Data Security and Privacy-Preserving in Edge Computing

The rapid massive growth of IoT and the explosive increase in the data used and created in the edge networks led to several complications in the cloud technology. Edge computing is an emerging technology which is ensuring itself as a promising technology. The authors mainly focus on the security and privacy issues and their solutions. There are a lot of important features which make edge computing the most promising technology. In this chapter, they emphasize the security and privacy issues. They also discuss various architectures that enable us to ensure safe technologies and also provide an analysis on various designs that enable strong security models. Next, they make a detailed study on different cryptographic techniques and trust management systems. This study helps us to identify the pros and cons that led us to promising implementations of edge computing in the current scenario. At the end of the chapter, the authors discuss on various open research areas which could be the thrust areas for the next era.

IoE-Enabled Healthcare 4.0 Systems

The whole world is changing quickly into a mechanical world. One of the most encouraging innovations is the smart sensor innovation which is presently accessible all over the place. Nowadays the utilization of internet is exaggerated in our lives everywhere so that most of the things we use in our day-to-day lives are dependent on internet, which leads to a new era of internet of everything (IoE). The internet of everything (IoE) has different applications in medication, from far off seeing to smart sensors and clinical appliances. It can ensure and screen patients and improve the degree of care. This technology shows improvements in different sectors, specifically in critical sectors which lead everything in the world to be very smart.

Edge Computing and IoT Technologies for Medical Applications

Dynamic observation of blood sugar levels is essential for patients diagnosed with diabetes mellitus in order to control the glycaemia. Inevitably, they must accomplish a capillary test three times per day and laboratory test once or twice per month. These regular methods make patients uncomfortable because patients have to prick their finger every time in order to measure the glucose concentration. Modern health monitoring systems rely on IoT. However, the number of advanced IoT-based continuous glucose monitoring systems is small and has several limitations. Here the authors study feasibility of invasive and continuous glucose monitoring system utilizing IoT-based approach. They designed an IoT-based system architecture from a sensor device to a back-end system for presenting real-time data in various forms to end-users. The results show that the system is able to achieve continuous glucose monitoring remotely in real time, and a high level of energy efficiency can be achieved by applying the nRF compound, power management, and energy harvesting unit altogether in the sensor units.

Edge Architecture Integration of Technologies

The enormous growth of the internet of things (IoT) and cloud-based services have paved the way for edge computing, the new computing paradigm which processes the data at the edge of the network. Edge computing resolves issues related to response time, latency, battery life limitation, cost savings for bandwidth, as well as data privacy and protection. The architecture brings devices and data back to the consumer. This model of computing as a distributed IT system aims at satisfying end-user demands with faster response times by storing data closer to it. The enormous increase in individuals and locations, connected devices such as appliances, laptops, smartphones, and transport networks that communicate with each other has raised exponentially. Considering these factors in this chapter, edge computing architecture along with the various components that constitute the computing platform are discussed. The chapter also discusses resource management strategies deliberate for edge computing devices and integration of various computing technologies to support efficient IoT architecture.

IoT Trends

The internet of things indicates a kind of system to interface anything with the internet dependent on stipulated conventions through data detecting hardware to direct data trade and correspondences so as to accomplish acknowledgments, situating, figuring out, checking, and organization. IoT empowers various advances about its engineering, qualities, and applications, but what are the future difficulties for IoT? IoT frameworks enable clients to accomplish further mechanization, investigation, and joining inside a framework. They improve the scope of these regions and their precision. IoT uses existing and developing innovation for detecting, systems administration, and apply autonomy. IoT abuses ongoing advances in programming, falling equipment costs, and current frames of mind towards innovation. Its new and propelled components acquire significant changes the conveyance of items, products, administrations, financial, and political effect of those changes.

Overview of Edge Computing and Its Exploring Characteristics

Edge computing is the concept of the distributed paradigm. In order to improve the response time and to save the bandwidth, it brings the computation and the storage of the data closer to the location whenever it is needed. Edge computing is one of the very famous and blooming concept in today's era. It has been used in so many applications for various purposes. Edge computing can be defined as infrastructure of physical compute which is placed between the device and the cloud to support various application and which brings the cloud closer to the end user or end devices. In this chapter, the authors discuss the origin of the edge paradigm, introduction, benefits. These are some of the criteria to be elaborated in the chapter overview of edge paradigm.

Constructive Solutions for Security and Privacy Issues at the Edge

The evolution in computing strategies has shown wonders in reducing the reachability issue among different end devices. After centralized approaches, decentralized approaches started to take action, but with the latency in data pre-processing, computing very simple requests was the same as for the larger computations. Now it's time to have a simple decentralized environment called edge that is created very near to the end device. This makes edge location friendly and time friendly to different kinds of devices like smart, sensor, grid, etc. In this chapter, some of the serious and non-discussed security issues and privacy issues available on edge are explained neatly, and for a few of the problems, some solutions are also recommended. At last, a separate case study of edge computing challenges in healthcare is also explored, and solutions to those issues concerning that domain are shown.

Innovative Concepts and Techniques of Data Analytics in Edge Computing Paradigms

Edge analytics is an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, network switch, or other device instead of waiting for the data to be sent back to a centralized data store. Cloud computing has revolutionized how people store and use their data; however, there are some areas where cloud is limited; latency, bandwidth, security, and a lack of offline access can be problematic. To solve this problem, users need robust, secure, and intelligent on-premise infrastructure for edge computing. When data is physically located closer to the users who connected to it, information can be shared quickly, securely, and without latency. In financial services, gaming, healthcare, and retail, low levels of latency are vital for a great digital customer experience. To improve reliability and faster response times, combing cloud with edge infrastructure from APC by Schneider electrical is proposed.

Deep Learning With Analytics on Edge

The steep rise in autonomous systems and the internet of things in recent years has influenced the way in which computation has performed. With built-in AI (artificial intelligence) in IoT and cyber-physical systems, the need for high-performance computing has emerged. Cloud computing is no longer sufficient for the sensor-driven systems which continuously keep on collecting data from the environment. The sensor-based systems such as autonomous vehicles require analysis of data and predictions in real-time which is not possible only with the centralized cloud. This scenario has given rise to a new computing paradigm called edge computing. Edge computing requires the storage of data, analysis, and prediction performed on the network edge as opposed to a cloud server thereby enabling quick response and less storage overhead. The intelligence at the edge can be obtained through deep learning. This chapter contains information about various deep learning frameworks, hardware, and systems for edge computing and examples of deep neural network training using the Caffe 2 framework.

Architecture of IoT and Challenges

Internet of things (IoT) architecture is an ecosystem of connected physical objects that are accessible through the internet. The ‘thing' in IoT could be a person with a heart monitor or an automobile with built-in-sensors (i.e., objects that have been assigned an IP address and have the ability to collect and transfer data over a network without manual assistance or intervention). The embedded technology in the objects helps them to interact with internal states or the external environment, which in turn affects the decisions taken. IoT world where all the devices and appliances are connected to a network and are used collaboratively to achieve complex tasks that require a high degree of intelligence, and IoT is an interaction between the physical and digital words using sensors and actuators. Furthermore, the IoT architecture may combine features and technologies suggested by various methodologies. IoT architecture is designed where the digital and real worlds are integrating and interacting constantly, and various technologies are merged together to form IoT.