Industry 4.0 refers to the phase of transition that is taking place, enabling automation and data interchange in industrial technologies and processes. Fog computing architecture can provide real-time processing, nearby storage, extremely low latency, dependability, large data rates, and other requirements for industrial Internet of Things (IIoT) applications. In the context of IoT applications, fog infrastructure and protocols are the main areas of interest. The phrase “fog computing,” sometimes known “edge cloud,” is a new paradigm. Between edge devices and Cloud Core, it adds another layer. Along with providing computing, storage, and networking capabilities, it also fills a need left by the cloud. The main features of fog computing are covered in this chapter, along with current research on the subject and a focus on the difficulties encountered when creating its architectural design.
In the digital age, cybersecurity has become an important issue. Data breaches, identity theft, captcha fracturing, and other similar designs abound, affecting millions of individuals and organizations. The challenges are always endless when it comes to inventing appropriate controls and procedures and implementing them as flawlessly as available to combat cyberattacks and crime. The risk of cyberattacks and crime has increased exponentially due to recent advances in artificial intelligence. It applies to almost all areas of the natural and engineering sciences. From healthcare to robotics, AI has revolutionized everything. In this chapter, the authors discuss certain encouraging artificial intelligence technologies. They cover the application of these techniques in cybersecurity. They conclude their discussion by talking about the future scope of artificial intelligence and cybersecurity.
Increasing demand for food quality and size has increased the need for industrialization and intensification in the agricultural sector. The internet of things (IoT) is a promising technology that offers many innovative solutions to transform the agricultural sector. Research institutes and scientific groups are constantly working to provide solutions and products for different areas of agriculture using IoT. The main objective of this methodological study is to collect all relevant research results on agricultural IoT applications, sensors/devices, communication protocols, and network types. The authors also talk about the main problems and encounters encountered in the field of agriculture. An IoT agriculture framework is also available that contextualizes the view of various current farming solutions. National guidelines on IoT-based agriculture were also presented. Finally, open issues and challenges were presented, and researchers were highlighted as promising future directions in the field of IoT agriculture.
Agriculture is the foremost factor which is important for the survival of human beings. Farming contributes to a very big part of GDP; still, several areas exist where improvements are required. One of those is crop recommendation. Crop productivity is boosted as a result of accurate crop prediction. As crop production has already started to suffer from climate change, improving crop output is consequently desirable because agronomists are impotent to select the appropriate crop(s) depending on environmental and soil parameters, and the mechanism of forecasting the selection of the appropriate crops manually has failed. Factors like soil characteristics, soil types, climate characteristics, temperature, rainfall, area, humidity, geographic location etc. affect crop forecast. This chapter focuses mainly on building a recommendation system, i.e., suggesting the kind of the crop by applying various machine learning and deep learning techniques depending upon several parameters. The system would help the farmers for the appropriate decision to be taken regarding the crop type.
The chapter focuses on cloud security audit mechanisms and models. Here the third-party auditor (TPA) will be provided with the authority access scheme where the security of the auditing system will be enabled. The TPA will check out the auditing verification and shows a message about the data audited. The purpose of this work is to develop an auditing scheme that is secure, efficient to use, and possesses the capabilities such as privacy preserving, public auditing, maintaining the data integrity along with confidentiality. It consists of three entities: data owner, TPA, and cloud server. The data owner performs various operations such as splitting the file to blocks, encrypting them, generating a hash value for each, concatenating it, and generating a signature on it. TPA performs the main role of data integrity check. It performs activities like generating hash value for encrypted blocks received from cloud server, concatenating them, and generates signature on it. Thus, the system frequently checks the security of the server-side resources.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.