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.

Internet of things (IoT) is a new paradigm that involves the interconnection of thousands of devices and home appliances. Due to the scarcity of the spectrum suitable for wireless electromagnetic transmission, many communication systems and devices are close to each other, or even overlapping in spectrum, thus incurring complicated interference situations. Therefore, interference in IoT is worthy of thorough investigation and should be well addressed. There have been numerous interference management (IM) proposals at the interfering transmitter or the interfered transmitter/receiver separately or cooperatively. Moreover, the existing IM schemes rely mainly on the use of channel state information (CSI). However, in some communication scenarios, the option to adjust the interferer is not available, and, in the case of downlink transmission, it is always difficult or even impossible for the interfered receiver to acquire necessary information for IM. Based on the above observations, we first propose a novel IM technique, called interference steering (IS). By making use of both CSI with respect to and data carried in the interfering signal, IS generates a signal to modify the spatial feature of the original interference, so that the steered interference at the interfered receiver is orthogonal to its intended signal. We then apply IS to a wireless local area network (WLAN) based IoT in which the same frequency band is reused by adjacent basic service sets (BSSs) with overlapping areas. With IS, multiple nearby access points (APs) could simultaneously transmit data on the same channel to their mobile stations (STAs), thus enhancing spectrum reuse. Our in-depth simulation results show that IS significantly improves network SE over existing IM schemes.

Accurate tracking of the hands and fingers allows users to employ natural gestures in various interactive applications. Hand tracking also supports health applications, such as monitoring face-touching, a common vector for infectious disease. However, for both types of applications, the utility of hand tracking is often limited by the impracticality of bulky tethered systems (e.g., instrumented gloves) or inherent limitations (e.g., Line of Sight or privacy concerns with vision-based systems). These limitations have severely restricted the adoption of hand tracking in real-world applications. We present MagX, a fully untethered on-body hand tracking system utilizing passive magnets and a novel magnetic sensing platform. Since passive magnets require no maintenance, they can be worn on the hands indefinitely, and only the sensor board needs recharging, akin to a smartwatch. We used MagX to conduct a series of experiments, finding a wearable sensing array can achieve millimeter-accurate 5 DoF tracking of two magnets independently. For example, at 11 cm distance, a 6cm × 6cm sensing array can achieve positional and orientational errors of 0.76 cm and 0.11 rad. At 21 cm distance, the tracking errors are 2.65 cm and 0.41 rad. MagX can leverage larger sensor arrays for improved long-distance tracking, e.g., a 9.8cm × 9.8cm array can achieve 2.62 cm and 0.55 rad tracking performance on two magnets at 27 cm distance. The robust performance can facilitate ubiquitous adoption of magnetic tracking in various applications. Finally, MagX can perform all compute locally and only requires 0.38W total (220mW on the sensor platform plus 159mW on the computing unit) to perform real-time tracking, offering "all day" fully untethered operation on a typical smartwatch-sized battery.

With an increasing demand for complex and powerful system-on-chips, modern real-time automotive systems face significant challenges in managing on-chip-temperature. We demonstrate, via real experiments, the importance of accounting for dynamic ambient temperature and task-level power dissipation in resource management so as to meet both thermal and timing constraints. To address this problem, we propose RT-TRM, a real-time thermal-aware resource management framework. We first introduce a task-level dynamic power model that can capture different power dissipations with a simple task-level parameter called the activity factor . We then develop two new mechanisms, adaptive parameter assignment and online idle-time scheduling . The former adjusts voltage/frequency levels and task periods according to the varying ambient temperature while preserving feasibility. The latter generates a schedule by allocating idle times efficiently without missing any task/job deadline. By tightly integrating the solutions of these two mechanisms, we can guarantee both thermal and timing constraints in the presence of dynamic ambient temperature variations. We have implemented RT-TRM on an automotive microcontroller to demonstrate its effectiveness, achieving better resource utilization by 18.2% over other runtime approaches while meeting both thermal and timing constraints.

Cache-enabled network architecture has great potential for enhancing the efficiency of content distribution as well as reducing the network congestion. This, in turn, has called for joint optimization of traffic engineering and caching strategies while considering both network congestion and content demands. In this paper, we present a distributed framework for joint request/data forwarding and dynamic cache placement in cache-enabled networks. Specifically, to retrieve the information about content demands and network congestion over the network, we establish a dual queue system for both requests and data, and define a dynamic mapping between the two queues with the help of dummy data such that the nodes can determine packet forwarding and caching strategies based only on local information. As the local objective function associated with Lyapunov optimization is time-varying due to the stochastic evolution of request/data queues, we develop a low-complexity distributed forwarding and caching algorithm via stochastic network utility maximization. We also prove the proposed algorithm achieves queue stability, and derive its region stability property for time-varying local optimization to demonstrate the convergence behavior. The simulation results verify queue stability and shows the proposed algorithm outperforms the existing ones.