Staff publications (AIRS)

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Browsing Staff publications (AIRS) by Type "Conference paper"

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    Blockchain and distributed digital watermarking effort on federated learning: innovating intellectual property protection
    (IEEE, 2024-12-02) Chao, Kailin; Li, JunJie; Jiang, Yirui; Xiao, Jianmao; Cao, Yuanlong
    Federated Learning with Digital Watermarks (FLDW) have been recognized as a promising solution for property protection. However, the existing FLDW-related technologies neglect the requirements of decentralized settings, leading to recurrent issues such as discrepancies in distributed client data. This paper introduces a Blockchain Federated Learning Intellectual Property Protection Framework (BFLIPR), to address the data security and model validation challenges in decentralized federated learning environments. BFLIPR merges blockchain, digital watermarking, and federated learning technologies. By harnessing the blockchain’s tamper-proof properties, digital watermarking’s concealment capabilities, and federated learning’s distributed feature, the framework offers a solution that aligns with intellectual property protection mechanism, to bolster data security and property safeguarding. Experimental findings demonstrate its high feasibility and robust for data privacy and model security in the federated learning.
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    Cascade network stability of synchronized traffic load balancing with heterogeneous energy efficiency policies
    (IEEE, 2024-12-08) Zou, Mengbang; Guo, Weisi
    Cascade stability of load balancing is critical for ensuring high efficiency service delivery and preventing undesirable handovers. In energy efficient networks that employ diverse sleep mode operations, handing over traffic to neighbouring cells' expanded coverage must be done with minimal side effects. Current research is largely concerned with designing distributed and centralized efficient load balancing policies that are locally stable. There is a major research gap in identifying largescale cascade stability for networks with heterogeneous load balancing policies arising from diverse plug-and-play sleep mode policies in ORAN, which will cause heterogeneity in the network stability behaviour. Here, we investigate whether cells arbitrarily connected for load balancing and having an arbitrary number undergoing sleep mode can: (i) synchronize to a desirable load-balancing state, and (ii) maintain stability. For the first time, we establish the criterion for stability and prove its validity for any general load dynamics and random network topology. Whilst its general form allows all load balancing and sleep mode dynamics to be incorporated, we propose an ORAN architecture where the network service management and orchestration (SMO) must monitor new load balancing policies to ensure overall network cascade stability.
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    Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization
    (IEEE, 2024-12-08) Li, Jiawei; Wang, Dawei; Zhang, Jiankang; Alfarraj, Osama; He, Yixin; Al-Rubaye, Saba; Yu, Keping; Mumtaz, Shahid
    This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.
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    Designing and testing of HDPE–N2O hybrid rocket engine
    (MDPI, 2025-03-13) Arora, Triyan Pal; Buttrey, Noah; Kirman, Peter; Khadtare, Sanmukh; Kamath, Eeshaan; del Gatto, Dario; Isoldi, Adriano
    Hybrid Rocket Engines (HREs) combine the advantages of solid and liquid propellants, offering thrust control, simplicity, safety, and cost efficiency. Part of the research on this rocket architecture focuses on optimising combustion chamber design to enhance performance, a process traditionally reliant on time-consuming experimental adjustments to chamber lengths. In this study, two configurations of HREs were designed and tested. The tests aimed to study the impact of post-chamber lengths on rocket engine performance by experimental firings on a laid-back test engine. This study focused on designing, manufacturing, and testing a laid-back hybrid engine with two chamber configurations. The engine features a small combustion chamber, an L-shaped mount, a spark ignition, and nitrogen purging. Data acquisition includes thermocouples, pressure transducers, and a load cell for thrust measurement. Our experimental findings provide insights into thrust, temperature gradients, pressure, and plume characteristics. A non-linear regression model derived from the experimental data established an empirical relationship between performance and chamber lengths, offering a foundation for further combustion flow studies. The post-chamber length positively impacted the engine thrust performance by 2.7%. Conversely, the pre-chamber length negatively impacted the performance by 1.3%. Further data collection could assist in refining the empirical relation and identifying key threshold values.
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    Enhancing performance and interpretability of multivariate time-series model through sparse saliency
    (IEEE, 2024-09-24) Kong, Xiangqi; Xing, Yang; Liu, Zeyu; Tsourdos, Antonios; Wikander, Andreas
    Explainable time-series modelling is an essential task for modern intelligent transportation systems (ITS). How-ever, balancing accuracy and interpretability in multivariate time series forecasting presents significant challenges. These challenges arise from the necessity to understand the significance of features and their temporal variations. Factors such as autocorrelation in time series and data processing techniques like sliding windows expand feature sets, thereby complicating pattern recognition using traditional post-hoc explanation methods and making the issue even more complex. To overcome these challenges, in this study, we propose a flexible post-process approach which generates sparse and normalized saliency values based on existing saliency generation methods such as GradientSHAP. Additionally, an optional window aggregation and alignment strategy is introduced to align with the original time series dataset, enhancing the intuitive understanding of feature importance. Furthermore, the potential use of sparse saliency for data augmentation to improve the model is explored. Lastly, we utilize naturalistic data from San Francisco airport to demonstrate our approach for ITS time-series prediction and explanation. The evaluation results indicate that integrating sparse saliency from high-performing models not only boosts the performance of XGBoost models by 10.92% but also simplifies model complexity, facilitating easier interpretation.
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    Mitigating no fault found phenomena through ensemble learning: a mixture of experts approach
    (IEEE, 2024-09-24) Liu, Zeyu; Kong, Xiangqi; Chen, Yang; Wang, Ziyue; Jia, Huamin; Al-Rubaye, Saba
    In the aviation industry, the reliance on precise fault diagnostic decision-making is critical for equipment maintenance. A significant challenge encountered is the erroneous categorization of components under 'No Fault Found' (NFF), which subjects these components to unwarranted repairs or further testing. Such misclassifications not only trap on airlines through costly cycles of unnecessary maintenance but also exacerbate degeneration and potential safety hazards. Consequently, there is a heightened demand for the development of effective fault diagnosis models that are adapting to the aircraft complex systems and adeptly addressing issues related to the NFF phenomenon. In this study, we draw inspiration from ensemble learning and propose a multiple Naive Bayes experts (MNBMoEs) approach based on a mixture of experts (MoEs) model. This method leverages the predictive advantages of each sub-model on specific features, allowing the hybrid expert decision to outperform any single expert. It also includes a quantitative analysis method for the NFF issue, derived from the confusion matrix according to the industrial definition of NFF. Experiments evaluated on public datasets results show that the ensemble learning approach, based on Mixture of Multiple Naive-Bayes expert models, can effectively utilize the strengths of different models, improving fault diagnosis accuracy to 96.96%, with a maximum reduction in NFF occurrence rates of up to 94.17% and 84.2% model performance improvement.
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    Mixed-precision federated learning via multi-precision over-the-air aggregation
    (IEEE, 2025-03-24) Yuan, Jinsheng; Wei, Zhuangkun; Guo, Weisi
    Over-the-Air Federated Learning (OTA-FL) is a privacy-preserving distributed learning mechanism, by aggregating updates in the electromagnetic channel rather than at the server. A critical research gap in existing OTA - FL research is the assumption of homogeneous client computational bit precision. While in real world application, clients with varying hardware resources may exploit approximate computing (AxC) to operate at different bit precisions optimized for energy and computational efficiency. Model updates with varying precisions among clients present a significant challenge for OTA - FL, as they are incompatible with the wireless modulation superposition process. Here, we propose an mixed-precision OTA-FL framework of clients with multiple bit precisions, demonstrating the following innovations: (i) the superior trade-off for both server and clients within the constraints of varying edge computing capabilities, energy efficiency, and learning accuracy requirements compared to homogeneous client bit precision, and (ii) a multi-precision gradient modulation scheme to ensure compatibility with OTA aggregation and eliminate the overheads of precision conversion. Through case study with real world data, we validate our modulation scheme that enables AxC based mixed-precision OTA-FL. In comparison to homogeneous standard precision of 32-bit and 16-bit, our framework presents more than 10% in 4-bit ultra low precision client performance and over 65% and 13% of energy savings respectively. This demonstrates the great potential of our mixed-precision OTA-FL approach in heterogeneous edge computing environments.
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    Resilient time dissemination fusion framework for UAVs for smart cities
    (MDPI, 2025-03-17) Negru, Sorin Andrei; Arora, Triyan Pal; Petrunin, Ivan; Guo, Weisi; Tsourdos, Antonios; Sweet, David; Dunlop, George
    Future smart cities will consist of a heterogeneous environment, including UGVs (Unmanned Ground Vehicles) and UAVs (Unmanned Aerial Vehicles), used for different applications such as last mile delivery. Considering the vulnerabilities of GNSS (Global Navigation System Satellite) in urban environments, a resilient PNT (Position, Navigation, Timing) solution is needed. A key research question within the PNT community is the capability to deliver a robust and resilient time solution to multiple devices simultaneously. The paper is proposing an innovative time dissemination framework, based on IQuila’s SDN (Software Defined Network) and quantum random key encryption from Quantum Dice to multiple users. The time signal is disseminated using a wireless IEEE 802.11ax, through a wireless AP (Access point) which is received by each user, where a KF (Kalman Filter) is used to enhance the timing resilience of each client into the framework. Each user is equipped with a Jetson Nano board as CC (Companion Computer), a GNSS receiver, an IEEE 802.11ax wireless card, an embedded RTC (Real Time clock) system, and a Pixhawk 2.1 as FCU (Flight Control Unit). The paper is presenting the performance of the fusion framework using the MUEAVI (Multi-user Environment for Autonomous Vehicle Innovation) Cranfield’s University facility. Results showed that an alternative timing source can securely be delivered fulfilling last mile delivery requirements for aerial platforms achieving sub millisecond offset.
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    Sustainable 6G-NTN for seamless air mobility: exploring channel propagation characteristics
    (IEEE, 2025-04-08) Chen, Yang; Bocciarelli, Hugo; Al-Rubaye, Saba; Tsourdos, Antonios
    The air transportation vision for sustainable sixthgeneration (6 G) wireless communications networks revolves around ensuring ubiquitous coverage and spectral efficiency with enhanced network intelligence in the diverse communication scenarios. This vision extends beyond terrestrial networks to include non-terrestrial networks (NTN) by incorporating GX Inmarsat satellites and aircraft networks. In the context of 6G GX satellite scenarios, aircraft seamless transportation plays a crucial role as a densely populated intermediate network layer between ground networks and space-based ones. The paper proposes a new sustainable mechanism with mathematical model to improve channel propagation, which has been validated by crucial analysis of propagation channel modeling within the framework of 6 G technology. It highlights the significance of such modeling with the guarantee of dependable communications, maximizing availability, and establishing system parameters like antenna layout and relay deployment. It explores industry trends and ongoing field trial initiatives, offering valuable insights into the progress and outcomes that will shape the future of 6G NTN.
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    Understanding the relevance of parallelising machine learning algorithms using CUDA for sentiment analysis
    (Association for Computing Machinery (ACM), 2024-10-17) Chai, Dakun Mang; Moulitsas, Irene; Bisandu, Desmond B.
    Sentiment classification is essential in natural language processing, leveraging machine learning algorithms to understand the sentiment expressed in textual data. Over the years, advancements in machine learning, particularly with Naive Bayes (NB) and Support Vector Machines (SVM), have tremendously improved sentiment classification. These models benefit from word embedding techniques such as Word2Vec and GloVe, which provide dense vector representations of words, capturing their semantic and syntactic relationships. This paper explores the parallelisation of NB and SVM models using CUDA on GPUs to enhance computational efficiency and performance. Despite the computational power offered by GPUs, the literature on parallelising machine learning methods, especially for sentiment classification, remains limited. Our work aims to fill this gap by comparing the performance of NB and SVM on CPU and GPU platforms, focusing on execution time and model accuracy. Our experiments demonstrate that NB outperforms SVM in execution time and overall efficiency, mainly when using GPU acceleration. The NB model consistently achieves higher accuracy, precision, and F1 scores with Word2Vec and GloVe embeddings. The results show the importance of leveraging GPU acceleration using varying numbers of threads per block for large-scale sentiment analysis and laying the foundation for parallelising sentiment classification tasks.