We propose AQM-LLM, a framework that distills Large Language Models for Active Queue Management in modern networks. Unlike conventional learning-based AQMs that require extensive feature engineering and struggle with dynamic conditions, our approach leverages the contextual reasoning capability of LLMs to enhance the Low Latency, Low Loss, and Scalable Throughput (L4S) architecture with minimal manual intervention. The L4S-LLM design introduces three key components: (i) a state encoder that transforms heterogeneous network telemetry into token embeddings, (ii) a specialized L4S-LLM head that produces congestion actions in a single inference step, and (iii) a data-driven Low-Rank Adaptation scheme that drastically reduces trainable parameters while preserving accuracy. Our open-source FreeBSD-14 implementation demonstrates improved queue delay stability and higher bandwidth utilization for both DCTCP and UDP Prague traffic. We emphasize that this work demonstrates architectural feasibility through controlled experiments; deployment on router-class hardware will require additional model optimization (e.g., compression, pruning, or quantization) and is left for future work.

With the growing deployment of robotic applications, fault diagnosis at the individual robot level (single-robot fault diagnosis) is increasingly insufficient to meet stringent safety and reliability requirements. To address these challenges, multi-robot fault diagnosis has emerged as a promising approach, which enables robots to collaboratively share sensor data from diverse tasks. This collaboration helps mitigate data scarcity and supports the development of robust global models with improved generalization capabilities. However, multi-robot fault diagnosis presents several key challenges: (1) mitigating negative transfer caused by sensor heterogeneity across different robots; (2) effectively capturing spatial-temporal dependencies within the sensor data; and (3) designing an efficient distributed learning framework that preserves data privacy while enabling collaborative model training. In this paper, we propose a novel federated spatial-temporal fault learning (FSTFL) framework based on a fused adjacency matrix. The adjacency matrix is dynamically updated and initially constructed using domain knowledge to guide the learning process. Experimental evaluations on real-world datasets demonstrate the effectiveness of the proposed FSTFL framework in achieving accurate and privacy-preserving multi-robot fault diagnosis.

Telerobotics is a key foundation in autonomous Industrial Cyber-Physical Systems (ICPS), enabling remote operations across various domains. However, conventional cloud-based telerobotics suffers from latency, reliability, scalability, and resilience issues, hindering real-time performance in critical applications. Cloud-Fog Telerobotics (CFTel) builds on the Cloud-Fog Automation (CFA) paradigm to address these limitations by leveraging a distributed Cloud-Edge-Robotics computing architecture, enabling deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. This paper synthesizes recent advancements in CFTel, aiming to highlight its role in facilitating scalable, low-latency, autonomous, and AI-driven telerobotics. We analyze architectural frameworks and technologies that enable them, including 5G Ultra-Reliable Low-Latency Communication, Edge Intelligence, Embodied AI, and Digital Twins. The study demonstrates that CFTel has the potential to enhance real-time control, scalability, and autonomy while supporting service-oriented solutions. We also discuss practical challenges, including latency constraints, cybersecurity risks, interoperability issues, and standardization efforts. This work serves as a foundational reference for researchers, stakeholders, and industry practitioners in future telerobotics research.

Industrial Cyber-Physical Systems (ICPS) technologies are foundational in driving maritime autonomy, particularly for Unmanned Surface Vehicles (USVs). However, onboard computational constraints and communication latency significantly restrict real-time data processing, analysis, and predictive modeling, hence limiting the scalability and responsiveness of maritime ICPS. To overcome these challenges, we propose a distributed Cloud-Edge-IoT architecture tailored for maritime ICPS by leveraging design principles from the recently proposed Cloud-Fog Automation paradigm. Our proposed architecture comprises three hierarchical layers: a Cloud Layer for centralized and decentralized data aggregation, advanced analytics, and future model refinement; an Edge Layer that executes localized AI-driven processing and decision-making; and an IoT Layer responsible for low-latency sensor data acquisition. Our experimental results demonstrated improvements in computational efficiency, responsiveness, and scalability. When compared with our conventional approaches, we achieved a classification accuracy of 86%, with an improved latency performance. By adopting Cloud-Fog Automation, we address the low-latency processing constraints and scalability challenges in maritime ICPS applications. Our work offers a practical, modular, and scalable framework to advance robust autonomy and AI-driven decision-making and autonomy for intelligent USVs in future maritime ICPS.

Reliable wireless connectivity is essential for the safe operation of Unmanned Aerial Vehicles (UAVs) in Low-Altitude Economy (LAE), particularly at low altitudes where network impairments can affect control accuracy and stability. In this paper, we present an empirical study on how network bandwidth, latency, and packet loss influence the performance of micro-UAVs, using an in-house Crazyflie-based experimental testbed. We conduct controlled experiments under varied network conditions to evaluate trajectory tracking and altitude control in vertical and square flight modes. Our results identify a critical bandwidth threshold, below which oscillations and control degradation emerge. We also observe increased waypoint dwell times and reduced responsiveness under constrained conditions. These findings provide practical insights for deploying UAVs in degraded communication environments and offer benchmark data to improve simulation fidelity. Our software and experimental datasets are publicly available to support reproducibility and further research in advancing network-aware UAV design for the growing LAE.

This demo presents a modular experimental testbed and lightweight visualization tool for evaluating TCP congestion control performance in wireless networks. We compare Google’s latest Bottleneck Bandwidth and Round-trip time version 3 (BBRv3) algorithm with loss-based CUBIC under varying Active Queue Management (AQM) schemes, namely PFIFO, FQ-CoDel, and CAKE, on a Wi-Fi link using a commercial MikroTik router. Our real-time dashboard visualizes metrics such as throughput, latency, and fairness across competing flows. Results show that BBRv3 significantly improves fairness and convergence under AQM, especially with FQ-CoDel. Our visualization tool and modular testbed provide a practical foundation for evaluating next-generation TCP variants in real-world AQM-enabled home wireless networks.

Enabling multiple autonomous machines to perform reliably requires the development of efficient cooperative control algorithms. This paper presents a survey of algorithms that have been developed for controlling and coordinating autonomous machines in complex environments. We especially focus on task allocation methods using computational intelligence (CI) and deep reinforcement learning (RL). The advantages and disadvantages of the surveyed methods are analysed thoroughly. We also propose and discuss in detail various future research directions that shed light on how to improve existing algorithms or create new methods to enhance the employability and performance of autonomous machines in real-world applications. The findings indicate that CI and deep RL methods provide viable approaches to addressing complex task allocation problems in dynamic and uncertain environments. The recent development of deep RL has greatly contributed to the literature on controlling and coordinating autonomous machines, and it has become a growing trend in this area. It is envisaged that this paper will provide researchers and engineers with a comprehensive overview of progress in machine learning research related to autonomous machines. It also highlights underexplored areas, identifies emerging methodologies, and suggests new avenues for exploration in future research within this domain.

ABSTRACT The importance of fault tolerance strategies for distributed streaming computing systems becomes more evident due to the increased diversity of failures. Checkpointing is considered a general and efficient method for ensuring fault tolerance. However, determining the checkpoint interval poses a challenge: shorter checkpoint intervals lead to higher overhead, while longer intervals result in extended fault recovery time. Therefore, optimizing the checkpoint interval becomes crucial for the efficient operation of streaming applications. There has been relatively limited exploration and analysis of optimal checkpoint interval settings in the context of stream computing. Many existing works considered adjusting this interval based on a single factor. This article proposes a checkpoint adaptive strategy with high availability, named Ca‐Stream. It considers multiple factors when adjusting checkpoint intervals. Specifically, it addresses the following aspects: (1) Using linear regression to predict the system's fault rate and dynamically adjusting the checkpoint interval based on these predictions. (2) Monitoring CPU time and memory consumption per task to dynamically trigger checkpoints, achieving high reliability, especially in resource‐constrained scenarios. (3) Detecting task execution times on nodes and volume of input data for tasks to identify slow tasks within the cluster. Experiments conducted on a Flink system demonstrate Ca‐Stream's benefits. It reduces checkpoint consumption time by over 38%, system recovery latency by 33%, CPU occupancy by up to 47%, and memory occupancy by 37% compared to Flink's approaches.

Guest Editorial: Co-Design of Communication, Computing, and Control in Industrial Cyber-Physical Systems—Part I

Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term “3C co-design”) is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.

Guest Editorial: Co-Design of Communication, Computing, and Control in Industrial Cyber-Physical Systems—Part II

Edge intelligence is an emerging distributed computing paradigm that has been driven by the rapid proliferation of Internet of Things (IoT) devices, along with the advancements in edge computing and artificial intelligence. With latency-sensitive data commonly cached across multiple Edge Servers (ESs), efficient Edge Data Integrity Verification (EDIV) has become increasingly critical. Traditional ‘challenge-response’ EDIV methods incur substantial computation and communication costs by indiscriminately verifying all ESs, even though not all ESs may be simultaneously corrupted. A recent Federated Learning (FL)-based framework partially addressed this inefficiency by identifying potentially corrupted ESs early, considering only homogeneous ES activity data. However, due to heterogeneous activity data across diverse ESs, this approach suffers from reduced detection accuracy of potentially corrupted ESs, slower FL convergence, and unclear guidance for subsequent verification rounds, thus limiting the overall reduction in EDIV computation and communication costs. To that end, we proposeFederated learning withAdaptiveRegularizer-basedEdgeDataIntegrityVerification (FedAR-EDIV), which is an effective FL-based framework integrating an adaptive objective regularization strategy specifically designed to handle heterogeneous data distributions. FedAR-EDIV efficiently identifies potentially corrupted ESs during the FL process, achieves faster convergence, and significantly reduces computation and communication costs in the final EDIV procedure. It achieves up to 16× communication speedup and 9.1× computation cost reduction compared to baseline EDIV methods, and reaches FL-based detection accuracy exceeding 99.78% under heterogeneous conditions using KDD99 activity data. Additionally, FedAR-EDIV incorporates a dynamic reputation mechanism after each EDIV round to strategically guide subsequent verification rounds, ensuring fewer checks for trustworthy ESs and greater scrutiny for suspicious ones, thus further minimizing EDIV-related costs. We provide a theoretical analysis that demonstrates the convergence of FedAR-EDIV during FL training, as well as correctness, efficiency, and security during the EDIV process. Extensive experiments conducted on two different heterogeneous activity datasets validated that FedAR-EDIV substantially outperforms baseline methods in terms of corrupted ES detection accuracy, FL convergence speed, and overall EDIV computation and communication costs.

Ensuring Edge Data Integrity (EDI) is imperative in providing reliable and low-latency services in mobile edge computing. Existing EDI schemes typically address single-vendor (App Vendor, AV) single-server (Edge Server, ES), single-vendor multi-server, and multi-vendor multi-server scenarios, which consider a single data replica cached by an ES from the AVs. However, the most practical scenario of Multi-Vendors and Multi-Servers with Multiple Data (MVMS-MD) cached by an ES from different AVs remains unexplored. Current solutions struggle when applied to this scenario due to increased computation and communication costs in the verification process across all ESs using the classicalchallenge-response per-data multi-roundstrategy. To tackle this issue, we propose a Multiple EDI-Verification (MEDI-V) approach in this paper. In particular, our MEDI-V utilizes an adaptive Merkle Hash Tree (ad-MHT) to efficiently generate a tree of multiple data replicas within each AV. Next, the dynamic mechanism computes minimal verification information using ad-MHT to create achallengefor individual ESs to produce EDI proofs. The ES then leverages its ad-MHT and the ES's proof to send the reconstructed ad-MHT root to the AV for verification. Theoretical insights into MEDI-V's correctness, efficiency, security, and comprehensive evaluations demonstrate its superiority in addressing MEDI issues in the MVMS-MD scenario.

A Data-Encoding Approach to Quantum Federated Learning: Experimenting with Cloud Challenges

The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless interconnected devices in Industrial Internet of Things environments. Most of the industrial automation systems today are based on the traditional International Society of Automation (ISA)-95 model, with some recently transitioned to Cloud Automation systems. Latest developments in network connectivity technologies, artificial intelligence, and Cloud/Fog computing technologies have motivated us to rethink the ISA-95 model. In this article, we propose a vision that aims to migrate most of the computational and automation tasks closer to the ground, which we term the collaborative “Cloud-Fog Automation” paradigm. We perform a comprehensive survey of the state-of-the-art and formulate the three pillars of this vision: Deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. In each of these pillars, we review their latency and reliability, security, and functional safety requirements and challenges. Finally, we articulate and highlight key future research directions to realize this vision.

Typical Internet of Things (IoT) deployments are resource-constrained, with limited computation and storage, high network latency, and low bandwidth. The introduction of Edge and Cloud computing provides a method of mitigating these shortfalls. This paper proposes a framework for structuring IoT applications to allow for seamless offloading (based on CPU load) of work from IoT nodes to Edge and Cloud computing resources. The proposed flexible framework utilises software to orchestrate multiple containerised IoT applications for optimal performance within available computational resources. Edge and Cloud servers co-operate autonomously to determine the appropriate resource allocation based on the requirements of running IoT applications in real-time. The result is a framework that is suited to perform with heterogeneous IoT hardware while improving overall computational performance, latency and bandwidth relative to IoT architectures that do not auto-scale. This framework is evaluated using an experimental setup with multiple IoT nodes, Edge nodes and Cloud computing resources. It demonstrates the approach is viable and results in a flexible and scalable IoT solution.

The progressive adoption of Active Queue Management (AQM) and the popularity of Dynamic Adaptive Streaming over HTTP (DASH)-based streaming services have motivated the development of adaptive chunklets. Chunklets significantly improves the Quality of Experience (QoE) of video streaming applications in the presence of cross-traffic, which is known as the Achieved Rate (AR) multiplication effect. However, the detailed behaviour of chunklets and their implications on queuing dynamics have not been well-explored. In this paper, we instrumented a fine-grained, packet-driven FreeBSD queue measurement kernel module, and present an experimentally validated system model to help us better understand the AR multiplication effect in FlowQueue-based AQM bottlenecks. We experimentally demonstrated the accuracy of our system model across a wide range of network settings, along with providing fine-grained insights into queuing behaviours, including the detection of hash collisions. Our approach can provide network operators and researchers with greater network visibility where AQM schemes are deployed.

Dynamic Adaptive Streaming over HTTP (DASH) is a widely adopted standard for delivering high Quality of Experience (QoE) for consumer video streaming applications. The progressive deployment of Active Queue Management (AQM) schemes - such as PIE and FQ-CoDel - at ISP bottlenecks or home gateways means that consumers' video streams are increasingly impacted by such AQM schemes. However, many existing approaches do not consider adjusting streaming strategies based on the bottleneck queue types. We have previously demonstrated the benefits of AQM schemes for DASH video streams, and proposed adaptive chunklets for an improved streaming performance. In this paper, we demonstrate the problems of queue-agnostic streaming and propose a queue-detection technique during DASH-like streaming. This entirely client-side and application-level technique is capable of detecting likely FIFO, PIE and FQ-CoDel AQM schemes at network bottlenecks.

This paper explores a possible use-case of creating an integrated multiuser-multidevice interaction (2MUDI) model in IoT collectives, in particular, in an aged care centre environment. A prototype has been designed and developed, which has given a name KATE. The system comprises Internet-connected robot(s), multiple mobile devices and multiple users. Family members of the seniors admitted to aged care centres can monitor the seniors via the robot. Staff members, including doctors and nurses, who look after these seniors can also interact with and use the robot(s). This data can also be accessible by family members via an application on their mobile devices. This work has modelled complex interactions and considered the implementation challenges, societal implications in a 2MUDI system and demonstrate its applicability with the KATE system.

Commercial streaming services such as Netflix and YouTube use proprietary HTTP-based adaptive streaming (HAS) techniques to deliver content to consumers worldwide. MPEG recently developed Dynamic Adaptive Streaming over HTTP (DASH) as a unifying standard for HAS-based streaming. In DASH systems, streaming clients employ adaptive bitrate (ABR) algorithms to maximise user Quality of Experience (QoE) under variable network conditions. In a typical Internet-enabled home, video streams have to compete with diverse application flows for the last-mile Internet Service Provider (ISP) bottleneck capacity. Under such circumstances, ABR algorithms will only act upon the fraction of the network capacity that is available, leading to possible QoE degradation. We have previously explored chunklets as an approach orthogonal to ABR algorithms, which uses parallel connections for intra-video chunk retrieval. Chunklets effectively make more bandwidth available for ABR algorithms in the presence of cross-traffic, especially in environments where Active Queue Management (AQM) schemes such as Proportional Integral controller Enhanced (PIE) and FlowQueue-Controlled Delay (FQ-CoDel) are deployed. However, chunklets consume valuable server/middlebox resources which typically handle hundreds of thousands of requests/connections per second. In this article, we propose ‘adaptive chunklets’ -- a novel chunklet enhancement that dynamically tunes the number of concurrent connections. We demonstrate that the combination of adaptive chunklets and FQ-CoDel is the most effective strategy. Our experiments show that adaptive chunklets can reduce the number of connections by almost 30% and consume almost 8% less bandwidth than fixed chunklets while providing the same QoE.

Multimedia streaming is a significant source of Internet traffic, with Netflix and YouTube accounting for more than 50% of North American fixed network peak download traffic in 2016. Dynamic Adaptive Streaming over HTTP (DASH) is a recent standard for live and on-demand video streaming services, where clients adapt the video quality on-the-fly to match the network capacity by requesting multi-rate video chunk-by-chunk. Emerging Active Queue Management (AQM) schemes such as PIE and FQ-CoDel are being progressively deployed either at the ISP-end and/or home gateway to counter bufferbloat and will impact consumer DASH streams. We propose using intra-chunk parallel connections (chunklets) to retrieve DASH content when bottlenecks implement AQMs. We experimentally evaluate and characterise the impact of using chunklets over traditional FIFO, symmetric/asymmetric PIE and FQ-CoDel AQM bottlenecks. We show FQ-CoDel's flow isolation and fair capacity sharing ability enables DASH chunklets to attain the best throughput multiplication effect, hence translating to better user experience in the presence of competing elastic flows.

Internet of Things (IoT) applications such as telehealth, smart appliances, and smart energy are becoming more common within the home. However, they must compete for bandwidth with traditional applications such as video streaming, video conferencing, and bulk file transfers. Such competition can be detrimental to the IoT applications when home gateways use traditional first-in-first-out (FIFO) queue management. Simply increasing bandwidth between the home gateway and the Internet Service Provider (ISP), even when possible, provides no guarantee of bandwidth for IoT applications since many traditional applications will consume as much bandwidth as is available. In this paper, we explore whether active queue management (AQM), now being implemented in home gateways, can provide protection for IoT flows. We investigate the effect of different AQM algorithms deployed at the home gateway in scenarios with multiple concurrent application flows. We find that deploying multiqueue FlowQueue Controlled Delay (FQ-CoDel) or the hybrid FlowQueue Proportional Integral Controller Enhanced (FQ-PIE) at the home gateway can provide excellent capacity sharing, flow isolation, and good protection in terms of throughput and queuing delays for IoT flows and other applications, which cannot be achieved with traditional FIFO or other single-queue AQMs such as Proportional Integral Controller Enhanced (PIE).

With Netflix and YouTube accounting for more than 50% of North American, fixed network peak download traffic in 2015, video streaming is a significant source of Internet traffic. Dynamic Adaptive Streaming over HTTP (DASH) is a recent standard for live and on-demand video streaming services, where clients adapt their behaviour on-the-fly to match regularly updated estimates of network capacity. Consumer DASH streams are likely to be bottlenecked by last-mile ISP links, and impacted by emerging active queue management (AQM) schemes being deployed to counter bufferbloat. We experimentally characterise and evaluate the impact of bottlenecks utilising PIE, FQ-PIE, CoDel and FQ-CoDel AQM schemes on DASH streams. We show that PIE's higher burst tolerance provides better streaming quality for single DASH stream over moderate to high RTT paths and when coupled with a FlowQueue scheduler's flow isolation capabilities, FQ-PIE protects DASH streams in the presence of cross-traffic.