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.

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.

The precise classification of human activities through sensor data collection and analysis addresses the broad demands in healthcare, security surveillance, and smart home applications amidst the rapid development of IoT technology. However, achieving high efficiency and accuracy remains a significant challenge for HAR algorithms. This paper proposes a HAR algorithm based on Transformer and Random Forest (Transformer-RF). The algorithm extracts and integrates multimodal features in the time domain, frequency domain, and statistical metrics, constructing one-dimensional and two-dimensional feature sets through feature transformation. The Transformer component, leveraging self-attention mechanisms, captures long-range dependencies and extracts global contextual information. Concurrently, the Random Forest component randomly selects features and samples, enhancing model diversity and improving complex human activity recognization capabilities. Experimental results demonstrate that compared with state-of-the-art algorithms, the Transformer-RF model achieves superior performance on both one-dimensional and two-dimensional feature sets, with an accuracy of up to 94.17%. The primary contribution of this paper lies in the introduction of an innovative Transformer-RF human activity recognization method, which not only ensures high accuracy but also exhibits excellent generalization capability and practical application potential. This study provides new insights and technical solutions for the field of human activity recognization, offering significant theoretical and practical value.

As promising industrial embedded networks, Controller Area Networks with Flexible Data-rate (CAN-FD) are widely used in time-sensitive domains, such as automotive networks. However, the absence of built-in security mechanisms in CAN-FD necessitates the development of security protection mechanisms. The existing Lightweight Authentication for Secure Automotive Networks (LASAN) framework focuses on enhancing the security of CAN/CAN-FD communication but neglects the conflict between security and delay. In this study, we conduct a thorough analysis of the causal mechanism related to the security and delay of LASAN and propose a static message scheduling method called Cooperative Swapping Approach (CSA) to achieve secure and low-delay CAN-FD communication. CSA is to minimize the end-to-end delay of precedence-constrained CAN-FD applications by swapping message positions in a valid message sequence. Nevertheless, exchanging message positions may impact the precedence dependencies between messages; therefore, we propose a novel Cooperative Transform Approach (CTA) within the CSA to efficiently preserve these precedence constraints. Valid message sequences with minimal end-to-end delays of a motivation example and an Adaptive Cruise Control (ACC) application are obtained in LASAN by CSA. These sequences are implemented on the embedded microcontroller platform of STM32H743IITs for evaluation. Experimental results show that our proposed CSA can effectively reduce the end-to-end delay of LASAN and outperform the state-of-the-art static message scheduling method in terms of low delay.

Controller Area Network with Flexible Data-rate (CAN-FD) is a promising industrial embedded network because of its high bandwidth and long data field length. However, CAN-FD does not deploy any security protection mechanisms, leaving it vulnerable to network attacks. In recent years, authentication and authorization frameworks have often been deployed in industrial embedded networks (e.g., automotive networks) to provide secure CAN/CAN-FD communication. However, these frameworks cannot simultaneously enhance confidentiality, integrity, and availability; moreover, these frameworks are mainly based on a distributed security management mechanism, resulting in large computation, communication, and memory overhead. This paper proposes a trinity-enabled and lightweight security framework called TrinitySec based on cryptographic algorithms for CAN-FD communication. TrinitySec ensures the availability of ECU and CAN-FD messages through authentication and authorization, as well as the confidentiality and integrity of CAN-FD messages through a symmetric-key algorithm and Hash-based Message Authentication Code (HMAC) function. TrinitySec proposes a low-overhead centralized security management mechanism instead of the existing distributed management mechanism. We formally verify the security of TrinitySec using the ProVerif tool. We implement TrinitySec on STM32H743IIT Micro Controller Unit (MCU) with ARM Cortex M7 core and evaluate that TrinitySec outperforms other state-of-the-art security frameworks in terms of computation, communication, memory, and storage overhead.

Training a robust classifier and an accurate box regressor are difficult for occluded pedestrian detection. Traditionally adopted Intersection over Union (IoU) measurement does not consider the occluded region of the object and leads to improper training samples. To address such issue, a modification called visible IoU is proposed in this paper to explicitly incorporate the visible ratio in selecting samples. Then a newly designed box sign predictor is placed in parallel with box regressor to separately predict the moving direction of training samples. It leads to higher localization accuracy by introducing sign prediction loss during training and sign refining in testing. Following these novelties, we obtain state-of-the-art performance on CityPersons benchmark for occluded pedestrian detection.