Edge Computing in IOT: A Comparative Review

In current years, Internet of Things (IoT) has come a long way and is well emerged within many organizations and fields covering the sector of healthcare. Continuously, encompassing of the Internet of Medical Things (IoMT) in the field of the medical sector helps in the rise of medical data and analysis of that. Smart healthcare plays important role in human life and various research projects are running on it. It consists of diverse ingredients like wireless communication technology, cloud computing, edge computing, medical sensors, artificial intelligence (AI), IoMT, and IoT. The chapter introduces IoMT with respect to the multiple challenges like privacy, security and low-power operation faced while incorporating IoT with healthcare sector. For emergency treatment, a cloud-based healthcare framework provides a platform to support patients over internet communication by medical experts. Privacy and security to the medical records are on higher priority due to the very sensitive nature of the content. Moreover, a comprehensive survey on IoMT-based cloud and edge-intelligent healthcare system is also described. The current advancement in the field of IoT, IoMT, cloud computing, and AI related to privacy and security, some pathways for forthcoming work in collaboration of healthcare sector and IoT based on a collection of challenges and open issues are also addressed.

The Internet of Things (IoT) is impacting the world’s connectivity landscape. More and more IoT devices are connected, bringing many benefits to our daily lives. However, the influx of IoT devices poses non-trivial challenges for the existing cloud-based computing paradigm. In the cloud-based architecture, a large amount of IoT data is transferred to the cloud for data management, analysis, and decision making. It could not only cause a heavy workload on the cloud but also result in unacceptable network latency, ultimately undermining the benefits of cloud-based computing. To address these challenges, researchers are looking for new computing models for the IoT. Edge computing, a new decentralized computing model, is valued by more and more researchers in academia and industry. The main idea of edge computing is placing data processing in near-edge devices instead of remote cloud servers. It is promising to build more scalable, low-latency IoT systems. Many studies have been proposed on edge computing and IoT, but a comprehensive survey of this crossover area is still lacking. In this survey, we first introduce the impact of edge computing on the development of IoT and point out why edge computing is more suitable for IoT than other computing paradigms. Then, we analyze the necessity of systematical investigation on the edge-computing-driven IoT (ECDriven-IoT) and summarize new challenges occurring in ECDriven-IoT. We categorize recent advances from bottom to top, covering six aspects of ECDriven-IoT. Finally, we conclude lessons learned and propose some challenging

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.

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.

Technologies such as cloud, fog, and edge computing offer exceptional solutions for many technical problems in the Internet of Things (IoT). Cloud computing offers enormous flexibility in terms of resource availability and demand. In this paper, the role of cloud, edge, and fog computing in IoT was discussed along with the emergence of numerous IoT smart scenarios which are difficult to implement. This research is important in guiding future network paradigms that require faster processing with less delay and disruption, and the advanced technologies in them can address the challenges of IoT. It also reviews cloud, fog, and edge computing and their relevance to the IoT.

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

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

The Internet of Things (IoT) has emerged as a transformative technology that connects the physical and digital worlds, revolutionizing how devices, objects, and systems interact. As the world becomes increasingly interconnected, IoT paves the way for the development of smart cities, homes, industries, and healthcare systems. This paper presents a comprehensive analysis of the Internet of Things and its role in shaping the smart world.The first section of this paper delves into the foundational concepts of IoT, outlining its history, principles, and key components. The discussion highlights the rapid growth of IoT devices, sensors, and communication protocols, which have enabled seamless data exchange and connectivity on a global scale. The potential benefits and challenges of IoT adoption are addressed, emphasizing the need for robust security and privacy measures to protect against potential threats. The second section explores the pivotal role of IoT in shaping smart cities. With urbanization on the rise, the need for efficient resource management, sustainable infrastructure, and enhanced citizen services becomes more pressing. IoT-driven solutions empower cities to optimize traffic flow, manage energy consumption, improve waste management, and enhance public safety through real-time data analytics and predictive modeling.In the context of smart homes, the third section investigates how IoT applications have revolutionized the way people interact with their living spaces. Smart home devices, such as smart thermostats, lighting systems, and voice-activated assistants, provide users with increased convenience and energy efficiency. However, concerns surrounding data privacy and potential vulnerabilities necessitate a discussion on the best practices for securing smart home ecosystems. The fourth section examines the impact of IoT on industries, from manufacturing to agriculture. IoT-enabled industrial processes streamline production, enhance supply chain management, and facilitate predictive maintenance, leading to increased productivity and reduced operational costs. Additionally, precision agriculture applications of IoT offer farmers real-time data on soil conditions, weather patterns, and crop health, leading to improved crop yield and resource utilization.The fifth section addresses the transformation of healthcare systems through IoT integration. From remote patient monitoring to wearable health devices, IoT has opened new avenues for personalized and proactive healthcare. This section emphasizes the potential of IoT to revolutionize healthcare delivery by facilitating timely interventions, reducing hospital readmissions, and empowering patients to take an active role in their health management.The sixth section discusses the challenges and barriers hindering the widespread adoption of IoT and the smart world. Interoperability issues, standardization, and the complexity of integrating diverse systems pose significant challenges for stakeholders. Moreover, concerns regarding data ownership, privacy, and cyber-security remain key obstacles that need to be addressed to build public trust in IoT technologies. The final section explores future trends and possibilities for IoT and the smart world. With the proliferation of 5G networks, edge computing, and artificial intelligence, the potential for IoT applications is boundless. The paper concludes by emphasizing the importance of collaboration among governments, industries, and researchers to shape a sustainable and inclusive smart world powered by IoT technologies.In summary, this paper provides a comprehensive analysis of the Internet of Things and its role in shaping the smart world. From its foundational principles to its diverse applications across various sectors, IoT has the potential to transform societies and redefine how we interact with technology. However, addressing challenges related to security, privacy, and standardization is crucial to unlock the full potential of IoT and pave the way for a truly interconnected and intelligent future.

The Internet of Things (IoT) is expected to grow faster than any other category of connected devices. IoT allows any device with an on-and-off switch to connect to the internet—a concept that has the ability to greatly change our lives and work. These modern systems collect inherently complex data streams due to the volume, velocity, value, variety, variability, and veracity of data, which leads to incremental growth in data traffic on networks and in the cloud. To fulfill the requirements of IoT, including geodistribution, low latency, location awareness, and mobility support, a new paradigm is proposed: edge computing. In edge computing, substantial computing and storage resources are placed at the edge of the network in mobile devices or sensors. The term “edge” is taken from network diagrams; normally, the edge of a network diagram represents the point at which data traffic enters or leaves the workable network. Using the concept of edge computing, an organization can shift huge amounts of data into processed data near the data origin, which helps to reduce data traffic in the network’s central repository (called the “cloud”). Edge computing uses a variety of data reduction techniques close to the data source at the network edge, including data pre-processing, local storage, and filtering. This approach can prevent some critical issues, such as I/O bottlenecks, storage and bandwidth limitations, data traffic increments, and high energy costs. A major advantage of edge computing is improvement of the request-response delay to milliseconds. Edge computing also supports security and network challenges. However, two major obstacles exist toward achieving the benefit of network-edge computing. First, the most efficient algorithms for data reduction in time series (one of the most common types of data in IoT) were developed to work posteriori upon big datasets, but they cannot make decisions for each incoming data item. Secondly, the state of the art lacks systems that can apply any of the possible data reduction methods without adding significant delays or major reconfigurations. Edge computing has also inherited some of the challenges of cloud computing, including data abstraction, naming, and programmability. This chapter presents a detailed taxonomic discussion of edge computing, along with its challenges, opportunities, and data reduction methods.

The Internet of Things (IoT) has expanded rapidly, allowing for a network of sensors and gadgets to collect and share information to make people's lives easier and more convenient. As the Internet of Things (IoT) grows, however, energy efficiency becomes a major issue, especially for portable and wireless gadgets. Low-power, long-range communication capabilities are needed, and Long-Range Wide Area Network (LoRaWAN) has emerged as a viable solution to meet this need. This study provides an in-depth analysis of the LoRaWAN-based, low-power Internet of Things. The suggested network architecture is optimized for low power consumption and high connectivity for numerous Internet of Things (IoT) use cases. This low-power Internet of Things network relies on LoRaWAN gateways, end devices, and a server to function. LoRaWAN is a technology that enables the low-power, long-range transmission of data packets. The results show that the optimized case and non-optimized case have a delivery ratio of 0.85 to 0.73 from node 100 to 500. LoRaWAN significantly reduces energy usage compared to conventional IoT connectivity alternatives, making it a fantastic option for battery-powered devices in far-flung or limited-resource locations. Finally, the adoption of LoRaWAN provides a viable solution to address the energy efficiency concerns in IoT networks, hence allowing for sustainable, long-lasting IoT installations and enabling a wide variety of new applications within the IoT ecosystem. Furthermore, addresses the potential applications of this technology in the future, including upgrades and integration with other technologies like edge computing and 5G networks.

Power line communication (PLC) technology has created a niche use in the Internet of Things (IoT) by offering flexible and reliable connection among power-driven IoT devices/sensors over existing wired networks. In IoT over PLC networks, massive real-time data generated by the ever-growing connected devices will eventually pose an overwhelming burden on the IoT cloud, which in turn severely degrades the network performance. To cope with these issues, edge computing (EC) has emerged as a complement to cloud computing, aiming at offloading a portion of computing in the cloud to the network edges closer to the IoT devices. However, confronting a practical scenario that some electrical devices cannot communicate with wireless and mobile networks directly, existing EC paradigms may not be directly applied to IoT over PLC networks. In this paper, we propose a novel EC-IoT over PLC paradigm to reduce the transmission latency while migrating a portion of computing from the cloud to the edges. First, we develop a distributed EC platform to serve terminal users (TUs) in different IoT systems with various IoT services. Second, we put forth a cache-enabled scheme to store the popular contents from the cloud and edge sensors to reduce redundant data transmissions between TUs and the cloud. Finally, our experimental results demonstrate that the proposed EC-IoT over PLC network can significantly reduce energy consumption and transmission latency.

Commonly, an Internet of Things (IoT) system follows the architecture of the Cloud‐centric Internet of Things (CIoT) in which the physical objects are represented in the form of Web resources that are managed by the servers in the global Internet. In general, the common IoT system involves three major technologies: embedded systems, middleware, and cloud services. Although the CIoT model is a common approach to implement IoT systems, it is facing the growing challenges in IoT. This chapter discusses foundations of computing paradigms for realizing emerging IoT applications, especially fog and edge computing, their background, characteristics, architectures and open challenges. It presents related technologies to fog and edge computing. The chapter describes how fog and edge can improve CIoT and explains the hierarchy of fog and edge computing environments. It illustrates the business models of fog and edge computing, and provides the information regarding the opportunities and challenges in fog and edge computing.

Recent advancements in technology now allow for the generation of massive quantities of data. There is a growing need to transmit this data faster and more securely such that it cannot be accessed by malicious individuals. Edge computing has emerged in previous research as a method capable of improving data transmission times and security before the data ends up in the cloud. Edge computing has an impressive transmission speed based on fifth generation (5G) communication which transmits data with low latency and high bandwidth. While edge computing is sufficient to extract important features from the raw data to prevent large amounts of data requiring excessive bandwidth to be transmitted, cloud computing is used for the computational processes required for developing algorithms and modeling the data. Edge computing also improves the quality of the user experience by saving time and integrating quality of life (QoL) features. QoL features are important for the healthcare sector by helping to provide real-time feedback of data produced by healthcare devices back to patients for a faster recovery. Edge computing has better energy efficiency, can reduce the electricity cost, and in turn help people reduce their living expenses. This paper will take a detailed look into edge computing applications around Internet of Things (IoT) devices, smart city infrastructure, and benefits to healthcare.

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.

The basics of Internet of Things (IoT) is the attribute of Internet of Objects, expected as a flexible strategy for giving several facilities. Condensed clever units create a critical phase of IoT. They vary extensively in practice, volume, service facility, and calculation strength. However, the incorporation of these clever matters in the widespread internetworking proposed IoT of safety threat due to the act the most internetwork technologies along with verbal exchange manners that had not been now designed to guide IoT. Furthermore, the trade of IoT has produced public safety matter, such as nonpublic secrecy issues, risk of cyber threats, and trained crime. Have guidelines or suggestions on the analysis of IoT security and contribute to improvement. This control offers full information of the open penetration and retrieval as opposed to the IoT edge side layer, which are done in three phases: edge nodes, transmission, and edge computing. The method to reach this target, first, we quickly define those popular IoT recommendation models and outline protection in the IoT's framework. We also talk about the feasible purposes of IoT and the inspiration of the intruder and set new goals in this new model. With such rise in IoT and with the advent of ongoing digital applications, huge amount of data is produced each and every day, leading to the emergence of the term big data. In this chapter, we will be collaborating the detailed security study in case of IoT and big data.