Jonathan Kua

Thien Tran, Khang Duong, Minh Tran et al.

The persistent challenge in scaling authentic manipulator education within university laboratories is a structural dichotomy: commercial digital twins are often cost-prohibitive and rigidly scripted, whereas open-source robotics middleware (ROS) imposes steep technical and syntax barriers for novices. To resolve this logistical and educational friction, this Work-in-Progress (WiP) paper proposes a scalable four-tier communication architecture tailored for sustainable robotic curricula. Rather than focusing on software application design, our study examines the underlying data exchange mechanisms required to bridge visual conceptual environments with physical robotic endpoints, utilizing the Graphical Open-Source Platform (GOSP) as a foundational instantiation. This WiP details the framework's technical integration of 3D visual armature modeling with a robust ROS middleware backend, emphasizing the serialization, routing, and encapsulation of intricate communication routines. Preliminary sim-to-real validation using multi-axis spatial trajectories confirms that encapsulating these communication pipelines provides a sufficient fidelity hardware-agnostic pathway. By bridging virtual design and physical execution, this architectural blueprint offers a viable infrastructure for engineering education.

Thien Tran, Jonathan Kua, Thuong Hoang et al.

Multi-robot systems (MRS) increasingly offload compute-intensive perception tasks to edge nodes to meet strict time-sensitive Quality-of-Service (QoS) constraints. However, static task orchestration on a shared edge node can severely degrade QoS due to network latency, jitter, and edge-resource contention. We present a pilot edge-centric MRS testbed using Raspberry Pi nodes to evaluate a camera-to-manipulator pipeline under three modes: local execution, static offloading, and a QoS-aware Adaptive Task Placement (ATP) controller. ATP scores candidate placements using a multi-metric cost (normalized latency, CPU utilization, and switching overhead) over two-second control windows. The closed-loop visual servoing testbed is instrumented with sub-millisecond clock synchronization, network emulation, and detailed monitoring of multiple metrics across nodes to capture realistic jitter. Experimental results under compute-stress and network-fault scenarios show that static edge offloading reduces on-board CPU load but amplifies tail latency and deadline misses. In contrast, the QoS-aware ATP controller, by switching task placement based on measured latency and utilization thresholds, consistently lowers deadline violations and tail latency. Overall, the results position ATP as a practical edge-side control primitive for MRS and concrete design guidelines for Cloud-Edge Robotics deployments within the broader cloud-fog automation, while motivating QoS-aware multi-objective workload orchestration for industrial cyber-physical systems.

Shyam Kumar Shrestha, Shiva Raj Pokhrel, Jonathan Kua

We evaluate TCP BBRv3 on Wi-Fi 6 home networks under modern AQM schemes using a fully wireless testbed and a simple cross-layer model linking Wi-Fi scheduling, router queueing, and BBRv3's pacing dynamics. Comparing BBR Internet traffic with CUBIC across different AQMs (FIFO, FQ-CoDel, and CAKE) for uplink, downlink, and bidirectional traffic, we find that FIFO destabilizes pacing and raises delay, often letting CUBIC dominate; FQ-CoDel restores fairness and controls latency; and CAKE delivers the best overall performance by keeping delay low and aligning BBRv3's sending and delivered rates. We also identify a Wi-Fi-specific effect where CAKE's rapid queue draining, while improving pacing alignment, can trigger brief retransmission bursts during BBRv3's bandwidth probes. These results follow from the interaction of variable Wi-Fi service rates, AQM delay control, and BBRv3's inflight limits, leading to practical guidance to use FQ-CoDel or CAKE and avoid unmanaged FIFO in home Wi-Fi, with potential for Wi-Fi-aware tuning of BBRv3's probing.

Thien Tran, Jonathan Kua, Thuong Hoang et al.

Current Physical AI (PAI) relies heavily on closed-loop visual-servoing pipelines, whose perception and planning stages may become computationally intensive onboard due to complex models embedded on robots. In practice, offloading the perception task to on-site edges statically is inappropriate for latency-sensitive, precise industrial settings over a standardized industrial network. This emphasizes the importance of Control-Communication-Computing (3C) co-design in industrial automation: monolithic local execution saturates AI-accelerated machine and robot hardware, while static edge offloading exposes the control loop to network jitter. Existing adaptive task placement (ATP) controllers can partially address the gap by relocating a single pipeline stage on binary threshold rules, without a multi-stage model and an explicit cost on placement switching. In this Work-in-Progress (WiP) paper, we propose a directed acyclic graph (DAG) based quality-of-service (QoS)-aware dynamic task placement (DTP) framework for sensing-perception-planning-control pipelines in networked robotics. This pipeline is formalized as a DAG with task-level and node-level attributes for compute cost, communication delay, and feasible placement sets; over a small interpretable candidate set (fully local, static offload, hybrid), a window-based cost function combines tail end-to-end latency, deadline violation rate, hardware utilization, and a Hamming-distance switching penalty, and a DTP algorithm with hysteresis and a minimum dwell-time bounds placement chatter. Our WiP paper presents the theoretical framework, a structured qualitative analysis, and a two-phase simulation plus hardware-in-the-loop validation roadmap.

Shiva Raj Pokhrel, Naman Yash, Jonathan Kua et al.

Quantum Federated Learning (QFL) is an emerging concept that aims to unfold federated learning (FL) over quantum networks, enabling collaborative quantum model training along with local data privacy. We explore the challenges of deploying QFL on cloud platforms, emphasizing quantum intricacies and platform limitations. The proposed data-encoding-driven QFL, with a proof of concept (GitHub Open Source) using genomic data sets on quantum simulators, shows promising results.

Thien Tran, Jonathan Kua, Minh Tran et al.

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.

Shiva Raj Pokhrel, Deol Satish, Jonathan Kua et al.

The growing complexity of network traffic and demand for ultra-low latency communication require smarter packet traffic management. Existing Deep Learning-based queuing approaches struggle with dynamic network scenarios and demand high engineering effort. We propose AQM-LLM, distilling Large Language Models (LLMs) with few-shot learning, contextual understanding, and pattern recognition to improve Active Queue Management (AQM) [RFC 9330] with minimal manual effort. We consider a specific case where AQM is Low Latency, Low Loss, and Scalable Throughput (L4S) and our design of AQM-LLM builds on speculative decoding and reinforcement-based distilling of LLM by tackling congestion prevention in the L4S architecture using Explicit Congestion Notification (ECN) [RFC 9331] and periodic packet dropping. We develop a new open-source experimental platform by executing L4S-AQM on FreeBSD-14, providing interoperable modules to support LLM integration and facilitate IETF recognition through wider testing. Our extensive evaluations show L4S-LLM enhances queue management, prevents congestion, reduces latency, and boosts network performance, showcasing LLMs' adaptability and efficiency in uplifting AQM systems.

Thien Tran, Quang Nguyen, Jonathan Kua et al.

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.

Rakshitha De Silva, Shiva Raj Pokhrel, Jonathan Kua et al.

Satellite Communication (SatCom) networks represent a fundamental pillar in modern global connectivity, facilitating reliable service and extensive coverage across a plethora of applications. The expanding demand for high-bandwidth services and the proliferation of mega satellite constellations highlight the limitations of traditional exclusive satellite spectrum allocation approaches. Cognitive Radio (CR) leading to Cognitive Satellite (CogSat) networks through Dynamic Spectrum Management (DSM), which enables the dynamic adaptability of radio equipment to environmental conditions for optimal performance, presents a promising solution for the emerging spectrum scarcity. In this survey, we explore the adaptation of intelligent DSM methodologies to SatCom, leveraging satellite network integrations. We discuss contributions and hurdles in regulations and standardizations in realizing intelligent DSM in SatCom, and deep dive into DSM techniques, which enable CogSat networks. Furthermore, we extensively evaluate and categorize state-of-the-art Artificial Intelligence (AI)/Machine Learning (ML) methods leveraged for DSM while exploring operational resilience and robustness of such integrations. In addition, performance evaluation metrics critical for adaptive resource management and system optimization in CogSat networks are thoroughly investigated. This survey also identifies open challenges and outlines future research directions in regulatory frameworks, network architectures, and intelligent spectrum management, paving the way for sustainable and scalable SatCom networks for enhanced global connectivity.

Thanh Thi Nguyen, Quoc Viet Hung Nguyen, Jonathan Kua et al.

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.

Shiva Raj Pokhrel, Jonathan Kua, Deol Satish et al.

In this work, we propose and develop a simple experimental testbed to study the feasibility of a novel idea by coupling radio frequency (RF) sensing technology with Correlated Knowledge Distillation (CKD) theory towards designing lightweight, near real-time and precise human pose monitoring systems. The proposed CKD framework transfers and fuses pose knowledge from a robust "Teacher" model to a parameterized "Student" model, which can be a promising technique for obtaining accurate yet lightweight pose estimates. To assure its efficacy, we implemented CKD for distilling logits in our integrated Software Defined Radio (SDR)-based experimental setup and investigated the RF-visual signal correlation. Our CKD-RF sensing technique is characterized by two modes - a camera-fed Teacher Class Network (e.g., images, videos) with an SDR-fed Student Class Network (e.g., RF signals). Specifically, our CKD model trains a dual multi-branch teacher and student network by distilling and fusing knowledge bases. The resulting CKD models are then subsequently used to identify the multimodal correlation and teach the student branch in reverse. Instead of simply aggregating their learnings, CKD training comprised multiple parallel transformations with the two domains, i.e., visual images and RF signals. Once trained, our CKD model can efficiently preserve privacy and utilize the multimodal correlated logits from the two different neural networks for estimating poses without using visual signals/video frames (by using only the RF signals).