IoT systems differ from traditional Internet systems in that they are different in scale, footprint, power requirements, cost and security concerns that are often overlooked. IoT systems inherently present different fail-safe capabilities than traditional computing environments while their threat landscapes constantly evolve. Further, IoT devices have limited collective security measures in place. Therefore, there is a need for different approaches in threat assessments to incorporate the interdependencies between different IoT devices. In this paper, we run through the design cycle to provide a security-focused approach to the design of IoT systems using a use case, namely, an intelligent solar-panel project called Daedalus. We utilise STRIDE/DREAD approaches to identify vulnerabilities using a thin secure element that is an embedded, tamper proof microprocessor chip that allows the storage and processing of sensitive data. It benefits from low power demand and small footprint as a crypto processor as well as is compatible with IoT requirements. Subsequently, a key agreement based on an asymmetric cryptographic scheme, namely B-SPEKE was used to validate and authenticate the source. We find that end-to-end and independent stand-alone procedures used for validation and encryption of the source data originating from the solar panel are cost-effective in that the validation is carried out once and not several times in the chain as is often the case. The threat model proved useful not so much as a panacea for all threats but provided the framework for the consideration of known threats, and therefore appropriate mitigation plans to be deployed.

This study explores recent advancements in emerging technologies for Demand Responsive Transport (DRT), focusing on the potential integration of autonomous bus services. We review the historical development and current state of research, extending the discussion into the era of Connected and Automated Mobility (CAM), particularly in mass transit. Key components of modern DRT systems are categorised from multiple perspectives, including vehicle types, infrastructure, devices, simulation approaches, decision-making algorithms, and optimisation factors. Each element is discussed in detail, supported by comparative tables to help readers interpret the results effectively. Our findings highlight the growing interest and ongoing research in this promising domain. We identify common practices across studies and areas needing improvement. To support this, we propose a System-of-Systems (SoS) approach to evaluate the technological maturity of DRT solutions, in line with our ongoing project. A System Readiness Level (SRL) analysis is performed by identifying constituent systems and assessing their Technology Readiness Levels (TRLs) and Integration Readiness Levels (IRLs). The study also explores the deployment of DRT in a specific use case: the Maylands Business Park in Hertfordshire, UK. It aims to serve as a comprehensive reference for addressing various dimensions of DRT in mass transit, particularly in the context of automation. It offers insights into current progress and outlines opportunities for deploying DRT as a cost-effective, scalable solution to improve sustainability in Intelligent Transportation Systems (ITS).

Electricity theft is a critical source of non-technical losses in modern power systems, causing substantial financial and operational challenges for utilities. Traditional detection methods, such as manual inspections, are inadequate to detect advanced theft techniques, including meter tampering and cyberattacks on smart grids. This study introduces a machine learning-based framework for electricity theft detection using the TDD2022 dataset (derived from OEDI) and evaluates multiple algorithms—Random Forest, Decision Tree, XGBoost, LightGBM, CatBoost, Extra Trees, and Logistic Regression. To address class imbalance, SMOTE is applied, while feature selection leverages LASSO and ReliefF. Experiments compare electricity-only data with multi-utility inputs (electricity and gas) under balanced and imbalanced conditions. Results show that tree-based ensembles, particularly Extra Trees combined with SMOTE and ReliefF, achieve superior performance (accuracy $>95\%$, AUC $\approx 0.99$). Consumer-specific models outperform global models, with commercial classes yielding near-perfect detection, while residential profiles remain challenging. The findings highlight the importance of tailored modeling and feature selection for scalable, accurate theft detection in smart grid environments.

In this paper, we propose a methodology for the fusion of different modes of speaker verification (SV) operation (fixed-passphrase, text-dependent and text-independent mode), using regression fusion models. The experimental results with and without spoofing attack conditions and using different single mode speaker verification engines, GMM-UBM, HMM-UBM and i-vector, indicated improvement in all the experiments. The 6.75 % in terms of EER is achieved as the best speaker verification performance, when using fusion of scores from three modes of operation of HMM-UBM based speaker verification systems. Relative improvement of 22.32 % achieved compare to the best performing single mode engine.

We present a comparative evaluation of different classification algorithms for a fusion engine that is used in a speaker identity selection task. The fusion engine combines the scores from a number of classifiers, which uses the GMM-UBM approach to match speaker identity. The performances of the evaluated classification algorithms were examined in both the text-dependent and text-independent operation modes. The experimental results indicated a significant improvement in terms of speaker identification accuracy, which was approximately 7% and 14.5% for the text-dependent and the text-independent scenarios, respectively. We suggest the use of fusion with a discriminative algorithm such as a Support Vector Machine in a real-world speaker identification application where the text-independent scenario predominates based on the findings.