Smart Buildings

The rapid growth of urbanization has intensified challenges such as congestion, pollution, climate change, resource inefficiency, and social inequalities, necessitating innovative solutions for sustainable development. Smart cities have emerged as a comprehensive framework that leverages information and communication technologies (ICT), the Internet of Things (IoT), artificial intelligence (AI), and big data analytics to enhance infrastructure, service delivery, and citizen participation. More than technological advancement, smart cities embody six dimensions-smart governance, smart economy, smart mobility, smart environment, smart people, and smart living-that collectively foster adaptive, inclusive, and resilient urban systems. Global examples such as Singapore, Barcelona, and Seoul illustrate how smart initiatives improve energy efficiency, mobility, environmental sustainability, and participatory governance while driving economic growth and innovation. At the same time, smart city development raises important challenges, including affordability, digital inequality, data privacy, cybersecurity risks, and the substantial investments required for implementation. The success of these initiatives depends not only on adopting advanced technologies but also on creating human-centered policies that ensure inclusivity, transparency, and equitable access to resources. By aligning sustainability with innovation, smart cities hold the potential to transform urban spaces into more livable, efficient, and environmentally responsible environments that meet the complex needs of modern societies.

Ever since the first use of the term of "smart city" scholars and researchers have asked what the concept means. Is there a commonly accepted definition of the term? Do we define it through the elements that are building up within? The "smartness" of the city, is it rather a stage or a process? The concept of "smart city" is more and more used in the public policy speech, so there has to be a commonly accepted method to establish which city is smarter than the other one. In the following article the "smartness" of a city will be analysed in terms of mobility and urban transport and we will determine whether in the process of becoming "smart", smaller cities are more likely to get there faster than bigger cities.

This paper presents a technical analysis of the application of the BC548 transistor in H-bridge configurations for controlling direct current (DC) motors used in small-scale industrial conveyor belts. The study explores the history and electrical characteristics of the BC548, highlighting its viability as an electronic switch in low-cost automation systems. The methodology included a literature review, practical circuit assembly, computer simulations, and compliance analysis with international IEC standards such as IEC 60204 and IEC 61131. The results showed that although the BC548 has current limitations (up to 100 mA), it can be effectively used in applications requiring bidirectional control of DC motors, provided that appropriate protection measures and proper sizing are adopted. The constructed H-bridge demonstrated fast response and stable operation, validating its use in controlled industrial environments. It is concluded that the BC548 is a functional and affordable alternative for light automation projects, combining technical simplicity with regulatory compliance.

This paper presents a control scheme based on distributed model predictive control (DMPC) for coordinating flexible distributed energy resources (DER) of heterogeneous type in the Smart Grid with minimum system integration effort. This approach can be used for reducing the peak power exchange between the grid and a cluster of units in the same feeder in pricedriven demand response applications. Preliminary simulations prove that the proposed coordination scheme for DMPC succeeds in coordinating flexible DER unit, achieving significant peak shaving when required. The rationale of this approach consists in coordinating independent units equipped with local MPC controller via simple information passing and hiding in the local controllers the units' dynamics.

El artículo analiza la evolución y aplicación de la inteligencia artificial (IA) en el ámbito de la vivienda, destacando tanto su potencial como sus riesgos. A través de un enfoque deductivo, se estudian hitos históricos de la domótica hasta las actuales smart homes, clasificadas en seis niveles de automatización (0 a 5), desde viviendas analógicas hasta hogares casi autónomos. La investigación muestra que la IA puede mejorar la eficiencia energética, la seguridad, la comodidad y la salud de los habitantes mediante el uso de sensores, algoritmos predictivos y sistemas de monitoreo en tiempo real. Estos avances permiten personalizar los espacios habitables, apoyar el envejecimiento en casa, facilitar la vida de personas con discapacidad y contribuir al bienestar físico y mental. Sin embargo, se advierte que su incorporación también implica desafíos significativos: Riesgos de privacidad y seguridad de datos. Brecha tecnológica y exclusión social en poblaciones con menor acceso. Contaminación ambiental por el ciclo de vida corto y la generación de residuos electrónicos. Dependencia tecnológica, con repercusiones físicas y psicológicas. Mayor exposición a campos electromagnéticos y sus posibles efectos en la salud. El texto concluye que la IA debe implementarse como herramienta complementaria y no sustitutiva de la acción humana, equilibrando innovación con ética, regulación y accesibilidad. La vivienda del futuro no solo debe ser inteligente, sino un espacio que promueva bienestar integral, sostenibilidad y equidad social

Fire safety in high-rise residential buildings is a complex issue, especially when it comes to electrical shaft fires. These enclosed vertical shafts help the rapid spread of smoke, toxic gases and heat across different floors and pose a high risk to the occupants and affect the evacuation process. However, the actual life fire outbreaks show that the existing measures are still wanting as far as evacuation safety is concerned. These issues are addressed in this research through the use of performance-based fire design (PBFD) to assess fire behaviour and evacuation patterns in high-rise buildings. The study then simulates using PyroSim and Pathfinder simulation software, the visibility of smoke, reduction of visibility, CO and CO2 levels, temperature changes, and congestion of the evacuation corridors. Unlike other studies, this paper combines both fire development and occupants’ response to give a holistic approach to the issue of evacuation challenges. One of the findings of this research is the analysis of the ASET and RSET whereby it was found that the current provisions in fire safety do not ensure a safe exit. It was established that RSET is much higher than ASET, which points to a severe lack in current fire safety solutions. Simulations incorporating NBC-2016 (National Building Code-2016) provisions—such as fire-rated doors, sprinklers, and mechanical ventilation—demonstrate improved outcomes, reducing RSET from 1,272 seconds to 746 seconds. However, persistent challenges such as congestion, bottlenecks, and hazardous gas levels highlight the need for enhanced fire safety strategies. This research offers practical recommendations to improve evacuation effectiveness through better ventilation, compartmentalization, and advanced suppression systems. The findings contribute to risk mitigation strategies, expand the knowledge base for fire safety, and provide a foundation for improving fire safety regulations in high-rise buildings.

Buildings require improvements in their structures to be more energy efficient and sustainable. This study focuses on holistic energy analysis that aids in enhancing energy efficiency and reducing consumption by retrofitting buildings. A quantitative approach is utilized in the study by using a unique methodology that integrated Building Information Modeling (BIM) with the Internet of Things (IoT) to develop a model that supports specialists in making well-informed decisions regarding future refits, and the research is based on a complex building at Jordan University of Science and Technology, located in Irbid, Jordon, as a case study. A comprehensive BIM model was developed specifically for this facility, and several retrofit methods were used in combination with IoT. Some of the IoT applications implemented included plug load efficiency, operation schedules, shading techniques, daylight management, lighting efficiency, HVAC system upgrades, and physical modifications like window glass types. Thereafter, a Building Energy Model (BEM) was established to evaluate energy consumption behavior. The results assessed several retrofitting options for their impact on total energy use (EUI) and identified ways to improve energy efficiency. According to the results, integrating BIM with IoT led to total annual energy savings of $9.19 per square meter.

Accurate prediction of energy consumption patterns is vital for attaining sustainability and efficient economic planning. In the USA energy consumption trends have evolved substantially because of factors such as population growth, technological advancements, and shifts in consumption behaviors. The prime objective of this research project was to develop and evaluate machine learning algorithms with accuracy in predicting trends in America's consumption of energy. By employing complex methodologies such as neural networks, regression analysis, and ensemble approaches, this work aims to enhance the accuracy of forecasts in terms of demand for energy in residential, commercial, and industrial sectors. The U.S. consumption datasets covered a wide variety of information representing the consumption of electricity, use of fuel, and integration of renewable sources in residential, commercial, and transportation sectors. Most datasets used for analysis are taken from the U.S. Energy Information Administration (EIA) and the Department of Energy (DOE), offering in-depth statistics about production, consumption trends, and price trends. With our dataset consisting of continuous values for consumption of energy and many predictor factors, we compared a variety of machine algorithms, encompassing XG-Boost, Logistic Regression, and Random Forest. The implemented code compared three algorithms-Logistic Regression, Random Forest, and XG-Boost-in terms of performance via calculation and visualization of key evaluation metrics. It devised a function calculate-metrics to calculate accuracy, precision, recall, and F1-score for a prediction of each model over a test set. Retrospectively, comparing accuracy values, one can observe that Logistic Regression got a high score, with Random Forest following closely, and XG-Boost following a little behind them. Overall, through strategic plots, the comparative strengths and weaknesses of all three algorithms were seen, proving that Logistic Regression was the most reliable algorithm for predicting values in a dataset. Utility companies can benefit a lot through machine learning (ML)-based prediction in terms of distribution efficiency and overall operational efficiency. With ML algorithms, utility companies can utilize humungous volumes of consumption in the past to make future demand predictions with high accuracy.

In today's digital age, mobile phones have become an essential part of our daily lives, serving as communication devices, personal assistants, and navigation tools. As smartphone usage continues to rise in Nigeria, so does the prevalence of mobile phone tracking and geolocation technologies. These technologies offer a wide range of benefits, from locating lost devices to providing location-based services. In this article, we will explore the various types of mobile phone tracking and geolocation methods used in Nigeria and their applications.

The construction and built environment sector is undergoing rapid digital transformation, but existing Human-Computer Interaction (HCI) approaches are inadequate for this complex, data-rich environment. Current methods fail to address the unique characteristics of construction contexts, including diverse data sources, multiple transformation opportunities, and varied human roles. EC3’s Human-Data Interaction (HDI) Technical Committee has developed the first comprehensive framework specifically designed for the Architecture, Construction, Engineering and Operation (AECO) sector. This white paper establishes HDI as a central research domain that unifies various interaction models under a human-centered approach.

The Navajo Nation faces critical challenges in developing housing that is resilient to climate change while honoring cultural heritage. Socio‐economic disparities, limited infrastructure, and extreme environmental conditions demand innovative solutions that integrate sustainable practices with traditional Navajo values. This study critically examines the potential of smart design‐build technologies to create resilient, culturally appropriate housing tailored to the Navajo Nation’s unique needs, while interrogating the normative assumptions that often accompany Western frameworks of sustainability and innovation. This research combines a multidisciplinary literature review with a graduate‐level design studio’s explorative and applied insight. The literature review synthesizes advancements in sustainable technologies—such as off‐grid power systems, alternative materials, and participatory design methods—through a decolonial lens that challenges dominant planning paradigms. A conceptual framework was constructed to evaluate the intersection of cultural coherence, technological viability, material sustainability, socio‐environmental adaptability, and governance. Off‐grid solutions, including solar panels and wind turbines, offer clean energy alternatives, while locally sourced materials, like earth‐based and carbon‐environmentally informed additive manufacturing solutions, provide cost‐effective, low‐carbon options suitable for the arid climate. The study emphasizes participatory design, engaging local communities in developing housing solutions that align with cultural values and modern needs. By combining traditional Navajo architectural principles—such as circular forms and earthen materials—with smart technologies, the resulting designs are resilient, sustainable, and socially relevant. The design studio component enabled graduate students to explore speculative housing prototypes grounded in this framework, evaluated in dialogue with Navajo cultural liaisons and contextual constraints, thereby centering Indigenous perspectives in both process and output. The findings contribute to the broader discourse on smart, resilient infrastructure, offering insights for policymakers, designers, and funders to support localized, culturally and environmentally informed housing solutions in Indigenous communities.

The rapid growth of technology worldwide in different ways drives the construction sector to take the same path. Smart cities, Digital Twins, Building Information Modeling (BIM), and the Internet of Things (IoT) are the trends in this way today. Also, integrating Building Information Modeling (BIM) and Internet of Things (IoT) technologies has revolutionized how buildings are designed, constructed, maintained, and managed. On the other hand, the complexity, high cost, need for expertise, and other things make the maintenance process and facility management by human inspections, commercial software, and different tools not suitable for the growth of the technology. This paper presents a proposal for a workflow of integration between BIM, and an algorithm of Maintenance System with IoT and highlights its potential to enhance building performance and facility management. The paper explores this innovative system's underlying principles, benefits, challenges, and implementation strategies. Furthermore, it discusses the implications of BIM, and the proposed algorithm of Maintenance System with IoT integration on various stakeholders, including building owners, facility managers, and occupants by using a case study. The findings collected by a questionnaire for some experts emphasize the importance of adopting this integrated approach to optimize building operations, improve maintenance practices, and create sustainable and intelligent built environments.

Green building is increasingly recognised in Nigeria as a strategic approach to achieving sustainable development across environmental, economic, and social dimensions within the construction industry. The integration of Green Building (GB) is fundamental to the realisation of green buildings. However, for GB to be successfully adopted and widely applied across the Nigerian built environment, it is crucial to understand the underlying factors that influence their uptake, especially from the perspective of key professionals such as architects, engineers, builders and quantity surveyors. This study's main objective is to investigate the relationship between the establishment year of firms and the Distribution of working experience among the built environment professionals (BEPs). A total of 312 valid responses were analysed using SPSS and exploratory factor analysis (EFA) techniques to examine the relationships between various influencing variables and the BEPs’ adoption behaviour. The findings reveal that factors such as BEPs’ motivation towards green building, the technical capabilities of the available green technologies, the BEPs’ knowledge base, and perceived shortcomings or limitations of the technologies significantly influence adoption decisions. Interestingly, organisational support and managerial encouragement were found to have no substantial impact on the willingness of BEPs to adopt GB. These insights offer practical implications for improving GB adoption within firms and promoting sustainable construction practices across Nigeria. The study contributes to the growing body of green building research by introducing a quantitative, theory-driven approach suited to the local context. It also serves as a basis for future research that may extend the scope of analysis to include other built environment professionals and the internal dynamics of Nigerian BEPs’ organisations

الفصل التاسع: التطورات المستقبلية : 6G  وتكنولوجيا ما بعد
(Future Developments: 6G and Beyond)

9.1 ما هو  6G وكيف سيغير العالم  (What is 6G and How Will It Change the World)
9.2  الابتكارات في الشبكات المستقبلية (Innovations in Future Networks)
9.3 دور الذكاء الاصطناعي في شبكات  G6 (Role of AI in 6G Networks)
9.4 إنترنت الأشياء الضخم في عصر G6 (Massive IoT in the Era of 6G)
9.5 الأمان السيبراني في شبكات الجيل السادس  (Cybersecurity in 6G Networks)
9.6 تكنولوجيا الفضاء والاتصالات عبر الأقمار الصناعية في  6G
(Space Technology and Satellite Communications in 6G)
9.7  تكنولوجيا الواقع الممتد والهولوغرام في شبكات G6
  (Extended Reality and Holograms in 6G Networks)
9.8 تمارين وحلول الفصل المتنوعة (لجميع المستويات: المبتدئين، الباحثين والمحترفين(

الفصل السابع: التطورات المستقبلية بعد UMTS
  (Future Developments Beyond UMTS)
7.1 الانتقال إلى HSPA و LTE (Transition to HSPA and LTE)
في إطار التطور المستمر في تقنيات الاتصالات اللاسلكية، ظهرت الحاجة إلى تحديث الشبكات لتلبية الزيادة الكبيرة في حجم البيانات التي يتم نقلها يوميًا عبر الإنترنت. بينما كانت تقنية UMTS تمثل قفزة كبيرة في تحسين أداء الشبكات اللاسلكية من خلال تقديم خدمات الجيل الثالث، إلا أن تزايد الطلب على البيانات عالية السرعة دفع إلى الحاجة لمعايير جديدة أكثر قدرة على تلبية هذه المتطلبات.
كان الانتقال إلى HSPA (High Speed Packet Access)  و (Long Term Evolution) LTE جزءًا من هذا التطور التكنولوجي الذي يهدف إلى زيادة السرعات وتقليل زمن الاستجابة، وتحسين القدرة على التعامل مع التطبيقات الحديثة مثل الفيديو عالي الدقة، الألعاب عبر الإنترنت، البث المباشر، والعديد من التطبيقات التي تعتمد على الإنترنت السريع.

The world is rapidly shifting toward cleaner, more sustainable energy solutions-and with good reason. As climate concerns grow and energy demands increase, traditional power systems are struggling to keep up. What we need now are energy infrastructures that are not only green but also smart able to adapt, self-optimize, and function efficiently under dynamic conditions [1]. This is where the powerful combination of Artificial Intelligence (AI) and the Internet of Things (IoT) comes into play.

] Abstrack Energy use in homes has been impacted, particularly by HVAC systems, as a result of conventional control (manual ON/OFF). It has been acknowledged that occupant prediction and detection play a major role in increasing building energy efficiency. Over the past ten years, a number of strategies have been put out to increase the accuracy of occupancy estimation and detection in buildings. One useful method for identifying and estimating building occupants during unknown behaviour is environmental sensing. However, the research shows that the low calibre of the training dataset employed in the model results in relatively poor environmental sensing performance. This study employed machine learning algorithm collect consistent and reliable datasets about occupancy that can be used for cross-validation. Although different technique might be readily incorporated into the suggested approach, this study uses various machine learning technqiues suggest best candidate for occupancy prediction which in turn provide the potential for higher energy saving. The findings show that the suggested approch can collect data, compute it, and make reliable prediction (up to 99.3%). Additionally, a room with two zones is modelled, each with air conditioning and a separate thermostat controller, to show how the number of occupants affects energy efficiency. IoFClime is used in the first zone, and a design-builder is used to modify IoFClime in the second zone. Using EnergyPlus software, ten tenants are randomly simulated together with local climatic data in three different situations. According to the Fanger model's thermal comfort study, the first and third scenarios can save up to 50% and 25% of energy, respectively.

Dynamic and Adaptive solar systems demonstrate a greater potential to enhance the satisfaction of occupants, in terms of indoor environment quality and the energy effi-ciency of the buildings, than conventional shading solutions. This study has evaluated Dynamic and Adaptive Photovoltaic Shading Systems (DAPVSSs) through a compre-hensive analysis of six shading designs in which their energy production and the comfort of occupants were considered. Energy generation, thermal comfort, daylight, and glare control have been assessed in this study, considering multiple orientations throughout the seasons, and a variety of tools, such as Rhino 6.0, ClimateStudio 2.1, Grasshopper, and Ladybug, have been exploited for these purposes. The results showed that the prototypes that were geometrically more complex, designs 5 and 6 in particular, had approximately 485 kWh higher energy production and energy savings for cooling and 48% better glare control than the other simplified configurations while maintaining the minimum day-light as the threshold (min DF: 2%) due to adaptive and control methodologies. Design 6 demonstrated optimal balanced performance for all the aforementioned criteria, achieving 587 kWh/year energy production while maintaining the daylight factor within the 2.1–2.9% optimal range and ensuring visual comfort compliance during 94% of oc-cupied hours. This research has established a framework that can be used to make well-informed design decisions that could balance energy production, occupants’ wellbeing, and architectural integration, while advancing sustainable building envelope technologies.

Smart home safety and security systems have gained much importance over the last few years owing to their notable impact in reducing and preventing losses in resources and human life caused by unwanted situations that could occur while homeowners are far away from their homes. To date, there is a lack of an in-depth literature analysis that could help researchers and developers better understand these systems and their applications in different contexts. It is therefore crucial that research evidence published in this area is presented. In this study, 63 research papers that examined smart home safety and security systems using the Arduino platform from popular literature databases were thoroughly surveyed to extract useful data. Then, the extracted data were analyzed to answer many research questions concerning state-ofthe-art applications of these systems, their architectures, their enabling technologies, their components, etc. In addition, several challenges that these systems currently face and how future research could enable better implementation and use of these systems were discussed.

Artificial intelligence (AI) is increasingly applied in mechatronic system optimization to improve efficiency,
adaptability, and performance. This paper compares important AI methods—like neural networks, genetic
algorithms, reinforcement learning, fuzzy logic, and hybrid approaches—showing their advantages and
disadvantages regarding accuracy, computing power needed, how easy they are to understand, and how well they
can be integrated. Neural networks provide strong modeling capabilities for nonlinear dynamics but require
extensive data. Genetic algorithms excel at global optimization but can be computationally costly. Reinforcement
learning offers adaptive policy learning but presents safety and data challenges. Fuzzy logic systems prioritize
interpretability, while hybrid approaches combine complementary strengths. By analyzing these trade-offs and
citing recent advancements such as physics-guided neural networks, this study guides practitioners in selecting
appropriate AI strategies for mechatronic optimization and outlines directions for future research.

Green building is a term that is considered for the new generation of buildings because most of the energy required in the building itself is supplied through the systems embedded in it that produce green energy, and because in these buildings no fossil fuels are used, they are free of carbon dioxide and have comprehensive systems to reduce energy consumption and increase efficiency. After the Industrial Revolution, especially during the era of architectural modernism, smart buildings emerged. The use of modern technologies and technologies in construction that originate from architecture is necessary to optimally use and control energy consumption, reduce waste, create comfort, and save time and building maintenance costs. Today, with the advancement of technology, it is possible to update building components to increase the level of interaction between humans and technology, to provide more comfort, to achieve safety, security, comfort, and better control of the space, and to bring a building as close as possible to the architectural indicators. There are various sustainable methods; one of the best methods is to use smart building systems. Therefore, in this article, smart green buildings with a sustainable architecture approach are discussed and investigated. The research method is to use a qualitative approach, and this article has been studied in the library method by studying books, articles, and magazines to examine the concepts of smart buildings, sustainable development, dimensions of sustainability, and existing examples related to sustainable architecture.

The rise in the Internet of Things (IoT)based smart building construction process offers an acceptable fix to the challenging issues of energy administration, equitable expansion, and economic optimization as the number of people living in metropolitan areas rises and the desire for ecofriendly lifestyles rises.This study investigates the construction process in smart buildings by offering a deep learning (DL) approach to energy optimization. The investigation outlines a novel architecture that uses DL methods to optimize energy use in IoT-based smart buildings. To enable intelligent energy optimization and performance choices, it leverages current statistics from various resources, such as detectors, gadgets, big data processing, and smart grids. By using DL systems, such as neural networks and recurrent neural networks (RNNs), the suggested method provides accurate demand predictions while adhering to flexible use patterns. The empirical study demonstrates the efficacy of the DL-based approach using actual case investigations and intricate models conducted in multiple intelligent construction processes across the globe. The results demonstrate notable energy consumption drops, financial savings, and noteworthy actions to the eradication of carbon emissions, thus enhancing ecological sustainability. Furthermore, the system's adaptability and scalability demonstrate how it may be used in a range of urban contexts. In addition to addressing contemporary energy optimization in construction process concerns, this research paper lays the groundwork for a more efficient and ecologically responsible metropolitan future.

This paper presents the design of a passivity-based iterative learning control (ILC) algorithm for coupled temperature and humidity in buildings. Since buildings are subjected to repeating diurnal patterns of disturbances, ILC algorithms can significantly improve performance. Moreover, since it is a feedforward control scheme, it can be used in conjunction with either model-free or model-based approaches such as the popular model predictive control techniques. However, model-based control is challenging for buildings because of the difficulty in identifying building thermohygrometric models. Furthermore, the control law should be designed in such a way as to address both temperature and humidity set points. We propose a model-free ILC design approach facilitated by the inherent passivity of building thermohygrometric dynamics. We first demonstrate that the building dynamics are strictly output-incremental passive. This property is then exploited to design ILC laws that guarantee convergence in the iteration domain, while being robust to model uncertainty. Since we wish to control both temperature and humidity using only one input -mass flow rate of supply air, convergence to a point is not guaranteed; instead convergence to an ellipse on the temperature-humidity plane is shown. The controller performance is demonstrated through simulation examples.

Building interior temperatures are affected by the outdoor air temperature. Note that outdoor weather patterns are somewhat repetitive in nature and historical records of outdoor temperature are readily accessible; we present a data-driven iterative learning approach to improve the room temperature tracking performance over time. By comparing the short-term temperature forecast with the past data, chains of (nonconsecutive) days exhibiting similar outside temperature patterns can be identified. The corresponding building operation record (heat input and temperature output trajectories) may then be used in the iterative learning control (ILC) to update the input based on the past temperature tracking error. Multizone buildings are strictly passive from the heat input to temperature output in all zones. This property assures the convergence of the ILC iteration if the update gain is suitably bounded, without the need of an accurate model. This means that for each chain, the zone temperature deviation from the specified profile will converge to zero as the number of days in the chain grows (i.e., as more iterations of ILC are performed). Using a six-zone physical test bed with programmable ambient temperatures, we demonstrate the practicality of the proposed approach in multiple experimental trials. Additional longer-duration simulations are performed based on the actual temperature recorded in Orlando, FL, USA and New York, NY, USA over a two-year period. In all cases, ILC is shown to improve the tracking error in the presence of ambient temperature fluctuations. Note to Practitioners-This paper uses historic operating data and weather forecasting to improve the temperature tracking in multizone buildings. The proposed approach exploits the inherent passivity property of the building thermal model and, therefore, does not require the accurate model identification prior to implementation. As a result, this method may be conveniently retrofitted to any existing heating, ventilation, and air conditioning system, provided that the system is capable of adjusting the heat input (e.g., by controlling the supply air flow rate) and

The transition to net-zero energy buildings requires innovative energy management solutions, with Smart Lighting Control Systems (SLCS) playing a crucial role in optimizing energy consumption. This study evaluates the techno-economic feasibility of SLCS implementation in large commercial buildings targeting net-zero performance. A comprehensive analysis integrates sensor-based adaptive lighting, daylight harvesting, and IoT-enabled automation with Building Energy Management Systems (BEMS). The research employs energy simulations, cost-benefit analysis, and payback period assessments to compare SLCS with conventional lighting systems. Findings indicate that SLCS can achieve 30-60% energy savings, significantly reducing operational costs and carbon footprint. Despite higher initial investments, return on investment (ROI) is observed within 3-7 years, depending on factors such as building occupancy patterns, lighting efficiency, and local energy tariffs. The study underscores the economic and environmental viability of SLCS as a strategic component for achieving net-zero goals in commercial buildings. Insights from this research benefit developers, policymakers, and facility managers in implementing smart lighting solutions for sustainable building projects.

The urbanization trend and the prevailing energy crisis in India, makes it important to study the potential of IoT technology in the Indian buildings market. This research work proposes a systems dynamics model of IoT based climate control systems diffusion in Intelligent Buildings. The modeling process leverages the generic Bass model of technology diffusion and augments it with causal loops based on the enablers and barriers to IoT diffusion in buildings identified through literature review. The model encompasses the technological, social, financial, business, regulatory and environmental aspects of the system, and uses stock-flow concepts to represent their dynamic interplay. The model will be useful to value chain players in the construction sector by providing them support for strategic decision-making concerning market entry and new product development. It will also help policy makers to assess industry readiness to tackle the prevailing energy crisis and devise strategies to mitigate it.

User-centric, localized energy and comfort management solutions that are essential in commercial buildings are realized by IoT enabled building management systems, involving networked sensors and artificial intelligence-based data processing. This paper employs the Delphi method to identify the major issues concerning IoT adoption in India and examines these across the value chain, specific to energy and comfort management in intelligent buildings. The Delphi exercise involved 3 expert-panels, consisting of a total of 24 subject matter experts representing all important stakeholders across the intelligent building value chain. A total of 140 issues have been identified, grouped across 8 themes, and ranked, leading to the development of an 'IoT adoption index'. A subset of the issues has been identified using two inputs from the literature, viz., key performance indicators of intelligent buildings and the business model canvas. The results can be practically used by the stakeholders while evaluating IoT systems for deployment in their projects. Further, the results should be useful in the context of intelligent buildings also in other emerging economies having similar climatic conditions.

Windows play a pivotal role in the building envelope, significantly influencing energy performance, aesthetic appeal, and overall occupant comfort. The design of a glazing system that optimally integrates all relevant factors poses considerable challenges. Smart glazing technologies, capable of dynamically adjusting their optical properties in response to varying outdoor conditions, have shown a promising potential to enhance energy efficiency without compromising the architectural integrity of structures. This paper provides a thorough review of commercially available smart window technologies, examining aspects such as their underlying technologies, manufacturing processes, marketing strategies, energy performance metrics, aesthetic qualities, and the barriers they face in capturing a larger market segment. The smart glazing systems considered include Electrochromic (EC), thermochromic (TC), thermotropic (TT), photochromic (PC), SPD (Suspended Particle Devices) Smart windows, PDLC (Polymer Dispersed Liquid Crystal) and PNLC (Polymer Network Liquid Crystals) smart windows. In the discussion section, we summarize the principal findings and explore strategic pathways to address the current challenges hindering the broader adoption of these advanced glazing solutions.

The rapid economic growth in China is accompanied by an ever-increasing demand for energy in both urban and rural areas. Since 1978, China has experienced an annual growth rate of 4.98% in primary energy resources production. The primary energy consumption from 2000 to 2009 recorded an annual growth rate of 9.4%. Ostensibly, it is important to upgrade the power generation units and improve air quality. To enhance air quality, this article suggests the adoption of small-scale hydropower and biomass energy in rural areas and the construction of green and smart buildings in urban areas. Improvement in regulations and enforcement is also considered to be another useful means in enhancing urban sustainability. The adoption of big data and artificial intelligence in optimising the building and architecture design may be one of the major directions in moving toward sustainable built environment in urban areas.

How are energy resources being managed in educational institutions? Conversely, how are they being wasted? Proposed reductions in electricity consumption within the business and home sectors currently average twenty-three and sixteen percent, respectively. These statistics signal that education, government, and industry may need to take a closer look at consumption in hopes of reducing wastage, thus working towards a more sustainable future. Approximately forty percent of all primary and secondary schools, advanced education centers, and universities are inefficient energy consumers. A vast majority of these and other institutions wishing to assess and work towards becoming more efficient in their use of electricity have a definite need for current information covering opportunities, levels of energy use, and strategies to aid in reducing large levels of wastage. In 2018, schools had to spend additional costs resulting from inefficiency, energy wastage, and outdated infrastructure. Maintenance and operation alone account for a significant percentage of the total expenses in our schools. The prompt repair and reconstruction of energy-inefficient buildings in educational institutions is a significant investment. To find the technical solutions in an institutional setting, focusing on energy efficiency of the premises through site visits and investigations has two principal goals: reducing energy expenses and increasing the environmental benefits. Major

This study highlights the necessity for developing smart building and cities across the globe in order to conserve natural resources and energy. The aim of the study is to investigate the efficacy of 5G technologies in the architecture and construction industry. The study benefits from an extensive literature review to discuss the advantages and challenges in incorporating 5G in smart buildings. 5G is found to be needed to maximize the use of artificial intelligence (AI) and IoT in architecture in order to be able to efficiently enhance the features of smart buildings by managing energy and waste.

The home automation or the smart home is a novel approach on smart living for smart people in Sri Lanka. The smart home concept is not very much popular in local customers as in other regions of the world. Modern homes provide electricity to electrical devices through the last element of the power supply chain, the power sockets and switches, which have been regarded traditionally as a mere junction. In fact, domestic power distribution system has not evolved as fast as other everyday devices, although they seem to be one of the best positioned candidates to be improved as smart homes are becoming increasingly popular. The paper addresses some of the most common problems that arise when interacting with the home automation and building management systems which show a novel approach to them: the prevention of unnecessary power consumption, wireless access and control of smart devices. The main tool for measuring system efficiency in homes and offices is the monitoring of the energy consumption of household appliances'. This paper describes the design and prototype implementation of a wireless home energy management system under a DC-distribution environment, which allows remote monitoring of appliances and energy monitoring. The proposed integrated prototype framework is embedded with three layers: ESP-12E IEEE 802.11 based communication layer for remote access, ESP-12E microcontroller as control layer to control the domestic appliances and DC input layer to input the DC power with voltage and current sensing. This system is suitable for maintaining a smart home which powered by a renewable energy such as solar or wind isolated from national grid on low voltage DC (LVDC) distribution systems. The research was tested for LVDC system in laboratory environment and need further modifications for low voltage AC (LVAC) distribution systems.

Collecting and storing smart city data is a task that requires thorough data exploration, configuring and testing to be of value. Configuring a data collection pipeline for data from a new data provider needs to take into account what the various fields represent, what parts of the data is of interest, which data fields should be stored, and more. In some cases the data follows a predefined, and known schema, in other cases the data may be undocumented. This thesis presents a framework and a software for automating the process of collecting and storing smart city data, and other event based data sets. The problem, and solution is illustrated in this thesis by a use case where the task consist of storing public transportation data in a structured way in a storage system that can handle big data.

Linear Construction Methods (LCM) have emerged as a transformative solution for managing linear projects such as road infrastructure, offering significant advantages over traditional scheduling methods like the Critical Path Method (CPM) and the Program Evaluation and Review Technique (PERT). Despite their potential to streamline scheduling, optimize resources, and reduce costs, the adoption of LCM faces several challenges. These include technical barriers, limited expertise, resource constraints, stakeholder resistance, and the lack of supportive policies, especially in developing regions. This research investigates these challenges while exploring innovative solutions that enhance LCM's effectiveness and applicability. The study utilizes secondary data from case studies, such as the Tamil Nadu Highway Project, and expert consensus through the Delphi method to analyze the key barriers to LCM implementation. Innovations like the integration of Building Information Modeling (BIM), Artificial Intelligence (AI), and Internet of Things (IoT) tools are evaluated for their role in addressing these challenges. The findings highlight the potential of LCM to revolutionize road infrastructure development by improving project timelines, cost efficiency, and sustainability. This research provides practical recommendations for engineers, policymakers, and project managers, including structured training programs, policy incentives, and advanced technology integration. The study underscores LCM's role as a critical tool for achieving efficient, sustainable, and future-ready road infrastructure.

Linear Construction Methods (LCM) have emerged as a transformative solution for managing linear projects such as road infrastructure, offering significant advantages over traditional scheduling methods like the Critical Path Method (CPM) and the Program Evaluation and Review Technique (PERT). Despite their potential to streamline scheduling, optimize resources, and reduce costs, the adoption of LCM faces several challenges. These include technical barriers, limited expertise, resource constraints, stakeholder resistance, and the lack of supportive policies, especially in developing regions. This research investigates these challenges while exploring innovative solutions that enhance LCM's effectiveness and applicability. The study utilizes secondary data from case studies, such as the Tamil Nadu Highway Project, and expert consensus through the Delphi method to analyze the key barriers to LCM implementation. Innovations like the integration of Building Information Modeling (BIM), Artificial Intelligence (AI), and Internet of Things (IoT) tools are evaluated for their role in addressing these challenges. The findings highlight the potential of LCM to revolutionize road infrastructure development by improving project timelines, cost efficiency, and sustainability. This research provides practical recommendations for engineers, policymakers, and project managers, including structured training programs, policy incentives, and advanced technology integration. The study underscores LCM's role as a critical tool for achieving efficient, sustainable, and future-ready road infrastructure.

The integration of smart technologies and automation systems in high-rise housing presents a promising avenue for addressing the complex challenges of urban living. This research paper explores the multifaceted implications of implementing smart technologies in tall residential buildings, with a focus on enhancing efficiency, safety, and comfort for residents. Drawing on a comprehensive review of existing literature, case studies, and expert interviews, this study examines the key features, benefits, and challenges associated with the adoption of smart technologies in high-rise housing. The paper begins by providing an overview of the current state-of-the-art smart technologies and automation systems applicable to high-rise residential buildings, including IoT devices, sensor networks, building management systems, and integrated smart home solutions. It delves into the potential benefits of these technologies, such as energy conservation, optimized resource management, improved security, and personalized user experiences. Additionally, the research explores how smart technologies can contribute to enhancing the overall sustainability and resilience of high-rise housing developments, through strategies such as demand response, predictive maintenance, and disaster preparedness measures. Furthermore, this paper examines the challenges and barriers that may impede the widespread adoption of smart technologies in high-rise housing.

The idea of smart homes has been completely transformed by the Internet of Things (IOT) rapidly expanding technological capabilities, which allow for seamless automation and remote device control. Because of Python's strong and versatile programming features, this thesis discusses the design and simulation of IOT frameworks and device communication in a smart home setting. The suggested system incorporates a number of simulated Internet of Things (IOT) devices, including security cameras, smart lights, and thermo stats. These device s use the MQTT (Message Queuing Telemetry Transport) protocol to connect with one another through a centralized message broker. The simulation demonstrates how various smart home components interact by simulating real-time communication between devices and a central controller using Python and the Paho MQTT module. Every gadget is represented as a MQTT client that reacts to simulated environmental changes and publishes status updates on a regular basis. Dynamic monitoring and control are made possible by the central controller, which processes incoming messages and subscribes to the device topics. The study simulates realistic smart home situations including automatic lighting, temperature control, and motion detection while highlighting the architectural concepts underlying effective communica tion, scalability, and security in IOT networks. This study advances knowledge about Internet of Things frameworks and how they may be used to improve the comfort, security, and energy efficiency of smart homes. It also lays the groundwork for future efforts to integrate more IOT protocols, cloud services, and sophisticated machine learning methods for predictive automation.

The increasing prevalence of smart buildings within urban environments necessitates advanced security measures to detect and mitigate potential threats. This study leverages the data by a private company ASHRAE, the ASHRAE-Great Energy Predictor III dataset (GEPIII). The research question is: How can anomalous energy consumption be used as a proxy for identifying intrusions in smart buildings? By establishing baseline energy consumption patterns for building operations, we investigate how deviations from these patterns may signal the presence of unauthorised individuals. The anomaly detection in this study focuses on deviations in energy consumption patterns, considering not only magnitude and frequency but also duration, timing, rate of change, consistency across similar conditions, correlation with external factors like weather, aggregate daily or monthly usage, geospatial distribution within the building, and statistical outliers. In this study, we employ a Long Short-Term Memory (LSTM) neural network for our anomaly detection task, capitalising on their ability to capture dependencies in sequential data. After training our LSTM model, we conducted extensive validation to assess its performance. The dataset provides meter readings from over 1300 commercial buildings, of which we used a subset of 100 randomly selected buildings for this study due to computational resource limitations. Using IoT with interconnected sensing devices in smart buildings to collect data, combined with AI is an emerging research area in building security. Results highlight the potential of this approach to provide tools for enhancing the security of smart buildings, with implications for broader urban safety systems. Broader implications are that threats can be detected pre-emptively by using the developed model, or buildings can be designed and then a simulation can be run against the developed AI model, influencing future building codes or policy changes for the governance of urban environments.

Brain computer interfaces are currently considered to greatly enhance assistive technologies and improve the experiences of people with special needs in the workplace. The proposed adaptive control model for smart offices provides a complete prototype that senses an environment’s temperature and lighting and responds to users’ feelings in terms of their comfort and engagement levels. The model comprises the following components: (a) sensors to sense the environment, including temperature and brightness sensors, and a headset that collects electroencephalogram (EEG) signals, which represent workers’ comfort levels; (b) an application that analyzes workers’ feelings regarding their willingness to adjust to a space based on an analysis of collected data and that determines workers’ attention levels and, thus, engagement; and (c) actuators to adjust the temperature and/or lighting. This research implemented independent component analysis to remove eye movement artifacts from the EEG sig...

The gas processing industry plays a vital role in global energy production. However, it faces increasing pressure to enhance efficiency and reduce environmental impact. This review paper explores advanced techniques in gas processing, focusing on cutting-edge technologies, strategies for optimizing efficiency, and methods for minimizing emissions and waste. It discusses adopting digital technologies, such as artificial intelligence and machine learning, revolutionizing process optimization and predictive maintenance. The paper also examines sustainable practices, including integrating renewable energy sources and using carbon capture and storage (CCS) technologies. Furthermore, it analyzes the anticipated regulatory changes and their implications for the industry, highlighting the challenges of adopting advanced techniques while balancing operational efficiency and environmental responsibility. By providing a comprehensive overview of the latest trends and challenges in gas processing, this paper aims to guide industry professionals in implementing effective strategies for sustainable and efficient operations.

Rapid urbanization and the increased construction activities worldwide have immensely contributed to the consumption of energy and degradation of the environment. Green construction minimizes these impacts by designing energy-efficient buildings and reducing waste. The integration of Artificial Intelligence in green construction presents innovative solutions that optimize design, resource management, security and sustainability practices. This paper analyzes the role of AI in achieving energy efficiency in building design and enhancing waste management during construction. It covers key aspects such as generative design, energy modeling, security, predictive analytics, and automated waste sorting. Real-world applications, challenges, and emerging trends in AI-driven sustainable construction are also included.

We introduce a collaborative learning framework allowing multiple parties having different sets of attributes about the same user jointly build models without exposing their raw data or model parameters. In particular, we propose a Federated Stochastic Block Coordinate Descent (FedBCD) algorithm, in which each party conducts multiple local updates before each communication to effectively reduce the number of communication rounds among parties, a principal bottleneck for collaborative learning problems. We analyze theoretically the impact of the number of local updates, and show that when the batch size, sample size and the local iterations are selected appropriately, within T iterations, the algorithm performs O( √ T ) communication rounds and achieves some O(1/ √ T ) accuracy (measured by the average of the gradient norm squared). The approach is supported by our empirical evaluations on a variety of tasks and datasets, demonstrating advantages over stochastic gradient descent (SGD) approaches.

One critical challenge for applying today’s Artificial Intelligence (AI) technologies to real-world applications is the common existence of data silos across different organizations. Due to legal, privacy and other practical constraints, data from different organizations cannot be easily integrated. Federated learning (FL), especially the vertical FL (VFL), allows multiple parties having different sets of attributes about the same user collaboratively build models while preserving user privacy. However, communication overhead is a principal bottleneck since the existing VFL protocols require per-iteration communications among all parties. In this paper, we propose the Federated Stochastic Block Coordinate Descent (FedBCD) to effectively reduce the communication rounds for VFL. We show that when the batch size, sample size and the local iterations are selected appropriately, the algorithm requires O( √ T ) communication rounds to achieve O(1/ √ T ) accuracy. Finally, we demonstrate t...

The Internet of Things (IoT) is transforming the management of Heating, Ventilation, and Air Conditioning (HVAC) systems by enabling real-time monitoring and intelligent control. Buildings account for about 40% of global energy use, with HVAC systems consuming the largest share. Yet many HVAC operations are inefficient, wasting 20-30% of energy due to rigid controls and lack of feedback. This study investigates the impact of IoT integration on HVAC performance, focusing on improvements in energy efficiency and occupant comfort. We present a comprehensive analysis combining literature review, experimental case studies, and data-driven evaluation. The results demonstrate that IoT-enabled HVAC systems can significantly reduce energy consumption-often by 20-30% or more-while maintaining or enhancing indoor comfort. Case studies from commercial buildings and campus facilities illustrate how smart sensors, connected thermostats, and data analytics optimize HVAC operation. Occupant comfort is addressed through adaptive controls and occupant feedback mechanisms, leading to more stable indoor environments. However, challenges such as data security, privacy, and integration costs remain and are discussed alongside future directions for widespread adoption. These findings underscore IoT's potential to revolutionize HVAC systems, achieving a synergy between sustainability and human comfort in modern buildings.