Intelligence Edge Computing (IEC) is the key enabler of emerging 5G technologies networks and beyond. IEC is considered to be a promising backbone of future services and wireless communication systems in 5G integration. In addition, IEC enables various use cases and applications, including autonomous vehicles, augmented and virtual reality, big data analytic, and other customer-oriented services. Moreover, it is one of the 5G technologies that most enhanced market drivers in different fields such as customer service, healthcare, education methods, IoT in agriculture and energy sustainability. However, 5G technological improvements face many challenges such as traffic volume, privacy, security, digitization capabilities, and required latency. Therefore, 6G is considered to be promising technology for the future. To this end, compared to other surveys, this paper provides a comprehensive survey and an inclusive overview of Intelligence Edge Computing (IEC) technologies in 6G focusing on main up-to-date characteristics, challenges, potential use cases and market drivers. Furthermore, we summarize research efforts on IEC in 5G from 2014 to 2021, in which the integration of IEC and 5G technologies are highlighted. Finally, open research challenges and new future directions in IEC with 6G networks will be discussed.

The promise of low latency connectivity and efficient bandwidth utilization has driven the recent shift from vehicular cloud computing (VCC) towards vehicular edge computing (VEC). This paper presents an advanced deep learning-based computational offloading algorithm for multilevel vehicular edge-cloud computing networks. To conserve energy and guarantee the efficient utilization of shared resources among multiple vehicles, an integration model of computational offloading, and resource allocation is formulated as a binary optimization problem to minimize the total cost of the entire system in terms of time and energy. However, this problem is considered NP-hard and it is computationally prohibitive to solve this type of problem, particularly for large-scale vehicles, due to the curse-of-dimensionality problem. Therefore, an equivalent reinforcement learning form is generated and we propose a distributed deep learning algorithm to find the near-optimal computational offloading decisions in which a set of deep neural networks are used in parallel. Finally, simulation results show that the proposed algorithm can exhibit fast convergence and significantly reduce the overall consumption of an entire system compared to the benchmark solutions.

Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization.

Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network’s performance has improved because training and testing time do not increase when we include more nodes in the network.

Smart cities provide citizens with smart and advanced services to improve their quality of life. However, it has been observed that the collection, storage, processing, and analysis of heterogeneous data that are usually borne by citizens will bear certain difficulties. The development of the Internet of Things, cloud computing, social media, and other Industry 4.0 influencers pushed technology into a smart society’s framework, bringing potential vulnerabilities to sensor data, services, and smart city applications. These vulnerabilities lead to data security problems. We propose a decentralized data management system for smart and secure transportation that uses blockchain and the Internet of Things in a sustainable smart city environment to solve the data vulnerability problem. A smart transportation mobility system demands creating an interconnected transit system to ensure flexibility and efficiency. This article introduces prior knowledge and then provides a Hyperledger Fabric-based data architecture that supports a secure, trusted, smart transportation system. The simulation results show the balance between the blockchain mining time and the number of blocks created. We also use the average transaction delay evaluation model to evaluate the model and to test the proposed system’s performance. The system will address residents’ and authorities’ security challenges of the transportation system in smart, sustainable cities and lead to better governance.

In recent years, autonomous vehicles have become more and more popular due to their broad influence over society, as they increase passenger safety and convenience, lower fuel consumption, reduce traffic blockage and accidents, save costs, and enhance reliability. However, autonomous vehicles suffer from some functionality errors which need to be minimized before they are completely deployed onto main roads. Pedestrian detection is one of the most considerable tasks (functionality errors) in autonomous vehicles to prevent accidents. However, accurate pedestrian detection is a very challenging task due to the following issues: (i) occlusion and deformation and (ii) low-quality and multi-spectral images. Recently, deep learning (DL) technologies have exhibited great potential for addressing the aforementioned pedestrian detection issues in autonomous vehicles. This survey paper provides an overview of pedestrian detection issues and the recent advances made in addressing them with the help of DL techniques. Informative discussions and future research works are also presented, with the aim of offering insights to the readers and motivating new research directions.