In recent years, with the increasing demand for computing power from various intelligent applications on mobile devices, heterogeneous multi-core architectures have received more attention. The big.LITTLE architecture, as a new type of energy-saving architecture, consists of high-performance cores and low energy consumption cores, which can better meet the real-time task requirements of various mobile devices. One of the challenges faced by these scheduling algorithms is how to fully utilize the advantages of big.LITTLE architectures to achieve a balance between system energy consumption and performance. To address this issue, we designed an initial task allocation strategy based on the characteristics of big.LITTLE architecture, introduced reinforcement learning algorithms for decision-making, and assigned different types of tasks to their appropriate processors. In order to balance task load and reduce total scheduling time, we proposed a dynamic migration algorithm, which dynamically adjusts task load by migrating them in the case of imbalanced utilization. Experimental results demonstrate that the proposed algorithm effectively balances energy consumption and performance, improves resource utilization, and reduces both scheduling time and energy consumption.

Task offloading is a key technology in fog computing, which allows resource-intensive tasks to be offloaded from terminal devices to fog nodes with higher computing capabilities. However, traditional task offloading methods usually use heuristic methods, which are highly dependent on the mode. They cannot effectively optimize the delay, resulting in a decline in service quality. To solve this problem, a task offloading method based on deep reinforcement learning (DRL) is proposed in this paper. We applied various methods to improve the Deep Q-Network (DQN) and utilized it for task offloading to enhance system performance in fog computing.

As automobiles continue to develop in the direction of intelligence and networking, the requirements for in-vehicle network bandwidth and deterministic time delay continue to increase. However, existing in-vehicle network standard protocols such as Controller Area Network (CAN) cannot meet the increasing bandwidth needs of in-vehicle networks, and Time Sensitive Networking (TSN) has emerged a research hot-spot for next-generation in-vehicle network standards. The next-generation in-vehicle network is developing towards a domain network architecture with TSN as the backbone and other conventional buses as branches. In this architecture, the TSN-CAN gateway is an important component that handles the data communication between the TSN domain and CAN. Due to the large difference in transmission rates between TSN and CAN, the TSN-CAN heterogeneous gateway suffers from congestion resulting in unguaranteed real-time transmission of messages across the gateway. To address this issue, a high response ratio priority scheduling algorithm (HRRP) for TSN-CAN gateways based on worst-case response time analysis theory is proposed in this paper. The algorithm assigns forwarding priority to CAN messages based on the value of their response ratio, and the experimental demonstrate show that the method can significantly improve the schedulability and reduce latency, improving the real-time performance of the system.

The development of the complexity and connectivity of modern automobiles has caused a massive rise in the security risks of in-vehicle networks (IVNs). Nevertheless, existing IVN designs (e.g., controller area network) lack cybersecurity consideration. Intrusion detection, an effective method for defending against cyberattacks on IVNs while providing functional safety and real-time communication guarantees, aims to address this issue. Therefore, the necessity of its research has risen. In this paper, an IVN environment is introduced, and the constraints and characteristics of an intrusion detection system (IDS) design for IVNs are presented. A survey of the proposed IDS designs for the IVNs is conducted, and the corresponding drawbacks are highlighted. Various optimization objectives are considered and comprehensively compared. Lastly, the trend, open issues, and emerging research directions are described.

The automotive electronic system is a typical heterogeneous distributed embedded system. For such a resource-constrained, cost-sensitive system, how to optimize the hardware cost and energy is a hot topic in current research. Meanwhile, industrial safety requirement must be satisfied according to safety standards. To address this complex problem, This study proposes an optimization algorithm, namely hardware cost and energy consumption optimization algorithm (HCECO), which is based on a genetic algorithm combined with simulated annealing and a state-of-the-art scheduling strategy. It aims to reduce the hardware cost and energy consumption of the embedded product while satisfying the hard real-time and reliability requirements of safety-critical applications during the early design phase. The experiment is completed under three real applications. The results demonstrate that the HCECO algorithm can effectively reduce the hardware cost and energy consumption under the hard real-time and reliability constraints.