and agricultural enterprises built using G-IoT and green nanotechnology are discussed. Furthermore, how the IoT and G-IoT empower agriculture with accurate and sustainable farming approaches is also discussed.
– Chapter 5 demonstrates the use of analytics and machine learning to improve crop data. For optimal crop growth, pH, water content, and humidity are key. Inadequate inputs might lead to a growth imbalance in crops. Machine learning approaches can profit from prediction and classification challenges. To monitor the health of crops in the field, CIoT (Crop Internet of Things) was mostly required to assist in humidity, soil pH, soil wetness, soil type, and crop quality monitoring. Various sensors, like humidity sensors, pH sensors, soil quality sensors, and temperature sensors, can be utilized to detect crop yield and ML algorithms to calculate yields alongside descriptive analytics. Decisions on types of pesticides to use and the nutritional requirements of individual crops can be made with ML algorithms. Farmers would benefit from the early use of ML for soil mapping to produce a better environment for future crops.
– Chapter 6 explores smart farming using deep learning (DL) techniques. In the modern world, trending automation techniques are mixed with traditional farming approaches. A variety of crops are cultivated in various seasons from farmland. Farmers face several obstacles such as climatic calamities, pests, seed quality, soil quality, etc. Therefore, DL technologies are being used to turn traditional farming into smart farming.
– Chapter 7 presents the use of G-IoT and ML for agriculture applications. The IoT is a network of diverse sensors, software, and other technologies embedded in a system that retains flexibility. This design combines many sensors and software to perform multiple functions simultaneously. Artificial neural networks, C-means, K-means, Bayesian model and so on are used to make cost-effective, low-power IoT devices. Approaches like green computing, green wireless sensor networks, etc., create a difference in criteria for energy consumption.
– Chapter 8 presents an IoT-enabled AI-based model to assess land suitability for crop production. It briefly covers the IoT sensors used in horticulture and presents a summary of ML, AI, and DL techniques for farmers. Current challenges in the field of agribusiness are shown with the evaluation of the study. The review reveals that explicit model assessment is supported by a wholly automated approach.
– Chapter 9 introduces green cloud computing (GCC) and G-IoT applications for agriculture and healthcare systems. In this chapter, the technology and issues relating to GCC and G-IoT, as well as the ability to minimize energy usage through the combination of these two approaches, are discussed. The green information and communication technologies (GICTs) that facilitate the G-IoT are extensively discussed. Additionally, the possibilities of a healthcare and G-IoT application system using a digital wireless sensor cloud with discrete integration or digital summation modeling are presented.
– Chapter 10 discusses how the G-IoT can be used for smart transportation. This is a global technical necessity since building an energy-efficient IoT ecosystem reduces CO2 emissions from sensors, devices, and services that are implemented in IoT applications. The IoT is used in smart transportation (cars, trains, buses, etc.) to provide an effective way to avoid more energy use. This chapter looks at the G-IoT, along with its various challenges and issues, and the various technologies utilized in it.
– Chapter 11 discusses the use of the G-IoT and ML in the banking industry. It provides an overview as to how banking institutions are making use of the G-IoT and its life cycle. Also, it focuses on the use of AI and ML in the banking industry, along with scrutinizing their important role in the banking sector, and identifies the latest technologies which can be adopted for reducing carbon footprints with the IoT.
– Chapter 12 presents the G-IoT technologies and the future challenges they face. The IoT appends everything in the smart world. Consequently, the utilization of IoT advancements in applications has become an energized area of research. Enlivened by achieving low power consumption using the IoT, a green IoT is proposed. This chapter fundamentally discusses the existing procedure of the G-IoT, which contains green development, green reprocessing, green execution, and green improvement. A study is presented that analyzes the principles of the G-IoT, and plots the pivotal energy of the G-IoT and its structure.
Machine learning and deep learning, along with the developments in the IoT, are accelerating the value of different applications, thereby increasing the prospects for boosting operational effectiveness and productivity. However, security and scalability still need to be achieved for many processes, goods, and services since many of the devices remain susceptible to malicious acts due to a lack of end-to-end security solutions. Nonetheless, ML-enabled IoT devices have faster, flexible, high-performance networks that connect a wide variety of devices that provide numerous functions and applications to the end user.
The Editors
November 2021
1
G-IoT and ML for Smart Computing
Karunendra Verma1*, Vineet Raj Singh Kushwah2 and Nilesh3
1Chitkara University School of Engineering & Technology, Chitkara University, Himachal Pradesh, India
2Department of Computer Science & Engineering, IPSCTM, Gwalior, M.P., India
3Department of Computer Science & Engineering, Rama University, Kanpur, U.P., India
Abstract
Today’s life is going to be easy with the use of various devices. Mostly, devices are based on various Machine Learning (ML) techniques, which is one of the most thrilling technologies of Artificial Intelligence (AI). If such devices are operated by internet, then this will increase the efficiency and effectiveness of the working and such type of technology is based on Internet of Thing (IoT). Every web search like Bing or Google is used to search on the internet using these techniques and retrieved results, efficient and accurate in short span of time as they used such learning algorithms to rank the web pages. Like every time, Facebook is used to identify the friends’ photo that is also ML. Product recommendation, online fraud detection, online customer support, videos surveillance, healthcare industries, face recognition, email spam, and malware are also based on various learning algorithms. The objective of this article is to give the brief introduction of various AI, ML, and IoT-based techniques with their applications in real life. Article also included the various aspect of Green IoT (G-IoT) which is based on utilization of IoT with environment friendly.
Keywords: Artificial intelligence, IoT, G-IoT, machine learning, deep learning, supervised learning, unsupervised learning
1.1 Introduction
Artificial Intelligence (AI) refers to the system where machines are given AI and such machines are called intelligent agents. These days AI is growing to be popular due to their features. It is simulating the natural intelligence in machines that are performing the mimic and learn
