Smart Homes

Enoncé de l'activité : la fonction logique OU Dans cette fiche de travaux pratiques, nous allons utiliser un circuit intégré de type DIL (l'abréviation en anglais Dual in-Line Package pour une forme particulière d'un boîtier de circuit intégré). C'est quoi un circuit intégré ? Un circuit intégré est un composant électronique de petite taille qui intègre en général une ou plusieurs fonctions analogiques ou numériques, ou voire même mixtes, dans une petite puce miniature fabriquée en silicium (le silicium est un composant obtenu à partir du silice). Pour vous faire découvrir l'univers de l'électronique numérique, nous vous proposons cette fiche d'activité pour pour découvrir et valider la fonction logique OU en réalisant l'expérience avec un circuit intégré; pour cela, nous allons utiliser l'une des 4 portes logiques d'un circuit intégré 74LS32. La figure ci-dessous nous le présente : Le 74LS32 est un circuit intégré de la famille 74LS qui contient 4 portes logiques de type OU et qui fonctionne avec une source de tension comprise entre 4.75V et 5.25V. Pour cela, nous allons l'alimenter avec une tension continue de 5V. La broche numéro 7 sera ainsi connectée à GND et la borche numéro 14 sera connectée à VCC soit les 5V dans notre cas de figure.

Despite the evident market growth and technological advancements, there is a lack of studies examining the strategies employed by Chinese smart home technology firms to encourage the adoption of AI-based smart home technologies. This qualitative multiple-case study aimed to fill that gap by exploring how five leading Chinese smart home technology firms (Haier, Aqara, Xiaomi, Huawei, and LifeSmart) embed TAM-related adoption predictors into their consumer communication strategies, focusing on Millennials and Gen Z. This purpose leads to the research question: "How do five leading Chinese smart home firms embed TAM-related adoption predictors into their consumer communication strategies, focusing on Millennials and Gen Z?" The findings reveal that all five case study firms effectively tailor their communication strategies by leveraging TAM predictors to resonate with the preferences and expectations of Milennials and Gen Z, providing implications for both academic theory and practical marketing management. Future research could add further case study firms to the sample, extend the results to other geographical markets, or use longitudinal studies to analyse changes in consumer adoption and communication strategies.

In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural networks to predict household energy usage based on power consumption data from common appliances, such as lamps, fans, air conditioners, televisions, and computers. The model comprises two interrelated submodels: one predicts the individual energy consumption and usage time of each device, while the other estimates the total energy consumption of connected appliances. This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. Following a systematic methodology that includes model building, training, and evaluation, the LSTM model achieved a low test set loss and mean squared error (MSE), with values of 0.0163 for individual consumption and usage time and 0.0237 for total consumption. Additionally, the predictive performance was strong, with MSE values of 1.0464 × 10-6 for usage time, 0.0163 for individual consumption, and 0.0168 for total consumption. The analysis of scatter plots and residuals revealed a high degree of correspondence between predicted and actual values, validating the model's accuracy and reliability in energy forecasting. This study represents a significant advancement in intelligent home energy management, contributing to improved efficiency and promoting sustainable consumption practices.

In the quest to improve energy efficiency in residential environments, home energy management systems (HEMSs) have emerged as an effective solution, leveraging artificial intelligence (AI) technologies to improve energy efficiency. This study proposes a deep learning-based approach employing Long Short-Term Memory (LSTM) neural networks to predict household energy usage based on power consumption data from common appliances, such as lamps, fans, air conditioners, televisions, and computers. The model comprises two interrelated submodels: one predicts the individual energy consumption and usage time of each device, while the other estimates the total energy consumption of connected appliances. This dual structure enhances accuracy by capturing both device-specific consumption patterns and overall household energy use, facilitating informed decision-making at multiple levels. Following a systematic methodology that includes model building, training, and evaluation, the LSTM model achieved a low test set loss and mean squared error (MSE), with values of 0.0163 for individual consumption and usage time and 0.0237 for total consumption. Additionally, the predictive performance was strong, with MSE values of 1.0464 × 10-6 for usage time, 0.0163 for individual consumption, and 0.0168 for total consumption. The analysis of scatter plots and residuals revealed a high degree of correspondence between predicted and actual values, validating the model's accuracy and reliability in energy forecasting. This study represents a significant advancement in intelligent home energy management, contributing to improved efficiency and promoting sustainable consumption practices.

Identifying malicious networks has been a subject of study for decades, and since the volume of network traffic is increasing day by day, there is a need for a successful intrusion-detection system that can make the identification process easier during attacks.

The Worcester Data Center and Aquaculture Project (2027-2057) proposes a thirty-year, integrated infrastructure initiative that combines exascale, AI-optimized supercomputing with an intensive, heat-integrated aquaculture system. The project addresses the rising global demand for computational capacity and sustainable food production through a co-located, symbiotic architecture. Modular data halls and power plant units are staged for progressive deployment between 2027 and 2039, while aquaculture facilities are designed to utilize the thermal output of computing operations for year-round production efficiency. The study presents consolidated technical, financial, and environmental projections across capital expenditures, operating costs, workforce dynamics, and long-term community impacts. Scenarios are modeled to examine risk factors including technological obsolescence, regulatory adaptation, and environmental stressors, while aspirational elements explore the potential of advanced cooling, circular economy principles, and artificial intelligence governance. The initiative's design emphasizes both regional economic revitalization and global knowledge contribution, targeting Worcester, Massachusetts, as a locus for innovation in sustainable supercomputing. This paper offers a comprehensive technical and socioeconomic assessment of the feasibility and long-horizon impacts of a dual-purpose infrastructure model, situating the Worcester project as a potential prototype for integrating next-generation computing facilities with environmentally restorative industries.

This paper provides a comprehensive analysis of the development of smart homes and their advantages for social sciences in providing housing for the unhoused through tiny communities and AI-integrated homes, focusing on economic development. The references cited offer a foundation for further research and exploration in this area.

Este artículo evidencia la creación y utilización de un sistema de monitoreo y gestión de recursos domiciliarios para agua y energía en el hogar que permite la automatización y visualización de los consumos presentes en el hogar, con el objetivo de que los usuarios puedan conocer los puntos más incidentes en el gasto, mostrar un perfil de consumo y controlar su activación y desactivación. Esto da como resultado un sistema de software y hardware libre que muestra que las aplicaciones manejadas con las herramientas actuales son elementos potenciales para la gestión y el cuidado del ambiente, reduciendo sustancialmente el costo de los servicios y fomentando el manejo de herramientas y soluciones económicas como el software y el hardware libre. En la actualidad, la gestión de recursos domiciliarios es uno de los temas más importantes a la hora de buscar sostenibilidad y eficiencia energética, por lo que la utilización de hardware y software libre permiten el desarrollo de aplicaciones iot (Internet of Things), fáciles de trabajar, económicas y confiables. Finalmente, se muestran los resultados obtenidos con la medición de los consumos desde el software libre de Arduino soportado en el hardware libre de nodemcu.

The increasing integration of smart technologies in residential settings is revolutionizing how modern homes are managed, leading to enhanced comfort, energy efficiency, and security. This study presents the design, development, and evaluation of a smart control system tailored for domestic applications. Leveraging the Internet of Things (IoT), artificial intelligence (AI), and wireless communication, the system enables real-time monitoring and automation of household devices such as lighting, fans, and security components.
A modular IoT architecture was implemented using sensors (PIR, DHT11, MQ-2), a NodeMCU ESP8266 microcontroller, and a cloud-based dashboard accessible via mobile applications. Adopting the V-Model development methodology, the system was thoroughly tested and evaluated based on latency, accuracy, reliability, scalability, and security.
Key results demonstrated motion detection latency of 1.1 seconds, 92% smoke detection accuracy, and system uptime of 98.6% over a 7-day period—exceeding industry benchmarks for performance. Despite limitations such as Wi-Fi dependency and prototype scale, the research confirms the system's viability as a scalable and energy-efficient solution for smart living. This work contributes meaningfully to the field of home automation by demonstrating an adaptable and user-friendly platform for intelligent domestic control.

In SPR imaging (SPRi), convolutional neural networks (CNNs) excel at extracting diagnostic features from complex images, achieving disease detection accuracies exceeding 90% . Dimensionality reduction techniques like Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) further support the visualization and clustering of high-dimensional data, facilitating interpretation and downstream analysis. ML techniques have revolutionized the design of SPR sensors by enabling predictive modeling and inverse design. Neural networks, including multilayer perceptrons (MLPs) and CNNs, have been trained to predict optical responses (e.g., resonance wavelength shifts) from nanostructure geometry and material parameters. These models have reached prediction accuracies of over 99% when combined with statistical techniques like Bayesian Ridge Regression. Advanced generative approaches, such as Generative Adversarial Networks (GANs) and reinforcement learning, allow for the inverse design of nanostructures. These models generate configurations that maximize field enhancement and sensitivity. Metaheuristic algorithms, including Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs), have also been combined with ML models to accelerate multi-parameter optimization, leading to a 75% reduction in design time and sensitivity enhancements up to 5,200 nm/RIU. ML-powered SPR biosensors have demonstrated remarkable promise in clinical diagnostics. Wearable SPR devices, integrated with lightweight ML models, are capable of real-time monitoring of biomarkers such as cardiac enzymes and inflammatory mediators. In infectious disease diagnostics, DL-enhanced SPR systems have achieved femtomolar-level detection of SARS-CoV-2 in under 30 minutes-surpassing the performance of standard RT-PCR tests. CNN-based systems have also been used for cancer detection, enabling identification of exosomes and multiple protein biomarkers (e.g., eight breast cancer indicators) at picogramper-milliliter sensitivity. ML integration has substantially improved the sensitivity, speed, and multiplexing capacity of SPR biosensors. Optimized sensor designs using hybrid materials (e.g., Au-TiO₂-CdS) have improved sensitivity by 30-45%, and CNNs have significantly reduced noise in complex biological media such as serum. However, several challenges remain. The development of high-performing ML models requires large, annotated datasets, which are difficult to generate XLI / Extended Abstracts in biomedical settings. Computational resource demands, particularly for training deep networks, limit the portability of these systems. Moreover, the "black-box" nature of many DL models hampers their interpretability, raising concerns in clinical settings. Lastly, SPR's inherent limitations in detecting low-molecular-weight analytes (<500 Da) and its susceptibility to interference in biological matrices persist as obstacles. Several promising strategies are emerging to address these limitations. Lightweight DL models suitable for edge computing are being developed for use in point-of-care devices. Transfer learning techniques reduce the dependence on large training datasets by repurposing pre-trained models for SPR-specific tasks . Hybrid physics-informed ML models combine domain-specific physical principles with data-driven learning, enhancing both performance and interpretability. Additionally, federated learning offers a path toward collaborative model training across institutions while preserving data privacy, a critical consideration in clinical applications. Machine learning has significantly advanced the capabilities of SPR biosensors. By improving signal interpretation, guiding sensor design, and enabling rapid multiplexed diagnostics, ML is pushing the limits of what SPR can achieve. Although challenges related to data availability, model transparency, and sensor physics remain, ongoing developments in computational modeling and materials science are steadily bridging these gaps. As the convergence between artificial intelligence and nanophotonics continues to evolve, SPR biosensors are poised to play a transformative role in personalized medicine, real-time diagnostics, and global health monitoring.

Contemporary construction projects are characterized by escalating complexity, voluminous data flows, and stringent sustainability requirements, rendering conventional project management methods increasingly inadequate. In response, artificial intelligence (AI) has emerged as a transformative enabler in construction project management, offering advanced capabilities in predictive analytics, process automation, and intelligent decision support. This paper explores the role of AI in the identified principal functions of construction project management, including time management, cost estimation, quality assurance, occupational health and safety, risk mitigation, resource optimization, and design management through a narrative literature review. Analysis demonstrates that AIdriven approaches significantly enhance operational efficiency and system resilience by enabling proactive identification of schedule delays, cost overruns, and safety hazards. For example, image-recognition systems integrated with Internet-of-Things sensors facilitate real-time monitoring of site conditions and adaptive response to disruptions, while neuralnetwork models trained on historical project data yield more accurate cost forecasts than traditional estimation techniques. In the design management domain, generative design algorithms and AI-enhanced BIM integration have the potential to automate clash detection, optimize form and function, and generate innovative design alternatives that align with cost, energy, and sustainability objectives. Beyond efficiency gains, AI fosters a paradigm shift toward predictive, data-driven, and adaptive management practices that strengthen project resilience, enabling teams to anticipate, absorb, and recover from unforeseen challenges while improving project performance and sustainability. Critical barriers to widespread AI adoption are also identified in this study. Fragmented and nonstandardized data ecosystems impede model training and interoperability with legacy systems, while organizational resistance and a shortage of professionals skilled in both AI and construction hinder implementation. Ethical and legal concerns-stemming from the "black-box" nature of many AI algorithms-further complicate accountability in safetycritical decisions. By synthesizing these challenges, the strategic role of AI is highlighted not only as a technological innovation but also as a catalyst for cultural and organizational transformation toward more resilient project delivery. Targeted future research directions include empirical validation of AI tools in live project environments, development of sectorspecific AI frameworks tailored to the peculiarities of the construction industry, interdisciplinary collaboration among engineers, data scientists, and managers, and educational initiatives to upskill the workforce. Collectively, these steps will help bridge the gap between theoretical potential and real-world impact, positioning AI as a cornerstone of intelligent, resilient, sustainable, and high-performing construction project management.

Rescue missions must be conducted within a strict timeframe, and the safety of all rescuers and civilians is prioritized. The proposed system aims to design a remote-operated aerial surveillance robot for disaster-affected areas for search and rescue missions. Real-time video transmission and RS-232 long-range communication enable operators to navigate rough environments and monitor data collected in real-time. This powerful tool ensures the protection of human life while collecting accurate and meaningful data. Cloud storage for data and surveillance strengthens the system, preventing part failure and fostering collaboration among users. This is a significant step towards using Internet of Things systems alongside remote-controlled robots in disaster response. The robot's key contribution to disaster management is identifying the environment, addressing issues of no visibility, complicated terrains, and speed. Its modification and expansion capabilities make it useful in armed surveillance, industrial monitoring, and environmental studies, making it an important innovation for many other fields.

Large-scale, distributed data storage is made possible by cloud computing, which also improves system performance and availability. However, a recurring and unsolved problem is making sure that data is consistently and completely deleted across several servers. Copies of a file that has been deleted by a server might still be present on other network nodes, which could result in data leakage, violations of privacy laws, and wasteful storage resource usage. In order to resolve the discrepancy, this work presents an intelligent system that combines Graph Neural Networks (GNNs) with a Publish/Subscribe (Pub/Sub) messaging to make this deletion. The entire storage infrastructure is modeled as a dynamic graph, where servers and data blocks are represented as nodes, and replication relationships as edges, enhanced with attributes like last access time, trust levels, and file size. When data deletion takes place, the Pub/Sub model notifies all subscribed nodes, which then use GNN to predict other possible data locations. Based on the prediction confidence, the system requests admin approval, or deletes the data automatically. This approach is highly scalable, and reduces duplicate actions and helps in proper detection of the deleted data from other servers. Integrated auditing feature which is similar to blockchain, provides a secure and record of all deletion operations, increasing liability. The proposed framework enables reliable, efficient, which is really crucial in data-sensitive sectors such as healthcare, finance, and education. It's design supports both operational efficiency and adherence to privacy regulations across distributed storage environments. Thus this approach has been taken.

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(

Cybersecurity threats have become increasingly sophisticated, requiring defense organizations to build highly skilled and adaptable cyber workforces. Traditional approaches to training and talent acquisition often fall short due to the lack of pre-training assessments, resulting in misaligned skill development and inefficient use of training resources. This paper presents the design, implementation, and deployment of the Cybersecurity Aptitude Assessment Platform (CAAP), a configurable system developed to evaluate the current competencies of individuals and recommend tailored training paths aligned with mission needs. The CAAP platform bridges the gap between talent identification and effective workforce integration by offering a structured, pre-enrollment assessment of candidates' cybersecurity knowledge across foundational and advanced domains. Deployed within defense departments and led by Unified Business Technologies, the platform has assisted in workforce upskilling, optimized training investments, and enabled strategic placement of personnel into cyber roles. Additionally, the platform supports scalability beyond cybersecurity, allowing use in other technical domains where aptitude-based assessments can inform role alignment and training needs. This paper outlines the challenges in traditional cyber training pipelines, describes the technical architecture and workflow of CAAP, and presents measurable outcomes from its implementation in a defense environment.

With the revolution of using wireless local area networks (WLAN), many connected things over the internet and other networks have evolved in many smart applications. The smart home is one such evolved technological advancement incorporated with IoT and WLAN for connected things using Wireless Fidelity (Wi-Fi) networking technology. In Wi-Fi-connected devices, the most prominent feature is to monitor and control them. In the domestic environment, smart technologies are used to convert the conventional home to a smart home with controlling lights, appliances, and electrical energy monitoring individually or as a whole. The modern era smart home technologies were not penetrated to the domestic users' even though the technology has evolved swiftly. The usage of this technology among domestic users raises economic and societal issues. The research predominantly addresses those questions on adopting smart home technology on existing domestic users over the live distribution system with the design of devices that can be used with domestic users without changing their existing distribution system. Further pointed to the design of circuit controller node (CCN) as developed system's main controllers and the design and development of an energy monitor.

The smart home is one of the evolved technological advancement incorporated with the use of Internet of Things (IoT) for connected things using Wireless Fidelity (Wi-Fi) technology. In Wi-Fi connected devices, the most prominent feature is to monitor and control them. In the domestic environment the smart technology used to convert the conventional home to a smart home with controlling lights, controlling appliances, and monitoring of electrical energy individually or as by whole. The modern era smart home technologies were not penetrated to the domestic users' even though the technology has evolved swiftly. The research predominantly addresses those matters of questions on adopting smart home technology on existing domestic users over the live distribution system. Further pointed to the design of Smart Socket Outlets (SSO) and Smart Light (SL) as the main controllers of the developed system and also design and develop the prototype of an energy monitor to monitor the energy consumption in individual appliances or as a whole using the web-based application and over the cloud-based system.

This paper presents a novel federated learning framework for implementing voice biometric authentication in multi-tenant cloud contact centers while preserving customer privacy and regulatory compliance. Traditional centralized voice biometric systems face significant challenges in cloud environments due to data sovereignty requirements, privacy regulations (GDPR, CCPA), and tenant isolation needs. Our proposed framework enables distributed learning across tenant boundaries without exposing raw voice data, achieving 97.3% authentication accuracy while maintaining strict data locality. The system leverages differential privacy mechanisms and homomorphic encryption to protect individual voice patterns during model aggregation. Implementation results from a Cisco Webex Contact Center environment serving 50+ enterprise tenants demonstrate 40% reduction in authentication latency and 99.8% compliance with data residency requirements. This approach addresses critical gaps in secure biometric deployment for cloud-native contact centers while enabling cross-tenant learning benefits.

As contact centers evolve into omnichannel customer experience hubs processing millions of interactions daily, they have become high-value targets for sophisticated cyber threats. This paper presents a comprehensive Zero Trust Architecture (ZTA) framework specifically designed for modern contact centers, addressing the unique challenges of hybrid workforces, cloud-native infrastructures, and stringent regulatory compliance requirements. Through analysis of real-world implementations across 50+ organizations, we demonstrate how Zero Trust principles can reduce security incidents by 75%, improve compliance scores by 40%, and enable seamless remote operations while maintaining enterprise-grade security postures.

The goal is to propose an achievable roadmap for creating AI systems that feel as accessible as fictional portrayals, while grounded in present technological and social realities. Summary of Contributions 1. Bridging Fiction and Reality-mapping "Jarvis" from pop culture into technically viable AI functions.

] 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.

The rapid growth of the ageing population triggers the demand for innovative solutions to support independent living for seniors, given the changes in physical health, cognitive function, and mobility. Smart Living and Universal Design principles are trusted to play crucial roles in improving the quality of life, especially for the elderly and disabled individuals. Hence, this paper aims to present a theoretical background on the potential adoption of smart living and universal design in housing development in response to cater to the ageing population's well-being. A systematic literature review utilizing bibliometric analysis through the visualization of similarity viewer software is conducted to synthesise existing research on smart living and universal design housing features catering to the ageing population. Findings highlighted the potential for extensive smart living and universal design adoption in housing, with several challenges derived from the lack of standard regulations. Despite the drawbacks, the incorporation of extensive smart living and universal design is noteworthy for its efficacy in extending the life expectancy of the ageing population, particularly the elderly residing in their residences. This study provides practical implications for policymakers, designers, and practitioners to implement smart living and universal design in Malaysian housing development to foster the well-being of the ageing population in Malaysia.

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.

The first edition of this book, Internet of Things A to Z: Technologies and Applications, was released in 2018 with the aim of providing a comprehensive resource on the subject, but given recent advancements in the field, updating the book was imperative. Notable developments include the widespread adoption of artificial intelligence (AI), edge computing, and modern big data technologies; the emergence of new technologies such as edge intelligence, digital twins, virtual and augmented reality, the metaverse, and quantum computing; and the growing rate and sophistication of cybersecurity attacks in recent years. This second edition has been thoroughly revised and expanded to capture these advances and address other critical areas not covered in the previous edition.

This peer-reviewed volume comprises a collection of 25 related chapters, organized into five parts, addressing introductory as well as more advanced themes. The chapters are authored by a group of leading domain experts from both academia and industry. The chapters collectively cite approximately 1500 references to support the provided discussions and help researchers and practitioners investigate the addressed concepts and technologies further.

This book is aimed at students, educators, researchers, practitioners, and information and communication technology (ICT) professionals working in the areas of IoT, mobile and wireless networks, distributed computing, and artificial intelligence. It is designed to be used both as a reference and a learning tool.

A decade on from the launch of Amazon's Alexa the smart home's breakout product the vision of semi-automated, pervasively sensed domesticity remains unrealised; industry losses are mounting; and big tech protagonists are in partial or full retreat from their smart home ambitions. Simultaneously, scholars draw attention to the dangers posed by the extractive business model underpinning this instantiation of the internet of things, in terms such as 'data colonialism', and call for an organised 'unmaking' of it. Against this backdrop, drawing on a 2 year study of six families living with smart home technologies (SHTs), we invoke the concept of 'mundane resistance', to describe the ways in which SHTs come to be diminished, or outright rejected, not by a singular user, but in the interplay of multiple members of the home in the course of their everyday participation in its moral economy. The outcome of these exchanges is a surveillance capitalism less panoptic than myopic. At a moment in time when the global economy is increasingly geared around the promise of generative AI, recognising the potential for such uncoordinated everyday acts to, in aggregate, disassemble the grand plans of the tech industry, is of considerable value. It serves both as a rejection of the industry's grandiose self-mythologising and as a statement of the ongoing importance of attending to the situated practices of everyday life, both in its repeating patterns and its quotidian idiosyncrasies.

Integrating Big Data and Internet of Things (IoT) platforms is the focus of this research, which aims to improve energy management. The problem statement is centered on the potential for development through advanced technologies and the inefficiencies in traditional energy management methods. The objectives are to analyze energy consumption patterns, develop an innovative Home Energy Management System (HEMS) architecture, and offer energy-saving solutions. Synthetic energy consumption data is generated, normalized, and divided into training and testing sets from a methodological perspective. K-nearest neighbors, Decision Trees, Support Vector Regression, and Random Forest are the machine learning models trained and evaluated. The Random Forest model outperforms other models in terms of the accuracy of its predictions of energy consumption. The integration of renewable energy sources with cutting-edge technology to revolutionize energy management practices is the essence of novelty. In conclusion, this investigation underscores the importance of utilizing advanced technologies to promote sustainable energy management, providing practitioners and policymakers with practical insights.

We investigated people's experience with living in shared smart homes, involving both smart home initiators and maintainers (primary users) as well as other inhabitants (secondary users). Through a cultural probe study with 35 participants from 16 shared homes and follow-up interviews with a subset, we gained insights into people's understanding of smart home technology, their ideas for the future, their experiences with the technology, and how they relate to their smart home. Our findings highlight how the role taken (primary or secondary user) influences how smart homes are experienced and understood in everyday life, and how 'smartness' is defined. The study further investigates how people describe their smart home 'as a living being', yielding a wide range of animal metaphors, that reveal character traits that people associate with smart home technology.

The negative portrayal of ageing as a human decline burdening society has prompted Ageing Technology industries (AgeTech) to foresee solutions rooted in the Ageing in Place paradigm. These ostensibly neutral future interventions are intertwined with socio-technical dynamics. While Science and Technology Studies (STS) and anthropology scholars have questioned these AgeTech practices, limited literature explores industry's predictions of future AgeTech. Drawing on STS and futures-anthropology literature, I interrogate AgeTech industry visions of future assemblages involving older people, smart home technology, and socio-material discourses rooted in their own discrepancies and dilemmas. To unpack AgeTech futures, my methods include a review of 49 industry reports and 29 interviews with industry experts. Based on the reports, I designed comics to be used in interviews with experts spanning CEOs and managers of companies designing technology for older people, consultants, and agedcare workers based in 12 countries. Ageing futures are far from being neutral or a chronological process, instead they are non-consensual and fragmented. In the review and interviews, I captured future assemblages of a fragmented AgeTech industry in relationships with governments and industry giants. The fragmentation continues unfolding in participants from diverse countries and professions contesting dominant AgeTech narratives. In dissecting future assemblages, I also unpack non-consensual futures based on diverging experts' values (e.g. safety versus activity) and humans' values like control and improvisation challenging predictive and surveillance technology. AgeTech Futures transcend physical matters or assemblages of technologies and humans. They encompass future normativities, tensions, divergent values, and ideological concepts. I propose not only alternatives to the visions found in industry narratives, but also encourage scholars to understand the AgeTech industry's dilemmas.

The adoption of home automation technologies has improved residential convenience and energy efficiency, but it has also introduced various cybersecurity risks. This qualitative literature review examines the primary security threats and risk mitigation strategies related to smart home environments. Key issues identified include unauthorized access, data breaches, device tampering, and privacy violations, often due to weak default configurations, user negligence, and the lack of unified security standards. The review synthesizes recent research findings and emphasizes the importance of integrating technical defences (e.g., encryption, multi-factor authentication), promoting cybersecurity literacy among users, and advancing regulatory frameworks. It stresses the need for collaboration among manufacturers, policymakers, researchers, and consumers to ensure smart home systems are secure, resilient, and privacy-preserving. These insights provide a foundation for future research and development aimed at improving security within the domain of smart home automation.

This presentation showcases the comprehensive interior design process for a 72-square-meter studio apartment in Lekki, Lagos, tailored for a young, tech-savvy engineer. It explores a minimalist yet luxurious design approach that combines smart technology integration, space optimization, and a calming aesthetic. Key highlights include detailed furniture and lighting plans, zoning diagrams, material selections, and conceptual renderings, all aimed at delivering a highly functional, decluttered, and elegant space. The work reflects an emphasis on natural light, premium textures, and user-centered design.

The rise of data-sharing platforms, driven by public demand for open data and legislative mandates, has raised several pertinent issues. These encompass uncertainties over data accuracy, provenance and lineage, privacy concerns, consent management, and the lack of equitable incentives for data providers. The advanced nature of blockchain makes it well suited to address these concerns. Yet, the limitations of blockchains, particularly their restricted performance, scalability, and high cost, make them less adept at managing the four "Vs" of big data-volume, variety, velocity, and veracity. As the body of work proposing blockchain-based data-sharing solutions grows, so does the confusion in selecting between these platforms, particularly in terms of sharing mechanisms, services, quality of services, and applications. In this article, we aim to fill this knowledge gap through an in-depth survey of blockchain-based data-sharing architectures and applications. We first identify the key challenges of existing data-sharing techniques and lay out the foundations of blockchains. Our focus then shifts to the intersection of blockchain and data sharing, wherein we aim to clarify the existing landscape and propose a reference architecture for blockchain-based data sharing. Subsequently, we explore various industrial applications of blockchain-based data sharing, spanning healthcare, smart grids, transportation, and decarbonization. For each application, we draw from real-world deployments to present key lessons learned in the implementation of blockchain-based data sharing. Lastly, we shed light on current research challenges and open avenues for further study in this space. This article aims to serve as a comprehensive resource for researchers/practitioners looking to navigate the complex terrain of blockchain-based data-sharing solutions.

This research aims to develop a secure smart home system capable of forecasting energy consumption and user profiles through deep learning techniques and an edge computing strategy utilizing blockchain technology. The proposed system monitors Internet of Things (IoT) devices, such as refrigerators, dishwashers, smart microwaves, and furnaces, leveraging blockchain for enhanced security and transparency. The study highlights the effectiveness of Long Short-Term Memory (LSTM) neural networks-a specialized deep learning approach for time series prediction-in achieving favorable forecasting outcomes. The findings indicate that the optimal window size significantly affects model uncertainty estimates and prediction performance. By employing the Equilibrium Optimization (EO) algorithm for hyperparameter tuning, the study enhances the efficiency of the LSTM model. Overall, the results demonstrate that the LSTM model is effective in accurately predicting the energy requirements of smart homes.

The expanding deployment of renewable energy sources as well as the widespread deployment of smart meters enables and encourages demand management in homes. Like smart meters, most solar or other renewable deployments allow homeowners to carefully monitor energy supply and past energy consumption, however, using this information to drive demand management is still a manual process. The overarching goal of our work is to automate the process of adapting energy demand to meet supply, which requires a comprehensive understanding of home energy use. Though home energy measurement systems exist, they are often intrusive-requiring several physical components and using often limited resources including energy and bandwidth. In this work, we present the design of a system for comprehensive home energy measurement and analyze the resource requirements of the basic system. Using data collected from six deployments, including one in an off-grid home, we then present two techniques for reducing the resource requirements of the system. Our techniques reduce the energy footprint of the system as well as the amount of physical infrastructure required, making adoption of the system more attractive, particularly to those who live in homes powered by renewable energy sources.

This work presents the evaluation of a system based on IROG (Infrared Oculography) to control home appliances in a smart environment by people with disabilities. One participant with disability used the system for 21 days. According to the results, the participant was able to control the appliances, felling safer and independent to execute her daily activities, and positively evaluating the system, training and the instruction manual. Resumo-Este trabalho apresenta a avaliação de um sistema de controle de ambiente inteligente (AI) controlado por movimentos do globo ocular (IROG-Infrared Oculography: Oculografia por Infravermelho) por pessoas com deficiência. Uma pessoa com deficiência fez uso do sistema por 21 dias. Na avaliação realizada, constatou-se que a participante foi capaz de controlar os equipamentos do AI por IROG, sentindo-se mais segura e independente nas suas atividades da vida diária (AVD), avaliando positivamente o sistema, o treino e o manual de instruções.

This work presents an assistive system, usercentered designed, for controlling and monitoring a smart home. The proposed system allows a person with motor disability to perform the tests in his/her own home, not only to increase convenience and comfort, but also so that the system can be tested where it should in fact be used. In addition, the system allows a healthcare professional (caregiver) to monitor the tests remotely, by internet, in real time. The system was tested by a person with disability, and the test lasted seven days. At the end of the test, the user responded to the System Usability Scale (SUS) tool and commented on her experience using the assistive system. Resumo-Este trabalho apresenta um sistema assistivo, projetado centrado no usuário, para controle e monitoramento de uma casa inteligente. O sistema proposto permite que uma pessoa com deficiência motora faça os testes em sua própria casa, não apenas para aumentar a conveniência e o conforto, mas também para que o sistema possa testado onde de fato deverá ser utilizado. Além disso, o sistema permite que um profissional da área da saúde (cuidador) acompanhe os testes remotamente, via internet, em tempo real. O sistema foi testado por uma pessoa com deficiência, e o teste teve duração de sete dias. Ao final do teste, a usuária respondeu o instrumento Escala de Usabilidade do Sistema e fez comentários sobre a sua experiência em utilizar o sistema assistivo.

This paper presents the development of a user interface based on eye/gaze tracking to control a smart home. The user interface proposed has some functionalities that can improve the usability of the system as a whole. In the experimental sessions, performed in an actual home, 29 participants performed the test protocol during about 5 to 10 minutes and then answered the SUS questionnaire. The SUS result showed that the participants evaluated the user interface with a mean of 89.9, positioning the tool as exceptional. Resumo-Este artigo apresenta o desenvolvimento de uma interface de usuário baseada em rastreamento de olhos para controlar uma casa inteligente. A interface do usuário proposta possui algumas funcionalidades que podem melhorar a usabilidade do sistema como um todo. Nas sessões experimentais, realizadas em uma casa real, 29 participantes realizaram o protocolo do teste durante cerca de 5 a 10 minutos e responderam ao questionário SUS. O resultado do SUS mostrou que os participantes avaliaram a interface do usuário com uma média de 89,9, classificando a ferramenta como excepcional.

This work presents a system for controlling a smart home by eye gaze. With this system it is possible to control everyday equipment of a residence, such as lamps, television, fan and radio. GlobalBox (gBox) is a new device controller module, which was assembled and customized in an actual home, where tests were conducted with 29 participants. At the end of the tests, users evaluated the system regarding the interest in using it, the available functionality, the complexity in using it, and the confidence in its functioning. Resumo-Este trabalho apresenta um sistema para controle de uma casa inteligente. Com este sistema é possível controlar equipamentos do cotidiano de uma residência, como lâmpadas, televisão, ventilador e rádio. Foi desenvolvida a GlobalBox (gBox), que é um novo circuito de acionamento de dispositivos, o qual foi montado e customizado em uma casa real, onde foram feitos testes com 29 participantes. Ao final dos testes, os usuários avaliaram o sistema com relação ao interesse em utilizá-lo, às funcionalidades disponíveis, à complexidade em utilizá-lo e à confiança no seu funcionamento.

In the era of smart city development, enhancing accuracy and operational efficiency at Realtime is crucial. To meet the complexity of advanced urban environments smart city technologies-including IoT and ICT-play a vital role. These innovations aim to improve quality of life and represent progress in urban monitoring and control. Key sectors such as traffic management, healthcare, surveillance, governance, and security are becoming increasingly intelligent. As urban populations grow, the demand for integrated and efficient smart city services-particularly in surveillance, transportation, public safety, and healthcare-continues to rise. This paper proposes a concept for comprehensive and centralized smart city model that unifies various autonomous departments to operate cohesively under an AI-driven system, thereby enhancing responsiveness and resource coordination.

This chapter shows how generational attitudes regarding robotics changed from dread to interest. The study found significant age disparities in robotic technology perceptions from 50 interviews and a 1,400-person survey. Due to their digital upbringing and positive media depictions, Generation Z and Millennials are more accepting and enthusiastic about robots. Due to unfavorable cultural narratives and technology upheavals, Generation X and Baby Boomers are more skeptical and concerned. The chapter discusses fear and fascination psychological theories and how exposure, familiarity, and culture shape them. It also points out the ethical and social issues of integrating robotics into society and the necessity for focused education and ethical frameworks to dispel myths and encourage a balanced perspective of robotic technology. Understanding generational variations allows stakeholders to build strategies to promote robotics adoption across all age groups, maximizing advantages while minimizing negatives.

PhD Thesis presented to the Postgraduate Program in Electrical Engineering of the Federal University of Espirito Santo, as a partial requirement to obtain the degree of Doctor in Electrical Engineering, in the area of concentration: Robotics and Intelligent Automation.

There is a need for scalable, widely accessible, and efficient solutions due the rise in mental health issues like depression, anxiety, and suicidal thoughts. Traditional mental health treatments have limitations in terms of availability, affordability, and accessibility. The study focuses on advancements in AI-powered solutions, such as chatbots and online therapies that improve accessibility and provide customized treatment. User engagement, retention, and privacy aspects which are crucial to the long-term effectiveness of digital treatments are also addressed in the research. The flexibility of digital mental health technologies for different populations, including children, low-income groups, and senior citizens, is also discussed in this research. The significance of patient engagement, moral responsibility, and treatment flexibility is also highlighted. This paper examines several studies to illustrate how digital solutions can improve user experience and mental health care without sacrificing moral values.

Home Automation and Smart Homes diffusion are providing an interesting opportunity to implement elderly monitoring. This is a new valid technological support to allow in-place aging of seniors by means of a detection system to notify potential anomalies. Monitoring has been implemented by means of Complex Event Processing on live streams of home automation data: this allows the analysis of the behavior of the house inhabitant through quantitative indicators. Different kinds of quantitative indicators for monitoring and behavior drift detection have been identified and implemented using the Esper complex event processing engine. The chosen solution permits us not only to exploit the queries when run "online", but enables also "offline" (re-)execution for testing and a posteriori analysis. Indicators were developed on both real world data and on realistic simulations. Tests were made on a dataset of 180 days: the obtained results prove that it is possible to evidence behavior changes for an evaluation of a person's condition.

Internet of Things A to Z offers a holistic approach to the Internet of Things (IoT) model. The Internet of Things refers to uniquely identifiable objects and their virtual representations in an Internet-like structure. Recently, there has been a rapid growth in research on IoT communications and networks that confirms the scalability and broad reach of the core concepts. With contributions from a panel of international experts, the text offers insight into the ideas, technologies, and applications of this subject.

The authors discuss recent developments in the field and the most current and emerging trends in IoT. In addition, the text is filled with examples of innovative applications and real-world case studies.

Written for ICT researchers, industry professionals, and lifetime IT learners, as well as academics and students, Internet of Things A to Z provides a much-needed and comprehensive resource to this burgeoning field.

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.

Seeing smart furniture as a design space where technology redefines the dwelling at home, we formed a design project, “Design for Being Home and Connected,” for third-year industrial design studio class. In this regard, we further analyzed the notions of “smartness” and “being connected” in the context of household furniture through student projects. Our analysis framework consisted of three types of connectivity: emotional, digital, and functional, and five types of smartness: the ability to cooperate, autonomy, environmental agility, upgradability, and two-way communication. Furthermore, our contribution is twofold: first, we reveal how humans are still omnipresent through smart furniture despite being at home, and second, we scrutinize how smart furniture can provide a more sustainable lifestyle in households.

Nowadays renewable energy generation techniques and their application for home energy management are becoming very common topics of discussion all across the globe. The increased user comfort, bill reduction, and government subsidy schemes make more consumers interested in installing these sustainable sources in their homes. Also, the utility company will be able to level its peak load and reduce its carbon footprint. Does installing renewable energy sources in homes with a conventional billing scheme help in reducing the carbon footprint of the utility company? Also, are there chances for an increased trend of electric load addition in homes installed with renewable energy plants having net metering schemes to lead to peak load management burden? This paper is an attempt to underline the benefits of using renewable energy sources at home but at the same time what are the precautions to be taken while using the same in the state of Kerala, India. The paper also proposes an economical portable solar-powered light tower that helps in leveling peak loads in homes with on-grid power plants which are billed under a conventional block rate pricing scheme through net metering.

Many reports estimated that in 2024, the number of Internet of Things (IoT) devices exceeded 18 billion worldwide, with predictions suggesting that it could reach nearly 40 billion by 2033. Despite primarily being consumer devices, a growing number of them will find use in industrial and enterprise applications. This shows the significance of IoT and how it shapes the future. However, to realize its full potential, we must address its emerging challenges and highlight recent applications, advances, and trends, which is the focus of this book. The main objective of this book is to capture the state of the art in IoT. This peer-reviewed book serves as a reference for researchers, academics, practitioners, and graduate-level students.

Architecture, as a sublime art, in conjunction with other artistic branches, provides a platform for the manifestation of thought, identity, and aesthetic values. The interaction between these two fields is not merely a formal amalgamation, but a dynamic and transformative process that expands the conceptual boundaries of design in response to contemporary challenges. In this context, interior design, as the point of convergence between form, function, and meaning, serves as an ideal platform for examining how the principles of art and architecture can be blended to create multifaceted and thought-provoking spaces. This research, focusing on the convergence of art and architecture, delves into contemporary methods of integrating these two domains in interior design. On one hand, the use of architectural drawings and visual statements as tools for representing reality and conveying abstract concepts plays a significant role in the design process. On the other hand, technological advancements, environmental challenges, and the need for social reconstruction have led to the emergence of new design paradigms that break previous boundaries and result in novel interpretations of space. In this regard, the analysis of key international works, the study of climatic considerations in interior design, and the exploration of creative patterns formed in response to contemporary cultural and social needs will be central to this research. The ultimate goal is to present a theoretical and practical framework through which interior design can become a platform for critical dialogue, artistic regeneration, and the formation of contemporary living spaces.