The growing adoption of edge AI in applications such as smart manufacturing and autonomous systems demands efficient deep neural network (DNN) inference on resource-constrained devices. However, mainstream intermediate representations (IRs), including ONNX and TVM Relay, rely heavily on static scheduling, which limits adaptability to the dynamic and heterogeneous nature of edge environments. To address this issue, we propose EdgeOpt-Sched, a dynamic operator scheduling framework that leverages Graph Neural Networks (GNNs) and Reinforcement Learning (RL) to optimize model inference graphs. Specifically, we introduce TopoGNN for modeling operator dependencies and PPO-Sched to learn adaptive scheduling strategies via Proximal Policy Optimization. Experimental results on edge devices using BERT, ResNet50 and Vision Transformer demonstrate that EdgeOpt-Sched reduces average inference latency by 31.4% and improves memory utilization by 26.2% over existing IR-based runtimes. Our framework offers a portable and lightweight solution for efficient DNN deployment across diverse edge platforms. We compare against state-of-the-art schedulers including TVM MetaSchedule and IOS and observe consistent end-to-end latency and throughput gains across BERT, ViT and ResNet workloads on ARMv8 edge devices. Compared with TVM MetaSchedule (offline search) and IOS (operator-group DP), our method reduces latency while lowering peak memory and remains stable under runtime variability (CPU load, thermal throttling).

To address the emerging security challenges in Multi-Access Edge Computing (MEC), it is imperative that solutions go beyond the current infrastructure-centric measures. These methods, including authentication and access control, are insufficient to combat malware that conceals itself within ME applications. The acknowledged flaws in the ME application layer necessitate an immediate call for creative solutions. In this work, we propose a non-invasive security architecture for MEC, meticulously designed to strike a balance between performance burden and security protection capabilities. The objective of the design contains three major aspects, i.e. user experience, service density and serviceability. We conduct a thorough evaluation that enables us to quantify the significance of high bandwidth, low user experience latency and MEC serviceability. The experimental results and ablation studies indicate that our proposed method effectively balances user experience and security capabilities. This not only provides a practical and cost-effective solution but also establishes a strong precedent for the community to develop a secure MEC with superior performance in real-world production environments.

The Mirai botnet, notorious for launching significant Distributed Denial of Service (DDoS) attacks and crippling portions of internet services in late 2016, has emerged as a significant threat. Its threat is magnified by the open-source nature of the original Mirai code, which enables a propagation and evolution rate that surpasses traditional malware and frequently defies common sense. As the primary targets of Mirai attacks, Internet of Things (IoT) devices must promptly adapt to the evolving variations of the Mirai threat scenario. In practice, however, IoT devices are frequently constrained by insufficient security detection resources. Therefore, there is an urgent need for a lightweight framework capable of handling Mirai variants and dynamically updating its rule set in order to effectively counter the threat. In response to these challenges, we present Efficient and lightweight Mirai Variants Detection (EVaDe), a novel, lightweight framework for detecting Mirai. EVaDe unleashes the power of sample function mining to efficiently automate the generation of detection rules, requiring limited hardware resources while maintaining effectiveness against Mirai and its numerous variants. In addition, to improve the efficacy of rule generation, we propose a sophisticated algorithm designed to optimize the maximum submatrix problem, thereby facilitating the efficient and rapid extraction of malicious rules from the sample group. We validated the experiments on actual IoT devices with significantly compressed performance overheads. An average sample detection time of 5 ms to make sure the system can be deployed in real production. According to the result, the approach has an average detection rate of 95% for Mirai and its variants, which beats every other well-known piece of commercial antivirus software on the market by 3% to 56%.