Federated learning (FL) in Unmanned Aerial Vehicle (UAV) swarms faces the challenges of data heterogeneity and resource constraints, limiting its large-scale deployment. Existing solutions attempt to leverage Clustered Federated Learning (CFL) to mitigate data heterogeneity by grouping clients based on data similarity. However, these data-driven approaches concentrate on application-layer features without comprehensive consideration of status in other layers, leading to unstable clustering and suboptimal routing. In this paper, we propose X-CFL, a novel cross-layer framework that co-optimizes clustering and routing by integrating application-layer data features with cross-layer node status. Specifically, X-CFL introduces a joint clustering mechanism that groups UAVs based on data similarity to minimize model discrepancy, while concurrently leveraging real-time physical conditions (e.g., location and energy) to enhance cluster robustness. Subsequently, a two-stage routing protocol is employed to establish reliable intra-cluster communication and enable efficient global aggregation among cluster heads. Extensive simulation results demonstrate that X-CFL significantly improves training throughput, network lifetime, and model training performance compared to state-of-the-art baselines.

The surge in mobile application services has led to diversified traffic and increased demands on network resources. Traditional multipath algorithms, designed for resource integration through subflow scheduling across paths, struggle with disharmonious transmission caused by terminal mobility and differentiated path resources. Especially when differentiated services are transmitted concurrently, disharmonious transmission can give rise to resource contention, causing a large number of subflows to congest a single path and leading to performance degradation. To mitigate these challenges, this paper introduces HarmonyPath, a fine-grained flexible multipath transmission mechanism that can ensure harmonious resource occupation. Specifically, HarmonyPath firstly employs an in-band telemetry protocol to gather path resource information, generating a network resource distribution map. Based on this map, it flexibly allocates path resources according to the network resource distribution and service requirements. Then, HarmonyPath establishes a collaborative matching model for service demands and path resources. Through matrix transformation and calculation, it rapidly generates and deploys the scheduling strategy. To further alleviate service contention, HarmonyPath employs heuristic algorithms to optimize the scheduling strategy and achieve precise multipath transmission. Experiments demonstrate that HarmonyPath surpasses traditional algorithms in the multipath transmission of differentiated services, offering flexible service resource guarantees and enhancing network resource utilization efficiency.

Delay-sensitive applications like telemedicine and VR/AR intensify competition for network resources and elevate congestion risks, particularly in mobile networks with highly dynamic link conditions. Traditional end-to-end congestion control methods suffer from prolonged response times, rendering them ineffective for Delay-sensitive applications. To this end, this paper proposes a novel In-Network Congestion Control (INCC) mechanism that accelerates congestion control by enabling network nodes to proactively identify bottlenecks and promptly notify end-hosts. Unlike traditional end-host-centric approaches, INCC facilitates collaborative congestion decision-making between end-hosts and in-network unit. INCC classifies congestion into two phases: “yellow” and “red” based on the local queue length bottleneck awareness and global congestion flow bottleneck statistics. For the “yellow” local congestion phrase, we design an in-network local control algorithm that performs proactive packet dropping and rate adjustment to mitigate emerging congestion. For the “red” global congestion phrase, we design an end-host and network cooperative global congestion control algorithm to make precise sending rate adaptation by proactive bottleneck awareness. We implement INCC via Linux kernel modifications and design three experiments to compare with Cubic, NewReno, and BBR. Experimental results demonstrate INCC has good performance on round-trip time and throughput, achieving 99.03% scheduling fairness in flow contention scenarios. Additionally, INCC has low execution overhead on CPU utilization and realize microsecond computational latency.

UAVs can significantly improve the access networks of next-generation mobile networks during the building of smart cities. Drones equipped with base stations can expand the coverage of communication networks and assist more users’ devices to access the 5G/6G network, in which reliable authentication for drones becomes essential. However, traditional authentication methods still utilize the overt channel to transmit identity and key information, which are vulnerable and very easy to be eavesdropped, hijacked, and forged by malicious third parties. Therefore, this paper proposes an authentication scheme (CCRA). It includes 1) covert channels to assist authentication and key negotiation, and 2) a covert channel algorithm (EIDOP). Specifically, CCRA transmits fake identity information, part of the key information, and unimportant data in the overt channel, while using the covert channel to transmit important data and another part of the key for authentication and key negotiation to enhance the reliability of authentication. In addition, this paper proposes an algorithm called EIDOP based on the order of packet delay intervals to establish the covert channel for embedding and hiding important information. Finally, we conduct experiments on physical machines and compare EIDOP with other covert timing mechanisms to conclude that our algorithm has better concealment and latency overhead and still guarantees a very low BER under such circumstances.

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.

Driven by the new era of big data and artificial intelligence (AI), as well as the increasing demands for the privacy protection, how to deployment the AI on wireless networks is drawing increasing attention. In this paper, we investigate the distributed learning mechanism of hosting AI over intelligent reflecting surface (IRS)-assisted wireless networks, where IRS is utilized to enhance communication in a cost-effective and energy-efficient manner. Firstly, a distributed learning framework is formulated based on the alternating direction method of multipliers (ADMM) to achieve the parallel processing of the objective function. Specifically, in the proposed architecture each user updates the learning model with its own data and uploads it to the global model through wireless networks. Thence, a joint passive phase shift of IRS and user scheduling scheme based on a metric of efficiency-efficacy weighted sum (EEWS) is formulated to explore both the learning efficiency and efficacy. In addition, aiming at improving the one-round learning efficiency, a grouping-based suboptimal solution about IRS’s phase is adopted to realize the max-min fair transmission. Simulation results demonstrate the relationship among the number of users involved, the scale of IRS and the learning performance.

Vehicular Edge Computing (VEC) is expected to be an effective solution to meet the ultra-low delay requirements of many emerging Internet of Vehicles (IoV) services by shifting the service caching and the computation capacities to the network edge. However, due to the constraints of the multidimensional (storage-computing-communication) resources capacities and the cost budgets of vehicles, there are two main issues need to be addressed: 1) How to collaboratively optimize the service caching decision among edge nodes to better reap the benefits of the storage resource and save the time-correlated service reconfiguration cost? 2) How to allocate resources among various vehicles and where vehicular requests are scheduled to improve the efficiency of the computing and communication resources utilization? In this paper, we formulate an edge caching and computation management problem that jointly optimizes the service caching, the request scheduling, and the resource allocation strategies. Our focus is to minimize the time-average service response delay of the random arriving service requests in a cost-efficient way. To cope with the dynamic and unpredictable challenges of IoVs, we leverage the combined power of Lyapunov optimization, matching theory, and consensus alternating direction method of multipliers to solve the problem in an online and distributed manner. Theoretical analysis shows that the developed approach achieves a close-to-optimal delay performance without relying on any prior knowledge of the future network information. Moreover, simulation results validate the theoretical analysis and demonstrate that our algorithm outperforms the baselines substantially.

Since urban rail unmanned train is considered as one of the core technologies of Intelligent Transportation System (ITS) , how to shorten the train headway as the driven interval between unmanned trains properly is still a challenge in Communications Based Train Control (CBTC) system. In this paper, we focus on analyzing the main factors which affect the train headway in inter-station barrier tracking mode, interstation stop tracking mode and station tracking mode and present an optimization scheme for Automatic Train Control (ATC) system with mobile block technology. Simulation results show that the proposed optimization scheme can reduce train headway to improve operational efficiency and reduce costs of urban rail transit system.

This paper presents a novel ZVZCS phase-shift full-bridge (PSFB) DC-DC converter with secondary-side energy storage inductor, which can be utilized in high voltage application such as electric vehicle. By employing an energy storage inductor and an output capacitive filter at the secondary side, there is little reverse recovery loss in output-diodes due to relatively slow downslope of the triangular current. Meanwhile, the voltage spike across the output diode can be also eliminated and clamped by the output capacitor directly. Moreover, the proposed converter introduces a passive auxiliary circuit to satisfy the ZVS operation of the converter from no-load to full-load conditions resulting in superior efficiency in very wide load variations. Finally, the operational principle and analysis of the proposed converter are presented in detail. Experimental results provided from a 1kW prototype verify the feasibility and superior performance of the proposed converter.