Guaranteeing safety is always a prerequisite in the process of realizing various automotive applications. However, the automotive functional safety design has been challenged by multiple factors, such as the complexity of the new generation automotive Electrical and Electronic (E/E) architecture, the continuous release and update of automotive functional safety standard ISO 26262, the release of new AUTOSAR adaptive platform standard, and the increase in different types of costs. In this paper, we summarize the recent advances of automotive functional safety design methodologies through analysis, design, optimization, and runtime phases, respectively: 1) functional safety analysis; 2) functional safety guarantee; 3) safety-aware cost optimization; and 4) safety-critical multi-functional scheduling. Then, we provide the future trends in functional safety design methodologies that will be directly oriented to autonomous vehicles and adapt to the next generation functional safety standard ISO 21448.

Automotive cyber-physical systems (ACPS) are energy-aware and safety-critical systems where energy consumption should be controlled from a perspective of design constraint and reliability should be enhanced towards functional safety goal assurance. In this paper, we solve the problem of reliability enhancement of an automotive function (i.e., functionality or application) under energy and response time constraints based on the dynamic voltage and frequency scaling (DVFS) technique. The problem is solved by a two-stage solution: (1) response time reduction under energy constraint and (2) reliability enhancement under energy and response time constraints. The first stage is solved by proposing average energy pre-allocation, and the second stage is solved by proposing a reliability enhancement technique based on the first stage. Examples and experiments show that the proposed solution not only can assure energy and response time constraints, but also enhances reliability as much as 16.66% compared with its counterpart.

Current Intellectual Property (IP) copyright protection technologies have low efficiency of authority management, traceability, and scalability. In this work, a blockchain-based IP copyright protection algorithm is proposed to address these issues by establishing a mathematical model of quadratic matrix transformation for IP circuit trading. This algorithm proposes the design of a distributed random embedding mechanism and position mapping function that, when IP trading occurs in blockchain, the traceable mapping function can trace the copyright information in IP trading with the mapping factor. Besides, this work analyzes the credibility, transparency, overhead, and complexity. Experimental results show that the proposed algorithm can resist replaying attacks, yet the copyright information can be rapidly retrieved after suffered from attacks. Still, the proposed algorithm has higher security, stability, and traceability.

The Controller Area Network with Flexible Data- Rate (CAN FD) is the new generation of CAN technology to meet the daily increasing bandwidth requirement for Automotive Cyber-Physical Systems (ACPS). However, ACPS is a security- critical system, an efficient security/timing-aware Design Space Exploration (DSE) method is required to fully utilize CAN FD's high data phase data-rate. In this paper, we propose an AUTOSAR-compliant system model that integrates both timing and security constraint, an integrated Mixed-Integer Linear Programming formulation (i-MILP) for the optimal DSE of CAN FD, and a divide-and-conquer approach to the i-MILP (dc-MILP) to address its timing complexity problem. The experiment results show that dc-MILP scales well for industrial-size systems, and saves 1.94% to 4.76% bandwidth utilization and guarantees the schedulability for more signal sets by comparing with state-of- the-art algorithm.

Water environmental Internet of Things (IoT) system, which is composed of multiple monitoring points equipped with various water quality IoT devices, provides the possibility for accurate water quality prediction. In the same water area, water flows and exchanges between multiple monitoring points, resulting in an adjacency effect in the water quality information. However, traditional water quality prediction methods only use the water quality information of one monitoring point, ignoring the information of nearby monitoring points. In this paper, we propose a water quality prediction method based on multi-source transfer learning for a water environmental IoT system, in order to effectively use the water quality information of nearby monitoring points to improve the prediction accuracy. First, a water quality prediction framework based on multi-source transfer learning is constructed. Specifically, the common features in water quality samples of multiple nearby monitoring points and target monitoring points are extracted and then aligned. According to the aligned features of water quality samples, the water quality prediction models based on an echo state network at multiple nearby monitoring points are established with distributed computing, and then the prediction results of distributed water quality prediction models are integrated. Second, the prediction parameters of multi-source transfer learning are optimized. Specifically, the back propagates population deviation based on multiple iterations, reducing the feature alignment bias and the model alignment bias to improve the prediction accuracy. Finally, the proposed method is applied in the actual water quality dataset of Hong Kong. The experimental results demonstrate that the proposed method can make full use of the water quality information of multiple nearby monitoring points to train several water quality prediction models and reduce the prediction bias.