Large Language Models (LLMs) demonstrate impressive capabilities in natural language understanding and generation, but incur high communication overhead and privacy risks in cloud deployments, while facing compute and memory constraints when confined to edge devices.Cloud–edge inference has emerged as a promising paradigm for improving privacy in LLM services by retaining sensitive computations on local devices.However, existing cloud–edge inference approaches apply uniform privacy protection without considering input sensitivity, resulting in unnecessary perturbation and degraded utility even for non-sensitive tokens. To address this limitation, we propose Privacy-aware Routing for Inference with Semantic Modulation (PRISM), a context-aware framework that dynamically balances privacy and inference quality. PRISM executes in four stages: (1) the edge device profiles entity-level sensitivity; (2) a soft gating module, also on the edge, selects an execution mode -cloud, edge, or collaboration; (3) for collaborative paths, the edge applies adaptive two-layer local differential privacy based on entity risks; and (4) the cloud LLM generates a semantic sketch from the perturbed prompt, which is then refined by the edge-side small language model (SLM) using local context.Our results show that PRISM consistently achieves superior privacy-utility trade-offs in various scenarios, reducing energy consumption and latency to 40–50% of baseline methods such as Uniform and Selective LDP, while maintaining high output quality under strong privacy constraints. These findings are validated through comprehensive evaluations involving realistic prompts, actual energy measurements, and heterogeneous cloud–edge model deployments.

In this paper, we address the novel problem of jointly considering dynamic function configuration, function download latency, multi-slot task offloading, and energy harvesting in order to process the maximum number of tasks. To this end, we formulate a mixed integer linear program (MILP) that optimizes (i) task offloading of servers, (ii) function configuration, (iii) function scheduling, (iv) function download decision, (v) energy usage, (vi) channel allocation. It provides a theoretical optimum, but leverages non-causal information and becomes computationally intractable in large scale networks. To address this challenge, we propose a fully distributed protocol called Distributed Task Offloading with Energy Harvesting (D-TOEH), where each server establishes a function importance ranking to activate functions required by more tasks. Our simulation results show that, on average, the performance of D-TOEH attains 71.36% of the theoretical optimum while ensuring low polynomial time complexity.

Future Internet of things (IoT) networks will host applications that involve data collection and computation tasks on one or more servers. To this end, this paper proposes the first mixed integer linear program (MILP) to schedule and embed applications on energy harvesting nodes, where it optimizes (i) the sampling time of devices, (ii) whether to run an application, and (iii) the energy usage of devices, gateways and servers. To ensure applications are run often, we adopt the maximum age of service (AoS) metric, and set the MILP’s objective to minimize the maximum AoS or min-max AoS of applications. This paper also proposes two novel solutions: (i) a receding horizon control (RHC) based method, and (ii) a solution that greedily embeds applications according to their AoS. The results show that the min-max AoS of RHC and greedy approach is respectively 1.07x and 1.13x higher than MILP.

This paper presents a robust Model Predictive Control (MPC) framework for trajectory tracking of unmanned aerial vehicles (UAVs), enhanced by a reinforcement learning (RL) policy for warm-start initialization and a sliding mode observer (SMO) for disturbance estimation. The proposed architecture addresses multifaceted performance degradation, including residual trajectory tracking errors, reduced control responsiveness, and slow early-stage convergence, primarily caused by external disturbances, model uncertainties, and time-varying environmental conditions. An enhanced adaptive super-twisting SMO is developed to estimate lumped disturbances in real time. These estimates are integrated into the MPC prediction model to compensate for mismatches between nominal dynamics and true system behavior. To accelerate control convergence, particularly in improving early stage performance, an offline RL warm-start policy is used to generate an initial control sequence for MPC. The overall framework preserves the constraint handling and predictive capabilities of conventional MPC, while significantly improving robustness, stability, and tracking accuracy. The simulation results validate the effectiveness of the proposed method in achieving reliable and precise trajectory tracking under challenging and uncertain operating scenarios.