Tianyue Zheng

The rise of digital twins, particularly in smart manufacturing and automated logistics, demands ultra-high-precision indoor localization that existing technologies struggle to provide. Conventional wireless localization methods, which rely on channel state information to estimate a limited set of parameters, neglect critical details of the sensing scene, leading to suboptimal accuracy. This paper presents RayLoc, a novel approach that addresses this limitation by reformulating wireless indoor localization as an inverse problem. By leveraging the digital twin of an environment, our method infers the precise scene parameters that generate the measured CSI, enabling highly accurate localization. At the core of our solution is a fully differentiable ray-tracing simulator that enables backpropagation to all sensing-critical parameters, including target locations. To establish a robust localization context, RayLoc first constructs a high-fidelity sensing scene by refining a coarse-grained background model, often derived from the initial digital twin. Furthermore, to overcome optimization unfriendliness of CSI-based loss landscapes, RayLoc introduces Gaussian kernal smoothing combined with an adaptive convergence strategy to mitigate sparse gradients and local minima. Extensive experiments showcase that RayLoc not only outperforms traditional localization baselines but is also able to generalize to different sensing environments, paving the way for its application in next-generation, high-accuracy-dependent systems.

With the advancement of 5G and AI technologies, vehicular networks supporting varied services now face high demands, which on-board processing alone cannot fulfill. Vehicular Edge Computing (VEC) addresses this by enabling vehicles to offload tasks to edge servers. However, efficient task allocation and resource utilization remain several challenges. First, in instance-level edge environments with heterogeneous resources, the optimal solution for task offloading is complex and intolerably time-consuming. Second, many studies ignore queuing delays introduced by task queuing dynamics, and fail to perform fine-grained modeling of the VEC system. Third, optimizing both latency and energy consumption during task offloading remains a crucial challenge. To overcome these challenges, we first introduce a heuristic node aggregation algorithm (HNA) that clusters edge devices into functionally complete computing clusters, reducing the scale of the problem for establishing the foundation of global resource utilization. Then, we propose a two-step offloading scheme (TSOS) that combines a greedy strategy with deep reinforcement learning to achieve energy–latency-aware task offloading. Specifically, we use Cluster-Based PPO (CBPPO) for inter-cluster decisions and Greedy Intra-Cluster Offloading Algorithm (GICOA) for task assignment within clusters to jointly minimize latency and energy consumption. Extensive experiments demonstrate the effectiveness of the proposed approach, showing significant improvements in system performance and robustness compared to baseline methods.

Service meshes built upon ubiquitous microservice architectures, as an emerging paradigm, promise to enhance the flexibility, scalability, and portability of energy-consuming and latency-sensitive applications in edge with limited resources. However, due to intricate microservice dependencies, service multiplexing, and parallel distributed instances, microservice deployment and request routing are highly interdependent. To reduce response latency and energy consumption, such collaborative optimization for efficient service mesh orchestration is necessary, but significantly challenging. Besides, strict service level objective (SLO) requirements and f ine-grained latency analysis with multi-nest routing further impose great difficulties to online orchestration. When considering multi instance modeling and multi-hop data communications for numerous microservices, the difficulty is extremely amplified. Nevertheless, most prevailing work failed to design sophisticate models and methods for addressing the above difficulties, and ignored the inherent transmission energy consumption for highly-concurrent multi-hop data interactions. Therefore, this paper investigates the energy aware service mesh deployment and online request routing in edge. First, we establish a multi-instance queuing network model to accurately analyze the end-to-end response latency with complicated dependencies and multi-hop communications, and optimize energy consumption in a fine-grained manner. Then, to boost the overall performance, we design an efficient multi-dimensional hierarchical deep reinforcement learning algorithm, which enables edges and service instances to cooperate with each other to handle massively concurrent requests. Besides, we propose an energy-aware proactive autoscaling algorithm to carefully adapt to exceedingly dynamic scenarios. Finally, extensive experiments are performed to show our superior performance compared to other baselines.

As a cornerstone for numerous sensing applications, wireless indoor localization has been a pivotal area of research over the last two decades. While techniques such as jamming, spoofing, and adversarial perturbation have been exploited to compromise wireless indoor localization, existing attacks face challenges in accessibility to wireless systems and stealthiness. To address these limitations, we introduce Loki, a novel physical-world attack on wireless indoor localization via differentiable object placement. Specifically, we develop a differentiable wireless ray-tracing technique that allows us to optimize object placement in the scene. By repositioning an existing object in the scene by just a few centimeters, Loki fools existing wireless indoor localization systems into generating erroneous localization results. We also show via experiments that the object placement generated by Loki aligns with wireless sensing theory (e.g., the forward scattering region and Fresnel zone), confirming its explainability. Additionally, Loki proves effective across various localization models and scenarios, highlighting its generalizability.

Low Earth Orbit (LEO) satellite networks, with their low latency, high bandwidth, and global coverage, are becoming key technologies for applications like real-time video transmission. As satellite networks expand, effectively managing multicast traffic and optimizing bandwidth utilization have become major challenges for efficient video distribution. Although Software-Defined Multicast (SDM) technology has made progress in bandwidth optimization, existing SDM methods are still focused on constructing Steiner trees, making it difficult to address the dynamic changes and high-load issues in LEO satellite networks. This paper frames the multicast tree construction problem as a Joint Path Optimization Game (JPOG). We propose a Cooperative Game-Theoretic Routing (CGMR) Algorithm based on game theory, which optimizes multicast path selection and achieves load balancing by introducing a link cost-sharing mechanism. Additionally, we propose a two-stage A* path generation algorithm to improve path search efficiency. Theoretically, this paper proves that JPOG is a potential game and can converge to a pure strategy Nash equilibrium (PSNE) within a finite number of iterations. The results showed that JPOG outperformed other algorithms, achieving lower link load, path cost, and superior load balancing, demonstrating its effectiveness in optimizing multicast routing and resource management in large-scale LEO satellite networks.

WiFi-based gesture recognition has emerged as a promising RF sensing paradigm for enabling non-contact and privacy-preserving human-computer interaction in AIoT environments. However, existing methods often suffer from limited generalization and semantic expressiveness due to the domain-sensitive nature of Channel State Information and the lack of high-level gesture abstraction. To address these challenges, we propose a novel generalization framework, termed Large-Model-Aware Semantic Distillation and Alignment (GLSDA), which leverages the semantic prior of pre-trained large foundation models to enhance gesture representation learning in both in-domain and cross-domain scenarios. Specifically, we first design a dual-path CSI encoding pipeline that captures geometric and dynamic gesture patterns via CSI-Ratio phase sequences and Doppler spectrograms. These representations are then fed into a Multiscale Semantic Encoder, which learns robust temporal embeddings and aligns them with gesture semantics through cross-modal attention mechanisms. To further enhance category discrimination, we introduce a Semantic-Aware Soft Supervision scheme that encodes inter-class correlations and reduces label ambiguity, especially for semantically similar gestures. Finally, we develop a Robust Dual-Distillation strategy to compress the aligned model into a lightweight student network, jointly distilling intermediate features and semantic-informed soft labels from the teacher model. Extensive experiments on the Widar3.0 benchmark show that GLSDA consistently outperforms state-of-the-art methods in both in-domain and cross-domain gesture recognition tasks, while significantly reducing model size and inference latency. Our method offers a scalable and deployable solution for generalized RF-based gesture interfaces in real-world AIoT applications.