Edge computing gained popularity for its promises of low latency and high-quality computing services to users. However, it has also introduced the challenge of mutual untrust between user and edge devices for service level agreement (SLA) compliance. This obstacle hampers wide adoption of edge computing, especially in pervasive edge computing (PEC) where edge devices can freely enter or exit the market, which makes verifying and enforcing SLAs significantly more challenging. In this paper, we propose a framework for verifying and enforcing SLAs in PEC, allowing a user to assess SLA compliance of an edge service and ensure correctness of the service results. Our solution, called VeriEdge, employs a verifiable delayed sampling approach to sample a small number of computation steps, and relies on randomly selected verifiers to verify correctness of the computation results. To make sure the verification process is non-manipulable, we employ verifiable random functions to post-select the verifier(s). A dispute protocol is designed to resolve disputes for potential misbehavior. Building upon the base VeriEdge framework, we further extend it with an anonymity-enhanced design to support privacy-sensitive applications, using blind signature-based tokens to enable anonymous access to edge services. Rigorous security analysis demonstrates that VeriEdge achieves a high probability of detecting SLA violation with a minimal overhead. Experimental results indicate that VeriEdge is lightweight, practical, and efficient.
Xiaojian Wang, Ruozhou Yu, Dejun Yang et al.· IEEE Transactions on Mobile...· 0 citations
STARS-GS is proposed, a truthful and feasibility-aware scheduling mechanism that combines bid-aware admission control, best-fit ground-station assignment, Earliest Deadline First (EDF)-based bandwidth scheduling, and critical-payment pricing, and scales smoothly to workloads containing up to 900 tasks.
Zhiying Wang, Xiaojian Wang, Huayue Gu et al.· 0 citations
Ensuring QoS provisioning in low-altitude wireless networks requires UAV positioning and navigation strategies that adapt to dynamic environments and generalizes across heterogeneous network scenarios. This paper proposes a digital twin (DT)-assisted meta reinforcement learning framework for multi-agent UAV trajectory planning. A high-fidelity network DT serves as a supervisory layer to generate key performance indicators (KPIs) and fine-grained channel knowledge, which guides both domain-specific learning and cross-domain validation. Building on the twin-informed UAV landmarks, we then develop a weakness-aware meta learning scheme: in the inner loop, agents are trained cooperatively toward the self-discovered landmarks under dynamic conditions; in the outer loop, navigation policies are evaluated via the DT to identify bottlenecks and generate targeted hard scenarios, enabling robust adaptation across diverse scenarios. Extensive simulations show that our framework achieves up to 4× higher service coverage compared to baselines, while the target-aware outer-loop adaptation further improves cross-scene performance and model generalization.
Jiayuan Huang, E. Tucker, Ruozhou Yu et al.· International Conference on...· 0 citations
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