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Cloud computing technologies for smart agriculture and healthcare
\"Cloud Computing Technologies for Smart Agriculture and Healthcare aims to cover the cloud management and framework. It discusses how cloud computing framework can be integrated with fog computing, edge computing, deep learning and IOT. This book will be divided in two application parts: Agriculture and Healthcare. Discusses fundamentals theories to practical and sophisticated applications of Cloud Technology for Agriculture and Healthcare Includes case studies Concepts are illustrated well with appropriate figures, tables and simple language This book is primarily aimed at graduates and researchers to understand the echo system of cloud technology for agriculture and healthcare\"-- Provided by publisher.
Book
Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge
Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.
Journal Article
Federated Learning in Edge Computing: A Systematic Survey
Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.
Journal Article
Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)
This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.
Journal Article
Digital Twin and Cloud BIM-XR Platform Development: From Scan-to-BIM-to-DT Process to a 4D Multi-User Live App to Improve Building Comfort, Efficiency and Costs
Digital twins (DTs) and building information modelling (BIM) are proving to be valuable tools for managing the entire life cycle of a building (LCB), from the early design stages to management and maintenance over time. On the other hand, BIM platforms cannot manage the geometric complexities of existing buildings and the large amount of information that sensors can collect. For this reason, this research proposes a scan-to-BIM process capable of managing high levels of detail (LODs) and information (LOIs) during the design, construction site management, and construction phases. Specific grades of generation (GOGs) were applied to create as-found, as-designed, and as-built models that interact with and support the rehabilitation project of a multi-level residential building. Furthermore, thanks to the sharing of specific APIs (Revit and Autodesk Forge APIs), it was possible to switch from static representations to novel levels of interoperability and interactivity for the user and more advanced forms of building management such as a DT, a BIM cloud, and an extended reality (XR) web platform. Finally, the development of a live app shows how different types of users (professionals and non-expert) can interact with the DT, in order to know the characteristics with which the environments have been designed, as well as the environmental parameters, increasing their degree of control, from the point of view of improving comfort, use, costs, behaviour, and good practices. Finally, the overall approach was verified through a real case study where the BIM-XR platform was built for energy improvements to existing buildings and façade renovations.
Journal Article
Privacy protection for fog computing and the internet of things data based on blockchain
With the development of the Internet of Things (IoT) field, more and more data are generated by IoT devices and transferred over the network. However, a large amount of IoT data is sensitive, and the leakage of such data is a privacy breach. The security of sensitive IoT data is a big issue, as the data is shared over an insecure network channel. Current solutions include symmetric encryption and access controls to secure the data transfer, but they have some drawbacks such as a single point of failure. Blockchain is a promising distributed ledger technology that can prevent the malicious tampering of data, offering reliable data storage. This paper proposes a distributed access control system based on blockchain technology to secure IoT data. The proposed mechanism is based on fog computing and the concept of the alliance chain. This method uses mixed linear and nonlinear spatiotemporal chaotic systems (MLNCML) and the least significant bit (LSB) to encrypt the IoT data on an edge node and then upload the encrypted data to the cloud. The proposed mechanism can solve the problem of a single point of failure of access control by providing the dynamic and fine-grained access control for IoT data. The experimental results of this method demonstrated that it can protect the privacy of IoT data efficiently.
Journal Article
Fostering digital transformation of SMEs: a four levels approach
PurposeThe purpose of this paper is to analyse how digital technologies trigger changes in the business process of manufacturing small and medium-sized enterprises (SMEs) in the Apulia Region (South Italy). As SMEs play an essential role in the process value creation of industries and countries, the article examines the enablers of Industry 4.0 in a regional contexts characterized by delay in research and development and innovation performances where the companies' competitiveness is based on limited knowledge and technological assets.Design/methodology/approachThe case study of Smart District 4.0, an ongoing project aimed to promote the digitalization of SMEs operating in the Agri–Food, Clothing–Footwear and Mechanics–Mechatronics in the Apulia Region (South Italy) is analysed. The project has been financed by the Italian Ministry of Economic Development with the final aim to sustain the digital transformation of SMEs in South Italy.FindingsThe results introduce a four levels approach of engagement of SMEs in the adoption of digital technologies, namely, digital awareness, digital enquirement, digital collaboration and digital transformation. Furthermore, for each level of engagement the study describes and discusses some relevant variables that could be used by managers and entrepreneurs to assess the level of readiness for utilization of digital technologies and how to digitalize some processes.Practical implicationsPractical implications regard the definition of a roadmap useful to assess and manage the level of digital transformation of SMEs. Limitations of the study regarding the temporal dimension of the evidences associated to the Smart District 4.0 as well as to the regional context was analysed.Originality/valueOriginality resides in the definition of a roadmap for the digital transformation of SMEs in a region where the profile of companies' digital maturity is still low.
Journal Article
On the Efficient Delivery and Storage of IoT Data in Edge–Fog–Cloud Environments
Cloud storage has become a keystone for organizations to manage large volumes of data produced by sensors at the edge as well as information produced by deep and machine learning applications. Nevertheless, the latency produced by geographic distributed systems deployed on any of the edge, the fog, or the cloud, leads to delays that are observed by end-users in the form of high response times. In this paper, we present an efficient scheme for the management and storage of Internet of Thing (IoT) data in edge–fog–cloud environments. In our proposal, entities called data containers are coupled, in a logical manner, with nano/microservices deployed on any of the edge, the fog, or the cloud. The data containers implement a hierarchical cache file system including storage levels such as in-memory, file system, and cloud services for transparently managing the input/output data operations produced by nano/microservices (e.g., a sensor hub collecting data from sensors at the edge or machine learning applications processing data at the edge). Data containers are interconnected through a secure and efficient content delivery network, which transparently and automatically performs the continuous delivery of data through the edge–fog–cloud. A prototype of our proposed scheme was implemented and evaluated in a case study based on the management of electrocardiogram sensor data. The obtained results reveal the suitability and efficiency of the proposed scheme.
Journal Article
Exploring Organizational Sustainability of Industry 4.0 under the Triple Bottom Line: The Case of a Manufacturing Company
There is an increasing interest in sustainability practices for organizations. Organizations act sustainably when they support the three dimensions the triple bottom line. Industry 4.0 (I40) promises to afford organizations to act sustainably. However, few empirical pieces of research targeted the impact of I40 on the social, economic, and environmental dimensions of sustainability. Our investigation considered the adoption of I40 in a manufacturing company which we analyzed as a single case study. We describe the level of I40 adoption and the process through which the unit has adopted them. Our case confirms that I40 applications support the triple bottom line through the improvement of productivity and product quality (economic), continuous energy consumption monitoring (environmental), and safer work environment and less intense work-load and job enrichment (social). We contribute to the literature by identifying two trajectories of interaction among the three dimensions of the triple bottom line in the shift from a traditional manufacturing company to a knowledge-intense organization. In the trajectories found, the three dimensions of sustainability influence and reinforce each other.
Journal Article
Presenting Cloud Business Performance for Manufacturing Organizations
This paper presents a new concept, Cloud Business Performance (CBP) and describes the method of measurement, data analysis, impacts to manufacturing and case studies about CPB. Three methods can be used for CBP with two case studies illustrated. The first case study presents a small and medium manufacturing enterprise that has adopted backup services for all manufacturing transactions and records. The second case study shows a software manufacturing organization’s forecasting on their business performance and risk. Methods used, results and analysis have been fully justifiable to support the case of CBP for manufacturing organizations. We demonstrate that the use of CBP calculation and prediction analysis is useful for manufacturing organizations that adopt Cloud Computing.
Journal Article