Serverless Platforms on the Edge: A Performance Analysis

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.

Data reliability and confidence in the data are very important issues, especially when the system integrates fraud or false information. The misusing of data collected may create serious problems. With the fast development of computing techniques, much data are collected from various terminals and industrial devices. Edge computing operates by driving data, software and computer resources from the centralized network to its extremes, allowing pieces of knowledge to lie on distributed cloud networks. Its target customers continue to use commercial Internet application software for every internet customer. Edge computing is used to provide delay-free customer experience assistance for features of the Internet of Things (IoT) services on the edge of the user network. The document identifies an IoT computing platform collaborating with the edge competitive data management latency (CDML) tool. This approach separately categorizes edge layer requests and response data over time using demand-density driven optimization. A difference-based optimization optimizes the frame limits for simultaneous request processing and exact allocation of data. The architectural efficiency of edge computing can be assessed by comparing latency, bandwidth usage, and overhead. Furthermore, estimating the availability, credibility and confidentiality of security solutions within each party would take into consideration security concerns in edge computing and propose a safety assessment process for IoT networks with edge computing. This procedure is finally validated using appropriate tests, and the resulting findings are examined to demonstrate the method’s accuracy. Experimental data are used to validate methods to request maintenance and processing, response time, resource utilization and contract period. In comparison to current approaches, the results of the proposed CDML are measured with a percentage of 97.90%. The proposed system enhances the request and response comparison ratio 97.5%, analyzing request performance ratio 98.1%, response with time analysis ratio of 98.3%, data allocation approach analysis ratio 97.7%.

Recent years have witnessed the proliferation of mobile computing and the Internet of Things (IoT), in which billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. However, it is challenging and infeasible to transfer and process zillions bytes of data using the current cloud-device architecture, due to bandwidth constraints of networks, potentially uncontrollable latency of cloud services, and privacy concerns while collecting data from IoT devices. To tackle these challenges, edge computing, an emerging computing paradigm, has received a tremendous amount of interest. By pushing data storage, computing, and controls closer to the network edge, edge computing has been widely recognized as a promising solution to meet the requirements of low latency, high scalability, and energy efficiency, as well as to mitigate the network traffic burdens. However, with the emergence of diverse IoT applications (e.g., smart city, industrial automation, and connected car), it becomes challenging for edge computing to deal with these heterogeneous IoT environments.

Introduction to the Special Issue on edge intelligence: Neurocomputing meets edge computing

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.

A novel framework for optimizing the edge network node for wearable devices

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.

SummaryIn a recent scenario of a computing environment, the Internet of Things (IoT) provides a greater impact in several applications and services. For that Edge Computing techniques are effectively incorporated, in which the sensitive data are stored on servers. As a result, edge computing allows for data storage and operation at the network's edge to emerge as optimistic advancement. Regardless, the distinctive characteristics of edge computing including real‐time computing, content perception and parallel processing have introduced a number of innovative challenges in the domain of privacy preservation and data security. Edge computing provides an extension of the cloud for designing and deploying new security resolutions in the area of IoT. In this paper, a Trusted Platform Module with Ciphertext‐policy attribute‐based encryption technique is introduced to provide a solution to a multilayer privacy‐preserving model for efficient data storage. The developed model is adaptable and has a reliable data access control mechanism. Further, the results of the experiments demonstrate that the suggested technique operates better and give 83% results in terms of storage cost, load testing, effective runtime execution, higher security, credibility, and efficiency.

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

Enhanced Dynamic Deep Q-Network for Federated Learning scheduling policies on IoT devices using explanation-driven trust

Edge computing, as an extension of cloud computing, brings computation and data storage closer to the data source. This shift offers significant advantages in terms of latency reduction, bandwidth optimization, and real-time processing capabilities. By minimizing the distance that data needs to travel, edge computing enhances the performance of applications that require rapid data processing and immediate response times. This is particularly beneficial for Internet of Things (IoT) devices, autonomous vehicles, smart grids, and other applications that demand low-latency interactions. However, the decentralization inherent in edge computing introduces a unique set of cybersecurity challenges that are distinct from those faced in traditional centralized cloud environments. The distributed architecture of edge computing creates numerous points of vulnerability, each of which can be exploited by cyber attackers. Additionally, edge devices often operate with limited computational resources and power, which complicates the implementation of robust security measures. This paper explores the cybersecurity implications of edge computing in the context of data engineering. It begins with a comprehensive review of existing literature to establish the current understanding of edge computing security issues. The review identifies the primary vulnerabilities associated with edge computing, including those related to distributed architecture, data transmission, and resource constraints. Following the literature review, the paper delves into specific security threats that edge computing environments face. These include man-in-the-middle (MitM) attacks, distributed denial-of-service (DDoS) attacks, malware and ransomware, and insider threats. Each threat is analyzed to understand its potential impact on edge computing systems and the data they process.

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.

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

Improving consumer satisfaction in smart cities using edge computing and caching: A case study of date fruits classification