Intelligent Decision Support Systems for Smart City Applications E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Concise Introductions to AI and Data Science
- Sprache: Englisch
INTELLIGENT DECISION SUPPORT SYSTEMS FOR SMART CITY APPLICATIONS This book provides smart city frameworks to address new difficulties by adding new features and allowing the city environment to react to collected data and information to increase the efficiency and sustainability of services for inhabitants. Making a smart city is an emerging strategy to mitigate the problems generated by urban population growth and rapid urbanization. This book aims to provide a better understanding of the concept of smart cities and the application of an intelligent decision support system. Based on the analysis of existing information there are eight critical factors of smart city initiatives: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. This book will focus on the application of the decision support system in managing these eight crucial aspects of smart cities. The intent in writing this book was also to provide a source that covers the stage-by-stage integration of the four key areas involving planning, physical infrastructure, ICT infrastructure, and deploying the smart solutions necessary for city transformation. With this as the motivation, "Decision Support Systems for Smart City Applications" provides the application of an intelligent decision support system for effectively and efficiently managing the transformation process, which can aid various supply chain stakeholders, academic researchers, and related professionals in building smart cities. Various chapters of this book are expected to support practicing managers during the implementation of smart solutions for city transformation. Audience This book is aimed at both academics and practitioners alike in the fields of intelligent computing, decision support systems, the manufacturing industry, supply chain managers, stakeholders, policymakers, and other technical and administrative personnel.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Dedication
Preface
Acknowledgment
1 Techno Agri for New Cities by Smart Irrigation
1.1 Introduction
1.2 Literature Review
1.3 Components Used
1.4 Proposed System
1.5 Android Mobile Application for Smart Irrigation
1.6 Novelty
1.7 Future Research Work
1.8 Limitations
1.9 Conclusions
References
2 A Case Study of Command-and-Control Center—A DSS Perspective
2.1 Introduction
2.2 Command and Control Center—A Critical System
2.3 Conclusion
References
3 Inverse Tree Interleavers in UAV Communications for Interference Mitigation
3.1 Introduction
3.2 Background
3.3 The Problem
3.4 Motivation
3.5 Interference Mitigation Using ITI
3.6 Interleavers for Interference Mitigation in UAV Communications
3.7 Inverse Tree Interleavers in UAV Communications
3.8 Decision Support System (DSS) in ITI Allocation
3.9 ITI-Based Clustered Interleaving and DSS for Smart City Framework
3.10 Conclusion
References
4 Introduction to DSS System for Smart Cities
4.1 Introduction
4.2 Smart City System Architecture
4.3 Types of Network Sensors
4.4 Role of Sensors in Smart Cities
4.5 Implications of Smart Sensors
4.6 Decision Modeling
4.7 Decision Support Systems (DSS)
4.8 Chandigarh: Becoming a Smart City
4.9 A Topology of Smart City Functions
4.10 Challenges for India’s Smart Cities
4.11 The Government Should Focus on the Following Main Areas for the Country’s Creation of Smart Cities
4.12 Conclusion
References
5 Evaluating Solutions to Overcome Blockchain Technology Implementation in Smart City Using a Hybrid Fuzzy SWARA-Fuzzy WASPAS Approach
5.1 Introduction
5.2 Research Methodology
5.3 Research Design
5.4 Application of Proposed Methodology
5.5 Conclusion
References
6 Identification and Analysis of Challenges and Their Solution in Implementation of Decision Support System (DSS) in Smart Cities
6.1 Introduction
6.2 Review of Literature
6.3 Research Methodology
6.4 Case Background
6.5 Case Description
6.6 Result Discussion
6.7 Conclusion
References
7 Evaluation of Criteria for Implementation of Capabilities in a Smart City’s Service Supply Chain: A Teacher Trainer’s Perspective
7.1 Introduction
7.2 Literature Review
7.3 Objectives
7.4 Problem Definition
7.5 Numerical Illustration
7.6 Conclusion
References
8 Industry 5.0: Coexistence of Humans and Machines
8.1 Introduction
8.2 Literature Review
8.3 Requirement of Fifth Industrial Revolution
8.4 Journey of Industry 4.0 to Industry 5.0
8.5 Industrial Revolution: Changes and Advancements
8.6 Conclusion
References
9 Smart Child Safety Framework Using Internet of Things
9.1 Introduction
9.2 Technology and Sensors Used
9.3 Advantages
9.4 Conclusion
9.5 Future Scope
References
10 Water Content Prediction in Smart Agriculture of Rural India Using CNN and Transfer Learning Approach
10.1 Introduction
10.2 Proposed Method
10.3 Results and Discussion
10.4 Conclusion
References
11 Cognitiveness of 5G Technology Toward Sustainable Development of Smart Cities
11.1 Introduction
11.2 Literature Review
11.3 5G: Overview
11.4 Smart Cities
11.5 Cognitiveness of 5G Network
11.6 Key Features for 5G Toward Sustainable Development of Smart Cities
11.7 Application Enabled by 5G
11.8 Sustainable 5G-Green Network
11.9 Electricity Harvesting for Smart Cities
11.10 Economic Impact of 5G Toward Sustainable Smart Cities
11.11 5G Challenges
11.12 Conclusion
References
12 Society 5.0 and Authenticity: Looking to the Future
12.1 Introduction
12.2 Theoretical Framework
12.3 Research Design and Methodology
12.4 Results
12.5 Conclusion
References
Appendix
13 IoT-Based Smart City Applications: Infrastructure, Research and Development
13.1 Introduction
13.2 Different Phases of Development
13.3 Current Scenario
13.4 Conclusion and Future Work
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 5
Table 5.1 Fuzzy evaluation scale.
Table 5.2 List of shortlisted challenges.
Table 5.3 List of shortlisted solutions.
Table 5.4 Results of fuzzy SWARA to weight challenges.
Table 5.5 Fuzzy waspas decision making matrix.
Table 5.6 FUZZY WASPAS normalized decision-making matrix.
Table 5.7 Fuzzy WASPAS weighted normalized decision-making matrix for summatio...
Table 5.8 Fuzzy WASPAS weighted normalized decision-making matrix for multipli...
Table 5.9 Fuzzy WASPAS result and ranking of the solutions.
Chapter 6
Table 6.1 List of challenges in implementing DSS in smart cities.
Table 6.2 List of challenges in implementing DSS in smart cities.
Table 6.3 Alternative rating.
Table 6.4 Decision matrix for pairwise table.
Table 6.5 Relative-closeness matrix.
Chapter 7
Table 7.1 Criteria for evaluation by teacher trainer’s in an educational suppl...
Table 7.2 Initial matrix for evaluation by one decision maker for DEMATEL.
Table 7.3 Final prominence vector and relationship vector for DEMATEL.
Chapter 10
Table 10.1 Comparative analysis in terms of accuracy with data augmentation.
Table 10.2 Comparative analysis: without data augmentation.
Chapter 11
Table 11.1 Discuss the application of 5G communication in the sustainable deve...
Chapter 12
Table 12.1 Measurement model assessment.
Table 12.2 Discriminant validity.
Table 12.3 Results of hypothesis testing.
Table 12.4 Measures for variables.
List of Illustrations
Chapter 1
Figure 1.1 Basic Arduino Uno, microcontroller [8].
Figure 1.2 Soil moisture sensor [8].
Figure 1.3 Temperature and humidity sensor [8].
Figure 1.4 Rainfall sensor [8].
Figure 1.5 Block diagram of smart irrigation system using IoT: future prospect...
Figure 1.6 Data flow diagram of smart irrigation system using IoT: future pros...
Figure 1.7 Data flow diagram of smart irrigation system using IoT: future pros...
Figure 1.8 Data flow diagram of smart irrigation system using IoT: future pros...
Figure 1.9 Activity diagram/flowchart of smart irrigation system using IoT: fu...
Figure 1.10 Login page of the app of smart irrigation system.
Figure 1.11 Home page of our working app.
Figure 1.12 Proposed system of smart irrigation system using IoT: future prosp...
Chapter 2
Figure 2.1 Illustrative architecture of integrated view of pan smart city comp...
Figure 2.2 Illustrative architecture of integrated view of pan smart city comp...
Chapter 3
Figure 3.1 Basic ITI (or FLRITI) concept.
Figure 3.2 (a) Interleaver allocation in UAV communication. (b) Interleaver al...
Figure 3.3 ITI allocation in UAV communication.
Figure 3.4 Basic DSS model for ITI allocation.
Figure 3.5 Coordination between base station and DSS.
Figure 3.6 ITI-based cluster interleaving with DSS for smart city framework.
Figure 3.7 Role of DSS in allocating temporary interleaver/ITI.
Chapter 5
Figure 5.1 Flowchart of the methodology.
Chapter 6
Figure 6.1 Fuzzy-TOPSIS methodology.
Chapter 7
Figure 7.1 Educational supply chain. Source: modified by authors based on vers...
Figure 7.2 Input-output relationships in educational service supply chain. Sou...
Figure 7.3 Steps of DEMATEL.
Figure 7.4 Diagraph of cause-and-effect group from DEMATEL.
Chapter 9
Figure 9.1 Conceptual architectural design of proposed system.
Figure 9.2 Demonstrates the necessary steps considered to track the child.
Figure 9.3 Sensory data collection.
Figure 9.4 Readings from sensors.
Figure 9.5 Turned ON buzzer.
Figure 9.6 Turned ON LED.
Figure 9.7 SMS notification via Twilio.
Chapter 10
Figure 10.1 Proposed method.
Figure 10.2 Field images of wet, dry, and extremely wet test cases.
Figure 10.3 Artificially creating new training data.
Figure 10.4 VGG-16 architecture.
Figure 10.5 VGG-19 architecture.
Figure 10.6 Inception model architecture.
Figure 10.7 Architecture of xception model.
Figure 10.8 ResNet50 architecture.
Figure 10.9 Comparison: validation accuracy.
Figure 10.10 Prediction by different algorithms on test data with augmentation...
Chapter 11
Figure 11.1 Global ICTs connection 2019-2030 (Source: Transforma Insights, 202...
Figure 11.2 Basic constraints.
Chapter 12
Figure 12.1 Transition to smart life.
Figure 12.2 Research’s model.
Figure 12.3 Structural model.
Chapter 13
Figure 13.1 Architecture of an IoT system.
Figure 13.2 IoT-based smart system over the years.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Dedication
Preface
Acknowledgment
Table of Contents
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Sustainable Computing and Optimization
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Scope: The objective of “Sustainable Computing and Optimization” series is to bring together the global research scholars, experts, and scientists in the research areas of sustainable computing and optimization from all over the world to share their knowledge and experiences on current research achievements in these fields. The series aims to provide a golden opportunity for global research community to share their novel research results, findings, and innovations to a wide range of readers, present globally. Data is everywhere and continuing to grow massively, which has created a huge demand for qualified experts who can uncover valuable insights from data. The series will promote sustainable computing and optimization methodologies in order to solve real life problems mainly from engineering and management systems domains. The series will mainly focus on the real life problems, which can suitably be handled through these paradigms.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Decision Support Systems for Smart City Applications
Edited by
Loveleen GaurVernika AgarwalandPrasenjit Chatterjee
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-89643-2
Cover image: Pixabay.ComCover design by Russell Richardson
Dedication
The Editors would like to dedicate this book to their parents, life partners, children, students, scholars, friends, and colleagues.
Preface
The current era of urbanization requires strong strategies and innovative planning to modernize urban life. Cities play a primary role in various aspects of social and economic development worldwide, and have a huge impact on the environment. A city is a large and permanent human ecosystem that provides services and opportunities to its citizens. Promoting sustainability has been a major challenge for urban cities. The aim of sustainable cities is to ensure that cities can respond to their inhabitant’s needs through sustainable solutions to socio-economic problems. The major challenge for cities worldwide is to have optimal solutions for transportation linkages, mixed land uses, and high-quality urban services with long-term positive effects on the economy.
Many cities are enhancing quality and performance of urban services by being digitalized, intelligent and smarter. Many of the new approaches related to urban services are based on harnessing technologies, including information and communication technologies (ICT), helping to create what some call “smart cities.” Over the last two decades, the concept of “smart city” has become more and more popular in scientific literature and international policies. Smarter cities start from the human capital side, rather than blindly believing that ICT can automatically create a smart city. Approaches towards education and leadership in a smart city should offer environments for an entrepreneurship accessible to all citizens. The stakeholders and policymakers are exploring solutions to deliver new services in an efficient, responsive and sustainable manner for a large population. This book explores various dimensions of the possible services available for making a city smart.
Making a smart city is an emerging strategy to mitigate the problems generated by urban population growth and rapid urbanization. There is very little academic literature in this context, with much of it focused on the definition of smart cities. The growth of an intelligent decision support system presented in the literature has given rise to various computational methods. The amalgamation of these two concepts in literature is still very scarce in research. This book aims to provide a better understanding of the concept of smart cities and the application of an intelligent decision support system. Based on analysis of existing information there are eight critical factors of smart city initiatives: management and organization, technology, governance, policy context, people and communities, economy, built infrastructure, and natural environment. This book will focus on the application of the decision support system in managing these eight crucial aspects of smart cities.
Our intent in writing this book was also to provide a source that covers the stage-by-stage integration of the four key areas involving planning, physical infrastructure, ICT infrastructure and deploying the smart solutions necessary for city transformation. With this as the motivation, this book provides the application of an intelligent decision support system for effectively and efficiently managing the transformation process, which can aid various supply chain stakeholders, academic researchers and related professionals in building smart cities. Various chapters of this book are expected to support practicing managers during the implementation of smart solutions for city transformation.
Chapter 1 deals with the development of an IoT-based smart agriculture device which can be a useful product and proven game changer in Next Gen Agriculture. Aided by the internet of things, this automated smart irrigation system maintains the adequate amount of water needed by crops by monitoring the amount of moisture, temperature and humidity in the soil. Temperature and humidity data is then maintained in a database for use in crop rotation and to help farmers select the appropriate crops. It will also benefit farmers by making it possible to monitor crop irrigation from a distant location, which in turn saves farmers time and reduces their workload. Chapter 2 describes a decision support system (DSS) for smart cities. Using standard mathematical and statistical models, DSS analyzes complex data, thereby producing the required information. Since the amount of information generated/used as input always deals with big data, it requires classificational analysis. Establishing an integrated command and control center as part of the Smart City Mission of creating an intelligent traffic system is discussed and illustrated with a case study. Chapter 3 evaluates the potential interleavers for burst error control and interference mitigation, whose possibilities for use in UAV communications are yet to be investigated. Hence, the idea of employing interleavers for interference mitigation in UAV communications is envisioned and analyzed. More specifically, we explore the role of ITI in UAV communication for this purpose. The chapter also provides a decision support system used for interleaver assignment to each UAV that is applicable within the smart city framework. Chapter 4 describes the difficulties faced when enhancing the consistency and sustainability of facilities for city residents, and also presents the characteristics of the ecosystem in smart cities. In this context, a DSS based on the development of the model of the analytical hierarchical process integrated into the logic representation system has recently become increasingly relevant. The DSS is positioned to be a unique benefit as a constantly evolving tool to prepare and complete exponential growth using IT infrastructure, cloud computing, web-based apps, and everything-as-a-service (XaaS) for the development of new mathematical models, artificial intelligence and data storage. Included in the chapter are decisions, decision modeling and decision management principles, support mechanisms for decision making and collaborative systems, as well as how they can assist in smart city planning initiatives.
Next, Chapter 5 discusses how technology enhancements that include social sustainability are driving the development of smart city economies. Blockchain technology, also known as “digital ledger,” is the building block for these smart cities, and the demand for it is increasing at a very rapid rate in all sectors and markets. This chapter describes the challenges which hinder the adoption of blockchain technology in smart cities and provides valuable solutions for solving them using fuzzy step-wise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assessment (WASPAS). These aim to determine the weight of each challenge and its importance and provide a rank to different solutions which will help in addressing them. The study is validated by taking Indian cities into consideration. Chapter 6 discusses the implementation of a DSS in modeling a smart city supported by the internet of things (IoT), cloud services, artificial intelligence (AI), and everything-as-a-service (XaaS), which position it ahead of many other software. The primary purpose of this chapter is to identify the challenges which hold back the use of DSS in smart cities and to provide optimal solutions to them by using a multicriteria decision-making (MCDM) approach. The technique for order preference by similarity to ideal solution (TOPSIS) is a fuzzy MCDM method. The results have been validated using the case of smart cities in northern India.
Next, Chapter 7 highlights the changes that have been implemented to help people adapt to new age technology. Those in different sectors have been forced to learn new ways of working; for example, smart cities which were only in the planning and policy development stage suddenly saw “Jugaad” technology playing a role in it. Therefore, the aim of this chapter is to engender understanding of the barriers to using technology in the education sector. Since there are many stakeholders to consider in this sector, it is impossible to consider all dimensions in one study. The aim is to determine the barriers faced by teacher’s training through an online teaching platform. The DEMATEL technique is a multicriteria decision-making tool to further establish the cause-and-effect between the criteria. It is necessary to remove the antecedents of the consequents and establish a better education system that leads to smart education for smart cities. Chapter 8 describes modern technology, from IoT to news. The integration of these innovations will turn industry 4.0 into industry 5.0 in the context of smart city technologies.
Next, Chapter 9 presents a framework for developing smart child safety using the internet of things; and Chapter 10 implements deep learning and transfer learning algorithms to predict soil moisture for smart city application, both with and without the effect of data augmentation.Chapter 11 presents the role of information and communication technologies (ICTs) in the sustainable development of smart cities toward futuristic communication. In the context of “smart life,” Chapter 12 proposes a new framework in which the value of knowledge is defined not only by competitiveness and productivity, but also by consumer demand. It is based on a quantitative study of 300 traditional product users in a digital environment. Synergies between technological, social, and economic systems are highlighted in the contributions. The concluding Chapter 13 presents surveys and analyses of smart city projects, as well as recommendations on how to sustain them while balancing the Earth, people, and profits. Furthermore, new technical implementations are recommended that are not only enticing but also tackle real-world challenges in emerging countries like India.
The transformation of “cities” into “smart cities” is the need of the hour. Due to the growth in population and high level of urbanization, policymakers, urban developers, government officials and service providers need to develop smart cities that are self-sustaining. A smart city is a framework, predominantly composed of information and communication technologies (ICT), in which to develop, deploy, and promote sustainable practices to address the growing challenges of urbanization. A big part of this ICT framework is essentially an intelligent network of connected objects and machines that transmit data using wireless technology and the cloud. Cloud-based IoT applications receive, analyze, and manage data in real time to help municipalities, enterprises, and citizens make better decisions that improve quality of life. Citizens engage with smart city ecosystems in a variety of ways, such as using smartphones and mobile devices, as well as connected cars and homes. Pairing devices and data with a city’s physical infrastructure and services can cut costs and improve sustainability. The major challenges in this framework are the application of computation techniques, including real-life data processing, connectivity issues, and application of IoT in everyday life. This book aims to address these challenges by describing how the transformation of cities into smart cities can be smoothed with the help of an intelligent decision support system.
Aimed at both academics and practitioners alike, this book was designed as a guide to developing smart cities. It focuses on the hierarchical decision-making process in the field of smart cities implementation; more specifically, it provides an essential framework in which policymakers and executives can make strategic decisions. The potential audience for the book includes researchers, industrial manufacturers, supply chain managers, stakeholders, policymakers, and other technical and administrative personnel. The book can be used as a textbook or to supplement graduate programs in smart cities, operations and management, sustainable management, and business administration.
The Editors
October 2022
Acknowledgment
The editors wish to express their warm thanks and deep appreciation to those who provided valuable input, support, constructive suggestions and assistance in editing and proofreading of this book.
The editors would like to thank all the authors for their valuable contributions in enriching scholarly content of the book.
Mere words cannot express the editors’ deep gratitude to the entire editorial and production teams of Scrivener Publishing, particularly Martin Scrivener for his great support, encourangement and guidance all through the publication process. This book would not have been possible without his significant contributions.
The editors would like to sincerely thank the reviewers who kindly volunteered their time and expertise for shaping such a high-quality book on a very timely topic.
The editors wish to acknowledge the love, understanding, and support of their family members during the book’s preparation.
Finally, the editors use this opportunity to thank all the readers and expect that this book will continue to inspire and guide them for their future endeavour.
The Editors
1Techno Agri for New Cities by Smart Irrigation
Rohit Rastogi*, Sunil Kumar Prajapati, Shiv Kumar and Satyam Verma
Computer Science & Engineering Department, ABES Engineering College, Ghaziabad, U.P., India
Abstract
Agriculture is the most important source of food production. It also plays a crucial role in the gross domestic product of the country. But there are various constraints in traditional methods of agriculture. These constraints include excessive use of water during cultivation of crops, time, money, etc. In order to overcome the various constraints involved in the agriculture sector, there is a need for an evolved irrigation system. This paper aims at developing an automated smart irrigation system with the help of the Internet of things. Its aim is to maintain an adequate amount of water needed by the crop by monitoring the amount of soil moisture, temperature, and humidity in the soil. The data of temperature and humidity are maintained in the database for backup. The data are used for crop rotation and also help the farmer for the selection of appropriate crops. We can also verify the different types of soil appropriate for different crops using this model. These will also benefit the farmers as they will be able to monitor the irrigation of the crop from a distant location. It would also save the time of the farmer and reduce the labor work. The manuscript deals with the IoT-based smart agriculture device, which can be developed as a useful product and be proved as a game changer in the next-gen agriculture. In South Asian continent, farmers are economically poor, and they will be highly benefitted by this application, if developed commercially. Smart cities concept in India will be supported by this.
Keywords: Arduino, soil moisture sensor, humidity and rain sensor, esp8266 wi-fi module, dht-11, smart irrigation, IoT
1.1 Introduction
Agriculture can be defined as a technique of cultivating the soil, growing crops and raising livestock. Agriculture is considered as the main source of food and fabrics. Cotton, wool, paper and leather are all agricultural products. Agriculture also provides wood for construction materials and other household activities. Before agriculture became an important factor people used to spend most of their lives searching for food and hunting wild animals. But around 2000 years ago, agriculture became the most important source of food and most of the Earth’s population became dependent on agriculture.
Water is a basic need of every living being in this world. It plays a vital role in carrying out day to day activities in human life. Agriculture is an area where water is required in a large quantity for better growth of the crops. But due to overuse of water the ground water level is depleting very rapidly. The main reason for this problem is the population growth and its increasing demand for water requirements. Overconsumption and wastage of water is another major problem, which is leading to water crisis in this world. Water crisis may lead to economic decline and poor living conditions if we continue the current scenario of water usage [5, 12].
With the estimated growth of the world’s population to 8 billion by the year 2050, the requirement for crops and food will also increase rapidly. On the other hand, the temperature is likely to increase by 4 to 5 degrees in the next few years due to global warming. Some climate models describe that there would be an increase in concentration of carbon dioxide on the crops. Therefore, climate change has the potential in affecting the productivity of agriculture. It is expected that there would be an increase in yearly dry days to about 15 extra dry days in the next few years. Which means that the dry areas would likely receive less rainfall throughout the year. This will have a direct impact on the total growth of agriculture [13].
Water is considered as the most important substance for running our life properly. Our bodies need water to function properly. According to Science humans can survive for weeks without food, but can survive only a few days without water. But we humans are depleting the fresh water sources very rapidly because we are not bothered to use the water in an efficient manner [13].
The Earth’s temperature is rising due to global warming and the hotter the earth will be the more would be the demand for water. The shortage of water will lead to less production in the agricultural field and thus the water crisis will become a food crisis. The main source of freshwater is groundwater, which is decreasing very rapidly. Ground water level could be increased by using the technique of rainfall harvesting [21].
Smart irrigation devices are the components that we are using in this project which will first analyze the climatic conditions like rainfall and temperature and then the will automatically operate the process of irrigation. Devices like rainfall, temperature, and humidity and moisture sensors are able to give precise values, which are used by Arduino to carry out the automated irrigation process. These values are used to match the threshold values and then the water pump is turned ON and OFF accordingly. Thus, using these smart irrigation devices, we can reduce water wastage, as well as increase the productivity [8].
IoT abbreviates to the Internet of Things. IoT is considered as a milestone when we talk about the evolution of superior technology. IoT comes into mind when we try to automate things. IoT can be applied in various sectors such as home automation, surveillance systems, and in the agriculture sector, there is a wide range of applications of IoT. As we already know that the crops require proper care for better yielding and irrigation is the most dominating factor that affects it most. Due to irregular monsoon, cultivated plants do not grow properly and result in low production. Using advanced technology like IoT, we can overcome this problem. By planting different sensors in the field, we can record important factors like temperature, humidity of the air and soil moisture content and make decisions accordingly using microcontrollers. Irrigation will be done automatically when the moisture of soil falls. It will be more helpful in the areas where there is a lack of water supply and fewer rainfall readings. The use of IoT in an irrigation system can bring a new revolution in the agriculture sector [10, 12].
The soil moisture of the field can be figured out by various techniques, such as by using the thermogravimetric method or by using a gypsum block and tensiometer methods. These methods are old and are put back by time domain reflectometry, frequency domain reflectometry, and optical sensor technology. Soil moisture estimation based on sensors provides data, which is real time, at an affordable cost. The sensor-based irrigation has a lot of positive points over the traditional method. It collects data that are real time and can be interpreted accordingly by different smart modules. It is cost-efficient and time-saving [11, 14].
Productivity increase,
Less water consumption,
Almost zero manpower consumption,
Cost efficient,
System have weather resistance,
Most efficient use of water.
Drip-irrigation System (Traditional): The most efficient way of irrigation is the traditional drip irrigation system. It allows water to ooze at the plant roots, resulting in less water wastage. It also helps in the efficient utilization of fertilizer, which is absorbed by soil uniformly with steady irrigation [12, 15].
Irrigation with Timer System: The best way to reduce water wastage in irrigation is by making a schedule. An irrigation system with an automatic timer can prevent over-watering in the field and can prevent from damaging the crop due to excessive irrigation. It helps to manage the water requirement for each season. It is cost efficient and reduces wastage of water while irrigation [12].
Smart Irrigation System: It uses MATLAB along with wireless sensors and IOT. Very good for the water usage optimization and can be operated remotely. It has auto and manual mode, which are very helpful, and cloud implementation makes it highly applicable [12, 16].
Research Objective
In this fast-growing digital world, we have thrust our thinking limit and are trying to replace normal brains with an artificially created one. Using AI we can make an intelligent machine. Machine learning with deep learning, ANN, CNN, sensors can intensify the machine work, which results in the development of more superior technology. The use of AI and ML in the agriculture sector along with different sensors to capture data can bring revolution and give birth to a happy and prosperous era [6].
1.2 Literature Review
The paper by Bobby Singla and others tells about how we can effectively control the water supply in our agricultural field. Sensors that are used for this application are DTH-11 sensor and soil moisture sensor. The information is provided on farmer mobile phones using Wi-fi and Arduino. In this manuscript, the DTH-11 temperature sensor and soil moisture sensor are connected to the input pins of Arduino Uno. The analog values produced by Arduino Uno are converted to digital output by the microcontroller. The obtained values are displayed by the mobile application. The motor is switched on/off based on the value obtained from the microcontroller with the already defined threshold value. The abovementioned system is found to be efficient in reducing the cost of the farmers and optimizing their agricultural production. The maintenance required by the system is also less [1, 17].
Rawal, Shrishthi and other team has found in their paper proposes an irrigation system which maintains and decides the required soil moisture content through automatic watering. The value obtained from soil moisture sensors helps to determine the exact quantity of water needed for irrigation. The system is divided into hardware and software components. Hardware comprises systems such as sensor, Arduino-uno whereas the software consists of a webpage displaying the data from the microcontroller. The sprinkler control is achieved using a threshold value. The value obtained from the system decides whether to turn on/off the sprinkler. The reading obtained is then put forward on the farmers’ website. The system uses value obtained from the microcontroller to on/off the sprinkler. This prevents the loss of the farmer and thereby avoiding crop damage [2, 18].
Nandhini and their team of researchers revealed that the proposed irrigation system helps to regulate the flow of water in the system. By using these systems, we can make effective use of water. The system uses soil and humidity sensors to find the level of moisture and humidity in the soil. The sensed values are then displayed on the screen. This system also uses various sensors, such as pH sensor, pressure sensor, DTH-11 sensor to find the sensed values from the Arduino UNO. The sensed values are then sent to be displayed on the screen of the web page application. If the value on the sensor crosses the threshold value, then the pump is turned on/off automatically. The main objective is to find the effective, user-friendly solution to the given problem. Due to readily available updates from the server, users can know about crop fields anytime [3, 19, 21].
In their experiments of agriculture, Aman Kumar and team proposed that their system is an automated irrigation system designed to save the time, power, and money of the farmer. By using these systems, we can make effective use of water. The system uses soil and humidity sensors to find the level of moisture and humidity in the soil. The sensed values are then displayed on the screen. The sensed values are then sent to be displayed on the screen of the web page application. If the value on the sensor crosses the threshold value, then the pump is turned on/off automatically. The main objective is to find the effective, user-friendly solution to the given problem. Due to readily available updates from the server, users can know about crop fields anytime [4, 20].
1.3 Components Used
Arduino System
Arduino UNO is basically a microcontroller, which has both hardware, as well as software components. Multiple sensors could be connected at a time to the Arduino board, and these sensors gave values to Arduino with the help of program codes. The board also consists of LED, which glows when our values are matched. We can run Arduino either by connecting to our computer or by using DC power supply. The main concept is to run a physical device by using software. With the help of programming, we can easily automate various devices using Arduino. An Arduino board generally consists of analog and digital pins, a USB port, a power jack as well as a reset button (as per Figure 1.1) [8].
Sensors Used in our Research Work
In this project, we are using three types of sensors in order to calculate the soil, as well as atmospheric conditions. Sensors used are soil moisture sensor, humidity, and temperature sensor, as well as rainfall sensor.
Soil Moisture Sensor
The moisture of the soil plays an important role in the irrigation of a field. The soil moisture sensor is a kind of sensor which is used to measure the content of water within the soil. Moisture of the soil is dependent on the amount of water within the soil. If the soil is dry, it will have less moisture as compared with the wet soil. The moisture sensor works by inserting it into the field and the water content in the soil is reported in the form of percentage. There are multiple uses of soil moisture sensor:
Figure 1.1 Basic Arduino Uno, microcontroller [8].
Figure 1.2 Soil moisture sensor [8].
Agriculture
Landscape Irrigation
Research (as per
Figure 1.2
) [
8
].
Temperature and Humidity Sensor
Temperature and humidity sensor (DHT11) is a combined low cost sensor that gives values for both temperature, as well as humidity in the environment. It works by inserting the sensor in the Arduino board and it gives climatic conditions of the surroundings. Humidity measurements do not mean to measure humidity directly; rather they depend on the measurement of quantities such as temperature, pressure, mass, resistivity to calculate humidity. These sensors give the output as digital values, which make them easy to interface and use with microcontrollers, such as Arduino, Raspberry Pi boards (as per Figure 1.3) [8, 9].
Rainfall Sensor
The rainfall sensor is a device that is used to calculate the amount of rainfall in a particular area. This sensor is used as a water preservation device, and this is connected to the irrigation system to check if there is rainfall going on and if the condition is true it shuts down the system at the time of rainfall. This sensor includes a board with nickel coated line, and it works on the resistance principle. When the rain droplets fall in the nickel coated board it gives the value of rainfall in the area. The four pins of the sensor are inserted in the Arduino while the board is kept in the field to calculate values (as per Figure 1.4) [8].
Figure 1.3 Temperature and humidity sensor [8].
Figure 1.4 Rainfall sensor [8].
1.4 Proposed System
Various sensors, microcontrollers, the android application can be used for making an automatic irrigation system. We generally go for low-cost humidity, temperature, and soil-moisture sensors. These sensors are connected to Arduino and continuously monitor the field.
The collected data by the Arduino through sensors are transmitted to the user wirelessly so that they can control the system remotely. The smart android application compares the value received from the sensors from its database and takes the appropriate decision. The proposed system has two modes auto and manual.
When the auto mode is on, the system acts automatically without any human interruption, while with manual mode ON, the motor can be operated with just a click of the switch. The motor toggles accordingly with soil moisture value, if the value is below the threshold motor turns ON else remains in OFF state.
The sensors are joined to the Arduino Uno and the hardware communicates through a microcontroller (ESP8266) which is a wi-fi module. All sensor values are displayed on the mobile interface so that the user has a continuous reach of the condition of the field. Programming of Arduino Uno is done in Embedded C.
