IoT streaming applications serve as critical drivers for the artificial intelligence of things (AIoT), characterized by their sensitivity to delays and resource requirements. Placing these application tasks on cooperative edge systems efficiently utilizes edge computing and network resources, enhancing data processing efficiency. However, existing placement methods often oversimplify the network environments or overlook dependencies of tasks, leading to suboptimal performance in real-world scenarios. In addition, the varying Quality of Service (QoS) requirements for IoT streaming applications emphasize the need to make different optimization decisions for applications with different QoS requirements. To address these challenges, we propose DAPNet, a neural adaptive dependent task placement method for IoT streaming applications. We model dependent tasks in an IoT streaming application as a directed acyclic graph (DAG) and then formalize the dependent task placement problem as a multi-objective optimization problem to maximize the IoT streaming application’s QoS. DAPNet adapts resource allocation to meet varying QoS requirements. It uses a deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) to optimize task placement and resource allocation for IoT streaming applications in dynamic network environments. Simulations with real-world datasets were conducted, comparing our approach with state-of-the-art methods across two network environments, evaluating completion time, energy consumption, and QoS. Results show that our method outperforms existing approaches in all three metrics.

As one of the most critical aspects of mobile ad hoc networks, vehicular ad hoc networks (VANETs) have attracted increasing attention with the growing demand for safety in transportation systems. Trust assessment is crucial in VANETs, as it can effectively identify and mitigate the impact of malicious vehicles, thereby ensuring the reliability and security of network communication. However, the models in most existing trust schemes cannot be dynamically updated over time, failing to adapt to the high dynamics of VANETs. Moreover, they lack protection for data privacy, increasing the risk of information tampering and leakage. To solve the above problems, this paper proposes a new Dynamic Model Update-based Trust Assessment Strategy for VANETs Considering Blockchain (DMTAS-VB). Specifically, DMTAS-VB addresses two primary aspects. Firstly, with respect to the dynamic model, the trust framework proposed in this study incorporates both direct trust and recommended trust models that are dynamically updated as interactions progress. Secondly, regarding privacy protection, blockchain is introduced into the trust model of this scheme. The innovative three-chain structure (MainBC, MesBC, and RepBC) is adopted to achieve functional separation, and core identity authentication, high-frequency message exchange, and reputation management are handled independently. The system can more flexibly optimize the performance parameters of each chain while enhancing data security and privacy protection. Simulation results demonstrate that the proposed scheme outperforms comparative approaches in detection performance under various conditions (vehicle number, vehicle speed, proportion of malicious vehicles) and different attack modes (SA, ZA, BMA, BA, and CA).

Scalable service-Oriented Middleware over IP (SOME/IP) is an Ethernet communication standard protocol in the Automotive Open System Architecture (AUTOSAR), promoting ECU-to-ECU communication over the IP stack. However, SOME/IP lacks a robust security architecture, making it susceptible to potential attacks. Besides, random hardware failure of ECU will disrupt SOME/IP communication. In this paper, we propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security. Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication, including Distributed Denial-of-Services, Man-in-the-Middle, and abnormal communication processes, assuming a malicious user accesses the in-vehicle network. Subsequently, SISSA designs a series of deep learning models with various backbones to extract features from SOME/IP sessions among ECUs. We adopt residual self-attention to accelerate the model’s convergence and enhance detection accuracy, determining whether an ECU is under attack, facing functional failure, or operating normally. Additionally, we have created and annotated a dataset encompassing various classes, including indicators of attack, functionality, and normalcy. This contribution is noteworthy due to the scarcity of publicly accessible datasets with such characteristics. Extensive experimental results show the effectiveness and efficiency of SISSA.