Personalized Health Tracking with Edge Computing Technologies

Investigation on big data evaluation and visualization of internet of things based on edge computing

Interacting with the Web of Things

Producción Científica

This research aims to investigate the possibility and viability of open-source Internet of Things (IoT) and edge-compatible equipment in the field of health monitoring. The focus is on exploring various IoT health monitoring environments used in e-health applications, taking into account crucial aspects such as sensor integration, data collection methods, communication protocols, security measures, scalability, and regulatory requirements. The research begins by examining existing IoT health monitoring environments to gain a comprehensive understanding of their strengths and limitations. This analysis helps identify the gaps and challenges that open-source IoT and edge devices can address in the context of health monitoring. Building upon this groundwork, a novel IoT-edge-powered deep learning system will be developed specifically for a targeted health monitoring environment. The system will leverage the capabilities of IoT devices, integrate various sensors to capture relevant health data, harness edge computing techniques to process data locally, and utilize deep learning algorithms for advanced analysis and inference. Special emphasis will be placed on optimizing data preprocessing, feature extraction, model training, and overall system performance. To assess the effectiveness of the proposed IoT-edge-deep learning environment, a comparison with currently implemented solutions will be done. Key indicators such as precision, accuracy, and efficiency will be the focus of the assessment, aiming to highlight the advantages and improvements offered by the developed system. The results of this research project are anticipated to make major contributions to the implementation of IoT and edge computing in health monitoring. By exploring the feasibility of open-source devices, the research will demonstrate their potential for democratizing access to health monitoring technology. Additionally, the exploration of e-health monitoring environments will provide valuable insights into best practices, challenges, and regulatory considerations. Finally, the introduction of an innovative deep learning system will enhance health monitoring capabilities, enabling more accurate and timely detection of health-related conditions. This research holds promise for advancing the field of health monitoring by combining IoT, edge computing, and deep learning, ultimately raising the standard of healthcare services and patient outcomes.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Internet of Things (IoT) is being applied to areas as smart-cities, home environment, agriculture, industry, etc. Developing, deploying and testing IoT projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software. New projects require high investments on devices, fog nodes, cloud nodes, analytic nodes, hardware and software before each system can be developed. In addition, the systems should be developed to test them, which implies time, effort and development costs. However, in order to decrease the cost associated to develop and test the system the IoT system can be simulated. Thus, simulating environments help to model the system, reasoning about it, and take advantage of the knowledge obtained to optimize it. Designing IoT simulation environments has been tackled focusing on low level aspects such as networks, motes and so on more than focusing on the high level concepts related to IoT environments. Additionally, the simulation users require high IoT knowledge and usually programming capabilities in order to implement the IoT environment simulation. The concepts to manage in an IoT simulation includes the common layers of an IoT environment including Edge, Fog and Cloud computing and heterogeneous technology. Model-driven development is an emerging software engineering area which aims to develop the software systems from domain models which capture at high level the domain concepts and relationships, generating from them the software artefacts by using code-generators. In this paper, a model-driven development approach has been developed to define, generate code and deploy IoT systems simulation. This approach makes it possible to design complex IoT simulation environments and deploy them without writing code. To do this, a domain metamodel, a graphical concrete syntax and a model to text transformation have been developed. The IoT simulation environment generated from each model includes the sensors, actuators, fog nodes, cloud nodes and analytical characteristics, which are deployed as microservices and Docker containers and where elements are connected by using publish-subscribe communication protocol. Additionally, two case studies, focused on smart building and agriculture IoT environments, are presented to show the simulation expressiveness.

This paper is a survey about the uprising of the Internet of Things (IoT) technologies, their evolution, their variety of the application domains of the IoT systems, which includes, but not limits to healthcare domain, smart cities, smart objects, integration of smart sensors, smart home appliances control, etc. and all the technology revolution that this evolution implies and forces to happen. The IoT system's revolution and fast-phased evolution will create and/or cause positive effects in the whole society and technology, like in the case when the IoT domain is redesigning the applied modern healthcare systems, promising and later offering technological, economic, and social benefits. This paper also reviews the state-of-the-art network architectures/platforms employed in the IoT systems, presents different applications, and also the future trends. IoT security and privacy features, including security requirements will be also discussed and presented. Ambient intelligence, and wearables can be leveraged in the IoT healthcare context, sensors are also becoming ubiquitous and very important in everyday life and also generates data at an unprecedented, unseen rate and scale. This article focuses on the current/existing trends in the domain of IoT and on the current stage of development of research for hardware and software solutions, possible future communication capabilities between human beings and machines, and any new technologies that will employ IoT. Future researches on IoT-based systems are based on a set of open issues and challenges; also, there is the vital step from current "Big Data" to "Big Information" that can be improved and faster created with the help and implication of the current and future IoT systems/setups. With the proliferation of IoT, humans will be able to interact with machines/computers and also control a variety of networked electronic devices, including sensors and actuators. Also, the emergence and evolution of wearable Brain-Machine Interfaces (BMIs), employing directly Electroencephalographyc (EEG) signals, can be a key catalyser of this interaction and evolution of IoT. As smart home appliances, smart sensors, smart glasses and wearable EEG/EMG headsets and any other smart objects are appearing in our lives every day, all of them connected or being a member of the IoT systems, it is important to be able to foresee what is lying ahead of us, as a society in view of the fact that their number grows every day. As the gathered data is growing each day, the computation power from the available IoT devices and infrastructure are becoming more important and more utilized.

Edge computing has gradually availed itself as one of the most important technologies in the Internet of Things (IoT) to influence the processing, management, and utilization of data.A major advantage that is offered by edge computing by bringing computation and storage resources closer to IoT devices is that it minimizes latency as well as conserves bandwidth and offers greater capabilities for real time decision making.These changes from massive cloud processing to multiple neighboring edge nodes solve IoT architectural challenges common in earlier types of design, including those with low-latency responses and offline capabilities of applications such as smart cities, self-driving cars, and industrial control systems.The potential advantages of edge computing in IoT include privacy and security, because the collected information can be analyzed on the fly on the endpoint without sending it to the cloud; scalability, given that a great amount of IoT devices can be monitored by edge nodes without putting extra load on the centralized facilities.However, the use of edge computing in IoT also poses some challenges for example restricted resources at edge nodes, issue of the network infrastructure and more so the security that has to be enhanced.This abstract discusses the existing works done on architecture of edge computing IoT, the advantages and the limitation of integrating edge computing IoT system and indicates how it is capable of revolutionizing industries and enable a new IoT generation application system equipped with intelligence and decentralization.

In recent years, the Internet of Things (IoT) has constituted a driving force of modern technological advancement, and it has become increasingly common as its impacts are seen in a variety of application domains, including healthcare. IoT is characterized by the interconnectivity of smart sensors, objects, devices, data, and applications. With the unprecedented use of IoT in industrial, commercial and domestic, it becomes very imperative to harness the benefits and functionalities associated with the IoT technology in (re)assessing the provision and positioning of healthcare to ensure efficient and improved healthcare delivery. In this research, we are focusing on two important services in healthcare systems, which are cancer care services and business analytics/cloud services. These services incorporate the implementation of an IoT that provides solution and framework for analyzing health data gathered from IoT through various sensor networks and other smart devices in order to improve healthcare delivery and to help health care providers in their decision-making process for enhanced and efficient cancer treatment. In addition, we discuss the wireless sensor network (WSN), WSN routing and data transmission in the healthcare environment. Finally, some operational challenges and security issues with IoT-based healthcare system are discussed.

Recent years have witnessed the widespread popularity of Internet of things (IoT). By providing sufficient data for model training and inference, IoT has promoted the development of artificial intelligence (AI) to a great extent. Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the massive data generated by IoT and meeting corresponding practical needs. In response, a new computing model called edge computing (EC) has drawn extensive attention from both industry and academia. With the continuous deepening of the research on EC, however, scholars have found that traditional (non-AI) methods have their limitations in enhancing the performance of EC. Seeing the successful application of AI in various fields, EC researchers start to set their sights on AI, especially from a perspective of machine learning, a branch of AI that has gained increased popularity in the past decades. In this article, we first explain the formal definition of EC and the reasons why EC has become a favorable computing model. Then, we discuss the problems of interest in EC. We summarize the traditional solutions and hightlight their limitations. By explaining the research results of using AI to optimize EC and applying AI to other fields under the EC architecture, this article can serve as a guide to explore new research ideas in these two aspects while enjoying the mutually beneficial relationship between AI and EC.

Human beings quest for making comfortable life is due to their inquisitiveness about technical arena. Over the last few decades, mankind had experienced technical transformational journey with the inventions of new technology frontiers. These frontiers have interacted with human beings and performed every possible work in shorter period of time and with a much greater accuracy. With the advent of ‘Smart Concepts’, the world is now becoming more connected. Precisely termed as hyper-connected world. The smart concepts includes smart phones, smart devices, smart applications and smart cities. These smarter concepts forms an ecosystem of devices whose basic work is to connect various devices to send and receive data. Internet of Things is one the dominating technology that keeps eye on the connected smart devices. Internet of Things has bought applications from fiction to fact enabling fourth industrial revolution. It has laid an incredible impact on the technical, social, economic and on the lives of human and machines. Scientists claim that the potential benefit derived from this technology will sprout a foreseeable future where the smart objects sense, think and act. Internet of Things is the trending technology and embodies various concepts such as fog computing, edge computing, communication protocols, electronic devices, sensors, geo-location etc. The chapter presents the comprehensive information about the evolution of Internet of Things, its present developments to its futuristic applications.

Minimizing energy consumption in UAV assisted NOMA-MEC networks

The last ten years have seen a shift in the healthcare monitoring systems, making them one of the most important systems. Because of a lack of timely medical attention for individuals suffering from various illnesses, humans are struggling with the issue of untimely mortality. The main objective was to create a dependable IoT-based patient monitoring system that would enable healthcare providers to keep track of their patients, whether they are in a hospital or at home, in order to provide better treatment. Sensors, a data collecting unit, and a microprocessor make up the majority of a mobile device-based wireless healthcare monitoring system that was built in order to offer real-time online information about a patient's physiological status. The Internet of Things (IoT), edge computing, cloud computing, and other emerging information technologies have all contributed to the development of smart healthcare. The Internet of Things (IoT) concepts have been widely applied to connect the available medical resources and provide patients with intelligent, dependable, and efficient healthcare. One of the paradigms that can harness the benefits of the IoT to improve the patient's lifestyle is health monitoring for active and supported living. We have introduced an IoT architecture that has been specially designed for healthcare applications in this project. Hence the proposed architecture collects the sensor data through Node MCU microcontroller

Chapter 28 - Moving to the cloud, fog, and edge computing paradigms: Convergences and future research direction

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in edge computing (EC), deep learning (DL), and deep transfer learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructures. DL architectures are particularly suitable for integration into a pandemic-compliant medical infrastructures when combined with medically oriented IoT setups. The development of an intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 4G and beyond systems including 5G wireless communication has enabled ultra-wide broadband data-transfer and efficient information processing, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such setups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge and cloud computing frameworks, and a suite of DL tools. The framework uses a composite attention-driven framework incorporating various DL pre-trained models (DPTMs) for protocol adherence and contact tracing, and can detect certain cyber-attacks when interfaced with public networks. The results confirm the effectiveness of the proposed methodologies.