Search Results (2,902)

The goals of the European Union towards the energy transition imply profound changes in the energy field, so as to promote sustainable energy development while fostering economic growth. To achieve these changes, the incorporation of sustainable technologies supporting decentralisation, energy efficiency, renewable energy production, and demand flexibility is of vital importance. Blockchain has the potential to change energy services towards this direction. To optimally exploit blockchain, innovative business models need to be designed, identifying the opportunities emerging from unmet needs, while also considering potential risks so as to take action to overcome them. In this context, the scope of this paper is to examine the opportunities and the risks that emerge from the adoption of blockchain in four innovative business models, while also identifying mitigation strategies to support and accelerate the energy transition, thus proposing optimal approaches of exploitation of blockchain in energy services. The business models concern Energy Performance Contracting with P4P guarantees, improved self-consumption in energy cooperatives, energy efficiency and flexibility services for natural gas boilers, and smart energy management for EV chargers and HVAC appliances. Firstly, the value proposition of the business models is analysed and results in a comprehensive SWOT analysis. Based on the findings of the analysis and consultations with relevant market actors, in combination with the examination of the relevant literature, risks are identified and evaluated through a qualitative assessment approach. Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. Furthermore, according to the results, technological and legal risks are the most significant, followed by political, economic, and social risks, while environmental risks of blockchain integration are not as important. Strategies to address risks relevant to blockchain exploitation include ensuring policy alignment, emphasising economic feasibility, facilitating social inclusion, prioritising security and interoperability, consulting with legal experts, and using consensus algorithms with low energy consumption. The findings offer clear guidance for energy service providers, policymakers, and technology developers, assisting in the design, deployment, and risk mitigation of blockchain-enabled business models to accelerate sustainable energy development. Full article

The growing use of mobile health (mHealth) technologies adds complexity and risk to the healthcare environment. This paper presents a multi-layered cybersecurity assessment of an in-house mHealth platform (PHGL-COVID), comprising a Docker-based server infrastructure, a Samsung Galaxy A55 smartphone, and a Galaxy Watch 7 wearable. The objective was to identify vulnerabilities across the server, mobile, and wearable components by emulating real-world attacks and conducting systematic penetration tests on each layer. Tools and methods specifically tailored to each technology were applied, revealing exploitable configurations, insecure Bluetooth Low Energy (BLE) communications, and exposure of Personal Health Records (PHRs). Key findings included incomplete container isolation, BLE metadata leakage, and persistent abuse of Android privacy permissions. This work delivers both a set of actionable recommendations for developers and system architects to strengthen the security of mHealth platforms, and a reproducible audit methodology that has been validated in a real-world deployment, effectively bridging the gap between theoretical threat models and practical cybersecurity practices in healthcare systems. Full article

Quantum state discrimination (QSD) is a fundamental task in quantum information processing, improving the computation efficiency and communication security. Non-Hermitian (NH) PT-symmetric systems were found to be able to discriminate two quantum states better than the Hermitian strategy. In this work, we propose a QSD approach based on P-pseudo-Hermitian systems with real spectra. We theoretically prove the feasibility of realizing QSD in the real-spectrum phase of a P-pseudo-Hermitian system, i.e., two arbitrary non-orthogonal quantum states can be discriminated by a suitable P-pseudo-Hermitian Hamiltonian. In detail, we decide the minimal angular separation between two non-orthogonal quantum states for a fixed P-pseudo-Hermitian Hamiltonian, and we find the orthogonal evolution time is able to approach zero under suitable conditions, while both the trace distance and the quantum relative entropy are employed to judge their orthogonality. We give a criterion to choose the parameters of a P-pseudo-Hermitian Hamiltonian that evolves the two initial orthogonal states faster than a fixed arbitrary PT-symmetric one with an identical energy difference. Our work expands the NH family for QSD, and can be used to explore real quantum systems in the future. Full article

In order to achieve the goals of carbon neutrality, large-scale storage of renewable energy sources has been integrated into the power grid. Under these circumstances, the power grid faces the challenge of peak shaving. Therefore, this paper proposes a coordinated variable-power control strategy for multiple battery energy storage stations (BESSs), improving the performance of peak shaving. Firstly, the strategy involves constructing an optimization model incorporating load forecasting, capacity constraints, and security indices to design a coordination mechanism tracking the target load band with the equivalent power. Secondly, it establishes a quantitative evaluation system using metrics such as peak–valley difference and load standard deviation. Comparison based on typical daily cases shows that, compared with the constant power strategy, the coordinated variable-power control strategy has a more obvious and comprehensive improvement in overall peak-shaving effects. Furthermore, it employs a “dynamic dispatch of multiple BESS” mode, effectively mitigating the risks and flexibility issues associated with single BESSs. This strategy provides a reliable new approach for large-scale energy storage to participate in high-precision peaking. Full article

To fully accommodate renewable and derivative energy sources in mine energy systems under supply and demand uncertainties, this paper proposes an optimized electricity–heat scheduling method for mining areas that incorporates Power-to-Gas (P2G) technology and Conditional Value-at-Risk (CVaR). First, to address uncertainties on both the supply and demand sides, a P2G unit is introduced, and a Latin hypercube sampling technique based on Cholesky decomposition is employed to generate wind–solar-load sample matrices that capture source–load correlations, which are subsequently used to construct representative scenarios. Second, a stochastic optimization scheduling model is developed for the mine electricity–heat energy system, aiming to minimize the total scheduling cost comprising day-ahead scheduling cost, expected reserve adjustment cost, and CVaR. Finally, a case study on a typical mine electricity–heat energy system is conducted to validate the effectiveness of the proposed method in terms of operational cost reduction and system reliability. The results demonstrate a 1.4% reduction in the total operating cost, achieving a balance between economic efficiency and system security. Full article

The increasing penetration of distributed renewable energy and the emergence of large-scale, flexible loads such as data centers pose significant challenges to the economic and secure operation of distribution systems. Traditional static pricing mechanisms are often inadequate, leading to inefficient resource dispatch and curtailment of renewable generation. To address these issues, this paper proposes a hierarchical pricing and dispatch framework modeled as a tri-level Stackelberg game that coordinates interactions among an upstream grid, a distribution system operator (DSO), and multiple virtual power plants (VPPs). At the upper level, the DSO acts as the leader, formulating dynamic time-varying purchase and sale prices to maximize its revenue based on upstream grid conditions. In response, at the lower level, each VPP acts as a follower, optimally scheduling its portfolio of distributed energy resources—including microturbines, energy storage, and interruptible loads—to minimize its operating costs under the announced tariffs. A key innovation is the integration of a schedulable data center within one VPP, which responds to a specially designed wind-linked incentive tariff by shifting computational workloads to periods of high renewable availability. The resulting high-dimensional bilevel optimization problem is solved using a Kriging-based surrogate methodology to ensure computational tractability. Simulation results verify that, compared to a static-pricing baseline, the proposed strategy increases DSO revenue by 18.9% and reduces total VPP operating costs by over 28%, demonstrating a robust framework for enhancing system-wide economic and operational efficiency. Full article

Rural regions face significant barriers to adopting IoT technologies, due to limited connectivity, energy constraints, and poor technical infrastructure. While urban environments benefit from advanced digital systems and cloud services, rural areas often lack the necessary conditions to deploy and evaluate secure and autonomous IoT solutions. To help overcome this gap, this paper presents the Smart Rural IoT Lab, a modular and reproducible testbed designed to replicate the deployment conditions in rural areas using open-source tools and affordable hardware. The laboratory integrates long-range and short-range communication technologies in six experimental scenarios, implementing protocols such as MQTT, HTTP, UDP, and CoAP. These scenarios simulate realistic rural use cases, including environmental monitoring, livestock tracking, infrastructure access control, and heritage site protection. Local data processing is achieved through containerized services like Node-RED, InfluxDB, MongoDB, and Grafana, ensuring complete autonomy, without dependence on cloud services. A key contribution of the laboratory is the generation of structured datasets from real network traffic captured with Tcpdump and preprocessed using Zeek. Unlike simulated datasets, the collected data reflect communication patterns generated from real devices. Although the current dataset only includes benign traffic, the platform is prepared for future incorporation of adversarial scenarios (spoofing, DoS) to support AI-based cybersecurity research. While experiments were conducted in an indoor controlled environment, the testbed architecture is portable and suitable for future outdoor deployment. The Smart Rural IoT Lab addresses a critical gap in current research infrastructure, providing a realistic and flexible foundation for developing secure, cloud-independent IoT solutions, contributing to the digital transformation of rural regions. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

Photovoltaic (PV) micro-installations producing renewable electricity and automatic pellet boilers producing renewable heat energy are promising solutions for single-family houses. A single-family house equipped with a prosumer 7.56 kWp PV micro-installation and a 26 kW pellet boiler was analyzed. This study aimed to analyze the production and use of electricity and heat over three successive years (from 1 January 2021 to 31 December 2023) and to identify opportunities for securing renewable energy supply for the house. Electricity production by the PV was, on average, 6481 kWh year⁻¹; the amount of energy fed into the grid was 4907 kWh year⁻¹; and the electricity consumption by the house was 4606 kWh year⁻¹. The electricity supply for the house was secured by drawing an average of 34.2% of energy directly from the PV and 85.2% from the grid. Based on mathematical modeling, it was determined that if the PV installation had been located to the south (azimuth 180°) in the analyzed period, the maximum average production would have been 6897 kWh. Total annual heat and electricity consumption by the house over three years amounted, on average, to 39,059 kWh year⁻¹. Heat energy accounted for a dominant proportion of 88.2%. From a year-round perspective, a properly selected small multi-energy installation can ensure energy self-sufficiency and provide renewable energy to a single-family house. Full article

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

Securing thermal comfort while minimizing energy consumption in educational buildings is vital for achieving sustainable development goals. Drawing on the Environmental, Social, and Governance (ESG) framework, this systematic review synthesizes findings from 84 peer-reviewed studies published over the past decade, with a focus on how thermal comfort and energy use are assessed in educational contexts. The review identifies three primary research themes: climate resilience, multidimensional human-centric design, and energy decarbonization. However, it also reveals that existing studies have placed disproportionate emphasis on the environmental dimension, with insufficient exploration of issues related to social equity and governance structures. To address this gap, this study introduces an ESG-driven theoretical framework encompassing seven dimensions: thermal environment stability, multimodal thermal comfort assessment integration, sustainable energy use, heterogeneous thermal demand equality, passive–active design synergy, participatory thermal data governance, and educational thermal well-being inclusivity. By fostering interdisciplinary convergence and emphasizing inclusive stakeholder engagement, the proposed framework provides a resilient and adaptive foundation for enhancing indoor environmental quality in educational buildings while advancing equitable climate and energy strategies. Full article

Trust evaluation has become a major challenge in the quickly developing Internet of Things (IoT) environment because of the vulnerabilities and security hazards associated with networked devices. To overcome these obstacles, this study offers a novel approach for evaluating trust that uses IOTA Tangle technology. By decentralizing the trust evaluation process, our approach reduces the risks related to centralized solutions, including privacy violations and single points of failure. To offer a thorough and reliable trust evaluation, this study combines direct and indirect trust measures. Moreover, we incorporate IOTA-based trust metrics to evaluate a node’s trust based on its activity in creating and validating IOTA transactions. The proposed framework ensures data integrity and secrecy by implementing immutable, secure storage for trust scores on IOTA. This ensures that no node transmits a wrong trust score for itself. The results show that the proposed scheme is efficient compared to recent literature, achieving up to +3.5% higher malicious node detection accuracy, up to 93% improvement in throughput, 40% reduction in energy consumption, and up to 24% lower end-to-end delay across various network sizes and adversarial conditions. Our contributions improve the scalability, security, and dependability of trust assessment processes in Internet of Things networks, providing a strong solution to the prevailing issues in current centralized trust models. Full article

Facial expression recognition (FER) is a critical research direction in artificial intelligence, which is widely used in intelligent interaction, medical diagnosis, security monitoring, and other domains. These applications highlight its considerable practical value and social significance. Face expression recognition models often need to run efficiently on mobile devices or edge devices, so the research on lightweight face expression recognition is particularly important. However, feature extraction and classification methods of lightweight convolutional neural network expression recognition algorithms mostly used at present are not specifically and fully optimized for the characteristics of facial expression images, yet fail to make full use of the feature information in face expression images. To address the lack of facial expression recognition models that are both lightweight and effectively optimized for expression-specific feature extraction, this study proposes a novel network design tailored to the characteristics of facial expressions. In this paper, we refer to the backbone architecture of MobileNet V2 network, and redesign LightExNet, a lightweight convolutional neural network based on the fusion of deep and shallow layers, attention mechanism, and joint loss function, according to the characteristics of the facial expression features. In the network architecture of LightExNet, firstly, deep and shallow features are fused in order to fully extract the shallow features in the original image, reduce the loss of information, alleviate the problem of gradient disappearance when the number of convolutional layers increases, and achieve the effect of multi-scale feature fusion. The MobileNet V2 architecture has also been streamlined to seamlessly integrate deep and shallow networks. Secondly, by combining the own characteristics of face expression features, a new channel and spatial attention mechanism is proposed to obtain the feature information of different expression regions as much as possible for encoding. Thus improve the accuracy of expression recognition effectively. Finally, the improved center loss function is superimposed to further improve the accuracy of face expression classification results, and corresponding measures are taken to significantly reduce the computational volume of the joint loss function. In this paper, LightExNet is tested on the three mainstream face expression datasets: Fer2013, CK+ and RAF-DB, respectively, and the experimental results show that LightExNet has 3.27 M Parameters and 298.27 M Flops, and the accuracy on the three datasets is 69.17%, 97.37%, and 85.97%, respectively. The comprehensive performance of LightExNet is better than the current mainstream lightweight expression recognition algorithms such as MobileNet V2, IE-DBN, Self-Cure Net, Improved MobileViT, MFN, Ada-CM, Parallel CNN(Convolutional Neural Network), etc. Experimental results confirm that LightExNet effectively improves recognition accuracy and computational efficiency while reducing energy consumption and enhancing deployment flexibility. These advantages underscore its strong potential for real-world applications in lightweight facial expression recognition. Full article

The rapid growth of Internet of Things (IoT) deployment has led to an unprecedented volume of interconnected, resource-constrained devices. Securing their communication is essential, especially in vehicular environments, where sensitive data exchange requires robust authentication, integrity, and confidentiality guarantees. In this paper, we present an empirical evaluation of TLS (Transport Layer Security)-enhanced MQTT (Message Queuing Telemetry Transport) on low-cost, quad-core Cortex-A72 ARMv8 boards, specifically the Raspberry Pi 4B, commonly used as prototyping platforms for On-Board Units (OBUs) and Road-Side Units (RSUs). Three MQTT entities, namely, the broker, the publisher, and the subscriber, are deployed, utilizing Elliptic Curve Cryptography (ECC) for key exchange and authentication and employing the AES_256_GCM and ChaCha20_Poly1305 ciphers for confidentiality via appropriately selected libraries. We quantify resource consumption in terms of CPU utilization, execution time, energy usage, memory footprint, and goodput across TLS phases, cipher suites, message packaging strategies, and both Ethernet and WiFi interfaces. Our results show that (i) TLS 1.3-enhanced MQTT is feasible on Raspberry Pi 4B devices, though it introduces non-negligible resource overheads; (ii) batching messages into fewer, larger packets reduces transmission cost and latency; and (iii) ChaCha20_Poly1305 outperforms AES_256_GCM, particularly in wireless scenarios, making it the preferred choice for resource- and latency-sensitive V2X applications. These findings provide actionable recommendations for deploying secure MQTT communication on an IoT platform. Full article

Tanzania’s Eastern Arc Mountains, a hotspot for biodiversity, are seriously threatened by deforestation and the loss of woodland cover. The loss of woodland cover has been associated with decreased access and availability of woodfuel for nearby communities, which may have detrimental effects on household energy security and livelihoods. This study, which employs geospatial techniques, looks at woodland change in the Eastern Arc Mountains region between 2001 and 2020 to prioritize areas that need more sustainable land use practices. We employed a “mixed methods” remote sensing approach linked to Landsat thematic mapper data to assess woodland change. The results showed that the Same District experienced a considerable loss of woodland, making up 37.4% of the total area lost between 2001 and 2020. These results suggest that access to woodfuel may become more difficult for the residents of Same District. Full article