Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%.

While 5G can facilitate high-speed Internet access and make over-the-horizon control a reality for unmanned aerial vehicles (UAVs; also known as drones), there are also potential security and privacy considerations, for example, authentication among drones. Centralized authentication approaches not only suffer from a single point of failure but they are also incapable of cross-domain authentication. This complicates the cooperation of drones from different domains. To address these limitations, a blockchain-based cross-domain authentication scheme for intelligent 5G-enabled Internet of drones is proposed in this article. Our approach employs multiple signatures based on threshold sharing to build an identity federation for collaborative domains. This allows us to support domain joining and exiting. Reliable communication between cross-domain devices is achieved by utilizing smart contract for authentication. The session keys are negotiated to secure subsequent communication between two parties. Our security and performance evaluations show that the proposed scheme is resistant to common attacks targeting Internet of Things (IoT) devices (including drones), as well as demonstrating its effectiveness and efficiency.

Edge intelligence is an emerging concept that refers to the processes in which data is collected, analyzed, and insights delivered close to where it is captured in a network using a selection of advanced intelligent technologies. As a promising solution to solve the problems of insufficient computing capacity and transmission latency, edge intelligence empowered Internet of Vehicle (IoV) is being widely investigated in both academia and industry. However, data sharing security in edge intelligent IoV becomes a challenge that should be solved with priority. Although the attribute-based encryption (ABE) is capable of addressing this challenge, a lot of time-consuming modular exponential operations and bilinear pair operations, as well as the serial computing, leads to a slow decryption speed of the ABE. It cannot address the response time requirement of edge intelligent IoV. Given this problem, an ABE model with parallel outsourced decryption for edge intelligent IoV, called ABEM-POD, is proposed. It includes a generic parallel outsourced decryption method for ABE based on the Spark and the MapReduce. This method is applicable to all ABE schemes with tree access structure and can be applied to edge intelligent IoV. Any ABE scheme based on the proposed model not only supports parallel outsourced decryption but also has the same security as the original scheme. In this paper, ABEM-POD has been applied to three representative ABE schemes and the experiments show that the proposed ABEM-POD is efficient and easy to use. It can significantly improve the speed of outsourced decryption to address the response time requirement for edge intelligent IoV.

It is expected that a mixture of autonomous and manual vehicles will persist as a part of the intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues arising from this mix of autonomous and manual vehicles before autonomous vehicles are entirely popularized is crucial. As the ITS system has increased in complexity, autonomous vehicles exhibit problems such as a low intention recognition rate and poor real-time performance when predicting the driving direction; these problems seriously affect the safety and comfort of mixed traffic systems. Therefore, the ability of autonomous vehicles to predict the driving direction in real time according to the surrounding traffic environment must be improved and researchers must work to create a more mature ITS. In this paper, we propose a deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled ITS. In this scheme, a driving trajectory dataset and a natural-driving dataset are employed as the network inputs to long-term memory networks in the 5G-enabled ITS: the probability matrix of each intention is calculated by the softmax function. Then, the final intention probability is obtained by fusing the mean rule in the decision layer. Experimental results show that the proposed scheme achieves intention recognition rates of 91.58% and 90.88% for left and right lane changes, respectively, effectively improving both accuracy and real-time intention recognition and improving the lane change problem in a mixed traffic environment.

The Internet revolution and Moore’s Law drove the rapid expansion of connected consumer electronics. As massive data is generated by Internet of Things (IoT) devices, edge computing has been developed and applied in consumer electronics to provide agile and real-time services. In this paper, we provide an overview of artificial intelligence-empowered edge computing in consumer electronics. We start with the background of consumer electronics and artificial intelligence and outline key characteristics and challenges, which are used to evaluate the state of research in smart homes, smart buildings, smart healthcare, and intelligent diagnosis. The evaluation shows that research in AI-empowered edge computing for consumer electronics has mainly focused on latency, robustness, and reliability over the past 5 years. However, explainability and load balancing are less mentioned and have great potential for future research. Although AI-empowered edge computing for consumer electronics is a thriving and widely used research area, there are still many unsolved problems and challenges that researchers can address.

Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the abovementioned issues, a blockchain-empowered decentralized horizontal FL framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy.