The increasing deployment of mobile robots in dynamic outdoor environments necessitates robotic systems capable of maintaining reliability amidst fluctuating wireless connectivity. While the Robot Operating System (ROS) has established itself as the de facto standard for such networked robotics, its abstraction of communication as an opaque, besteffort utility creates a critical bottleneck: it fails to leverage physical layer (PHY) information, resulting in degraded performance and unreliable execution in fluctuating networks. To address this, this paper presents MobileROS, a wireless-native robot operating system that transforms wireless communication from an external service into a core system resource. Grounded in the Symbiotic Paradigm, MobileROS establishes a bidirectional exchange where network conditions inform robotic decisions and mission requirements guide network resource allocation. Based on service mesh principles and domain-driven design, our architecture implements a Hub-Engines-Cells (HEC) model. It features a central Hub for global optimization, three specialized engines (the Radio Information Engine, the Cross Domain Engine, and the Physical Adaptive Engine) for crosslayer intelligence, and distributed Cells as functional units. A key mechanism, Application-Driven Bidirectional Dynamic Slicing, allows robots to actively reconfigure network resources based on semantic urgency, transforming the robot from a passive observer into an active network controller. We systematically evaluate MobileROS across three cities (London, Hong Kong, and Shenzhen) in five scenarios: distributed visual SLAM, cross-domain LiDAR perception, V2X autonomous driving, hybrid multi-robot collaboration against WebRTC baselines, and partition recovery validating CAP-theorem-aware failsafe mechanisms. Results demonstrate that MobileROS maintains significantly more stable performance than standard ROS in mobile wireless deployments.We provide implementation details athttps://github.com/MobileROS.

The proliferation of Internet of Things (IoT)-generated distributed personal data enables user-specific large language model (LLM) adaptation at the edge. The split federated learning (SFL) facilitates collaborative learning and reduces memory footprint by model splitting, which necessitates the transmission of intermediate activations, rendering it susceptible to reconstruction attacks and privacy breaches. In this paper, we present a privacy-aware SFL scheme addressing the accuracy-efficiency-privacy trilemma in LLM fine-tuning over heterogeneous IoT devices. Particularly, we develop a privacy quantification metric based on Fisher information to assess layer-wise privacy risks in smashed data transmission. Guided by this metric, we establish an analytical model that captures the intricate relationships between privacy leakage, fine-tuning convergence time, and device energy consumption. To optimize these three aspects, we formulate a multi-objective mixed-integer programming problem. Then, an -constraint-based block coordinate descent (BCD) algorithm is proposed to jointly determine the optimal LLM split layer, transmit power, and bandwidth allocation for IoT devices under their memory and network constraints. Extensive simulation results demonstrate the proposed scheme’s effectiveness in achieving 24% faster convergence, 40% lower energy consumption, and 7% reduced privacy leakage compared to baseline approaches, while maintaining competitive model accuracy.

The open radio access network (O-RAN) architecture is a promising RAN virtualization solution which provides interfaces for various timescale RAN intelligent control (RIC) schemes, thus achieving AI-based, cross layer resource scheduling. However, obtaining sufficient data to train AI models is difficult and costly. Digital twin (DT) technology can create high fidelity virtual world, which can not only be used to generate high-quality training data, but also provide an interactive platform for trial and error algorithms such as reinforcement learning (RL) to reduce the cost of interacting with real systems. Therefore, in this paper, we introduce our scheme that integrating DT and RL techniques to achieve cross layer resource scheduling in O-RAN system. We focus on the following two issues: i) how to reduce the gap between virtual and reality, ii) how to continuously evolve AI models to cope with unseen scenarios, and our preliminary ideas are also presented.

This paper studies the trade-off between the comunication capacity of unmanned aerial vehicle (UAV) and the UAV's unintentional interference in a shared spectrum scenario. In fact, UAV will also cause unintentional interference to other ground users (GUs) who do not communicate with the UAV while transmitting data to a specific ground node (GN). And the location of these GUs is usually unknown. For this problem, we introduce the concept of unacceptable area in which the interference power received by GUs from UAV exceeds their anti-jamming tolerance, and study the trade-off between UAV's average communication capacity and average unacceptable area by joint UAV's 3D trajectory and transmit power optimization. Further, we propose an effective iterative algorithm to solve this non-convex problem by using successive convex approximation (SCA) and block coordinate descent (BCD) method. Numerical results show the proposed scheme can meet the communication requirements of UAV and reduce the UAV's unintentional interference at the same time.