In this paper, we study the scheduling problem of the im- precise mixed-criticality model (IMC) under earliest deadline first with virtual deadline (EDF-VD) scheduling upon uniprocessor systems. Two schedulability tests are presented. The first test is a concise utilization- based test which can be applied to the implicit deadline IMC task set. The suboptimality of the proposed utilization-based test is evaluated via a widely-used scheduling metric, speedup factors. The second test is a more effective test but with higher complexity which is based on the concept of demand bound function (DBF). The proposed DBF-based test is more generic and can apply to constrained deadline IMC task set. Moreover, in order to address the high time cost of the existing deadline tuning algorithm, we propose a novel algorithm which significantly improve the efficiency of the deadline tuning procedure. Experimental results show the effectiveness of our proposed schedulability tests, confirm the theoretical suboptimality results with respect to speedup factor, and demonstrate the efficiency of our proposed algorithm over the existing deadline tunning algorithm. In addition, issues related to the implementation of the IMC model under EDF-VD are discussed.

Energy harvesting promises to power billions of Internet-of-Things devices without being restricted by battery life. The energy output of harvesters is typically tiny and highly unstable, so the computing system must store program states into non-volatile memory frequently to preserve the execution progress in the presence of frequent power failures. Task-based intermittent computing is a promising paradigm to provide such capability, where each task executes atomically and only states across task boundaries need to be saved. This paper presents LATICS, a low-overhead adaptive task-based intermittent computing system, which dynamically decides the granularity of atomic execution to avoid unnecessarily frequent state saving when energy supply is sufficient. The novel feature of LATICS is to drastically reduce the amount of states to be saved at task boundaries compared with existing solutions. Notably, we disclose that skipping state saving at some task boundary may cause the system to store more states at other places, and thus leads to higher overall overhead. Therefore, LATICS enforces mandatory state saving at certain task boundaries regardless of the current energy condition to reduce state saving overhead. We implement LATICS on a real energy-harvesting platform based on MSP430 and experimentally compare against the state-of-the-art under different settings. The experimental results show that LATICS significantly reduces state saving overhead and improves execution efficiency compared to existing solutions.

Community detection has a very important role in data processing and analysis, which is very hot in recent years. However, traditional algorithms have shortcomings in both time complexity and precision. In this paper, we introduce a Modified Genetic Algorithm (MGA) that with alleles encoding and half uniform crossover to detect community structure. In the algorithm, each allele of the chromosome stands for the community index of the corresponding node. At the same time, half uniform crossover can better prevent the elite individuals from destroying. And we choose modularity function as its fitness function. It does not need to know how many communities the network has. In order to identify our algorithm is effective. We use both artificial random network and real networks to test our algorithm. The experimental results show that the MGA algorithm can be applied to community detection, and its accuracy and time complexity can reach the effect of classical algorithms.

Recent work has shown that the timing behavior of a real-time system can be utilized by attackers for various adverse purposes via schedule-based timing side-channel attacks. An important assumption in this type of attacks is the prior knowledge of attackers about the task parameter information, including the number of tasks in the system and the period and execution time of each task. The attackers can use such information, together with the execution sequence of the task system, to reconstruct the exact schedule of the tasks and perform various subsequent attacks. In this paper, we show that the schedule-based timing side channel attacks can actually be performed even without knowing the task parameter information in prior. We develop methods to infer the number of tasks in the system and the period and execution time of each task directly from the execution sequence. This removes the task parameter prior knowledge requirement of the attackers and shows greater threats of the schedule-based timing side channel attacks. Both simulation experiments with synthetic task sets and a Zedboard-based evaluation with control system for a 3DOF helicopter are conducted to evaluate the proposed task parameter analysis method.

Robot operating system (ROS) is a widely used robotic software development framework. In safety-critical applications that require timing guarantees, the first generation of ROS falls short. The introduction of ROS 2 has addressed some of these limitations, but its multithreaded executor still struggles to meet real-time requirements. To address this issue, we design a new multithreaded executor called RTeX for ROS 2. The goal of RTeX is to improve system performance in terms of both run-time efficiency and timing predictability. We have implemented RTeX in the latest version of ROS 2 and conducted experiments on a real platform. The experimental results demonstrate that RTeX outperforms both the default ROS 2 multithreaded executor and its state-of-the-art variant, achieving significant real-time performance improvements.

Micro-ROS is widely used to bridge the performance gap between resource-constrained microcontrollers and powerful computing devices in ROS-based robotic applications. After modeling the callback scheduling module and the communication module in micro-ROS, we found that there are some design flaws that significantly impact the real-time performance of micro-ROS. To improve the timing predictability and run-time efficiency of micro-ROS, we design and implement a priority-driven chain-aware scheduling system (PoDS) based on the existing micro-ROS architecture. The experimental results demonstrate that our proposed PoDS exhibits significantly improved real-time performance compared to the default micro-ROS.