arXiv subjects

Omer Rana

Publications and source records attributed to Omer Rana.

At least 19 recordsLinked to original sources

arXiv2026

Trustworthy Second-hand Marketplace for Built Environment

The construction industry faces significant challenges regarding material waste and sustainable practices, necessitating innovative solutions that integrate automation, traceability, and decentralised decision-making to enable efficient material reuse. This paper presents a blockchain-enabled digital marketplace for sustainable construction material reuse, ensuring transparency and traceability using InterPlanetary File System (IPFS). The proposed framework enhances trust and accountability in material exchange, addressing key challenges in industrial automation and circular supply chains. A framework has been developed to demonstrate the operational processes of the marketplace, illustrating its practical application and effectiveness. Our contributions show how the marketplace can facilitate the efficient and trustworthy exchange of reusable materials, representing a substantial step towards more sustainable construction practices.

cs.DC↗

arXiv2026

A Hierarchical Optimisation Framework for Integrated Electric-Hydrogen-Transport Systems

Integrated electric-hydrogen infrastructures are becoming increasingly important with the growing deployment of electric vehicles (EVs) and hydrogen vehicles (HVs) in transport systems. However, the strong coupling between vehicle scheduling and multi-energy dispatch introduces significant operational challenges. This paper models an integrated electric-hydrogen-transport system (EHTS) and proposes a hierarchical optimisation framework that couples vehicle scheduling and downstream energy dispatch through a sequential, demand-driven two-layer structure. In the vehicle scheduling layer, a solver-free greedy heuristic (SFGH) algorithm is developed to avoid repeated optimisation solving, enabling real-time EV charging and HV refuelling under non-preemptive service and within-interval sequential assignment. The resulting charging and refuelling demands are subsequently passed to the energy dispatch layer, where a deep reinforcement learning (DRL)-based approach is designed to optimise battery operation, hydrogen-tank operation, and PV generation allocation to minimise the overall operational cost of the EHTS while satisfying the scheduled transport demand. Representative case studies, together with comparative, ablation, and generalisation analyses, demonstrate the effectiveness and robustness of the proposed framework. Furthermore, the learned dispatch policy maintains strong performance across diverse transport-demand scenarios without retraining, demonstrating robust generalisation capability for practical deployment.

eess.SY↗

arXiv2026

EEG-FuseFormer: A Transformer-Driven Feature Fusion Framework for Seizure Onset Prediction

Epilepsy is one of the most common neurological disorders globally, characterized by recurring seizures and significantly impacting the quality of life. Despite advancements in diagnostic techniques, the mitigation of risks faced by epilepsy patients remains challenging due to the unpredictability of seizure events. An accurate forecast of seizure onset helps to reduce risks in epilepsy patients. In this paper, we propose EEG-FuseFormer, a transformer-based feature fusion framework for seizure-onset prediction that combines intermediate features extracted from Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) and ResNet-18 networks. The CNN-LSTM architecture captures both spatial and temporal features directly from the raw signal, whereas the ResNet-18 extracts features from the Short-Time Fourier Transform (STFT) representation of the EEG signals. Fusion is carried out using a transformer encoder, and the final prediction is generated using fully connected dense layers. The CHB-MIT dataset was used to validate the proposed model. The results show that the proposed model achieves a mean recall of 98.85% and outperforms most of the state-of-the-art methods. This study evaluates the ability of the proposed feature fusion model to generalize in cross-patient testing scenarios. Fine-tuning pre-trained models on limited target patient data (target adaptation) within the cross-patient validation framework results in higher recall, precision, and F1-score metrics in comparison to the conventional cross-patient validation approach. Finally, the runtime-based computational complexity of the model is assessed across diverse hardware platforms to highlight the performance-complexity trade-off.

cs.LG↗

arXiv2026

Exploring Blockchain Interoperability: Frameworks, Use Cases, and Future Challenges

Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption, enabling users to work in a trustworthy manner. The early generations of blockchain have a problem; they cannot share information with other blockchains. As more and more entities move their applications to the blockchain, they generate large volumes of data, and as applications have become more complex, sharing information between different blockchains has become a necessity. This has led to the research and development of interoperable solutions allowing blockchains to connect together. This paper discusses a few blockchain platforms that provide interoperable solutions, emphasising their ability to connect heterogeneous blockchains. It also discusses a case study scenario to illustrate the importance and benefits of using interoperable solutions. We also present a few topics that need to be solved in the realm of interoperability.

cs.CR↗

arXiv2026

EVECTOR: An orchestrator for analysing attacks in electric vehicles charging system

Electric Vehicle (EV) charging infrastructure is critical for the widespread adoption of EVs, ensuring efficient and secure charging processes. Evaluating the security and performance of EV charging systems in real-world infrastructure poses significant challenges due to the diversity of information exchange between vehicles and charging stations/Electric Vehicle Supply Equipment (EVSE), including complex network protocols, scale of deployment and a variety of potential threats. Existing simulation frameworks are unable to handle complex security scenarios across these differing data exchange protocols. In this paper, we propose a novel EV orchestration framework: EVECTOR, which addresses the limitations of existing simulation systems by enabling both quantitative and qualitative analyses of EV charging scenarios. EVECTOR also provides a flexible attack orchestrator to simulate realistic attack behaviours on EV charging infrastructure. We validate the EVECTOR framework through two case studies: (a) cyber-physical attacks such as broken wire; and (b) cyber-specific attacks such as frame fuzzification. The case studies highlight the effectiveness of EVECTOR in providing deeper insights into the security and performance of EV charging systems.

cs.OH↗

arXiv2025

Knowledge Grafting: A Mechanism for Optimizing AI Model Deployment in Resource-Constrained Environments

The increasing adoption of Artificial Intelligence (AI) has led to larger, more complex models with numerous parameters that require substantial computing power -- resources often unavailable in many real-world application scenarios. Our paper addresses this challenge by introducing knowledge grafting, a novel mechanism that optimizes AI models for resource-constrained environments by transferring selected features (the scion) from a large donor model to a smaller rootstock model. The approach achieves an 88.54% reduction in model size (from 64.39 MB to 7.38 MB), while improving generalization capability of the model. Our new rootstock model achieves 89.97% validation accuracy (vs. donor's 87.47%), maintains lower validation loss (0.2976 vs. 0.5068), and performs exceptionally well on unseen test data with 90.45% accuracy. It addresses the typical size vs performance trade-off, and enables deployment of AI frameworks on resource-constrained devices with enhanced performance. We have tested our approach on an agricultural weed detection scenario, however, it can be extended across various edge computing scenarios, potentially accelerating AI adoption in areas with limited hardware/software support -- by mirroring in a similar manner the horticultural grafting enables productive cultivation in challenging agri-based environments.

cs.AI↗

arXiv2025

A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network

Connected and Autonomous Vehicles (CAVs) enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network (CAN) bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robust security solutions. In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time. This survey provides a comprehensive review of state-of-the-art research on learning-based in-vehicle IDSs, focusing on Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) approaches. Based on the reviewed studies, we critically examine existing IDS approaches, categorising them by the types of attacks they detect - known, unknown, and combined known-unknown attacks - while identifying their limitations. We also review the evaluation metrics used in research, emphasising the need to consider multiple criteria to meet the requirements of safety-critical systems. Additionally, we analyse FL-based IDSs and highlight their limitations. By doing so, this survey helps identify effective security measures, address existing limitations, and guide future research toward more resilient and adaptive protection mechanisms, ensuring the safety and reliability of CAVs.

cs.CR↗

arXiv2025

A Circular Construction Product Ontology for End-of-Life Decision-Making

Efficient management of end-of-life (EoL) products is critical for advancing circularity in supply chains, particularly within the construction industry where EoL strategies are hindered by heterogenous lifecycle data and data silos. Current tools like Environmental Product Declarations (EPDs) and Digital Product Passports (DPPs) are limited by their dependency on seamless data integration and interoperability which remain significant challenges. To address these, we present the Circular Construction Product Ontology (CCPO), an applied framework designed to overcome semantic and data heterogeneity challenges in EoL decision-making for construction products. CCPO standardises vocabulary and facilitates data integration across supply chain stakeholders enabling lifecycle assessments (LCA) and robust decision-making. By aggregating disparate data into a unified product provenance, CCPO enables automated EoL recommendations through customisable SWRL rules aligned with European standards and stakeholder-specific circularity SLAs, demonstrating its scalability and integration capabilities. The adopted circular product scenario depicts CCPO's application while competency question evaluations show its superior performance in generating accurate EoL suggestions highlighting its potential to greatly improve decision-making in circular supply chains and its applicability in real-world construction environments.

cs.AI↗

arXiv2025

Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research

In this paper, a dataset of IoT network traffic is presented. Our dataset was generated by utilising the Gotham testbed, an emulated large-scale Internet of Things (IoT) network designed to provide a realistic and heterogeneous environment for network security research. The testbed includes 78 emulated IoT devices operating on various protocols, including MQTT, CoAP, and RTSP. Network traffic was captured in Packet Capture (PCAP) format using tcpdump, and both benign and malicious traffic were recorded. Malicious traffic was generated through scripted attacks, covering a variety of attack types, such as Denial of Service (DoS), Telnet Brute Force, Network Scanning, CoAP Amplification, and various stages of Command and Control (C&C) communication. The data were subsequently processed in Python for feature extraction using the Tshark tool, and the resulting data was converted to Comma Separated Values (CSV) format and labelled. The data repository includes the raw network traffic in PCAP format and the processed labelled data in CSV format. Our dataset was collected in a distributed manner, where network traffic was captured separately for each IoT device at the interface between the IoT gateway and the device. Our dataset was collected in a distributed manner, where network traffic was separately captured for each IoT device at the interface between the IoT gateway and the device. With its diverse traffic patterns and attack scenarios, this dataset provides a valuable resource for developing Intrusion Detection Systems and security mechanisms tailored to complex, large-scale IoT environments. The dataset is publicly available at Zenodo.

cs.CR↗

arXiv2024

Towards Enhancing Linked Data Retrieval in Conversational UIs using Large Language Models

Despite the recent broad adoption of Large Language Models (LLMs) across various domains, their potential for enriching information systems in extracting and exploring Linked Data (LD) and Resource Description Framework (RDF) triplestores has not been extensively explored. This paper examines the integration of LLMs within existing systems, emphasising the enhancement of conversational user interfaces (UIs) and their capabilities for data extraction by producing more accurate SPARQL queries without the requirement for model retraining. Typically, conversational UI models necessitate retraining with the introduction of new datasets or updates, limiting their functionality as general-purpose extraction tools. Our approach addresses this limitation by incorporating LLMs into the conversational UI workflow, significantly enhancing their ability to comprehend and process user queries effectively. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems employing conventional chatbots. This integration facilitates a more nuanced and context-aware interaction model, critical for handling the complex query patterns often encountered in RDF datasets and Linked Open Data (LOD) endpoints. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries, indicating a promising direction for future research in this area. This investigation not only underscores the versatility of LLMs in enhancing existing information systems but also sets the stage for further explorations into their potential applications within more specialised domains of web information systems.

cs.IR↗

arXiv2024

A Robust Multi-Stage Intrusion Detection System for In-Vehicle Network Security using Hierarchical Federated Learning

As connected and autonomous vehicles proliferate, the Controller Area Network (CAN) bus has become the predominant communication standard for in-vehicle networks due to its speed and efficiency. However, the CAN bus lacks basic security measures such as authentication and encryption, making it highly vulnerable to cyberattacks. To ensure in-vehicle security, intrusion detection systems (IDSs) must detect seen attacks and provide a robust defense against new, unseen attacks while remaining lightweight for practical deployment. Previous work has relied solely on the CAN ID feature or has used traditional machine learning (ML) approaches with manual feature extraction. These approaches overlook other exploitable features, making it challenging to adapt to new unseen attack variants and compromising security. This paper introduces a cutting-edge, novel, lightweight, in-vehicle, IDS-leveraging, deep learning (DL) algorithm to address these limitations. The proposed IDS employs a multi-stage approach: an artificial neural network (ANN) in the first stage to detect seen attacks, and a Long Short-Term Memory (LSTM) autoencoder in the second stage to detect new, unseen attacks. To understand and analyze diverse driving behaviors, update the model with the latest attack patterns, and preserve data privacy, we propose a theoretical framework to deploy our IDS in a hierarchical federated learning (H-FL) environment. Experimental results demonstrate that our IDS achieves an F1-score exceeding 0.99 for seen attacks and exceeding 0.95 for novel attacks, with a detection rate of 99.99%. Additionally, the false alarm rate (FAR) is exceptionally low at 0.016%, minimizing false alarms. Despite using DL algorithms known for their effectiveness in identifying sophisticated and zero-day attacks, the IDS remains lightweight, ensuring its feasibility for real-world deployment.

cs.CR↗

arXiv2024

PrivacyCube: Data Physicalization for Enhancing Privacy Awareness in IoT

People are increasingly bringing Internet of Things (IoT) devices into their homes without understanding how their data is gathered, processed, and used. We describe PrivacyCube, a novel data physicalization designed to increase privacy awareness within smart home environments. PrivacyCube visualizes IoT data consumption by displaying privacy-related notices. PrivacyCube aims to assist smart home occupants to (i) understand their data privacy better and (ii) have conversations around data management practices of IoT devices used within their homes. Using PrivacyCube, households can learn and make informed privacy decisions collectively. To evaluate PrivacyCube, we used multiple research methods throughout the different stages of design. We first conducted a focus group study in two stages with six participants to compare PrivacyCube to text and state-of-the-art privacy policies. We then deployed PrivacyCube in a 14-day-long field study with eight households. Our results show that PrivacyCube helps home occupants comprehend IoT privacy better with significantly increased privacy awareness at p < .05 (p=0.00041, t= -5.57). Participants preferred PrivacyCube over text privacy policies because it was comprehensive and easier to use. PrivacyCube and Privacy Label, a state-of-the-art approach, both received positive reviews from participants, with PrivacyCube being preferred for its interactivity and ability to encourage conversations. PrivacyCube was also considered by home occupants as a piece of home furniture, encouraging them to socialize and discuss IoT privacy implications using this device.

cs.CR↗

arXiv2024

PriviFy: Designing Tangible Interfaces for Configuring IoT Privacy Preferences

The Internet of Things (IoT) devices, such as smart speakers can collect sensitive user data, necessitating the need for users to manage their privacy preferences. However, configuring these preferences presents users with multiple challenges. Existing privacy controls often lack transparency, are hard to understand, and do not provide meaningful choices. On top of that, users struggle to locate privacy settings due to multiple menus or confusing labeling, which discourages them from using these controls. We introduce PriviFy (Privacy Simplify-er), a novel and user-friendly tangible interface that can simplify the configuration of smart devices privacy settings. PriviFy is designed to propose an enhancement to existing hardware by integrating additional features that improve privacy management. We envision that positive feedback and user experiences from our study will inspire consumer product developers and smart device manufacturers to incorporate the useful design elements we have identified. Using fidelity prototyping, we iteratively designed PriviFy prototype with 20 participants to include interactive features such as knobs, buttons, lights, and notifications that allow users to configure their data privacy preferences and receive confirmation of their choices. We further evaluated PriviFy high-fidelity prototype with 20 more participants. Our results show that PriviFy helps simplify the complexity of privacy preferences configuration with a significant usability score at p < .05 (P = 0.000000017, t = -8.8639). PriviFy successfully met users privacy needs and enabled them to regain control over their data. We conclude by recommending the importance of designing specific privacy configuration options.

cs.CR↗

arXiv2024

Modeling and Characterizing Service Interference in Dynamic Infrastructures

Performance interference can occur when various services are executed over the same physical infrastructure in a cloud system. This can lead to performance degradation compared to the execution of services in isolation. This work proposes a Confirmatory Factor Analysis (CFA)-based model to estimate performance interference across containers, caused by the use of CPU, memory and IO across a number of co-hosted applications. The approach provides resource characterization through human comprehensible indices expressed as time series, so the interference in the entire execution lifetime of a service can be analyzed. Our experiments, based on the combination of real services with different profiles executed in Docker containers, suggest that our model can accurately predict the overall execution time, for different service combinations. The approach can be used by a service designer to identify phases, during the execution life-cycle of a service, that are likely to lead to a greater degree of interference, and to ensure that only complementary services are hosted on the same physical machine. Interference-awareness of this kind will enable more intelligent resource management and scheduling for cloud systems, and may be used to dynamically modify scheduling decisions.

cs.DC↗

arXiv2024

Modern Computing: Vision and Challenges

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress.

cs.DC↗

arXiv2023

Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported hallucinations within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial.

cs.CY↗

arXiv2023

Hierarchical and Decentralised Federated Learning

Federated learning has shown enormous promise as a way of training ML models in distributed environments while reducing communication costs and protecting data privacy. However, the rise of complex cyber-physical systems, such as the Internet-of-Things, presents new challenges that are not met with traditional FL methods. Hierarchical Federated Learning extends the traditional FL process to enable more efficient model aggregation based on application needs or characteristics of the deployment environment (e.g., resource capabilities and/or network connectivity). It illustrates the benefits of balancing processing across the cloud-edge continuum. Hierarchical Federated Learning is likely to be a key enabler for a wide range of applications, such as smart farming and smart energy management, as it can improve performance and reduce costs, whilst also enabling FL workflows to be deployed in environments that are not well-suited to traditional FL. Model aggregation algorithms, software frameworks, and infrastructures will need to be designed and implemented to make such solutions accessible to researchers and engineers across a growing set of domains. H-FL also introduces a number of new challenges. For instance, there are implicit infrastructural challenges. There is also a trade-off between having generalised models and personalised models. If there exist geographical patterns for data (e.g., soil conditions in a smart farm likely are related to the geography of the region itself), then it is crucial that models used locally can consider their own locality in addition to a globally-learned model. H-FL will be crucial to future FL solutions as it can aggregate and distribute models at multiple levels to optimally serve the trade-off between locality dependence and global anomaly robustness.

cs.LG↗

arXiv2022

AI-based Fog and Edge Computing: A Systematic Review, Taxonomy and Future Directions

Resource management in computing is a very challenging problem that involves making sequential decisions. Resource limitations, resource heterogeneity, dynamic and diverse nature of workload, and the unpredictability of fog/edge computing environments have made resource management even more challenging to be considered in the fog landscape. Recently Artificial Intelligence (AI) and Machine Learning (ML) based solutions are adopted to solve this problem. AI/ML methods with the capability to make sequential decisions like reinforcement learning seem most promising for these type of problems. But these algorithms come with their own challenges such as high variance, explainability, and online training. The continuously changing fog/edge environment dynamics require solutions that learn online, adopting changing computing environment. In this paper, we used standard review methodology to conduct this Systematic Literature Review (SLR) to analyze the role of AI/ML algorithms and the challenges in the applicability of these algorithms for resource management in fog/edge computing environments. Further, various machine learning, deep learning and reinforcement learning techniques for edge AI management have been discussed. Furthermore, we have presented the background and current status of AI/ML-based Fog/Edge Computing. Moreover, a taxonomy of AI/ML-based resource management techniques for fog/edge computing has been proposed and compared the existing techniques based on the proposed taxonomy. Finally, open challenges and promising future research directions have been identified and discussed in the area of AI/ML-based fog/edge computing.

cs.DC↗