Over-The-Air (OTA) software updates are widely used in automotive embedded systems to remotely address software defects and vulnerabilities. However, the distribution of software packages is vulnerable to malicious attacks, posing severe security threats. Various cryptographic algorithms are used to secure automotive OTA software updates. However, existing secure OTA software updates rely on digital certificates for identity authentication. These digital certificates are often provided by third-party Certificate Authorities (CAs) and issued based on physical identifiers (e.g., Vehicle Identification Number (VIN), engine number, or Electronic Control Unit-ID (ECU-ID)), which are susceptible to illegal modification. Additionally, these secure OTA software updates fail to provide integrated security that encompasses authentication, confidentiality, integrity, access control, and data freshness. To tackle these existing drawbacks, we propose LigSecOTA, a lightweight OTA software update with integrated security based on a one-machine-one-certificate digital identity management system. The one-machine-one-certificate digital identity management system issues a unique and trusted digital certificate for each ECU based on bit time information instead of physical identifiers; these certificates are then used for ECU authentication. LigSecOTA ensures integrated security, including authentication, confidentiality, integrity, access control, and data freshness, through three processes: authentication, authorization, and package distribution. The authorization dy namically provides keys for the package distribution, significantly enhancing security. The security attributes of LigSecOTA are formally verified using the ProVerif tool. Finally, we evaluate LigSecOTA on the NXP LS1028A platform with an ARM Cortex A72 core. Experimental results demonstrate that LigSecOTA outperforms state-of-the-art secure OTA software updates in terms of computation and communication overhead, highlighting its lightweight nature.

Time-sensitive networking (TSN) is an emerging technology widely used in real-time systems for its high bandwidth and deterministic timing properties. To ensure the deterministic transmission of Time-triggered (TT) messages, a guard band mechanism is employed to prevent interference from other messages, such as Audio-Video Bridging (AVB) and Best-effort (BE) messages, before transmitting the TT messages in TSN. However, this mechanism introduces transmission delays for non-TT messages and bandwidth wastes for the physical links. Another challenge arises from the default First-in-first-out (FIFO) order of incoming messages, resulting in a relatively low acceptance rate for non-TT messages. To address these issues, a hybrid scheduling algorithm based on the min-heap structure (HSMH) is proposed. For AVB messages, HSMH sorts them in ascending style on the basis of deadlines, guaranteeing the earliest deadline message to be sent first. For BE messages, a threshold is designed to diverge them into two queues: a FIFO queue and a STF (shortest-time-first) queue. The former outputs the messages in a FIFO style, while the latter outputs messages in a STF style. All the output order of AVB messages and STF-queue messages are arranged in a min-heap structure. The algorithm can efficiently improve the transmission rate of AVB messages, the sending rate of BE messages, and the overall link utilization. Experimental results demonstrate that the proposed algorithm outperforms existing approaches in all these three aspects.

This paper introduces a novel Real Relative encoding Genetic Algorithm (R$^{2}$GA) to tackle the workflow scheduling problem in heterogeneous distributed computing systems (HDCS). R$^{2}$GA employs a unique encoding mechanism, using real numbers to represent the relative positions of tasks in the schedulable task set. Decoding is performed by interpreting these real numbers in relation to the directed acyclic graph (DAG) of the workflow. This approach ensures that any sequence of randomly generated real numbers, produced by cross-over and mutation operations, can always be decoded into a valid solution, as the precedence constraints between tasks are explicitly defined by the DAG. The proposed encoding and decoding mechanism simplifies genetic operations and facilitates efficient exploration of the solution space. This inherent flexibility also allows R$^{2}$GA to be easily adapted to various optimization scenarios in workflow scheduling within HDCS. Additionally, R$^{2}$GA overcomes several issues associated with traditional genetic algorithms (GAs) and existing real-number encoding GAs, such as the generation of chromosomes that violate task precedence constraints and the strict limitations on gene value ranges. Experimental results show that R$^{2}$GA consistently delivers superior performance in terms of solution quality and efficiency compared to existing techniques.

Integrated Mapping and Scheduling (IMS) problems can be found in many domains, such as electronic design automation (EDA) and modern manufacturing systems. Optimization algorithms to solve the IMS problems can be used to minimize execution time, implementation cost, energy consumption, etc. Genetic Algorithms (GAs) are powerful evolutionary algorithms for tackling many of such IMS op-timization problems. By utilizing biological principles like selection, crossover, and mutation, GAs excel in generating high-quality solutions. Chromosome encoding and decoding, in addition to evolutionary operators, significantly influence GA's efficiency. This paper introduces a relative-priority genetic algorithm (RPGA), a novel GA for IMS problems, such as those in EDA. RPGA employs a unique encoding scheme tailored for IMS problems, especially those with OR nodes representing alternative operation paths. It encodes the relative priority of an operation in a chromosome, which can be divided into two parts: one for path selections and the other for operation scheduling and mappings. Efficient decoding of every chromosome into a solution is facilitated through the concept of a ready operation set. The study extensively compares RPGA and established meta-heuristics using a benchmark set. The experimental results demonstrate that RPGA achieves high solution quality and rapid convergence.

The problem of minimizing the energy consumption of a real-time parallel application on a heterogeneous system has been studied recently, and slack time reclamation based on the dynamic voltage and frequency scaling (DVFS) energy-efficient design technique has been proposed as a solution. However, the state-of-the-art algorithms merely minimize energy consumption through an “upward” approach (i.e., from exit to entry tasks) and do not apply the “downward” approach (i.e., from entry to exit tasks) to energy consumption minimization. This study solves the same problem by employing “downward” and “upward” approaches. The concepts of deadline-slack and task level are introduced to transfer the deadline of the parallel application to each task, that is, “downward” energy consumption minimization is implemented. “Upward” energy consumption minimization by reclaiming the slack time is then included to implement “downward” and “upward” energy consumption minimization with low time complexity. Results of the experiments using real parallel applications show that the proposed algorithm can generate the minimum energy consumption compared with the state-of-the-art algorithms under different real-time and scale conditions.