Welcome to our Publications page!

This paper addresses how the increasing demand for machine learning (ML) technologies has led to a significant rise in energy consumption and environmental impact, particularly within the context of distributed computing environments like the Edge-Fog-Cloud Continuum. This paper addresses the critical challenge of optimizing ML processes not only for performance but also for sustainability by introducing a novel Green Automated Machine Learning (AutoML) framework. The proposed framework integrates energy-aware task allocation into the AutoML pipeline, strategically distributing computational tasks across edge, fog, and cloud layers to minimize energy usage and carbon emissions without compromising model accuracy. To achieve this, the framework incorporates real-time monitoring and dynamic task allocation based on energy consumption, latency, and carbon footprint, utilizing a hierarchical approach that leverages the unique strengths of each layer within the continuum. The framework is supported by mathematical models that quantify energy consumption, communication latency, and environmental impact, offering a comprehensive metric for evaluating the sustainability of ML deployments. Through extensive simulations and real-world experiments, the framework demonstrates substantial improvements in energy efficiency and significant reductions in environmental footprint compared to traditional AutoML approaches. This research contributes to the advancement of sustainable AI by providing a practical solution for deploying ML models in a manner that balances performance with environmental responsibility.

In this paper, we propose an improved Transmission Control Protocol (TCP) algorithm called HYBIC, building upon existing CUBIC and HYBLA algorithms. This HYBIC algorithm is designed for improving capacity utilisation and transmission rate of heterogeneous networks such as the Integrated Satellite-Terrestrial Networks (ISTN) and long delay High Throughput Satellites (HTS) networks on Geostationary Earth Orbit that are characterised with high bandwidth-delay product path. Results analysed indicated that better performance is achieved using the proposed HYBIC algorithm. Considering the results, HYBIC achieved better performance in terms of window growth of 23×10 3 segments, transmission rate of 3Gbps, and capacity utilisation of 60 % compared with both CUBIC and HYBLA. However, the proposed HYBIC inherits the features of Round-Trip Time fairness, scalability, and friendliness of HYBLA and CUBIC algorithms.

In this work, we developed and proposed a real testbed with Integrated Satellite-Terrestrial Network (ISTN) scenario. This topology was used to measure the actual parameters that were used as the Smart Grid (SG) Quality of Service (QoS) metrics. Performance was evaluated with reference to the QoS requirements of SG applications. The emergence of new and evolving technologies, such as smarter energy utilities enabled by advanced communications technologies, necessitated evolutionary enhancements of both satellite and terrestrial communications systems. These would help improve energy efficiency and sustainability through the effective acquisition and analysis of data from energy systems. Hybrid communication technologies can be used to collect and share energy data efficiently and ubiquitously from generation stations to consumption sites. Thus, a topology using Inmarsat-4 satellites is presented that connects a robust, portable, and energy-efficient Broadband Global Area Network ground terminal. Performance evaluation was performed using measured parameters from the real ISTN topology testbed against key SG applications. The latency and bandwidth requirements for the 80-90% key SG applications were found to be within the QoS requirements range.

This work is part of Pillar 3 – Building Trustworthy Systems and examines the impact of customising the endorsement policy (EP) in Hyperledger Fabric (HLF) for data sharing in Autonomous Vehicles (AVs). In HLF, the EP can be tailored for each application, specifying the required approvals from the endorsing peers of participating organisations. Customisation is often essential to ensure that the transaction validation process aligns with the application’s specific security and business requirements. In our AV data-sharing platform, we implemented both default and customised EP configurations to assess their effectiveness.

In their latest paper, Dr. Yun Tang and Prof. Weisi explore the core of automated network and AI service provisioning, which relies on two key components:

1. Comprehension intelligence – This component understands both the use case requirements of end users and the (potentially dynamic) availability of networking (and AI) resources within the network (e.g., RAN and MEC).

2. Decision-making intelligence – This component monitors and configures the telecom network to deploy on-demand networking (and AI) services using RAN and MEC resources.

By adopting O-RAN, we gain the capability to monitor and configure RAN functions. Furthermore, by leveraging the intelligence of large language models (LLMs) equipped with use case and network knowledge through a RAG database, we enhance our ability to understand users’ needs and the network’s status, enabling automated network configurations and controls. This approach makes the vision of automated network (and AI) service provisioning feasible.

This paper outlines our initial step toward realizing this blueprint. It focuses on understanding and translating user-provided use case descriptions into actionable network specifications using RAG-LLMs. To advance this initiative, we propose establishing a use case knowledge database through crowd-sourcing of potential or real-life 6G use cases from the public. This database will be accessible to the public and integrated into the LLMs to support the entire network service automation pipeline.

Stay tuned as we work on integrating our current implementation with open-source O-RAN projects and 5G simulators!

This paper introduces the Search-Voxel Ellipse Normalisation technique designed to enhance the accuracy and reliability of respiratory rate monitoring using MIMO FMCW radar in environments impacted by metal-induced multipath distortions. The key contributions include the implementation of an ellipse normalisation method that effectively reduces noise and phase distortions, and the introduction of a voxel selection policy that integrates the Least Squares Method with Dynamic Time Warping and K-Nearest Neighbours to exclude low SNR voxel and refine signal quality. Additionally, the adoption of a Gaussian estimator offers a robust mechanism for continuous and precise respiratory rate estimation. Together, these innovations significantly enhance the efficacy of radar-based health monitoring systems, especially in complex indoor environments.

The aviation and air mobility sectors are undergoing rapid transformation, driven by 6G advancements and the need for secure and reliable airborne communication systems. The integration of Unmanned Aerial Systems (UAS) into commercial and logistical operations is redefining air mobility. The Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) protocol enhances UAS communication safety by minimizing data transmission collisions and ensuring efficient communication. Combining CSMA/CA with Sixth Generation (6G) technology further strengthens UAS operations’ reliability and effectiveness. MATLAB simulations provide insights into signal quality and latency as a function of distance, critical for analysing safety communications. Additionally, understanding handover procedures, especially vertical handovers in UAS operations, is vital for public safety and the development of advanced Unmanned Traffic Management (UTM) and Air Traffic Management (ATM) systems.

This new paper ‘Mixed-Precision Federated Learning via Multi-Precision Over-The-Air Aggregation’ demonstrates that Over-the-Air Federated Learning (OTA-FL) offers numerous advantages in challenging environments, including ultra-high radio resource efficiency and enhanced privacy preservation. It achieves this by combining client updates through the superposition of modulated waveforms in the propagation channel. Traditional research in OTA-FL assumes that the FL clients have the same computation bit resolutions (e.g., 4-bit, 8-bit 16-bit…etc.), and differential approximate computing (AxC) bit resolution updates pose a problem when reflected into the modulation constellation space superposition (e.g., QPSK, 8-PSK, 16-QAM). Our paper is due to appear in 2025 IEEE Wireless Communications and Networking Conference (WCNC) develops 2 innovations: (1) to create a common modulation basis for multi-resolution OTA-FL, and (2) to create a performance vs. energy efficiency trade-off mapping by adapting the client side bit-resolution. Our work paves the way for Auto-ML: the automatic orchestration of resources in distributed 6G and IoT computing architectures.

The work presented in this paper addresses the risk of misconfiguration introduced by logical inconsistencies when trying to balance QoS, for example between service availability and power efficiency, during the adoption of Open Radio Access Networks (O-RAN) and the deployment of AI/ML-driven applications (xApps) to enhance and simplify network management. This work proposes an approach to use probabilistic model checkers (PRISM and Storm) to quantitatively compute optimal thresholds between energy efficiency and service availability for a defined policy in a given scenario with three radio cells (RCs) and nine user equipments (UEs) while UEs are dynamically switched on and off under uncertainty. As a result, the work can prevent logical inconsistencies in xApp development and safeguard AI-driven applications to ensure network decisions are logically consistent, enhancing reliability and preventing misconfigurations.

The work presented in this paper addresses the accessibility issue of formal verification to security protocol designers by introducing animation as a formal way to verify protocols and the soundness issue of animation. This paper proposes an innovative and iterative workflow to allow designers to carry our verification in the early stage of design with a guarantee that a problem detected during animation must be a bug in the design. Most importantly, animators are automatically generated from security protocol models. As a result, designers can find bugs earlier and deliver secure protocols efficiently without a loss of guarantee.

The integration of machine learning (ML) algorithms with edge sensor systems has fundamentally transformed numerous industries. This convergence empowers real-time data processing, analysis, and decision-making at the network’s periphery. This paper investigates the latest advancements in this domain by examining two key communities: Sensys-ML and TinyML. While Sensys-ML concentrates on optimizing ML for sensor systems, TinyML prioritizes deploying ML models on resource-constrained devices. Through a critical analysis of these communities’ contributions and interactions, this work aims to provide a comprehensive overview of cutting-edge methodologies, persistent challenges, and promising future directions for ML at the edge within sensor systems. By tracing the trajectory of advancements in this field, we offer a critical reflection on the broader research landscape and its scope. Additionally, we identify emerging research areas as reflected in prominent forums and underscore persisting knowledge gaps that call for further investigation.

In the rapidly expanding landscape of Internet of Things (IoT) device manufacturing and deployment, concerns about security have become prominent. This demonstration involves practical attacks on a thread-mesh network within a controlled environment, exploiting vulnerabilities in various components of the Thread network stack. Our attack vectors successfully identified nearby Thread networks and devices by gathering 2-byte Personal Area Network ID (PAN ID) and device frequency information, serving as reconnaissance for potential additional attacks. The focus was on investigating susceptibility to replay attacks and packet injection into thread-mesh networks. Although the experiment attempted to capture thread packets to emulate an authorised sender, the cryptographic encryption and sequence numbers employed for integrity checks resulted in packet rejection by the network. Despite this, our successful injection of packets highlights the potential for battery depletion attacks.

Lip-reading has become a critical research challenge, aiming to recognize speech by analysing lip movements. Traditional methods rely on cameras and wearable devices, but these face limitations like occlusion, lighting conditions, privacy concerns, and user discomfort. In the COVID-19 era, with face masks being the norm, such systems are ineffective for hearing aids. To overcome these issues, this paper introduces an RFID-based smart mask for lip-reading, enabling effective speech recognition even under face masks. The system collects data using RF sensing, capturing three classes: vowels (A, E, I, O, U), consonants (F, G, M, S), and words (Fish, Goat, Meal, Moon, Snake). Machine learning models classify the data, achieving high accuracy across individual classes. On the combined dataset, the Random Forest model achieves 80% accuracy, demonstrating the system’s potential to enhance accessibility and speech recognition for individuals with hearing impairments.

The paper titled “Terahertz Photoconductive Antenna Based on Tai-Chi Totem Structure for Cancer Detection” introduces an innovative design for a terahertz (THz) photoconductive antenna (PCA) utilising a Tai-Chi totem structure. This design aims to enhance the efficiency and sensitivity of THz radiation sources, which are crucial for non-invasive and accurate cancer detection. By leveraging the unique properties of the Tai-Chi totem configuration, the PCA achieves improved performance in generating and detecting THz waves, offering significant potential in medical diagnostics. This research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “A Fractal Dual-Band Polarization Diversity Antenna Array for 5G Communications” introduces a compact, dual-polarized antenna array tailored for the 28 GHz and 38 GHz frequency bands, which are pivotal for 5G applications. With dimensions of 32×20 mm², the antenna array achieves broad impedance bandwidths of 2.7 GHz (26.6–29.3 GHz) and 5.2 GHz (35–40.2 GHz). To facilitate polarization diversity, the design incorporates two orthogonal feed lines, enabling the antenna to support multiple polarization states. This innovation holds significant potential for enhancing the performance and versatility of 5G communication systems.

The paper titled “RIS-Enabled Near-Field Localisation with EMI” explores the utilisation of Reconfigurable Intelligent Surfaces (RIS) to enhance near-field localisation accuracy in environments with Electromagnetic Interference (EMI). By dynamically controlling electromagnetic signal characteristics such as scattering, reflection, and refraction, the study demonstrates how RIS technology can mitigate EMI effects, leading to improved precision in position estimation. This research holds significant potential for applications in wireless communication systems, indoor navigation, and IoT networks. The findings were presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “Ultrahigh Sensitive Terahertz Metasurface with 2D MoS₂ for Refractive Index Biosensing” introduces a highly sensitive terahertz (THz) metasurface integrated with two-dimensional molybdenum disulfide (2D MoS₂) for advanced refractive index biosensing applications. By leveraging the unique properties of 2D MoS₂, the metasurface achieves an enhanced quality (Q) factor, leading to improved sensitivity in detecting minute changes in the refractive index. This innovation holds significant potential for applications in biosensing, medical diagnostics, and environmental monitoring. The research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “Design of Intelligent Reflective Surface Unit Cell for 5G mmWave Communication Enhancement” presents a novel approach to improving 5G millimeter-wave (mmWave) communications through the development of an intelligent reflective surface (IRS) unit cell. The proposed design utilises a PIN diode switch to achieve a 180° phase shift in reflection mode between its ON and OFF states. The geometry of the unit cell’s top layer is optimised to support transverse electric (TE) polarized reflection with an angular stability of 30°, ensuring consistent performance across a range of incident angles. This research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “An Innovative Metasurface Polarizer Working in 5G Frequency Bands” introduces a novel metasurface design aimed at enhancing polarization control in 5G communications. The proposed metasurface comprises unit cells featuring a ring-shaped configuration with an internal star pattern, constructed on a Rogers 5880 substrate with a thickness of 1.575 mm and a loss tangent of 0.009. This design facilitates multi-band capabilities, supporting both cross-polarization and circular polarization, thereby improving signal quality and efficiency in 5G networks. The research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “RF-Based Respiration Disorders Sensing and Classification Using USRP” explores the application of Radio Frequency (RF) sensing technologies for real-time, contactless monitoring of respiratory disorders. Utilising Universal Software Radio Peripherals (USRP), the study demonstrates how RF signals can detect and classify various respiratory conditions, offering a non-invasive alternative to traditional monitoring methods. This research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper titled “DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN” explores the application of Deep Reinforcement Learning (DRL) to optimise resource allocation in Open Radio Access Networks (O-RAN). By jointly scheduling Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) services, the study aims to enhance network efficiency and meet diverse Quality of Service (QoS) requirements. This research was presented at the 2024 IEEE International Conference on Communications Workshops (ICC Workshops).

The paper “DRL-based Joint Resource Scheduling of eMBB and URLLC in O-RAN” explores the application of Deep Reinforcement Learning (DRL) to tackle the challenge of resource scheduling in Open Radio Access Networks (O-RAN). By jointly optimising Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low-Latency Communications (URLLC) traffic, the proposed DRL framework ensures efficient allocation of network resources while meeting the diverse Quality of Service (QoS) requirements of these services. This innovative approach paves the way for scalable and adaptive resource management in 5G and beyond, enhancing network performance and reliability. Presented at the 2024 IEEE International Conference on Communications Workshops, the research highlights significant advancements in AI-driven solutions for modern wireless networks.

Efficient utilisation of scarce resources, particularly radio spectrum and power, is a critical challenge in the development of future Internet of Things (IoT) networks. The paper “Throughput Optimisation in Ambient Backscatter-Based Cognitive IoT Networks” addresses this issue by proposing an innovative approach that combines ambient energy harvesting with backscatter communication in energy-constrained cognitive IoT networks. In this scheme, secondary network nodes effectively utilise the primary network’s resources. Depending on the availability of spectrum and energy, these nodes can switch between energy harvesting and backscatter communication modes, thereby optimising throughput and enhancing overall network efficiency.

In their study, “Performing QoS Measurements in UAV Field Trials with 5G-Advanced Communications at Various Altitudes,” presented at the IET 6G and Future Networks Conference (IET 6G 2024) in London, UK, the authors conduct field trials to assess Quality of Service (QoS) parameters of 5G networks in air mobility scenarios. They investigate latency, throughput, and handover attempts at altitudes of 50, 72, 88, and 110 meters, providing insights into the performance of UAV-to-ground connections across different heights. 

In “Moving Target Defence in 6G UAV Networks,” presented at the IEEE/AIAA 43rd Digital Avionics Systems Conference (DASC) in San Diego, Oct 2024, the authors explore innovative methods to enhance security in UAV networks using 6G technology. By leveraging predictive cyber-attack analysis, network monitoring, and management functions, the study demonstrates how to proactively secure communication data links. This work, recognized with the Best Session Award, highlights the potential of 6G-enabled computing to fortify UAV network defenses.  

Exploring the seamless integration of satellite and terrestrial networks, this publication addresses key challenges in 6G communications, including latency and dynamic user traffic. By leveraging a deep Q-learning-based algorithm for optimizing satellite trajectories, the research offers innovative solutions to enhance network reliability and performance. 

“This paper introduces a multi-band ultrathin reflective metasurface that supports polarization conversion across Ku, K, and Ka bands. The metasurface offers both linear and circular polarization options, making it a versatile solution for advanced communication and sensing applications.”

“This study delves into the use of reconfigurable intelligent surfaces (RIS) for precise indoor localization in ‘coded environments.’ By leveraging data-driven approaches, this research enhances indoor navigation, with potential applications in smart buildings and automated environments.”

“This paper presents the concept of ‘intelligent wireless walls,’ designed for contactless in-home monitoring. The technology enables continuous health and safety monitoring by transforming walls into sensor networks, offering potential applications in elder care and home security through seamless, unobtrusive monitoring.”

Papers before 2023 were not funded by CHEDDAR but are here as they influenced the design of the hub.

“In this innovative study, the authors explore the use of remote RF sensing to achieve lip reading even when individuals are wearing face masks. This research pushes the boundaries of RF technology, presenting methods that could be transformative for communication in healthcare and other masked environments.”

Papers before 2023 were not funded by CHEDDAR but are here as they influenced the design of the hub.

“This paper introduces a comprehensive multimodal dataset aimed at advancing contactless lip reading and acoustic analysis. By combining visual and audio modalities, the dataset provides valuable resources for developing robust algorithms that can accurately interpret speech in challenging environments, enhancing applications in fields like accessibility and remote communication.”

“In this paper, the authors explore data-driven approaches to indoor localization using reconfigurable intelligent surfaces (RIS). This ‘coded environment’ approach enhances accuracy in indoor navigation, making it a promising solution for applications such as smart buildings and automated logistics.”

“This study investigates the use of reconfigurable intelligent surfaces (RIS) to improve near-field localization accuracy. By implementing a practical phase shift model, the research demonstrates how RIS technology can enable precise localization, with potential applications in indoor navigation and IoT.”

“This research explores the development of a 60 GHz programmable dynamic metasurface antenna (DMA), showcasing its potential for next-generation communication and imaging systems. The paper provides a detailed journey from concept to prototype, emphasizing DMA’s role in enhancing wireless communication and imaging precision.”

“This paper introduces a wideband series-fed patch antenna array optimized for 5G Vehicle-to-Everything (V2X) communications. The array’s high gain and low sidelobe characteristics, coupled with its linearly and circularly polarized configurations, make it ideal for improving connectivity and reliability in 5G-enabled transportation systems.”

“This study presents a novel sub-terahertz (THz) planar antenna array designed to enhance sensing and imaging applications. With a focus on achieving high performance in THz frequencies, the research highlights significant advancements in antenna design, providing applications in fields such as medical imaging and environmental monitoring.”