greater demand for data transfers and puts increased pressure on networks. Additionally, connectivity to cloud services can be costly and inefficient. Fog computing provides resources in proximity to user devices to overcome these drawbacks. However, optimisation of quality of service (QoS) in IoT applications and the management of fog resources are becoming challenging problems. This paper describes a dynamic online offloading scheme in vehicular traffic applications that require execution of delay-sensitive tasks. This paper …
The introduction of cloud data centres has opened new possibilities for the storage and processing of data, augmenting the limited capabilities of peripheral devices. Large data centres tend to be located away from the end users which increases latency and power consumption in the interconnecting networks. These limitations led to the introduction of edge processing where small distributed data centres or fog units are located at the edge of the network close to the end user. Vehicles can have substantial processing capabilities, often un-used, in their on-board-units (OBUs). These can be used…
Vehicular ad hoc networks (VANETs) facilitate vehicles to broadcast beacon messages to ensure road safety. Rogue nodes in VANETs cause a Sybil attack to create an illusion of fake traffic congestion by broadcasting malicious information leading to catastrophic consequences, such as the collision of vehicles. Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes, but they suffer from high processing delay, overhead, and false-positive rate (FPR). We propose a fog computing-based Sybil attack detection for VANETs (FSDV), which utilizes onboard un…
Vehicular fog computing (VFC) is envisioned as an extension of cloud and mobile edge computing to utilize the rich sensing and processing resources available in vehicles. We focus on slow-moving cars that spend a significant time in urban traffic congestion as a potential pool of on-board sensors, video cameras, and processing capacity. For leveraging the dynamic network and processing resources, we utilize a stochastic mobility model to select nodes with similar mobility patterns. We then design two distributed applications that are scaled in real-time and placed as multiple instances on sele…
Autonomous Vehicles (AVs) generated a plethora of data prior to support various vehicle applications. Thus, a big storage and high computation platform is necessary, and this is possible with the presence of Cloud Computing (CC). However, the computation for vehicular networks at the cloud computing suffers from several drawbacks, such as latency and cost, due to the proximity issue. As a solution, the computing capability has been recently proposed at the edge of vehicular networks, which is known as Vehicle Edge Computing (VEC). This leads to other open problems for vehicles to offload and c…
Vehicular Ad-hoc Networks (VANET) is a derived subclass of Mobile Ad-hoc Networks (MANET) with vehicles as mobile nodes. VANET facilitate vehicles to share safety and non-safety information through messages. Safety information includes road accidents, natural hazards, roadblocks, etc. Non-safety information includes tolling information, traveler information, etc. The main goal behind sharing this information is to enhance road safety and reduce road accidents by alerting the driver about the unexpected hazards. However, routing of messages in VANET is challenging due to packet delays arising f…
University of Birjand（比尔詹德大学）；Mazandaran University of Science and Technology（马赞达兰科技大学）；Qom University of Technology（库姆理工大学）；Deakin University（迪肯大学）；Singapore University of Social Sciences（新加坡社会科学大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）
With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computa…
