Controller Area Network (CAN) is a masterless serial bus designed and widely used for the exchange of mission and time-critical information within commercial vehicles. Invehicle communication is based on messages sent and received by Electronic Control Units (ECUs) connected to this serial bus network. Although unencrypted, CAN messages are not easy to interpret. In fact, Original Equipment Manufacturers (OEMs) attempt to achieve security through obscurity by encoding the data in their proprietary format, which is kept secret from the general public. As a result, the only way to obtain clear data is to reverse engineer CAN messages. Driven by the need for in-vehicle message interpretation, which is highly valuable in the automotive industry, researchers and companies have been working to make this process automated, fast, and standardized. In this paper, we provide a comprehensive review of the state of the art and summarize the major advances in CAN bus reverse engineering. We are the first to provide a taxonomy of CAN tokenization and translation techniques. Based on the reviewed literature, we highlight an important issue: the lack of a public and standardized dataset for the quantitative evaluation of translation algorithms. In response, we define a complete set of requirements for standardizing the data collection process. We also investigate the risks associated with the automation of CAN reverse engineering, in particular with respect to the security network and the safety and privacy of drivers and passengers. Finally, we discuss future research directions in CAN reverse engineering.

Controller Area Network is the most frequently used in-vehicle communication system in the automotive industry today. The communication inside the Controller Area Network (CAN) bus is typically encoded using proprietary formats in order to prevent easy access to the information exchanged on the bus. However, it is still possible to decode this information through reverse engineering, performed either manually or via automated tools. Existing automated CAN bus reverse engineering methods are still time-consuming and require some manual effort, i.e., to inject diagnostic messages in order to trigger specific responses. In this paper, we propose CANMatch – a fully automated CAN bus reverse engineering framework that does not require any manual effort and significantly decreases the execution time by exploiting the reuse of CAN frames across different vehicle models. We evaluate the proposed solution on a dataset of CAN logs, or traces , related to 479 vehicles from 29 different automotive manufacturers, demonstrating its improved performance with respect to the state of the art.

With the emergence of self-driving technology and the ever-increasing demand of bandwidth-hungry applications, providing the required latency, security and computational capabilities is becoming a challenging task. Although being evolving, traditional vehicular radio access technologies, namely WLAN/IEEE 802.11p and cellular networks cannot meet all the requirements of future Cooperative, Connected and Automated Mobility (CCAM). In addition, current vehicular architectures are not sufficiently flexible to support the highly heterogeneous landscape of emerging communication technologies, such as mmWave, Cellular Vehicle-to-Everything (C-V2X), and Visible Light Communication (VLC). To this aim, Multi-access Edge Computing (MEC) has been recently proposed to enhance the quality of passengers experience in delay-sensitive applications. In this paper, we discuss the in-premises features of MEC and the need of supporting technologies, such as Software Defined Networking (SDN) and Network Function Virtualization (NFV), to fulfil the requirements in terms of responsiveness, reliability and resiliency. The latter is of paramount importance for automated services, which are supposed to be always-on and always-available. We outline possible solutions for mobility-aware computation offloading, dynamic spectrum sharing, and interference mitigation. Also, by revealing MEC-inherent security vulnerabilities, we argue for the need of adequate security and privacy-preserving schemes in MEC-enabled vehicular architectures.

We consider a network of agents that send periodic updates to their neighbors. A trade-off between load on the shared communication channel and data timeliness is obtained by looking at the Age of Information (AoI) metric. We develop a model of a Carrier-Sense Multiple Access (CSMA) network with partial sensing, to calculate the AoI of one-hop broadcast messages exchanged among the agents. The model is applied to beacon messages in a vehicular network to gain insight into the impact of system parameters.

We develop traffic management and data networking mechanisms and study their integrated design for an autonomous transportation system. The traffic management model involves a multi-lane multi-segment highway. Ramp managers regulate admission of vehicles into the highway and their routing to designated lanes. Vehicles moving across each lane are organized into platoons. A Platoon Leader (PL) is elected in each platoon and is used to manage its members and their communications with the infrastructure and with vehicles in other platoons. We develop new methods that are employed to determine the structural formations of platoons and their mobility processes in each lane, aiming to maximize the realized flow rate under vehicular end-to-end delay constraints. We set a limit on the vehicular on-ramp queueing delay and on the (per unit distance) transit time incurred along the highway. We make use of the platoon formations to develop new Vehicle-to-Vehicle (V2V) wireless networking cross-layer schemes that are used to disseminate messages among vehicles traveling within a specified neighborhood. For this purpose, we develop algorithms that configure a hierarchical networking architecture for the autonomous system. Certain platoon leaders are dynamically assigned to act as Backbone Nodes (BNs). The latter are interconnected by communications links to form a Backbone Network (Bnet). Each BN serves as an access point for its Access Network (Anet), which consists of its mobile clients. We study the delay-throughput performance behavior of the network system and determine the optimal setting of its parameters, assuming both TDMA and IEEE 802.11p oriented wireless channel sharing (MAC) schemes. Integrating these traffic management and data networking mechanisms, we demonstrate the performance tradeoffs available to the system designer and manager when aiming to assure an autonomous transportation system operation that achieves targeted vehicular flow rates and transit delays while also setting the data communications network system to meet targeted message throughput and delay objectives.