In-Vehicle networks such as the Controller Area Network (CAN) are highly vulnerable to cyberattacks due to the absence of encryption and authentication mechanisms. Intrusion detection systems (IDSs) have therefore become a critical line of defense. Among them, graph-based IDSs that model message sequences as graphs have attracted attention for their effectiveness against attacks that disrupt communication structures. However, replication experiments show that a representative method, GDTIDS, performs poorly against masquerade attacks, which preserve structural properties while manipulating payload contentsTo address this limitation, we propose a new feature, the outlier ratio, which incorporates payload-level information into the IDS. We define three variants of the feature based on value range, Hamming distance, and Mahalanobis distance. Evaluations on three publicly available CAN datasets demonstrate that the proposed method, particularly the Mahalanobis-Distance based variant, significantly improves detection of masquerade attacks while maintaining high performance against other attack types.

This study aims to explore how to improve the reliability of the next-generation mobility model—Mobility as a Service (MaaS) based on autonomous vehicles, with a particular focus on the system’s resilience to uncertainty. Currently, the application of reliability engineering in the field of smart mobility services is primarily concentrated on technical details, lacking unified standards and methods to enhance the reliability of service levels. This paper attempts to fill this research gap. In this study, we adopt a combined approach of system analysis and optimization algorithms. First, we design a system reliability analysis method by examining the potential discrepancies between the system’s capability to provide mobility services and stakeholder demands. Subsequently, we propose a reinforcement learning-based system service capacity optimization algorithm aimed at enhancing the system’s resilience at the service level to tackle challenges posed by uncertainty. To validate the effectiveness of the proposed method, we conduct a case study on a practical intelligent mobility service framework. Through system simulation, we generate and collect data on system service capacity, demand discrepancies, and uncertainty, as well as stakeholders’ expectations for the MaaS framework evaluation. Case studies and experimental data analysis confirm that the proposed resilience engineering approach effectively identifies potential risks in system service capacity and provides a compromise system resilience engineering solution in the context of conflicting stakeholder demands. To facilitate reproducibility and further research, the core code is available at https://github.com/zzs-code/MaaS-RE-MARL.git.

The demand for hazardous materials (HazMat) logistics is on the rise, and the importance of safety engineering of HazMat logistics is clear. Freight services based on intelligent transportation systems (ITS) offer a more secure, efficient, and environmentally-friendly method of HazMat logistics. At the same time, the voice of digital transformation (DX) in logistics continues to grow. However, there are currently some barriers to the digital transformation of the HazMat logistics industry, in which safety is always prioritized. One of the issues is how to promote the use of information technology in the logistics business process in a reasonable and orderly manner, given limited funds and technology, while also ensuring safety and improving the business process. Existing safety analysis methods, such as the Functional Resonance Analysis Method (FRAM) and its quantitative expansion, can be used to identify safety risks and make safety recommendations for business process improvement, but they cannot assist us with the rationale for DX decision making. To close the gap, we present in this article an optimization method of HazMat transportation safety engineering based on FRAM, which will assist HazMat logistics companies in making decisions regarding the implementation of ITS. We use the HazMat transportation of a Japanese logistics company as an example to verify and validate the proposed decision support scheme.

In recent decades, the research of energy-aware scheduling on heterogeneous multiprocessor systems is becoming more and more popular. A classic method for real-time task allocation is Linear Programming (LP). However, existing LP formulations are usually regarded as ineffective in solving large-scale allocation problems due to the unacceptable time consumption. In this work, we propose two integer linear programming (ILP) formulations to deal with the allocation problems for large task sets. One exact ILP(1) is formulated to derive an intermediate solution, and the other relaxed ILP(2) is considered to calculate the desired minimum energy. Then the desired minimum energy can be taken as a reference to evaluate the optimality of the intermediate solution. Experimental results on randomly generated task sets demonstrate that our method achieves average 19.2% less energy within limited time than the classic greedy and the state-of-the-art heuristic algorithm.

In 2015, the population of people over the age of 65 is 25.0% in Japan. This means that Japan has already become a super-aging society. In such society, the number of elderly people living alone has been also increased. For such people, a fall accident is serious because it can lead to serious injury or death. Researches and services to monitor behaviours of such people have been proposed. For example, by monitoring the status of use of home appliances, something unusual happened to them can be predicted. However, such systems cannot recognize the detailed behaviours like fall. Surveillance cameras have been introduced only outside the house because of the privacy issues. In this paper, we propose a mobile robot to detect human fall and report it to their observers. The mobile robot consists of a household mobile robot (Yujin Robot's Kobuki), a sensor (Microsoft's Kinect), and a computer (PC) to detect a human and control the robot. For simplicity of the robot and accurate fall detection, the sensor is installed on the robot to follow the target harmoniously. Thus, the sensor can move around with the robot to minimize blind area. The results of our experiments show that improvement of up to 80% in fall detection rate compared to a conventional monitoring technique using position-fixed sensors. Finally, we discuss the capabilities and future works of the robot.

There are several scheduling simulators to verify the behavior of real-time applications under different task scheduling algorithms. Current simulators cannot model the application accurately, and consequently the result of the simulation differs considerably from the actual behavior on a real computing system. This paper presents a scheduling simulator with application modeling capabilities for real-time applications. The proposed approach supports modeling of complex task control flows and dependency relations between tasks. In order to evaluate the modeling capabilities, we modeled a real engine control application and simulated it. We measured the response times of the application model running on our scheduling simulator and compared them with the ones obtained by running the real engine application binaries on an instruction set simulator. The average percentage errors in mean and worst-case response time between both simulations were only 9.6% and 8.6% respectively.

In recent years, embedded system technologies are evolving rapidly and in short-handedness of experienced engineers has become a serious problem. To solve this problem from the point of view of students and young engineers, a committee is organized by those members. We, the committee members, planned to provide a new educational material and a curriculum for students and young engineers to have an opportunity to learn embedded system technologies during summer time as Summer School on Embedded System Technologies (SSEST). In this school, we aim to provide an education for basic knowledge and techniques about embedded systems through a whole development process and an interchange among people of different universities and companies through the processes. In this paper, we introduce the plan of summer school, and its educational material and the curriculum of SSEST in 2005 and 2006.