Cloud‐Based Data Analytics for Monitoring Smart Environments

The Internet of Things (IoT) is a computing paradigm whereby everyday life objects are augmented with computational and wireless communication capabilities, typically through the incorporation of resource-constrained devices including sensors and actuators, which enable their connection to the Internet. The IoT is seen as the key ingredient for the development of smart environments. Nevertheless, the current IoT ecosystem offers many alternative communication solutions with diverse performance characteristics. This situation presents a major challenge to identifying the most suitable IoT communication solution(s) for a particular smart environment. In this paper we consider the distinct requirements of key smart environments, namely the smart home, smart health, smart cities and smart factories, and relate them to current IoT communication solutions. Specifically, we describe the core characteristics of these smart environments and then proceed to provide a comprehensive survey of relevant IoT communication technologies and architectures. We conclude with our reflections on the crucial features of IoT solutions in this setting and a discussion of challenges that remain open for research.

Identification of Plantae Diseases is emerging as a developing field in India, as agriculture sector plays an important role in socio‐economic life. In the early days, irrational methods were used for Plantae disease detection. With specialized and logical headway, dependable techniques through most reduced turnaround time have been created and proposed for early recognition of Plantae infections. In this paper we propose an application of machine learning for the agriculture sector, rendering a particular solution to a problem of analyzing crop diseases which is relied on Plantae images captured using a Smartphone. In the flow of quickening ecological difficulties, for example, water shortage, climatic change, concerns in regards to nourishment supply, security and the inconvenient natural effects, feasible farming is a critical step to adapt to a quickly developing total population. The scope of cultivation which is plausible outreaches short‐term yield amplification and efficiency steered exploitation of resources.

The Smart Grids that connect Smart Homes are being built by Power Utilities to collectively shape Smart Cities. These new generation Power Utilities will also play a distinctive role to facilitate many new applications and services. In the trend of moving towards Smart Cities, the Telecommunications infrastructure and Services for Power Grid Utilities have been a challenge due to its complex and costly operations and constant improvements that are needed for the networks which span over multiple domains. Additionally, data centers and public and/or private clouds have already become parts of the Power Grid IT Infrastructure (IT/OT Convergence). The current Power Grid IT and Telecom network infrastructure are based on purpose‐built hardware for each network function and use technology‐specific interfaces. Recent paradigm shift in networking towards SDN/NFV have already started and next generation Power Grids should be part of this modernization. An obvious benefit is the simplification that manifests itself in cost reduction for network devices (white‐boxes) and their operations. In addition to improving quality of life, new applications will help revenue grow for Smart Grid Utilities and create sustainable environment for Smart Cities. The chapter covers issues, problems, and solutions of Smart Grid by the introduction of SDN and NFV to the Power Utilities Telecommunications Networks.

Artificial intelligence (AI) is a technical mix, and machine learning (ML) is one of the most important techniques in highly personalized marketing. AI ML presupposes that the system is re-assessed and the data is reassessed without human intervention. It is all about shifting. Just as AI means, for every possible action/reaction, that a human programmer does not have to code, AI machine programming can evaluate and test data to replicate every customer product with the speed and capacity that no one can attain. The technology we have been using has been around for a long time, but the influence of machines, cloud-based services, and the applicability of AI on our position as marketers have changed in recent years. Different information and data orientation contribute to a variety of technical improvements. This chapter focuses on the use of large amounts of information that enables a computer to carry out a non-definitive analysis based on project understanding. It also focuses on data collection and helps to ensure that data analysis is prepared. It also defines such data analytics processes for prediction and analysis using ML algorithms. Questions related to ML data mining are also clearly explained.

The potential Internet of Things (IoT) will have a profoundly prudent business and social impact on our lives. A hub of interest in IoT systems is typically an asset obligation, which makes them focus on digital assault baiting. In this way, broad attempts have been devoted to identifying security and safety difficulties in trendy IoT, primarily through conventional cryptographic methodologies. In any case, the remarkable qualities of IoT hubs make it the main objective of the association and collaboration between objects and objects sent through remote systems to satisfy the target set for them as a united element, to achieve a superior domain for the use of big data. What is more, based on the creativity of remote systems, both platforms and IoT may grow quickly and together. In this section, it methodically audits the security needs, the assault vectors, and the current security responses for IoT systems. It also addresses insights into current machine learning solutions for solving various security issues in IoT systems and a few future cryptography cloud analysis headlines.