Intelligent Life Guard: An IoT-Enabled Smart Helmet for Vital Monitoring & Fall Detection

With the rapid advancements in mobile devices and wireless network technologies, the Internet of Things (IoT) has become more powerful and popular than ever. The aim of IoT is to efficiently control various types of objects through wireless communications. This paper aims to design an IoT-based smart lighting system that reduces development costs and saves power consumption. Unlike public open spaces, the focus of this paper is on ship cabin spaces. As ship cabins have unique properties, such as requiring gas-based power generation and preferring a wireless environment, designing a smart cabin lighting system is crucial and has significant commercial value. The smart cabin lighting system is designed with four features. Firstly, it can automatically control the lighting devices around people using position-sensitive devices. Secondly, it enables setting on/off and adjusting the luminance for lighting devices through Touch Keypads. Thirdly, the system can be controlled using an app to turn on/off and adjust the luminance of lighting devices. Lastly, the lighting devices equipped with sensors collect specific data on cloud servers for analysis. The underlying communication protocol used to interconnect the smart lighting devices, sensors, and Touch Keypads is Zigbee. The smart cabin lighting system can be applied to marine lighting, thus improving the commercial value of enterprises related to marine lighting.

Purpose: Agricultural workers, especially those who farm full-time, are at substantial risk for developing long-term health problems related to occupational dust, chemical, and vibratory noise exposures. There is clear evidence of risk factors for acute agricultural injury; however, literature regarding risk factors for long-term occupational health problems, including noise-induced hearing loss (NIHL), agricultural chemical exposure (ACE), and inhalation injury, is more limited. The literature poses conflicting information on agricultural workers and personal protective equipment (PPE); agricultural workers understand the importance of PPE use, yet the lack of PPE use is pervasive in agricultural workers across numerous studies. Education of agricultural workers on the role of PPE in chronic disease prevention has shown promise. Methods: Our research seeks to identify a replicable model of Physician Assistant (PA) student education on agricultural worker health and safety, including patient education and disease prevention. This model will equip future health-care providers with the knowledge and skills to provide patient education on farming-associated, preventable chronic diseases, as it is a documented tool for increasing PPE use. Didactic curriculum objectives include agriculturally related respiratory health issues, chemical exposure, and noise-induced hearing loss. Curricular content delivery includes traditional classroom learning as well as adjunctive small group and simulation activities. Upon completion of the didactic phase, students complete a 15-month clinical learning phase, including a 12-week primary care rotation in a rural setting, serving many agricultural workers throughout the Midwest. Student logs of patient encounters track diagnoses related to agricultural exposure, as well as monitor agricultural patient treatment modalities, including patient education on PPE use. Distribution of patient education and PPE materials occurs through clinical site visits to primary care rotation sites. Practical Application: Our educational model intends to improve health outcomes of patients in the rural Midwest. Our ongoing research will collect data on agriculturally related diagnoses in the Midwest and patient-provider communication, ideally enhancing patient awareness of risk factors and compliance with treatment plans, as well as decreasing chronic disease severity. The measured effects of this educational intervention will serve as a basis for future research and health-care provider education.

Preventive healthcare is really important these days in big cities where people have a lot of health problems because of the way they live. A lot of health issues happen because peoaple do not see the early warning signs they do not go to the doctor when they should or they do not keep track of their health regularly. So healthcare systems usually focus on treating people when they're already very sick. To fix this problem this project is making a health monitoring system that uses Internet of Things technology to keep track of a persons health all the time and remind them to take care of themselves. This system uses Internet of Things sensors to collect health information like heart rate how much a person moves and the air quality around them. This information is then sent to a computer. Analyzed to see how a persons health is changing over time. By looking at this information in a way and using intelligent methods the system can find unusual changes in a persons health and send them a message or give them some advice. This helps people take care of themselves before they get really sick which reduces the chance of having health problems. The system is made to be simple and not too expensive so it is easy to use and good for people who live in cities and also for people who do not have a lot of access to healthcare. Keeping track of health all the time helps people know more about their health and take care of themselves. It also helps doctors make better decisions. The preventive healthcare system is a thing because it helps people stay healthy and it shows how Internet of Things solutions can make it easier for people to take care of their health. This project is also related to Sustainable Development Goal 3 which is about having good health and well-being by promoting preventive healthcare and using Internet of Things technology to make health monitoring better. Keywords: Good Health and Well-Being (SDG 3), Health Monitoring System, Internet of Things (IoT), Preventive Healthcare, Real-Time Data Analysis, Smart Healthcare Systems.

<p style="text-align: justify;">The Internet of Things (IoT) is a key technology in the development of smart cities, enabling the connection of different devices and systems for more efficient management of urban resources. By integrating IoT solutions into the everyday infrastructure of cities, it is possible to improve the quality of life of citizens, optimize resource consumption and ensure sustainable development. One of the significant examples of IoT applications is smart lighting, where street lighting automatically adjusts according to the presence of people or vehicles, thereby achieving significant energy savings and increasing safety. Traffic management using sensors and smart traffic lights allows for reduced congestion, better management of vehicle flow and faster response in case of accidents. Also, air quality monitoring through networked sensors allows for continuous monitoring of pollutants in real time, which provides city authorities with the data needed for timely environmental measures. By using IoT technologies, smart cities become more flexible, adaptable and efficient, and data collected in real time enables informed decisions that improve urban life.</p>

With the pervasive deployment of the Internet of Things (IoT) technology, the number of connected IoT end devices increases in an explosive trend, which continuously generates a massive amount of data. Real-time analytics of the IoT data can timely provide useful information for decision-making in the IoT systems, which can enhance both system efficiency and reliability. More specifically, real-time data analytics in IoT systems is utilized to effectively process the discrete IoT data series within a bounded completion time and provide services such as data classification, pattern analysis, and tendency prediction. However, the continuous generation of IoT data from heterogeneous devices brings huge technical challenges to real-time analytics. Thus, how to timely process the massive and heterogeneous IoT data needs to be seriously considered in the design of IoT systems. This chapter provides a comprehensive study of real-time data analytics in IoT systems. The characteristics of real-time analytics in IoT systems are firstly elucidated. Suitable architectures of IoT systems that can support real-time data analytics are thoroughly analyzed. Afterward, a comprehensive survey on the existing applications of real-time analytics in IoT systems is conducted from the perspectives of system design and shortcomings of performance. Finally, the main challenges remaining in the application of real-time analytics in IoT systems are pointed out, and the future research directions of related areas are also identified.KeywordsReal-time analyticsBig data analyticsEdge computingCloud computingInternet of Things (IoT)

The aim of this thesis to present a novel framework that enables intelligent compromise between competing requirements for performance, protection and comfort in cricket batting personal protective equipment (PPE). The proposed framework uses information collected from the Internet of Things (IoT) technologies in cricket and demonstrates its future potential in improving player safety. The literature presented in this thesis shows that the current commercially available cricket batting PPE are suboptimal in addressing competing demands for batter performance, protection and comfort. Therefore, a novel smart PPE framework with two critical subsystems: an active decision-making subsystem and a state-change enabled protective response system are suggested in this study to enable intelligent protection. The thesis outlines and evaluates a potential active decision-making system based on data harvested from body-worn Inertial Measuring Units (IMU). [Continues.]

Personal protective equipment is a tool used to protect part or all of the body from existing risks. Work related to pesticides can be said to be one of the jobs that has a high level of risk of danger. Personal protective equipment for farmers related to the use of pesticides must be considered because it is one of the preventive and control measures for the risks that farmers will receive. Farmers who do not use personal protective equipment will experience a decline in their health status and work productivity. Farmers who spray pesticides are at risk of experiencing various health problems, Acute Poisoning Symptoms such as dizziness, nausea, vomiting and headaches. Farmers who spray pesticides are at high risk of experiencing various health problems due to exposure to dangerous chemicals. Symptoms such as skin and eye irritation, as well as respiratory problems, Chronic effects Diseases that develop over time, such as endocrine disorders, organ damage, cancer, and reproductive problems. Long-term health problems such as nervous system disorders, respiratory problems, reproductive disorders, and increased risk of cancer. Eye Irritation Direct contact with pesticides can cause skin and eye irritation. The personal protective equipment that can be used includes gloves, masks, safety glasses, long-sleeved clothes and shoes. The method used in writing this journal uses a literature review approach. The sources used in this literature review come from journals obtained from Google Schoolar.

In the last decade, there has been tremendous development in Information and Communication Technology which revolutionized the different aspects of human life, especially healthcare. With the rapid growth of the Internet-of-Things ecosystem, which conglomerates physical objects, computing devices, hardware as well as software, it is now possible to present a seamless platform that connects human beings with digitally enhanced objects or “things”. They can interact, collect, exchange, and communicate among themselves to enrich the experience from a computer-based centralized approach to an application-based distributed environment. This technological advancement with Internet-of-Things (IoT) has promised great potential in health monitoring and propagating information with accuracy in time with other benefits like providing appropriate medical support to a patient, especially in remote places, enhancing the quality of living as well as saving lives during medical emergencies. IoT ecosystem renders time-saving services through technologies, provides expert diagnosis to critical ailments remotely, increases the timeliness of treatments, and also eases financial pressure on a patient. The IoT-based healthcare system also benefits the medical experts to cover more cases with less staff which improves their coordination with patients with more administrative efficiency The system is also beneficial for preventive health hazards, proactive health monitoring, follow-up, and management of patients suffering from chronic diseases. However, the IoT healthcare system is not devoid of challenges and they need to be addressed for making the ecosystem more stable and ubiquitous. This chapter provides a precise overview of several benefits of IoT and its potential applications in healthcare along with the main concerns/challenges faced by stakeholders of the ecosystem to implement the same seamlessly. The chapter also focuses on the importance of technologies like cloud computing and edge computing to empower IoT in healthcare to provide a quality life to the patient and ensure a healthy life throughout the patient’s lifetime.KeywordsIoTBenefitsApplicationsIoT challengesIoT security

Proactive monitoring of one's health could avoid serious diseases as well as better maintain the individual's well-being. In today's Internet of Things (IoT) world, there has been numerous wearable technological devices to monitor/measure different health attributes. With the increasing number of attributes and wearables, it becomes unclear to individuals which ones they should be using. The aim of this paper is to provide a novel recommendation engine for personalized advised wearables and IoT solutions for any given individual. The way the engine works is through first identifying the diseases that this person is at risk of, given his/her attributes and medical history. This is done via analyzing the individual's unstructured medical history using text mining, adding it to his/her structured demographic attributes, and then feeding this data to a machine learning classification model that predicts eventual diseases. Then, we map these diseases to the attributes that need to be measured in order to monitor them. Lastly, we use a mathematical optimization model that we developed to recommend the optimal wearable devices and IoT solutions for the individual. Thus, our solution enables proactive health monitoring and can thus provide a significant human benefit.

The amount of data in the aerospace industry is being generated and collected at unprecedented levels. This is primarily due to the increasingly connected nature of aerospace systems and devices, which could be classified under the generic term of the Internet of Things (IoT). The nature of data being generated and collected is also changing, primarily in the attributes of amount, format, and sources of data. Hence, new approaches, techniques, and corresponding designs need to be developed to store, analyze and derive insight from these data sets or also called “Big Data.” The traditional approach for Analytics requires the movement of the collected dataset to a core location on the network like a data center or cloud. However, many modern and emerging applications and aerospace use cases require Network Edge Analytics capability, where analytics and insights need to be performed at the edge of the network without moving the entire dataset to the core location. Thus, a hybrid model of Big Data Analytics with Network Core and Edge Analytics needs to be reviewed and considered. This paper will discuss the Analytics capabilities that can be leveraged both at the Network Edge and Network Core, and will also review all the above considerations from analytics techniques, technologies and use cases point of view. In addition, the paper will discuss various technologies and their implementation both at the core and edge, including software and infrastructure elements. Lastly, this paper will focus on analytics technologies and approaches for structured, unstructured and semi-structured data, including, review of real time and batch analytics techniques in a distributed data environment. Consequently, an approach is required to integrate these disjointed analytics and data sources techniques in a hybrid model of network core and edge analytics. Select techniques and technologies like Hadoop, NoSql, and real time analytics will be discussed as examples. Finally, the paper will review Analytics use cases, including Aerospace industry use cases, where such a hybrid model of core and edge analytics can be leveraged for practical implementations.

The COVID-19+ pandemic has brought into keen focus the necessity to utilize and enforce our digital infrastructure for remote patient monitoring based on IoT (Internet of Things) technology since quarantines and isolations are playing a vital role in containing its spread. As of date, many viral tests and vaccines are in use while few drugs are in experimental stages, but there is always need for possibilities for increasing reliability of disease detection and monitoring at both levels of individual and society, and such aim can be supported by wearable biomedical sensors devices. Previously, wearable devices have been used to monitor physiological parameters during daily human living activities. Still, the investment of such technologies toward predicting infection by COVID-19+ remains essential to alert potential patients and start sequence of health systems intervention. It was found that wearable devices increased patients’ compliance to healthcare advice. Thus, in this perspective review, we have proposed an IoT based system to monitor the quarantine / isolation subjects during COVID-19+ and similar pandemic and quarantine observation. This wearable prototype, associated with the bundled mobile app, act to reports and tracks/monitoring the quarantined individuals. IoT based quarantine/isolation monitoring system is contact-free that could benefit especially healthcare professionals to lower the risk of exposure to infective pathogens. Current manuscript describes clinically relevant physiological human parameters that can be measured by wearable biomedical sensors and monitored based on IoT technology and their role in health tracking, stability, and recovery of COVID-19++ve individuals and front-line health workers. This paper aimed at initiation of an approach among front-line healthcare workers as well as biomedical engineers for developing digital healthcare platforms of monitoring and managing such pandemic.

Internet of Things (IoT) devices are prevalent in all aspects of our lives, e.g., thermostat and smart lights. Nowadays, IoT devices are controlled by various end-user applications. There is a lack of a standard interface that allows the communication among various IoT devices. In this context, the functionalities of IoT devices may be published as IoT services. IoT services are RESTful services that connect to IoT devices. The uniform interface of IoT services allows them to be integrated with existing applications. We propose an approach that automatically transforms functionalities of IoT devices to IoT services hosted on the cloud. Our approach identifies the code methods from IoT applications that have to be transformed and also extracts service specifications (e.g., input/output parameters) from these methods. Our case study result shows that our approach obtains a precision and a recall above 70%. The identified methods and service specifications are converted to IoT services. Our approach generates IoT services with an accuracy of 96%.

This research investigates the impact of IoT (Internet of Things) technology, real-time analytics, and digital asset management on energy efficiency and productivity in the Indonesian manufacturing industry. A quantitative approach, employing structural equation modeling (SEM) with Partial Least Squares (PLS) 3 software, is utilized to analyze survey data collected from manufacturing firms across different sectors and sizes in Indonesia. The results reveal significant positive relationships between digital technologies (IoT, real-time analytics, digital asset management) and energy efficiency/productivity. Specifically, Digital Asset Management, IoT Technology, and Real-Time Analytics are found to have statistically significant effects on enhancing energy efficiency and productivity levels in Indonesian manufacturing operations. These findings underscore the transformative potential of digital technologies in driving operational performance and sustainability in the manufacturing sector. Policymakers, industry stakeholders, and practitioners can leverage these insights to inform strategic decision-making and investments in digital infrastructure, workforce development, and regulatory frameworks to foster innovation and competitiveness in the Indonesian manufacturing landscape.

Abstract: This book presents a comprehensive exploration of the convergence between Artificial Intelligence (AI) and Edge Computing as a transformative framework for optimizing Internet of Things (IoT) systems. It addresses the core challenge of deploying intelligent decision-making capabilities in latency-sensitive, bandwidth-constrained, and privacy-critical environments where traditional cloud-centric architectures prove insufficient. Through the integration of lightweight AI models, distributed inference, and federated learning, the book develops a conceptual and technical foundation for enabling scalable, real-time analytics at the edge. The methodology combines theoretical modeling with empirical case studies and performance benchmarking across diverse IoT applications, including smart cities, industrial automation, autonomous systems, and healthcare monitoring. Key AI techniques—such as reinforcement learning, spatiotemporal modeling, and knowledge distillation—are evaluated in the context of resource-constrained edge hardware. The book further explores secure and ethical deployment through privacy-preserving learning, encrypted model updates, and explainable AI frameworks aligned with international regulatory standards. Findings highlight significant improvements in system responsiveness, energy efficiency, and data sovereignty when AI is embedded at the edge. The book concludes with an assessment of emerging innovations—neuromorphic computing, quantum edge AI, and 6G-enabled edge-satellite intelligence—and proposes a cross-sectoral roadmap for future research, policy, and system design. This volume offers foundational insights for scholars, engineers, and policymakers navigating the evolution of intelligent, decentralized IoT infrastructures. Keywords Artificial Intelligence, Edge Computing, Internet of Things, IoT Optimization, Federated Learning, Real-Time Analytics, Edge AI, Reinforcement Learning, Spatiotemporal Modeling, Lightweight Neural Networks, Distributed Inference, Privacy-Preserving AI, Explainable AI, Smart Systems, Low-Latency Processing, Secure Edge Intelligence, 6G Networks, Neuromorphic Computing, Quantum AI, Autonomous IoT Systems

Smart lighting is an underlying concept that links three main aspects: solid-state lighting (SSL) technologies, advanced control and universal communication interfaces following global standards. However, this conceptualization is constantly evolving to comply with the guidelines of the next generation of devices that work in the Internet of Things (IoT) ecosystem. Modern smart lighting systems are based on light emitting diode (LED) technology and involve advanced drivers that have features such as dynamic spectral light reproduction and advanced sensing capabilities. The ultimate feature is of additional advanced services serving as the hub for optical communications that allows coexistence with traditional Wi-Fi gateways in indoor environments. In this context, lighting systems are evolving to support different wireless communications interfaces compatible with the IoT ecosystem. Market tendencies of SSL systems predict the accelerated expansion of connected IoT lighting control systems in different markets from smart homes and industrial environments. These systems offer advanced features never seen before such as advanced spectral control of the light source and also, the inclusion of several communication interfaces. These are mainly wired, radiofrequencies (RF) and optical wireless communications (OWC) interfaces for advanced services such as sensing and visible light communications (VLC). In this paper we present how to design and realize IoT-based smart lighting systems for different applications using different IoT-centric lighting architectures. Finally, different standards and aspects related to interoperability and web services are explained taking into account commercial smart lighting platforms.