Search Results (297)

Device-to-device (D2D) communication enhances network efficiency by enabling direct, low-latency links between nearby users or devices. While most existing research assumes that D2D connections occur with the nearest neighbor, this assumption often fails in real-world scenarios—such as dense indoor environments, smart buildings, and industrial IoT deployments—due to factors like channel variability, physical obstructions, or limited user participation. In this paper, we investigate the performance implications of connecting to the n-th nearest neighbor in a cellular network supporting underlay D2D communication. Using a stochastic geometry framework, we derive and analyze key performance metrics, including the coverage probability and average data rate, for both D2D and cellular links under proximity-aware connection strategies. Our results reveal that non-nearest-neighbor associations are not only common but sometimes necessary for maintaining reliable connectivity in highly dense or constrained spaces. These findings are directly relevant to IoT-enhanced localization systems, where fallback mechanisms and adaptive pairing are essential for communication resilience. This work contributes to the development of proximity-aware and spatially adaptive D2D frameworks for next-generation smart environments and 5G-and-beyond wireless networks. Full article

In the IT sector, the relevance of looking at security from many different angles and the inclusion of different areas is already known and understood. This approach is much less pronounced in the area of cyber physical systems and not present at all in the area of building automation. Increasing interconnectivity, undefined responsibilities, connections between secured and unsecured areas, and a lack of understanding of security among decision-makers pose a particular threat. This systematic review demonstrates a paucity of literature addressing real-world scenarios, asymmetric/hybrid threats, or composite vulnerabilities. In particular, the attack surface is significantly increased by the deployment of smart sensors and actuators in unprotected areas. Furthermore, a range of additional hybrid threats are cited, with practical examples being provided that have hitherto gone unnoticed in the extant literature. It will be shown whether solutions are available in neighboring areas and whether these can be transferred to building automation to increase the security of the entire system. Consequently, subsequent studies can be developed to create more accurate behavioral models, enabling more rapid and effective analysis of potential attacks to building automation. Full article

Radio frequency (RF)-based wireless systems are broadly used in communication systems such as mobile networks, satellite links, and monitoring applications. These systems offer outstanding advantages over wired systems, particularly in terms of ease of installation. However, researchers are looking for safer alternatives as a result of worries about possible health problems connected to high-frequency radiofrequency transmission. Using the visible light spectrum is one promising approach; three cutting-edge technologies are emerging in this regard: Optical Camera Communication (OCC), Light Fidelity (Li-Fi), and Visible Light Communication (VLC). In this paper, we propose a Multiple-Input Multiple-Output (MIMO) modulation technology for Internet of Things (IoT) applications, utilizing an LED array and time-domain on-off keying (OOK). The proposed system is compatible with both rolling shutter and global shutter cameras, including commercially available models such as CCTV, webcams, and smart cameras, commonly deployed in buildings and industrial environments. Despite the compact size of the LED array, we demonstrate that, by optimizing parameters such as exposure time, camera focal length, and channel coding, our system can achieve up to 20 communication links over a 20 m distance with low bit error rate. Full article

Tungsten-doped vanadium dioxide (W-VO₂) shows semiconductor-to-metal phase transition properties at room temperature, which is an ideal thermo-chromic smart window material. However, low visual transmittance and solar modulation limit its application in building energy saving. In this paper, a W-VO_2−x@AA core-shell nanoparticle is proposed to improve the thermo-chromic performance of W-VO₂. Oxygen vacancies were used to promote the connection of W-VO_2−x nanoparticles with L-ascorbic acid (AA) molecules. Oxygen vacancies were tuned in W-VO₂ nanoparticles by thermal annealing temperatures in vacuum, and W-VO_2−x@AA nanoparticles were synthesized by the hydrothermal method. A smart window was formed by dispersing W-VO_2−x@AA core-shell nanoparticles into PVP evenly and spin-coating them on the surface of glass. The visual transmittance of this smart window reaches up to 67%, and the solar modulation reaches up to 12.1%. This enhanced thermo-chromic performance is related to the electron density enhanced by the AA surface molecular coordination effect through W dopant and oxygen vacancies. This work provides a new strategy to enhance the thermo-chromic performance of W-VO₂ and its application in the building energy-saving field. Full article

The automation of smart building design processes remains a significant challenge, particularly in translating complex natural language requirements into structured design parameters within Computer-Aided Design (CAD) environments. Traditional design workflows rely heavily on manual input, which can be inefficient, error-prone, and time-consuming, limiting the integration of adaptive, real-time inputs. To address this issue, this study proposes an intelligent Natural Language Processing (NLP)-based workflow for automating the conversion of design briefs into CAD-readable parameters. This study proposes a five-step integration framework that utilizes NLP to extract key design requirements from unstructured inputs such as emails and textual descriptions. The framework then identifies optimal integration points—such as APIs, direct database connections, or plugin-based solutions—to ensure seamless adaptability across various CAD systems. The implementation of this workflow has the potential to enable the automation of routine design tasks, reducing the reliance on manual data entry and enhancing efficiency. The key findings demonstrate that the proposed NLP-based approach may significantly streamline the design process, minimize human intervention while maintaining accuracy and adaptability. By integrating NLP with CAD environments, this study contributes to advancing intelligent design automation, ultimately supporting more efficient, cost-effective, and scalable smart building development. These findings highlight the potential of NLP to bridge the gap between human input and machine-readable data, providing a transformative solution for the architectural and construction industries. Full article

Modular construction has the potential to transform the construction industry, as most (80–95%) of the modules, which are considered prefabricated buildings, are manufactured off-site, which is more efficient, safe, cost-effective, sustainable, productive, and faster than traditional construction. It is not without challenges, however, as it requires detailed and comprehensive planning, high initial costs, and navigating transportation and design constraints. The goal of this study was to identify and categorize the benefits and challenges of modular construction and offer strategies for resolving the challenges. This study also provides a comprehensive review of modular construction methods, including permanent modular construction (PMC), movable modular construction (RMC), volumetric modular construction (VMC), and panelized construction, and examines the connectivity of the modules, as well as the integration of advanced technologies like artificial intelligence (AI). The results revealed that the most frequently cited benefits of modular construction were reducing construction time by up to 50%, 20% cost savings, and material waste reduction of up to 83%. The most common challenges included transportation complexity, limited design flexibility, and high initial costs. The results of this study will assist project managers, construction professionals, and company owners in evaluating modular construction by providing quantified benefits and challenges, a comparative analysis of different modular methods, and insights into effective mitigation strategies, allowing them to assess its suitability based on project timelines, budgets, design requirements, and logistical constraints. Full article

In response to the escalating threat of cyberattacks on smart connected vehicles, numerous Intrusion Detection Systems (IDSs) have emerged. However, existing IDSs often prioritize enhancing detection accuracy while overlooking the time needed for training and detection. Moreover, they may not fully leverage the combined utilization of CAN bus IDs and the data field with external network data. Consequently, these systems frequently struggle to meet the real-time demands and broader attack scenarios inherent in in-vehicle systems. To overcome these challenges, we propose a stacked-model IDS architecture deployed across the CAN bus and central gateway, capable of detecting both internal and external vehicular network attacks. The system extracts key features from in-vehicle and external network data, builds base learners (CART, LightGBM, XGBoost), and integrates them through stacking with a meta-learner. Feature selection and training efficiency are enhanced using information gain and maximal information coefficient algorithms. Experiments show that the proposed IDS achieves an average detection accuracy of 99.99% for internal CAN bus attacks and 99.81% for external network attacks, with fast detection times of 0.018 ms and 0.088 ms, respectively. These results highlight the system’s real-time capability, high accuracy, and adaptability to complex attack scenarios. Full article

This paper discusses the development of an Integrated Building Energy Model (IBEM) in MATLAB (R2024b) for a university campus building. In the general context of the development of integrated energy district models to guide the evolution and planning of smart energy grids for increased efficiency, resilience, and sustainability, this work describes in detail the development and use of an IBEM for a university campus building featuring a heat pump-based heating/cooling system and PV generation. The IBEM seamlessly integrates thermal and electrical aspects into a complete physical description of the energy performance of a smart building, thus distinguishing itself from co-simulation approaches in which different specialized tools are applied to the two aspects and connected at the level of data exchange. Also, the model, thanks to its physical, white-box nature, can be instanced repeatedly within the comprehensive electrical micro-grid model in which it belongs, with a straightforward change of case-specific parameter settings. The model incorporates a heat pump-based heating/cooling system and photovoltaic generation. The model’s components, including load modeling, heating/cooling system simulation, and heat pump implementation are described in detail. Simulation results illustrate the building’s detailed power consumption and thermal behavior throughout a sample year. Since the building model (along with the whole campus micro-grid model) is implemented in the MATLAB Simulink environment, it is fully portable and exploitable within a large, world-wide user community, including researchers, utility companies, and educational institutions. This aspect is particularly relevant considering that most studies in the literature employ co-simulation environments involving multiple simulation software, which increases the framework’s complexity and presents challenges in models’ synchronization and validation. Full article

Discrete-event simulation (DES) is a powerful tool for modeling and analyzing complex systems where state changes occur at discrete points in time. This paper presents a practical and sustainable approach to implementing DES using free mathematical and programming software, making it accessible to a wider audience including educators, students, and practitioners. This study explores the use of open-source tools, such as Python and Octave, highlighting their capabilities in building and optimizing DES models without the need for expensive and unaffordable software. In the context of Industry 4.0 and smart manufacturing, the ability to simulate and optimize discrete processes with open tools contributes to the development of digital twins, the integration of cyberphysical systems, and data-driven decision-making. Through detailed case studies in industrial fields, including manufacturing, maintenance, and logistics, this study demonstrates the effectiveness of these tools in simulating real processes and promoting their sustainability. Case studies are also re-examined to emphasize their relevance to smart manufacturing, particularly in terms of predictive maintenance, process optimization, and operational flexibility. Several challenges were encountered during the research process, such as adapting DES methodologies to the limitations of general-purpose mathematical software, ensuring accurate time management and event scheduling in environments not specifically designed for simulation, and balancing model complexity with accessibility for nonexpert users. The integration of free software not only reduces costs but also promotes collaborative learning and innovation. Additionally, the paper discusses the best practices for model validation and experimentation, providing a comprehensive guide for those new to DES. By linking open-source DES tools to the objectives of Industry 4.0, we aim to reinforce the applicability of our approach to modern, connected industrial environments. By leveraging free mathematical and programming software, this approach aims to democratize the use of DES, fostering a deeper understanding and broader application of simulation techniques across diverse fields and various regions of the world. Full article

Smart building applications require robust security measures to ensure system functionality, privacy, and security. To this end, this paper proposes a Federated Learning Intrusion Detection System (FL-IDS) composed of two convolutional neural network (CNN) models to detect network and IoT device attacks simultaneously. Collaborative training across multiple cooperative smart buildings enables model development without direct data sharing, ensuring privacy by design. Furthermore, the design of the proposed method considers three key principles: sustainability, adaptability, and trustworthiness. The proposed data pre-processing and engineering system significantly reduces the amount of data to be processed by the CNN, helping to limit the processing load and associated energy consumption towards more sustainable Artificial Intelligence (AI) techniques. Furthermore, the data engineering process, which includes sampling, feature extraction, and transformation of data into images, is designed considering its adaptability to integrate new sensor data and to fit seamlessly into a zero-touch system, following the principles of Machine Learning Operations (MLOps). The designed CNNs allow for the investigation of AI reasoning, implementing eXplainable AI (XAI) techniques such as the correlation map analyzed in this paper. Using the ToN-IoT dataset, the results show that the proposed FL-IDS achieves performance comparable to that of its centralized counterpart. To address the specific vulnerabilities of FL, a secure and robust aggregation method is introduced, making the system resistant to poisoning attacks from up to $20\%$ of the participating clients. Full article

This paper presents a case study of Fog Computing, Edge Computing (EC) and Intelligent EC applied to Smart Buildings, focusing on the deployment of innovative services and smart IoT devices, discussing new architecture as Software-Defined Network (SDN). Specifically, a comprehensive solution of a Smart Building case is proposed to validate main statements and conclusions are drawn, providing a general guideline to address the problems of choosing between Edge or Fog Computing and the specific category of IoT devices. The methodology employed in this study is based on field research conducted in buildings within the metropolitan region of São Paulo, Brazil, that aim to enable their transformation into Smart Buildings (SBs). Moreover, principles of Electronic Systems Engineering and Cloud Computing such as reliability, scalability and security are applied. In that way, this study integrates advanced multimedia technical services to enhancing security and communication within the SBs through centralized control. The method focuses on identifying and analyzing the most common problems observed in field research within SBs in early stages of development, prior to the intensive implementation of IoT devices and Fog or Edge Computing technologies on the state of the art. The research adopts a comparative approach, investigating the best solutions for each application category. The results are consolidated in a main table within the article, correlating solutions to the four main problems identified in the field research, such as impairments in voice over IP and video communication using IoT devices; latency and delays in communication between SBs and the Cloud center; access security issues; and the Quality of Experience of video over IP communication, both in live transmissions and recordings between SBs. Regarding applications, this study considers the use of specific IoT devices and Cloud Computing architectures, such as Fog or IEC. Furthermore, it explores the implementation of new open network and communication models, such as SDN and NFV, to optimize communication between the various SBs and the SB’s connection to the control center of a Smart City. Full article

The exponential growth of connected devices and sensor networks has revolutionized data collection and monitoring across industries, from healthcare to smart cities. However, the true value of these systems lies not merely in gathering data but in transforming it into actionable intelligence. The integration of IoT, cloud computing, edge computing, and AI offers a robust pathway to achieve this transformation, enabling real-time decision-making and predictive insights. This paper explores innovative approaches to combine these technologies, emphasizing their role in enabling real-time decision-making, predictive analytics, and low-latency data processing. This work analyzes several integration approaches among IoT, cloud/edge computing, and AI through examples and applications, highlighting challenges and approaches to seamlessly integrate these techniques to achieve pervasive environmental intelligence. The findings contribute to advancing pervasive environmental intelligence, offering a roadmap for building smarter, more sustainable infrastructure. Full article

This paper presents an adaptive Machine Learning (ML)-based framework for automatic load optimization in Connected Smart Green Townhouses (CSGTs) The system dynamically optimizes load consumption and transitions between grid-connected and island modes. Automatic mode transitions reduce the need for manual changes, ensuring reliable operation. Actual occupancy, load demand, weather, and energy price data are used to manage loads which improves efficiency, cost savings, and sustainability. An adaptive framework is employed that combines data processing and ML. A hybrid Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) model is used to analyze time series and spatial data. Multi-Objective Particle Swarm Optimization (MOPSO) is employed to balance costs, carbon emissions, and efficiency. The results obtained show a 3–5% improvement in efficiency for grid-connected mode and 10–12% for island mode, as well as a 4–6% reduction in carbon emissions. Full article

The Internet of Things (IoT) is experiencing rapid growth, with an increasing number of devices connected to the Internet. By 2020, approximately 54% of the 21.7 billion active internet-connected devices worldwide were IoT devices. This number is projected to reach 30 billion by 2025, with an average of four IoT devices per person globally. IoT devices use communication protocols, such as Bluetooth, Wi-Fi, and RFID, to facilitate data exchange. However, the absence of standardized communication protocols and reprogrammable architectures presents significant challenges for IoT applications. Smart buildings, which heavily depend on IoT technology, are particularly affected by the diversity of protocols and standards used by different devices. The Web of Things (WoT) framework has been introduced to address these challenges, enabling interoperability among devices with heterogeneous communication protocols and enhancing system programmability. The increasing adoption of IoT devices necessitates more efficient communication protocols and integrated architectures to meet the demands of modern innovative building systems. This study presents a WoT-based modular architecture designed to ensure compatibility among devices and protocols while providing scalable, flexible, and secure solutions tailored to the current IoT trends. In this study, an Application Programming Interface (API) and a Worker Service were developed using .NET Core technology and the WoT framework for modular intelligent building automation. This system integrates various subsystems, leveraging hardware and communication protocols for seamless functionality. The API facilitates device monitoring and control, while the Worker Service manages scheduling and database operations. The system supports asynchronous communication by employing the HTTP and WebSocket protocols and provides multi-user access with role-based authorization. The proposed automation system was implemented and evaluated, demonstrating its practical applicability and effectiveness in managing complex, innovative building environments. Full article

The term “urban planning for the contemporary age” describes the process of creating and overseeing urban areas to meet the many complex issues of the current times, such as social justice, climate change, growing urbanisation, and technological advancement. It places a strong emphasis on flexible, inclusive, and progressive approaches that strike a balance between sustainable development and the changing demands of various urban populations. Urban planning, particularly in the contemporary age, is a multidisciplinary discipline that tackles the complexity of contemporary cities that is influenced by social injustices, fast urbanisation, climate change, and technology breakthroughs. This entry paper examines how planners deal with these issues by using flexible, inclusive, and sustainable approaches that strike a balance between community well-being, environmental conservation, and growth. It looks at important topics including incorporating smart technology, building climate impact resilience, and creating inclusive, accessible, and egalitarian urban environments. The conflict between tradition and modernisation is also highlighted in the chapter, underscoring the necessity of creative solutions that protect cultural legacy while meeting contemporary needs. By examining global trends, this study offers insights into how urban planning is changing to satisfy the demands of a dynamic and connected society. The study used a literature review to identify the main areas that are influencing innovation in urban planning. It then delves into discussions on contemporary issues and complexities in shaping urban futures. The main research methods were a literature review and empirical analysis. In the end, it makes the case for rethinking urban futures with an emphasis on resilience, sustainability, and the welfare and well-being of urban residents. Full article