Development of a Smart Internet of Things Based Motorcycle Theft Prevention System

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

The Internet of Things (IoT) and Cloud Computing both are developing technologies. Cloud Computing blows up to provide support to IoT by working as a sort of front-end and it is based on the concept of permitting users to do computing tasks using services delivered with internet. The cloud computing empower an appropriate, on-demand, and scalable network access to a shared pool of configurable computing resources. The cloud-based IoT architecture includes features of cloud-based IoT platform and its interaction with three main cloud computing models: IaaS (infrastructure as a service), Paas (platform as a service), and SaaS (software as a service). The cloud and IoT integration empowers new scenarios, for smart services and applications, as Sensing as a Service (SaaS), DataBase as a Service (DBaaS), Video Surveillance as a Service (VSaaS), and many more. Various live company products, research projects, and projects with freely available source code in various areas of Cloud Computing and IoT integration are Nimbits, ThingSpeak, Paraimpu, Device Cloud, Sensor Cloud. REpresentational State Transfer (REST) architectural style web services and Constrained Application Protocol (COAP), Message Queue Telemetry Transport (MQTT), web transfer protocols are used for communication for the IoT resource-constrained things. Networking protocols like IPv6 over Low power Wireless Personal Area Network (6LoWPAN) and IPv6 over Bluetooth Low Energy are used for constrained networks in IoT and cloud integration. The data link layer protocols for IoT devices like IEEE 802.15.4, IEEE 802.11ah, Z-Wave, WirelessHART, Bluetooth, Zigbee are used for short range communication for IoT things. The applications of integrated cloud and IoT include agriculture, video surveillance, healthcare, smart city, smart home and smart metering, etc. IoT and cloud integration involves several challenges and issues as standardization of machine to machine (M2M) communication and interoperability, power and energy efficiency of devices for data transmission and processing, big data generated by several devices, security and privacy, integration methodology, pricing and billing, network communications, storage, etc. In this chapter, the introduction of cloud and IoT, their integration architecture, integration applications, and challenges and issues involved are discussed.

This study explores the role of smart materials, sustainable engineering practices, and Internet of Things (IoT) integration in advancing modern engineering solutions. The primary objective is to qualitatively analyze the literature on how these innovative components contribute to the development of efficient, sustainable, and intelligent engineering systems. The research employs a qualitative literature review methodology, examining a broad range of academic articles, technical reports, and case studies related to smart materials, sustainable engineering, and IoT applications. The literature review methodology involves systematically collecting and analyzing relevant scholarly sources to identify key trends and insights. The study categorizes the literature into major themes, such as the properties and applications of smart materials, the principles and benefits of sustainable engineering, and the impact of IoT integration on engineering processes. By synthesizing findings from diverse sources, the research provides a comprehensive overview of the advancements and challenges associated with these technologies. The findings reveal that smart materials, such as shape-memory alloys, piezoelectric materials, and self-healing composites, offer significant potential for enhancing the functionality and durability of engineering systems. Sustainable engineering practices, including the use of renewable materials and energy-efficient designs, are critical for reducing environmental impact and promoting long-term sustainability. IoT integration enables real-time monitoring, data-driven decision-making, and improved system efficiency, thus transforming traditional engineering approaches.

Since its inception, Blockchain has proven itself as an emerging technology that revolutionizes diverse industries. Among others, Internet of Things (IoT) is one of the application domains that reaps large benefits from Blockchain. The Blockchain’s potential to overcome different challenges of IoT services has shifted the research interests of many scientists towards addressing the integration of two disruptive technologies, i.e., IoT and Blockchain. This resulted in publishing more research papers in this emerging field. Thus, there is a need to conduct research studies through which a broad overview of research contributions in this field could be investigated. To respond to this need, a number of review papers have been published recently, each of which has considered the integration of IoT and Blockchain from a different perspective. Nonetheless, none of them has reported a bibliometric analysis of the state-of-the-art in the integration of IoT and Blockchain. This gap stimulated us to investigate a thorough analysis of the current body of knowledge in this field, through a bibliometric study. In this paper, we conducted a bibliometric analysis on the Scopus database to assess all scientific papers that addressed the integration of IoT and Blockchain. We have analyzed those collected papers against four criteria including annual publication and citation patterns, most-cited papers, most frequently used keywords, and most popular publication venues. The results disseminate invaluable insights to the researchers before establishing a research project on IoT and Blockchain integration.

The integration of the Internet of Things (IoT) with Machine Learning (ML) is a transformative advancement that is revolutionizing the way data-driven decision-making occurs across various industries. IoT systems comprise interconnected devices that collect and transmit vast amounts of real-time data from sensors, machines, and appliances. However, merely collecting data is not sufficient; the real value lies in the analysis and interpretation of this data to generate actionable insights. This is where ML comes into play. ML techniques allow systems to learn from the data generated by IoT devices, enabling predictive analysis, automation, and enhanced decision-making processes. This integration of IoT and ML is paving the way for smarter, more efficient systems that can be applied in a wide array of fields such as healthcare, manufacturing, transportation, home automation, and smart cities. For instance, in healthcare, wearable IoT devices track vital health statistics like heart rate and blood pressure, while ML algorithms process these data in real-time to detect anomalies, predict potential health risks, and provide healthcare professionals with alerts for timely interventions. Similarly, in manufacturing, IoT devices collect sensor data from machines, which is analyzed by ML algorithms to predict maintenance needs, preventing costly breakdowns and improving operational efficiency. The sheer scale and complexity of data produced by IoT devices pose significant challenges for traditional data processing methods. ML algorithms are essential for managing and extracting value from this data, as they can handle large datasets, identify patterns, and make predictions in a scalable manner. By utilizing ML models such as deep learning, reinforcement learning, and clustering techniques, IoT systems are capable of adapting to changing environments, learning from their surroundings, and making intelligent decisions without human intervention. This paper will review the various ways ML can be leveraged within IoT systems to provide scalable, intelligent decision-making processes for analyzing the vast amounts of data produced by IoT devices. It will examine key use cases across different sectors where the integration of ML and IoT has shown significant promise. Specific case studies will be highlighted, including healthcare, where ML models enhance the monitoring and prediction of patient health; industrial IoT (IIoT), where predictive maintenance and anomaly detection improve operational efficiency; and smart cities, where ML-optimized IoT systems are used to manage traffic flow, energy consumption, and public services. By exploring these case studies, this paper aims to demonstrate the immense potential of integrating IoT with ML. It will also examine the challenges that arise in implementing such systems, including issues of scalability, data privacy, and security, and discuss potential solutions to these challenges. The paper will conclude with insights into the future of IoT-ML integration and how these technologies can continue to evolve to create even more intelligent, autonomous, and efficient systems across a broad range of industries.

The construction industry is one of the most hazardous and resource-intensive sectors, often facing challenges related to worker safety, operational inefficiencies, and project delays. The emergence of the Internet of Things (IoT) offers innovative solutions to address these issues by leveraging interconnected sensors, real-time monitoring, and data analytics. This study investigates the role of IoT sensors in enhancing construction site safety and efficiency. The research examines how IoT-enabled technologies, such as wearable sensors, environmental monitors, and asset-tracking devices, contribute to hazard detection, accident prevention, and resource optimization. A mixedmethod approach is adopted, combining case studies, interviews with industry professionals, and data analysis to evaluate the implementation and outcomes of IoT solutions on construction sites. Findings reveal that IoT sensors significantly improve safety by enabling real-time risk identification, worker tracking, and environmental monitoring. Additionally, IoT enhances operational efficiency through predictive maintenance, equipment tracking, and automated data-driven decision-making, resulting in reduced downtime and improved resource allocation. Despite the proven benefits, challenges such as implementation costs, technical integration, and user adoption remain significant barriers. The study concludes that IoT sensors play a pivotal role in modernizing the construction industry, offering substantial improvements in safety and efficiency. Recommendations for integrating IoT technologies into construction workflows are provided, alongside suggestions for future research to explore cost-benefit analyses and the integration of IoT with complementary technologies like Artificial Intelligence (AI) and Building Information Modeling (BIM).

Clinics around the globe are adopting newer, highly advanced technologies. The Internet of Things (IoT) has emerged due to advancements in information and communication technology. Integration of the IoT with accessible medical devices, and patients and their surroundings has opened up new possibilities for data collecting and more intelligent decisions in recent years. IoT technologies benefit healthcare professionals and patients in today’s medical context because they are used in several treatment domains (like real-time monitoring, patient information and healthcare management). Diverse and distributed devices will gather, analyse, and transmit real-time medical data to open clouds in the upcoming world of IoT for healthcare, allowing for the accumulation, piling, and analysis of elementary data streams in many new procedures and the activation of context-dependent intimations. Healthmonitoring and remote treatment are possible with an IoT paradigm that uses sensors (environmental, wearable, implanted) distributed throughout the local environment.It has been critical to create new technology for storing large amounts of clinical data in recent years. Cloud storage has become enormous, and it is managed by merging IoT and Edge Computing (EC) technologies. According to recent research, the integration of IoT and the cloud has a considerable impact on remote healthcare. This survey report focuses on howIoT devices with EC can enhance people’s health while guaranteeing that medicines and medicine delivery are error-free. This research will act as a source and reference of knowledge regarding smart healthcare for healthcare professionals, researchers, and others interested in learning more about IoT with EC-based healthcare applications.

Healthcare delivery is anticipated to undergo a revolution through the infusion of Internet of Things (IoT) technology into pharmacy information systems through optimized medication management, better patient safety, and increased efficiency in operations. The chapter looks at the combined impact of IoT, pharmacy information systems, and healthcare integration. Some IoT devices can help improve its operational aspects, such as real-time monitoring and data collection for pharmacies. For instance, if IoT is incorporated into their system, they will have an advantage in terms of streamlining workflows and reducing medication errors, thus ensuring adherence to regulatory standards. The introduction of IoT into pharmacy information systems poses challenges such as connectivity problems, the integrity of data, and the development of powerful analytics platforms. To address these challenges, companies should adopt industry practices and use encryption methods and techniques for advanced analysis that provides high-level recommendations based on IoT device data. Nevertheless, the benefits resulting from integration are considerable in pharmacy. Better compliance with drug-taking schedules, better inventory management, and live follow-up on patient results are some of them. The future of pharmacy information systems’ IoT integration is very promising as it could be further optimized through the use of AI and machine learning algorithms. Besides, with the advent of telepharmacy services using IoT technology, there is an enormous chance to broaden access to healthcare. In conclusion, IoT-based integration with pharmacy information systems will be a game changer in the delivery of healthcare. Despite remaining challenges, patient care outcomes and system efficiency gains are immense, thus requiring more investigation and commitment on this novel technology confluence.

The increasing demand for clean and reliable water resources, coupled with the growing threat of water pollution, has made real-time water quality (WQ) monitoring and assessment a critical priority in many urban areas. Urban environments encounter substantial challenges in maintaining WQ, driven by factors such as rapid population growth, industrial expansion, and the impacts of climate change. Effective real-time WQ monitoring is essential for safeguarding public health, promoting environmental sustainability, and ensuring adherence to regulatory standards. The rapid advancement of Internet of Things (IoT) sensor technologies and smartphone applications presents an opportunity to develop integrated platforms for real-time WQ assessment. Advances in the IoT provide a transformative solution for WQ monitoring, revolutionizing the way we assess and manage our water resources. Moreover, recent developments in Location-Based Services (LBSs) and Global Navigation Satellite Systems (GNSSs) have significantly enhanced the accessibility and accuracy of location information. With the proliferation of GNSS services, such as GPS, GLONASS, Galileo, and BeiDou, users now have access to a diverse range of location data that are more precise and reliable than ever before. These advancements have made it easier to integrate location information into various applications, from urban planning and disaster management to environmental monitoring and transportation. The availability of multi-GNSS support allows for improved satellite coverage and reduces the potential for signal loss in urban environments or densely built environments. To harness this potential and to enable the seamless integration of the IoT and LBSs for sustainable WQ monitoring, a systematic literature review was conducted to determine past trends and future opportunities. This research aimed to review the limitations of traditional monitoring systems while fostering an understanding of the positioning capabilities of LBSs in environmental monitoring for sustainable urban development. The review highlights both the advancements and challenges in using the IoT and LBSs for real-time WQ monitoring, offering critical insights into the current state of the technology and its potential for future development. There is a pressing need for an integrated, real-time WQ monitoring system that is cost-effective and accessible. Such a system should leverage IoT sensor networks and LBSs to provide continuous monitoring, immediate feedback, and spatially dynamic insights, empowering stakeholders to address WQ issues collaboratively and efficiently.

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities’ conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities’ real time monitoring with fallouts on the maintenance timeliness.

The main objective of this study is to examine and highlight the substantial impact of integrating Internet of Things (IoT) technology and biosensors in the healthcare sector, focusing on their potential to drive substantial advancements and improvements in healthcare. Emphasis is placed on tackling the global challenge posed by chronic diseases by proposing an all-encompassing healthcare system that facilitates real-time monitoring, early detection, and remote management of these conditions. Chronic diseases, distinguished by their prolonged duration and gradual progression, have emerged as a marked challenge for healthcare systems worldwide. This paper seeks to illustrate how biosensors, with the capability to identify specific biomarkers, can play a pivotal role in delivering personalized patient care, enhancing outcomes, and mitigating healthcare expenses. This review was conducted using a systematic and comprehensive approach to analyze the integration of Internet of Things (IoT) technology with biosensors for real-time monitoring and early detection of chronic diseases. Relevant literature was sourced from reputable databases, including IEEE Xplore, PubMed, and Elsevier's ScienceDirect, focusing on studies published between 2014 and 2024. Keywords such as "IoT in healthcare," "biosensors for chronic diseases," and "real-time monitoring systems" guided the selection process. This review included original research articles, review papers, and case studies, which were critically analyzed to assess current advancements, challenges, and future directions in this interdisciplinary field. The findings were synthesized to provide an in-depth understanding of how IoT-enabled biosensors are transforming healthcare, particularly in chronic disease management. This research explores the integration of IoT and biosensors for real-time monitoring of chronic diseases. The combination offers personalized healthcare, early detection, and cost reduction. Applications include remote patient monitoring, cardiac health, glucose management, and elderly care. Despite challenges, ongoing advancements promise to optimize accuracy, efficiency, and ethical soundness, ushering in a patient-centric healthcare era. The integration of IoT-enabled biosensors approach to addressing global challenges posed by chronic diseases. This study highlights the potential of this convergence in healthcare by facilitating real-time monitoring, early detection, and personalized care. By surpassing limitations of traditional monitoring systems, IoT-enabled biosensors provide continuous insights into patients' health, enabling proactive interventions. Their applications are demonstrated in diverse domains, including remote monitoring, cardiac health, glucose management, and elderly care, showcasing their role in advancing precision medicine and improving patient outcomes. Despite technical hurdles, ongoing advancements in miniaturization, edge computing, and AI-driven analytics aim to enhance accuracy, efficiency, and ethical practices, paving the way for a proactive and patient-centric healthcare era.

The demand for renewable energy, particularly solar energy, has surged as concerns about climate change and environmental sustainability intensify. Solar photovoltaic (PV) systems play a central role in renewable energy generation, but their efficiency is influenced by dynamic environmental factors such as sunlight intensity and temperature. Maximum Power Point Tracking (MPPT) techniques are used to optimize power extraction under varying conditions. With the advent of the Internet of Things (IoT), the operation and monitoring of solar PV systems can be significantly enhanced. This paper explores the integration of IoT with MPPT techniques, emphasizing how IoT technologies, including real-time monitoring, predictive maintenance, and data analytics, can optimize solar PV system performance. The synergy between MPPT and IoT enhances the adaptability and efficiency of solar systems, offering potential solutions for real-time optimization and remote diagnostics. Despite challenges such as data security, cost, and connectivity, the integration of IoT with MPPT presents a promising pathway for optimizing solar power generation. Keywords: Solar PV, MPPT, IoT, renewable energy, optimization, real-time monitoring, energy management

The research here investigates how Internet of Things (IoT) technologies can be integrated into responsive architecture in order to create sustainable and energy-efficient smart buildings. The main issue being solved is that there is no clearly defined framework for integrating IoT in responsive architectural design that makes it challenging to attain real-time adaptability, reduce energy consumption, and enhance the user experience. The research aims to evaluate existing IoT implementations in architecture by analyzing real-world case studies and developing a conceptual framework that articulates IoT's impact on building sustainability and efficiency. In an attempt to realize this goal, the research explores the following hypotheses: IoT integration greatly lowers a building's energy use. IoT-enabled buildings are more flexible and more satisfactory to users than conventional architectural buildings. An IoT-responsive architecture framework that is well designed can be employed as a scalable model for future smart city infrastructure. Through IoT-enabled building case studies, it has been found that IoT deployment results in a 30% reduction in energy consumption and substantial improvement in occupant comfort and operational efficiency. Keeping these findings in perspective, the research proposes a holistic framework with specific guidance to architects and urban planners to design more adaptive, efficient, and user-friendly buildings.

Background: The healthcare sector is experiencing a transformative shift due to advancements in technology, particularly with the Internet of Things (IoT). IoT integration in healthcare is poised to revolutionize patient monitoring and management, particularly for individuals with chronic conditions. The Grand View Research Inc. analysis projected a significant increase in IoT penetration in healthcare, with a market value of approximately $409.9 billion by 2022. Aim: This article aims to explore the applications, benefits, and future potential of IoT devices in real-time health monitoring for patients with chronic conditions. Methods: The review encompasses various IoT-based health monitoring systems, including wearable and implantable devices, biosensors, and remote patient monitoring systems. The methodologies of existing IoT applications, such as the UbiMon project and various ZigBee-based systems, are analyzed to understand their impact on patient care. Results: IoT technologies facilitate real-time monitoring of vital signs, improve chronic disease management, and enhance emergency response systems. Examples include smart inhalers, ECG monitors, and remote surgery systems. The integration of IoT in healthcare has led to improved patient outcomes, reduced emergency waiting times, and better resource management in hospitals. Conclusion: IoT is transforming healthcare by enabling continuous, real-time monitoring of chronic conditions and enhancing overall patient care.