Air-ground cooperative perception (AGCP) integrates connected and autonomous vehicles (CAVs), roadside units (RSUs), and uncrewed aerial vehicles (UAVs) to provide wide-area coverage and high-resolution perception by leveraging their complementary perception and communication capabilities. However, the dynamic and heterogeneous characteristics of the air-ground network introduce strong cross-layer coupling across perception, communication, and computation, thereby complicating the coordination of cooperation update intervals and cooperation partner selection. To address these challenges, we develop a unified AGCP framework that jointly models LiDAR-based multi-agent perception, together with its associated communication bandwidth allocation and computation latency models, under dynamic mobility and time-varying resource conditions. Building on this framework, a multi-objective optimization problem is formulated to characterize the interplay between update interval selection and cooperation partner choice, aiming to balance perception accuracy and end-to-end latency. A Tchebycheff distance-based formulation is utilized to normalize and integrate multiple objectives into a unified optimization metric. To efficiently solve this highly coupled problem, an attention-enhanced hierarchical reinforcement learning algorithm is proposed, which leverages a two-level Markov decision process combined with an attention-enhanced actor-critic architecture. Simulation results validate that the proposed algorithm achieves a desirable trade-off between perception performance and end-to-end latency.
Hai-Xia Peng, Yixin Fan, Zhou Su et al.· IEEE Transactions on Cogniti...· 0 citations
Mobile Edge Computing (MEC) enables resource-constrained Internet of Things (IoT) devices to offload computation-intensive workloads to nearby edge servers, reducing latency and energy consumption. However, wireless offloading exposes transmitted data to eavesdropping, raising serious privacy concerns. Existing physical-layer security techniques either introduce additional overhead or fail to conceal semantic information. This paper proposes a diffusion-based semantic encoding framework for secure and efficient computation offloading in MEC systems. By applying a forward diffusion process, task inputs are transformed into approximately noise-like latent representations whose distribution is statistically close to a standard Gaussian distribution prior to transmission. Authorized edge servers equipped with learned reverse diffusion models can reliably recover task-relevant semantics, while intercepted representations reveal negligible information. We analyze the secrecy and robustness of diffusion-based encoding from an information-theoretic perspective and integrate the proposed encoder-decoder into a practical MEC offloading pipeline. Extensive experiments under realistic wireless conditions demonstrate that diffusion-based encoding significantly improves secrecy, reduces transmission overhead, and maintains high task performance. Compared with autoencoder and variational autoencoder baselines, the proposed approach offers substantially stronger resistance to reconstruction and inference attacks while preserving computational efficiency at the edge.
