arXiv subjects

Alistair Barros

Publications and source records attributed to Alistair Barros.

8 recordsLinked to original sources

arXiv2026

EVTradeMatch: A Mobility-Aware Multi-Objective Matching Framework for EV--EV Energy Trading

Peer-to-peer energy trading among electric vehicles (EVs) can improve charging flexibility under limited charging infrastructure, but effective EV--EV trading requires coordinated provider--consumer matching under journey-specific conditions. This paper proposes EVTradeMatch, a prediction-guided multi-objective optimization framework for mobility-aware EV--EV energy trading. Building on the EVNextTrade study, a prior learning-to-rank model for charging-node recommendation, we define charging-node suitability as a prediction-derived score reflecting the appropriateness of assigning a provider--consumer pair to a candidate charging node based on mobility, energy, and contextual trading features. This score is used as a guidance signal and as an explicit optimization objective rather than as a hard selection rule. The EV--EV matching problem is formulated as a multi-objective mixed-integer linear program that maximizes matching coverage, transferred energy, and charging-node suitability while minimizing mobility cost under spatial, temporal, one-to-one matching, and charging-node capacity constraints. To approximate Pareto-efficient solutions in wide-area dynamic settings, we develop a tailored non-dominated sorting genetic algorithm II (NSGA-II). Experimental results show that EVTradeMatch improves transferred energy by 74.2--82.8% and charging-node suitability by 8.3--84.4% compared with proximity- and auction-based state-of-the-art methods, while improving matching coverage by 3.74--25.07 percentage points. Balanced NSGA-II solutions achieve 53.07$\pm$0.76% matching coverage and transfer 1701.94$\pm$17.03 kWh, with higher mobility cost as an explicit trade-off against travel-minimizing methods. Pareto-front analysis shows that the framework supports flexible selection among high-coverage, high-energy, low-mobility-cost, and high-suitability solutions according to operational priorities.

math.OC↗

arXiv2026

EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading

Peer-to-peer energy trading among electric vehicles (EVs) has been increasingly studied as a promising solution for improving supply-side resilience under growing charging demand and constrained charging infrastructure. While prior studies on EV-EV energy trading and related EV research have largely focused on transaction management or isolated mobility prediction tasks, the problem of identifying which charging nodes are more suitable for EV-EV trading in journey contexts remains open. We address this gap by formulating next charging nodes recommendation as a learning-to-rank problem, where each EV decision event is associated with a set of candidate charging locations. We propose a supervised ranking framework applied to a large-scale urban EV mobility dataset comprising millions of journey records and multidimensional EV trading-related features, including EV energy level, trading role, distance to charging locations, charging speed, and temporal station popularity. To account for uncertainty arising from the mobility of both energy providers and consumers, as well as the presence of multiple viable charging nodes at a decision point, we employ probabilistic relevance refinement to generate graded labels for ranking. We evaluate gradient-boosted learning-to-rank models, including LightGBM, XGBoost, and CatBoost, on EV journey records enriched with candidate charging nodes. Experimental results show that LightGBM consistently achieves the strongest ranking performance across standard metrics, including NDCG@k, Recall@k, and MRR, with particularly strong early-ranking quality, reflected in the highest NDCG@1 (0.9795) and MRR (0.9990). These results highlight the effectiveness of uncertainty-aware learning-to-rank for charging node recommendation and support improved coordination and matching in decentralized EV-EV energy trading systems.

cs.IR↗

arXiv2025

Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data

Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where pixel perturbations maintain visual similarity, tabular data lacks intuitive similarity metrics, making it difficult to define imperceptible modifications. Additionally, traditional gradient-based methods prioritise $\ell_p$-norm constraints, often producing adversarial examples that deviate from the original data distributions. To address this, we propose a latent-space perturbation framework using a mixed-input Variational Autoencoder (VAE) to generate statistically consistent adversarial examples. The proposed VAE integrates categorical embeddings and numerical features into a unified latent manifold, enabling perturbations that preserve statistical consistency. We introduce In-Distribution Success Rate (IDSR) to jointly evaluate attack effectiveness and distributional alignment. Evaluation across six publicly available datasets and three model architectures demonstrates that our method achieves substantially lower outlier rates and more consistent performance compared to traditional input-space attacks and other VAE-based methods adapted from image domain approaches, achieving substantially lower outlier rates and higher IDSR across six datasets and three model architectures. Our comprehensive analyses of hyperparameter sensitivity, sparsity control, and generative architecture demonstrate that the effectiveness of VAE-based attacks depends strongly on reconstruction quality and the availability of sufficient training data. When these conditions are met, the proposed framework achieves superior practical utility and stability compared with input-space methods. This work underscores the importance of maintaining on-manifold perturbations for generating realistic and robust adversarial examples in tabular domains.

cs.LG↗

arXiv2024

Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis

Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks have been extensively studied in unstructured data like images, applying them to tabular data, poses new challenges. These challenges arise from the inherent heterogeneity and complex feature interdependencies in tabular data, which differ from the image data. To account for this distinction, it is necessary to establish tailored imperceptibility criteria specific to tabular data. However, there is currently a lack of standardised metrics for assessing the imperceptibility of adversarial attacks on tabular data. To address this gap, we propose a set of key properties and corresponding metrics designed to comprehensively characterise imperceptible adversarial attacks on tabular data. These are: proximity to the original input, sparsity of altered features, deviation from the original data distribution, sensitivity in perturbing features with narrow distribution, immutability of certain features that should remain unchanged, feasibility of specific feature values that should not go beyond valid practical ranges, and feature interdependencies capturing complex relationships between data attributes. We evaluate the imperceptibility of five adversarial attacks, including both bounded attacks and unbounded attacks, on tabular data using the proposed imperceptibility metrics. The results reveal a trade-off between the imperceptibility and effectiveness of these attacks. The study also identifies limitations in current attack algorithms, offering insights that can guide future research in the area. The findings gained from this empirical analysis provide valuable direction for enhancing the design of adversarial attack algorithms, thereby advancing adversarial machine learning on tabular data.

cs.LG↗

arXiv2024

Microservices-based Software Systems Reengineering: State-of-the-Art and Future Directions

Designing software compatible with cloud-based Microservice Architectures (MSAs) is vital due to the performance, scalability, and availability limitations. As the complexity of a system increases, it is subject to deprecation, difficulties in making updates, and risks in introducing defects when making changes. Microservices are small, loosely coupled, highly cohesive units that interact to provide system functionalities. We provide a comprehensive survey of current research into ways of identifying services in systems that can be redeployed as microservices. Static, dynamic, and hybrid approaches have been explored. While code analysis techniques dominate the area, dynamic and hybrid approaches remain open research topics.

cs.SE↗

arXiv2024

Edge-Mapping of Service Function Trees for Sensor Event Processing

Fog computing offers increased performance and efficiency for Industrial Internet of Things (IIoT) applications through distributed data processing in nearby proximity to sensors. Given resource constraints and their contentious use in IoT networks, current strategies strive to optimise which data processing tasks should be selected to run on fog devices. In this paper, we advance a more effective data processing architecture for optimisation purposes. Specifically, we consider the distinct functions of sensor data streaming, multi-stream data aggregation and event handling, required by IoT applications for identifying actionable events. We retrofit this event processing pipeline into a logical architecture, structured as a service function tree (SFT), comprising service function chains. We present a novel algorithm for mapping the SFT into a fog network topology in which nodes selected to process SFT functions (microservices) have the requisite resource capacity and network speed to meet their event processing deadlines. We used simulations to validate the algorithm's effectiveness in finding a successful SFT mapping to a physical network. Overall, our approach overcomes the bottlenecks of single service placement strategies for fog computing through composite service placements of SFTs.

cs.DC↗

arXiv2024

Service Colonies: A Novel Architectural Style for Developing Software Systems with Autonomous and Cooperative Services

This paper presents the concept of a service colony and its characteristics. A service colony is a novel architectural style for developing a software system as a group of autonomous software services co-operating to fulfill the objectives of the system. Each inhabitant service in the colony implements a specific system functionality, collaborates with the other services, and makes proactive decisions that impact its performance and interaction patterns with other inhabitants. By increasing the level of self-awareness and autonomy available to individual system components, the resulting system is increasingly more decentralized, distributed, flexible, adaptable, distributed, modular, robust, and fault-tolerant.

cs.SE↗

arXiv2020

A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission

To slow down the spread of COVID-19, governments around the world are trying to identify infected people and to contain the virus by enforcing isolation and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission and mass infection. To address this problem, we develop an e-government Privacy Preserving Mobile and Fog computing framework entitled PPMF that can trace infected and suspected cases nationwide. We use personal mobile devices with contact tracing app and two types of stationary fog nodes, named Automatic Risk Checkers (ARC) and Suspected User Data Uploader Node (SUDUN), to trace community transmission alongside maintaining user data privacy. Each user's mobile device receives a Unique Encrypted Reference Code (UERC) when registering on the central application. The mobile device and the central application both generate Rotational Unique Encrypted Reference Code (RUERC), which broadcasted using the Bluetooth Low Energy (BLE) technology. The ARCs are placed at the entry points of buildings, which can immediately detect if there are positive or suspected cases nearby. If any confirmed case is found, the ARCs broadcast pre-cautionary messages to nearby people without revealing the identity of the infected person. The SUDUNs are placed at the health centers that report test results to the central cloud application. The reported data is later used to map between infected and suspected cases. Therefore, using our proposed PPMF framework, governments can let organizations continue their economic activities without complete lockdown.

cs.CR↗