An intelligent transportation system (ITS) plays an important role in public transport management, security and other issues. Traffic flow detection is an important part of the ITS. Based on the real-time acquisition of urban road traffic flow information, an ITS provides intelligent guidance for relieving traffic jams and reducing environmental pollution. The traffic flow detection in an ITS usually adopts the cloud computing mode. The edge of the network will transmit all the captured video to the cloud computing center. However, the increasing traffic monitoring has brought great challenges to the storage, communication and processing of traditional transportation systems based on cloud computing. To address this issue, a traffic flow detection scheme based on deep learning on the edge node is proposed in this article. First, we propose a vehicle detection algorithm based on the YOLOv3 (You Only Look Once) model trained with a great volume of traffic data. We pruned the model to ensure its efficiency on the edge equipment. After that, the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm is optimized by retraining the feature extractor for multiobject vehicle tracking. Then, we propose a real-time vehicle tracking counter for vehicles that combines the vehicle detection and vehicle tracking algorithms to realize the detection of traffic flow. Finally, the vehicle detection network and multiple-object tracking network are migrated and deployed on the edge device Jetson TX2 platform, and we verify the correctness and efficiency of our framework. The test results indicate that our model can efficiently detect the traffic flow with an average processing speed of 37.9 FPS (frames per second) and an average accuracy of 92.0% on the edge device.

An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System

In the traditional cloud-based Internet of Vehicles (IoV) architecture, it is difficult to guarantee the low latency requirements of the current intelligent transportation system (ITS). As a supplement to cloud computing, fog computing can effectively alleviate the bottlenecks of cloud computing bandwidth and computing resources and improve the quality of service (QoS) of the IoV. However, as a distributed system that operates near users, fog computing has a complicated network structure. In the complex and dynamic IoV environment, to effectively manage these computing resources with different attributes and provide high-quality services, it is necessary to design an efficient architecture and a resource allocation algorithm. Therefore, on the basis of fog-cloud computing and software-defined networking (SDN), a novel 5G IoV architecture is designed. In addition, after fully considering the service requirements of the IoV, a model of four objectives is constructed, and a many-objective optimization algorithm is proposed. The experiment results show that the proposed algorithm outperforms the other state-of-the-art algorithms.

Resource Allocation in 5G IoV Architecture Based on SDN and Fog-Cloud Computing

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Artificial Intelligence for the Metaverse: A Survey

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.

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Holistic Network Virtualization and Pervasive Network Intelligence for 6G

The development of the Internet of Vehicles (IoV) has made transportation systems into intelligent networks. However, with the increase in vehicles, an increasing number of data need to be analyzed and processed. Roadside units (RSUs) can offload the data collected from vehicles to remote cloud servers for processing, but they cause significant network latency and are unfriendly to applications that require real-time information. Edge computing (EC) brings low service latency to users. There are many studies on computing offloading strategies for vehicles or other mobile devices to edge servers (ESs), and the deployment of ESs cannot be ignored. In this paper, the placement problem of ESs in the IoV is studied, and the six-objective ES deployment optimization model is constructed by simultaneously considering transmission delay, workload balancing, energy consumption, deployment costs, network reliability, and ES quantity. In addition, the deployment problem of ESs is optimized by a many-objective evolutionary algorithm. By comparing with the state-of-the-art methods, the effectiveness of the algorithm and model is verified.

Large-Scale Many-Objective Deployment Optimization of Edge Servers

The automotive industry, a key part of industrial Internet of Things, is now converging with cognitive computing (CC) and leading to industrial cognitive Internet of Vehicles (CIoV). As the major data source of industrial CIoV, social media has a significant impact on the quality of service (QoS) of the automotive industry. To provide vehicular social media services with low latency and high reliability, edge computing is adopted to complement cloud computing by offloading CC tasks to the edge of the network. Generally, task offloading is implemented based on the premise that edge servers (ESs) are appropriately quantified and located. However, the quantification of ESs is often offered according to empirical knowledge, lacking analysis on real condition of intelligent transportation system (ITS). To address the abovementioned problem, a c ollaborative method for the q uantification and p lacement of ESs, named CQP, is developed for social media services in industrial CIoV. Technically, CQP begins with a population initializing strategy by Canopy and K-medoids clustering to estimate the approximate ES quantity. Then, nondominated sorting genetic algorithm III is adopted to achieve solutions with higher QoS. Finally, CQP is evaluated with a real-world ITS social media data set from China.

Edge Server Quantification and Placement for Offloading Social Media Services in Industrial Cognitive IoV

With the potential of implementing computing-intensive applications, edge computing is combined with digital twinning (DT)-empowered Internet of vehicles (IoV) to enhance intelligent transportation capabilities. By updating digital twins of vehicles and offloading services to edge computing devices (ECDs), the insufficiency in vehicles’ computational resources can be complemented. However, owing to the computational intensity of DT-empowered IoV, ECD would overload under excessive service requests, which deteriorates the quality of service (QoS). To address this problem, in this article, a multiuser offloading system is analyzed, where the QoS is reflected through the response time of services. Then, a service offloading (SOL) method with deep reinforcement learning, is proposed for DT-empowered IoV in edge computing. To obtain optimized offloading decisions, SOL leverages deep Q-network (DQN), which combines the value function approximation of deep learning and reinforcement learning. Eventually, experiments with comparative methods indicate that SOL is effective and adaptable in diverse environments.

Service Offloading With Deep Q-Network for Digital Twinning-Empowered Internet of Vehicles in Edge Computing

Dynamic server placement in edge computing toward Internet of Vehicles

With the advent of the Internet of Vehicles (IoV), drivers are now provided with diverse time-sensitive vehicular services that usually require a large scale of computation. As civilian vehicles are generally insufficient in computational resources, their service requests are offloaded to cloud data centers and edge computing devices (ECDs) with ample computational resources to enhance the quality of service (QoS). However, ECDs are often overloaded with excessive service requests. In addition, as the network conditions and service compositions are complicated and dynamic, the centralized control of ECDs is hard to achieve. To tackle these challenges, a dynamic task offloading method with minority game (MG) in cloud-edge computing, named DOM, is proposed in this paper. Technically, MG is an effective tool with a distributed mechanism which can minimize the dependency on centralized control in resource allocation. In the MG, reinforcement learning (RL) is applied to optimize the distributed decision-making of participants. Finally, with a real-world dataset of IoV services, the effectiveness and adaptability of DOM are evaluated.

Bowen Shen

Papers

Edge Server Quantification and Placement for Offloading Social Media Services in Industrial Cognitive IoV

Service Offloading With Deep Q-Network for Digital Twinning-Empowered Internet of Vehicles in Edge Computing

Dynamic server placement in edge computing toward Internet of Vehicles

Dynamic Task Offloading with Minority Game for Internet of Vehicles in Cloud-Edge Computing