Search Results (80)

The rapid deployment of electric vehicle (EV) charging infrastructure, coupled with the integration of 5G/6G and Internet of Vehicles (IoV) technologies, has transformed charging stations into cyber–physical systems that rely on wireless communication for authentication, control, and grid coordination. While existing security standards such as ISO 15118 provide cryptographic protection at upper layers, they are insufficient to address physical-layer threats inherent to wireless connectivity. In particular, wireless active eavesdropping attacks can corrupt channel estimation during the authentication phase, enabling impersonation, unauthorized charging, and disruption of grid operations. This paper proposes a machine learning-based physical layer security (PLS) framework for detecting active eavesdropping attacks in 5G/6G-enabled EV charging systems. By modeling malicious EVs as pilot-spoofing attackers, three discriminative features, namely mean power, power ratio, and angle-based feature, are extracted from received pilot signals at the charging station. Three classifiers are evaluated: single-class support vector machine (SC-SVM), Random Forest (RF), and DNN. Simulation results demonstrate that the SC-SVM maintains a stable accuracy between 94% and 96% across all attacker power levels, while RF and DNN significantly outperform it under stronger attack conditions. Specifically, under strong attacker conditions, RF achieves an accuracy of 99.9%, and DNN reaches 99.8%, both exceeding 99% detection accuracy. By preventing pilot-spoofing-based impersonation during authentication, the proposed framework enhances charging availability, billing integrity, and grid-aware scheduling in intelligent EV charging infrastructure. Full article

The electric vehicles (EVs) market keeps growing globally; thus, it is critical to secure the EV charging communication protocols in order to guarantee reliable and fair charging operations among the customers. The Open Charge Point Protocol (OCPP) 2.0.1 supports the communication between the Electric Vehicle Supply Equipment (EVSE) and Charging Station Management Systems (CSMSs); therefore, it becomes vulnerable to several types of attacks, which aim to jeopardize smart charging, billing, and energy management. Specifically, OCPP 2.0.1 allows the self-reporting of the State of Charge (SOC) values, which makes it vulnerable to spoofing-based cyberattacks, which target manipulating the scheduling priorities, distorting the load forecasts, and extending the charging sessions in an unfair manner. In this paper, we try to address this type of attack by providing a comprehensive analysis of the SOC spoofing attacks and introducing a novel unsupervised detection framework based on the One-Class Support Vector Machine (OCSVM) algorithm. Specifically, two types of attack scenarios are analyzed (i.e., priority manipulation and session extension) by deriving engineered features that capture the nonlinear relationships under normal charging behavior. Detailed simulation-based results are derived by utilizing the DESL-EPFL Level 3 EV charging dataset. Our results demonstrate high F1-score and recall in identifying spoofed SOC values and that the proposed OCSVM model demonstrates superior performance compared to alternative clustering and deep-learning based detectors. Full article

Electromobility provides an effective solution for developing countries to reduce dependence on fossil fuels, enhance energy security, and increase environmental sustainability. The current study evaluates the feasibility of implementing electric vehicles (EVs) powered by renewable energy in developing countries. Based on qualitative methods, including expert interviews, it discusses existing transportation systems, the benefits of EVs, and significant constraints such as poor infrastructure, high initial investment, and ineffective policy structures. Evidence further suggests that EV adoption is likely to bring considerable benefits, particularly in cities with high population densities, adequate infrastructure, and supportive regulations that facilitate rapid adoption. Countries like India and Kenya have reduced their fuel import bills and created new jobs. At the same time, cities such as Bogota and Nairobi have seen improved air quality through the adoption of electric public transit. However, the transition requires investments in charging infrastructures and improvements in power grids. Central to this is government backing, whether through subsidy or partnership. Programs like India’s Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME) initiative and China’s subsidy program are prime examples of such support. The study draws on expert interviews to provide context-specific insights that are often absent in global EV discussions, while acknowledging the limitations of a small, regionally concentrated sample. These qualitative findings complement international data and offer grounded implications for electromobility planning in developing contexts. It concludes that while challenges remain, tailored interventions and multi-party public–private partnerships can make the economic and environmental promise of electromobility in emerging markets a reality. Full article

The proliferation of Internet of Things (IoT) applications in safety-critical domains, such as healthcare, smart transportation, and industrial automation, demands robust solutions for data integrity, traceability, and security that surpass the capabilities of centralized databases. This paper analyzes how blockchain technology can be integrated with core IoT service functions—including data management, security, device management, group coordination, and automated billing—to enhance immutability, trust, and operational efficiency. Our analysis identifies practical use cases such as consensus-driven tamper-proof storage, role-based access control, firmware integrity verification, and automated micropayments. These use cases showcase blockchain’s potential beyond traditional data storage. Building on this, we propose a novel framework that integrates a permissioned distributed ledger with a standardized IoT service layer platform through a Blockchain Interworking Proxy Entity (BlockIPE). This proxy dynamically maps IoT service functions to smart contracts, enabling flexible data routing to conventional databases or blockchains based on the application requirements. We implement a Dockerized prototype that integrates a C-based oneM2M platform with an Ethereum-compatible permissioned ledger (implemented using Hyperledger Besu) via BlockIPE, incorporating security features such as role-based access control. For performance evaluation, we use Ganache to isolate proxy-level overhead and scalability. At the proxy level, the blockchain-integrated path achieves processing latencies (≈86 ms) comparable to, and slightly faster than, the traditional database path. Although the end-to-end latency is inherently governed by on-chain confirmation (≈0.586–1.086 s), the scalability remains high (up to 100,000 TPS). This validates that the architecture secures IoT ecosystems with manageable operational overhead. Full article

Accelerating the diffusion of renewable energy requires clear evidence on which determinants enable or hinder deployment across contexts. This study aims to identify the most frequently discussed contemporary determinants of renewable energy deployment. To this end, we conduct a PRISMA-guided systematic review within the SALSA framework, complemented by VOSviewer bibliometric mapping, synthesizing 110 peer-reviewed studies published between 2013 and 2025. We group the most frequently examined determinants into eight domains (economic, environmental, energy, political, regulatory, regional, technological, and social) and summarize the prevalent direction of effect reported in the literature. Economic conditions (e.g., economic growth, financial development, green finance, and trade) and policy/regulation (e.g., institutional quality, instrument stringency, and feed-in and net-billing schemes) emerge as pivotal. Environmental co-benefits (emissions reduction and air quality improvements) and energy system factors (security and energy poverty) are influential, with context-dependent roles for fossil fuel prices and consumption. Regional context (e.g., geopolitical risk) and technological progress (eco-innovation, storage, and grid integration) shape outcomes, while public acceptance, awareness, perceived benefits/costs, and demographics condition uptake. We also document contradictory findings (e.g., foreign direct investment and oil price effects) and gaps (especially social/demographic determinants and causal evaluation of specific policies). Overall, the review offers a coherent synthesis of evidence and an actionable framework of determinants to inform policy design and investment targeting for large-scale diffusion of renewable energy technologies. Full article

The European Union’s Cyber Resilience Act (CRA) introduces a complex set of binding lifecycle security obligations, presenting a significant compliance challenge for the Internet of Things (IoT) industry. This study addresses this challenge by developing a comprehensive CRA mapping framework specifically tailored to the IoT sector. The core contribution is a detailed lifecycle-based checklist that translates the regulation’s legal mandates into an actionable blueprint for manufacturers. Beyond the checklist itself, this paper’s core contribution is a transparent two-phase methodology. The first phase provides a structured pipeline to translate dense legal text (from CRA Articles 13–14 and its annexes) into atomic testable engineering requirements. The second phase builds a quantitative rating tree using the Analytic Hierarchy Process (AHP) to weigh these requirements, providing a consistent and evidence-based scoring rubric. By synthesizing the complex regulatory landscape and the technical state of the art, this paper operationalizes the CRA’s requirements for governance, secure design, vulnerability management, and conformity assessment. The framework is validated in the TRUEDATA case, yielding a weighted readiness score and a sensitivity analysis that underpin the reliability of the findings. Full article

The implementation of the Internet of Things (IoT) in logistics has the ability to transform the whole logistics industry by improving business models, operational efficiency, traceability, security, and customer experience. The manual logistics process causing a lot of late deliveries, wrong deliveries, and line stoppages in a multinational automotive company. That led to the pursuit of this research work to convert the manual call-off process to a fully system-controlled process. The main objective of this research was to implement system-controlled warehouse call-offs and scheduling processes to reduce line stoppages caused by late and incorrect delivery of parts to the line, as well as hot call-offs, and to improve the overall efficiency of line supply routes. The introduction of IoT in the warehouse comes with a takted process, meaning that each step of the line supply process is timed. The process introduces scanners to support process confirmation and link every process step to System Applications and Products in Data Processing (SAP) to allow for traceability. The interconnected devices and system in this study connect line-side reality (using Rapid Frequency Identification (RFID), optic sensors, and the Integrated Production System Logistics (IPSL) bill of material information) with the SAP demand and part requirements. The IoT implementation results show a great improvement in the overall logistics of line supply processes. A decrease in line stoppages is witnessed, with a reduction of 69%, and line-side confirmation makes tracing easier, thereby enhancing process transparency. The addition of scanners provides line supply employees transparency with respect to where parts are going, further reducing the probability of wrong deliveries. Waste reduction is also a result of this research, as the takted processes allow for time saving on the round-trip time, which is reduced by 32%. Conclusively, this research adds to the expanding corpus of research on the application of IoT in logistics and offers useful advice to policymakers and logistics managers who wish to integrate IoT technologies into their operations. Full article

The rapid expansion of the Internet of Things (IoT) demands scalable, efficient, and user-friendly gateway solutions that seamlessly connect resource-constrained edge devices to cloud services. Low-cost, widely available microcontrollers, such as the ESP32 and its ecosystem peers, offer integrated Wi-Fi/Bluetooth connectivity, low power consumption, and a mature developer toolchain at a bill of materials cost of only a few dollars. For smart-home deployments where budgets, energy consumption, and maintainability are critical, these characteristics make MCU-class gateways a pragmatic alternative to single-board computers, enabling always-on local control with minimal overhead. This paper presents the design and implementation of an embedded IoT gateway powered by the ESP32 microcontroller. By using lightweight communication protocols such as Message Queuing Telemetry Transport (MQTT) and REST APIs, the proposed architecture supports local control, distributed intelligence, and secure on-site data storage, all while minimizing dependence on cloud infrastructure. A real-world deployment in an educational building demonstrates the gateway’s capability to monitor energy consumption, execute control commands, and provide an intuitive web-based dashboard with minimal resource overhead. Experimental results confirm that the solution offers strong performance, with RAM usage ranging between 3.6% and 6.8% of available memory (approximately 8.92 KB to 16.9 KB). The initial loading of the single-page application (SPA) results in a temporary RAM spike to 52.4%, which later stabilizes at 50.8%. These findings highlight the ESP32’s ability to serve as a functional IoT gateway with minimal resource demands. Areas for future optimization include improved device discovery mechanisms and enhanced resource management to prolong device longevity. Overall, the gateway represents a cost-effective and vendor-agnostic platform for building resilient and scalable IoT ecosystems. Full article

Electric vehicle (EV) charging infrastructures raise significant concerns about data security and user privacy because traditional centralized authorization and billing frameworks expose sensitive information to breaches and profiling. To address these vulnerabilities, we propose a novel decentralized framework that couples a permissioned blockchain with fully homomorphic encryption (FHE). Unlike prior blockchain-only or blockchain-and-machine-learning solutions, our architecture performs all authorization and billing computations on encrypted data and records transactions immutably via smart contracts. We implemented the system on Hyperledger Fabric using the CKKS-based TenSEAL library, chosen for its efficient arithmetic on real-valued vectors, and show that homomorphic operations are executed off-chain within a secure computation layer while smart contracts handle only encrypted records. In a simulation involving 20 charging stations and up to 100 concurrent users, the proposed system achieved an average authorization latency of 610 ms, a billing computation latency of 310 ms, and transaction throughput of 102 Tx min while maintaining energy overhead below 0.14 kWh day per station. When compared to state-of-the-art blockchain-only approaches, our method reduces data exposure by 100%, increases privacy from “moderate” to “very high,” and achieves similar throughput with acceptable computational overhead. These results demonstrate that privacy-preserving EV charging is practical using present-day cryptography, paving the way for secure, scalable EV charging and billing services. Full article

This paper investigates the dynamics of energy poverty across EU Member States from 2015 to 2023, a period characterized by economic recovery, the COVID-19 pandemic, and a significant energy crisis. Utilizing Eurostat EU-SILC data, the study analyzes trends in four key indicators: the inability to keep homes adequately warm, arrears on utility bills, housing cost overburden rate, and the at-risk-of-poverty rate. Data processing and trend analysis were performed using R and RStudio, while a k-means cluster analysis, executed in Python via Visual Studio Code, identified and compared distinct country groupings based on their energy poverty profiles in 2015 and 2023. The findings reveal a general improvement in energy poverty indicators across the EU until 2019, followed by a marked deterioration, particularly in energy affordability metrics post-2021 due to the energy crisis. This impact was observed to be distinct from general income poverty trends. While significant geographical disparities persist, with Southern and Eastern European countries often more vulnerable, the analysis also points to notable improvements in several Central and Eastern European nations. The cluster analysis, which identified eight clusters in 2015 and seven in 2023, suggests a degree of partial convergence. Key shifts include Poland’s transition to a lower-risk cluster and Spain’s move to a higher-risk group, while Southern Europe generally remains highly susceptible. This research underscores the dynamic and multifaceted nature of energy poverty, highlighting the necessity for targeted, context-specific policies. Addressing energy poverty is crucial for enhancing household resilience and achieving truly comprehensive energy security throughout the EU, especially amid the ongoing energy transition and potential future socio-economic shocks. Full article

Social determinants of health (SDoH) are considered significant determinants of mental health. This study examines the association between SDoH and mental health status in the United States. We analyzed 2023 Behavioral Risk Factor Surveillance System (BRFSS) data from 183,318 U.S. adults using multinomial logistic regression. Several SDoH were significantly linked to the frequency of poor mental health days. After adjusting for all covariates, individuals facing difficulty paying utility bills had lower odds of experiencing episodic (vs. chronic) poor mental health (AOR = 0.47, p = 0.031). Transportation challenges were associated with lower odds of episodic distress rather than chronic mental health issues (AOR = 0.35, p = 0.026). Individuals who were unable to afford a doctor or who experienced employment loss had significantly lower odds of reporting no poor mental health days compared to reporting chronic poor mental health, with adjusted odds ratios of 0.37 and 0.84, respectively. Non-Hispanic Whites and males were more likely to report chronic poor mental health. Policies that prioritize economic stability and job security, reliable transportation, and equal access to education and healthcare are crucial for promoting mental health equity across diverse populations. Full article

Energy efficiency has emerged as a crucial focal point in global agendas, being recognized for its pivotal role in combatting climate change, bolstering energy security, and fostering economic growth. Governments worldwide are formulating ambitious targets and enacting comprehensive strategies to optimize energy utilization across various sectors. This involves the formulation of policies, provision of incentives, and facilitation of collaborations to encourage energy-efficient practices, ultimately steering towards a sustainable and energy-efficient future. Notably, the residential sector stands as a pivotal component in these efforts due to its substantial share of energy consumption. This paper evaluates the strategic vision of Morocco concerning energy efficiency within the residential sector from its inception to the projected initiatives up to 2030. The analysis focuses on the current iteration of thermal regulations and its implications. Although specific numerical outcomes are not discussed herein, the implementation of these regulations is observed to yield notable benefits, including reductions in energy bills and gains in annual primary energy. These advantages are estimated to result in a substantial decrease in final energy consumption, equating to significant savings for end-users. Additionally, to cover the expenses associated with building repairs and thermal enhancements, an extra fee is levied, varying based on building typology and climatic region. Despite this additional investment, the associated costs typically exhibit a favorable payback period, on average, underscoring the efficacy of regulatory and profitability measures in driving energy efficiency within the residential sector. This paper examines Morocco’s strategic approach to energy efficiency in the residential sector, focusing on its thermal building regulation RTCM (Moroccan thermal regulation on construction). Energy efficiency is recognized as essential for reducing GHG (greenhouse gas) emissions, enhancing energy security, and lowering costs. Using simulation models across six climatic zones and three residential building types, the study highlights RTCM’s significant impact—achieving national energy savings between 39% and 68%. Despite added costs for thermal improvements, the measures show favorable payback periods, confirming RTCM’s strong energy and economic performance and its potential role in shaping future policies. Full article

Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems. Full article

This research investigates the integration of blockchain technology into smart grids, focusing on optimizing both electrical billing and peer-to-peer energy trading between producers and consumers. Using blockchain smart contracts, the system automates and secures energy consumption recording, bill calculation, payment processing, and energy transactions. In the electrical billing framework, a blockchain-based approach was developed to model these functionalities, utilizing an EnergyBilling smart contract to calculate bills and an EnergyPayment smart contract to ensure payment accuracy. Validation using actual consumption data from Sinoma Handan’s project site confirmed the system’s accuracy and reliability when cross-verified with mathematical models. Simultaneously, the study explores peer-to-peer energy trading, where producers (represented by Askari Cement Plant.Nizampur, Pakistan) and consumers (Sinoma Handan Ltd, Handan, China.) conduct automated, transparent transactions. Blockchain’s decentralized nature ensures transparency, data immutability, and a secure, tamper-proof record of transactions. The system eliminates intermediaries, enhancing operational efficiency and reducing costs. Key outcomes demonstrate successful transaction execution with detailed settlements, ensuring financial accountability. Our research highlights blockchain’s transformative potential in revolutionizing electrical billing and energy trading. It offers a secure, transparent, and efficient solution while acknowledging scalability, transaction costs, and regulatory hurdles. Future work could focus on real-world implementation, integration with IoT devices for real-time data collection, and scaling these technologies for broader industrial applications in global energy markets. Full article

The advanced metering infrastructure (AMI) of the smart grid plays a critical role in energy management and billing by enabling the periodic transmission of consumers’ power consumption readings. To optimize data collection efficiency, AMI employs a “change and transmit” (CAT) approach. This approach ensures that readings are only transmitted when there is enough change in consumption, thereby reducing data traffic. Despite the benefits of this approach, it faces security challenges where malicious consumers can manipulate their readings to launch cyberattacks for electricity theft, allowing them to illegally reduce their bills. While this challenge has been addressed for supervised learning CAT settings, it remains insufficiently addressed in unsupervised learning settings. Moreover, due to the distortion introduced in the power consumption readings due to using the CAT approach, the accurate prediction of future consumption for energy management is a challenge. In this paper, we propose a two-stage approach to predict future readings and detect electricity theft in the smart grid while optimizing data collection using the CAT approach. For the first stage, we developed a predictor that is trained exclusively on benign CAT power consumption readings, and the output of the predictor is the actual readings. To enhance the prediction accuracy, we propose a cluster-based predictor that groups consumers into clusters with similar consumption patterns, and a dedicated predictor is trained for each cluster. For the second stage, we trained an autoencoder and a one-class support vector machine (SVM) on the benign reconstruction errors of the predictor to classify instances of electricity theft. We conducted comprehensive experiments to assess the effectiveness of our proposed approach. The experimental results indicate that the prediction error is very small and the accuracy of detection of the electricity theft attacks is high. Full article