Guest editorial: Collaborative intelligence for vehicular Internet of Things

Celimuge Wu, Kok-Lim Alvin Yau, Carlos Tavares Calafate, Lei Zhong
2021 China Communications  
China Communications • July 2021 uture vehicular Internet-of-Things (IoT) systems feature a large number of devices and multi-access environments where different types of communication, computing, and storage resources must be efficiently utilized. At the same time, novel services such as cooperative autonomous driving and intelligent transportation systems (ITS), that demand unprecedented high accuracy, ultra-low latency, and large bandwidth, are emerging. These services also have an extreme
more » ... riance in user requirements and resource demands with respect to time, location, and context. Hence, current research is no longer confined to improving reliable communication and system operation in the presence of highly mobile vehicles, which has been the main focus in the past. It is therefore important to empower future vehicular IoT systems with advanced features, such as real-time reactive and proactive cooperation and coordination among different agents (or decision makers), including vehicles, roadside units, base stations, pedestrians, and other entities. Recently, artificial intelligence (AI) based approaches have been attracting great interest in empowering computer systems. Some collaborative learning approaches, such as federated learning and multi-agent systems, have been used to reduce network traffic and improve the learning efficiency of some smartphone applications. In vehicular IoT systems, collaborative intelligence can be achieved via an efficient collaboration among heterogeneous entities, including vehicles, edges, and the cloud. This feature topic focuses on the technical challenges and the synergistic effect of collaboration among heterogeneous entities and AI in enabling intelligent perception of the environment, intelligent networking, and intelligent processing of big data in vehicular IoT systems. We were successful at attracting 22 high-quality submissions. All of he sub-mitted papers were evaluated according to the standard reviewing process of China Communications. Following a rigorous peer review process, 12 papers were accepted in this special issue. The accepted papers cover a wide range of topics for enabling collaborative intelligence in vehicular IoT applications, including intelligent perception, radio resource allocation, routing protocols, data sharing, task offloading, and security enhancement. We hope this special issue will open up many exciting and critical future research activities in related fields. The first paper, "V2I based Environment Perception for Autonomous Vehicles at Intersections" by Duan et al., proposes a novel approach for collaborative perception about complex road environments while driving. In this approach, a vehicle-road collaborative system is built through vehicle-to-infrastructure (V2I) communications at intersections. Sensors are deployed on roadside to sense the traffic environment around intersections in real-time, and the object detection results are sent back to the autonomous vehicles via V2I links. Compared with the traditional perception methods, this approach uses roadside sensors to assist the surrounding autonomous vehicles for achieving perception enhancement. Therefore, the perception range of the autonomous vehicles in an intersection environment is improved, resulting in a better perception result. The article by Santa et al., "Machine Learning-Based Radio Access Technology Selection in the Internet of Moving Things," proposes a machine learning-based approach for selecting the most adequate transmission interface to transmit a certain message in a multi-access Internet-of-moving-things (IoMT) environment where the cellular, vehicular WiFi, and low power wide area network technologies coexist. The authors explore different machine
doi:10.23919/jcc.2021.9495349 fatcat:3zybrihi4ze37lvmokf7yqjtlu