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Introduction: With many new technologies requiring real-time data processing, cloud computing has become challenging to implement due to high bandwidth and high latency requirements.

Purpose: To overcome this issue, edge computing is used to process data at the network’s edge. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed. It is used to process time-sensitive data.

Methodology: The authors implemented the model using Linux Foundation’s open-source platform EdgeX Foundry to create an edge-computing device. The model involved getting data from an on-board sensor (on-board diagnostics (OBD-II)) and the GPS sensor of a car. The data are then observed and computed to the EdgeX server. The single server will send data to serve three real-life internet of things (IoT) use cases: auto insurance, supporting a smart city, and building a personal driving record.

Findings: The main aim of this model is to illustrate how edge computing can improve both latency and bandwidth usage needed for real-world IoT applications.

This chapter presents the Smart Irrigation system using the Internet of Things (IoT). IoT Technology is a network of physical objects that are connected with sensors, software, etc. This chapter concludes the project based on the agriculture field that automates the irrigation process and on the agriculture field that automates the irrigation process and solves the challenge of water consumption in those areas. We have developed the system using different sensors like (1) Soil Moisture sensor, which measures the moisture present in the soil, (2) Humidity and Temperature Sensor (DHT11), which traces the temperature change. All these sensors are connected to the Node MCU ESP8266 microcontroller, which is also a Wi-Fi module. It uploads the data to the cloud and displays it in the form of readings detected by the Blynk Application. This sensor's reading values control the pump for emergency purposes, such as stopping the pump for irrigation. Thus, this project can automate the irrigation process by analyzing soil moisture and climatic conditions, covering essential aspects like less labor, power consumption, reliability, and cost.

This study compares the performance of two types of clustering methods, time-based and non-time-based clustering, in the identification of river discharge patterns at the Johor River basin during the northeast monsoon season. Time-based clustering is represented by employing dynamic time warping (DTW) dissimilarity measure, whereas non-time-based clustering is represented by employing Euclidean dissimilarity measure in analysing the Johor River discharge data. In addition, we combine each of these clustering methods with a frequency domain representation of the discharge data using Discrete Fourier Transform (DFT) to see if such transformation affects the clustering results. The clustering quality from the hierarchical data structures of the identified river discharge patterns for each of the methods is measured by the Cophenetic Correlation Coefficient (CPCC). The results from the time-based clustering using DTW based on DFT transformation show a higher CPCC value as compared to that of non-time-based clustering methods.

While rapid increase in demand for foods but limited availability of croplands has forced to adopt input-intensive farming practices to increase yield, there are serious long-term ecological implications including degradation of biodiversity. It is increasingly recognised that ensuring agricultural sustainability under the changing climatic conditions requires a change in the production system along with necessary policies and institutional arrangements. In this context, this chapter examines if climate-smart agriculture (CSA) can facilitate adaptation and mitigation practices by improving resource utilisation efficiency in India. Such an attempt has special significance as the existing studies have very limited discussions on three main aspects, viz., resource productivity, adaptation practices and mitigation strategies in a comprehensive manner. Based on insights from the existing studies, this chapter points out that CSA can potentially make significant contribution to enhancing resource productivity, adaptation practices, mitigation strategies and food security, especially among the land-constrained farmers who are highly prone to environmental shocks. In this connection, staggered trench irrigation structure has facilitated rainwater harvesting, local irrigation and livelihood generation in West Bengal. However, it is necessary to revisit the existing approaches to promotion of CSA and dissemination of information on the design of local adaptation strategies. This chapter also proposes a change in the food system from climate-sensitive to CSA through integration of technologies, institutions and policies.

The impact of rapidly developing technologies on digitalization of production and planning has affected all sectors in a short period. This impact has led to both positive and negative outcomes. While it is helping the decision-makers to make better and more productive choices, it also can create a cluster of information and data that can put an undue burden on processes. Today, we call this Industry 4.0, which is defined as the use of digital technologies, based on data processing, the end-to-end connection of value chains, and ensuring data fluidity. Industry 4.0 consists of technologies such as cyber-physical systems, internet of things, cloud computing, big data and analysis, autonomous vehicles, and augmented reality. With the digitalization of the entire production and planning processes, data-based applications are made; thus it is important to protect data in this context, pointing to the critical importance of cybersecurity. Companies are constantly working on taking the necessary cybersecurity measures to prevent exposure to any cyberattacks. One of the biggest steps toward the development of production and planning is undoubtedly the integration and adaptation of blockchain technology. The use of blockchain technologies has been a major breakthrough for the sector in order to effectively meet customer needs, ensure information security, reduce costs, and achieve rapid growth. With digital transformation, production and planning must comply with the principle of transparency. In this context, integrating blockchain technologies into the production and planning ecology for data security will provide companies with a serious competitive advantage.

Global recession is a serious issue to both the developed and developing economies. Reports published by the Ministry of Statistics and Programme Implementation (2019–20) have revealed that the growth of gross domestic products (GDPs) has shrunk significantly in the last few quarters. Due to such recession productions by many, manufacturing industries have reduced significantly, and a large number of people have lost their work, and scope of new job creations has also decreased. Food sector has also been affected by global recession (Agbedeyi & Adigwe, 2018). Food Processing Industry (FPI) is India's one of the most sunshine manufacturing industries and ranks fifth among the Indian industries in terms of production, consumption, and exports. The country ranks second in global ranking in terms of producing food products next to China. Despite the global recession, the FPIs helped the Indian economy to maintain the growth of the GDP and have created new job opportunities. Around 70 lakh persons are employed in both registered and unregistered food processing units in India. The value of food exported in the year 2018–19 was US $35.30 billion which was 10.69% of India's total export (i.e. US$330.67 billion) (MoFPI report, 2018–19). In this backdrop, the present chapter will try to find out the role of FPI in the Indian economy and will also highlight the prospects of this industry in the coming years.