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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
MACHINE LEARNING PARADIGM FOR INTERNET OF THINGS APPLICATIONS As companies globally realize the revolutionary potential of the IoT, they have started finding a number of obstacles they need to address to leverage it efficiently. Many businesses and industries use machine learning to exploit the IoT's potential and this book brings clarity to the issue. Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies, and business people. The book addresses the problem and new algorithms, their accuracy, and their fitness ratio for existing real-time problems. Machine Learning Paradigm for Internet of Thing Applications provides the state-of-the-art applications of machine learning in an IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store 'contextualized marketing', and intelligent transportation systems. Readers will gain an insight into the integration of machine learning with IoT in these various application domains.
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Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
Preface
1 Machine Learning Concept–Based IoT Platforms for Smart Cities’ Implementation and Requirements
1.1 Introduction
1.2 Smart City Structure in India
1.3 Status of Smart Cities in India
1.4 Analysis of Smart City Setup
1.5 Ideal Planning for the Sewage Networking Systems
1.6 Heritage of Culture Based on Modern Advancement
1.7 Funding and Business Models to Leverage
1.8 Community-Based Development
1.9 Revolutionary Impact With Other Locations
1.10 Finding Balanced City Development
1.11 E-Industry With Enhanced Resources
1.12 Strategy for Development of Smart Cities
References
2 An Empirical Study on Paddy Harvest and Rice Demand Prediction for an Optimal Distribution Plan
2.1 Introduction
2.2 Background
2.3 Methodology
2.4 Results and Discussion
2.5 Conclusion
References
3 A Collaborative Data Publishing Model with Privacy Preservation Using Group-Based Classification and Anonymity
3.1 Introduction
3.2 Literature Survey
3.3 Proposed Model
3.4 Results
3.5 Conclusion
References
4 Production Monitoring and Dashboard Design for Industry 4.0 Using Single-Board Computer (SBC)
4.1 Introduction
4.2 Related Works
4.3 Industry 4.0 Production and Dashboard Design
4.4 Results and Discussion
4.5 Conclusion
References
5 Generation of Two-Dimensional Text-Based CAPTCHA Using Graphical Operation
5.1 Introduction
5.2 Types of CAPTCHAs
5.3 Related Work
5.4 Proposed Technique
5.5 Text-Based CAPTCHA Scheme
5.6 Breaking Text-Based CAPTCHA’s Scheme
5.7 Implementation of Text-Based CAPTCHA Using Graphical Operation
5.8 Graphical Text-Based CAPTCHA in Online Application
5.9 Conclusion and Future Enhancement
References
6 Smart IoT-Enabled Traffic Sign Recognition With High Accuracy (TSR-HA) Using Deep Learning
6.1 Introduction
6.2 Experimental Evaluation
6.3 Conclusion
References
7 Offline and Online Performance Evaluation Metrics of Recommender System: A Bird’s Eye View
7.1 Introduction
7.2 Evaluation Metrics
7.3 Related Works
7.4 Experimental Setup
7.5 Summary and Conclusions
References
8 Deep Learning–Enabled Smart Safety Precautions and Measures in Public Gathering Places for COVID-19 Using IoT
8.1 Introduction
8.2 Prelims
8.3 Proposed System
8.4 Math Model
8.5 Results
8.6 Conclusion
References
9 Route Optimization for Perishable Goods Transportation System
9.1 Introduction
9.2 Related Works
9.3 Proposed Methodology
9.4 Proposed Work Implementation
9.5 Conclusion
References
10 Fake News Detection Using Machine Learning Algorithms
10.1 Introduction
10.2 Literature Survey
10.3 Methodology
10.4 Experimental Results
10.5 Conclusion
References
11 Opportunities and Challenges in Machine Learning With IoT
11.1 Introduction
11.2 Literature Review
11.3 Why Should We Care About Learning Representations?
11.4 Big Data
11.5 Data Processing Opportunities and Challenges
11.6 Learning Opportunities and Challenges
11.7 Enabling Machine Learning With IoT
11.8 Conclusion
References
12 Machine Learning Effects on Underwater Applications and IoUT
12.1 Introduction
12.2 Characteristics of IoUT
12.3 Architecture of IoUT
12.4 Challenges in IoUT
12.5 Applications of IoUT
12.6 Machine Learning
12.7 Simulation and Analysis
12.8 Conclusion
References
13 Internet of Underwater Things: Challenges, Routing Protocols, and ML Algorithms
13.1 Introduction
13.2 Internet of Underwater Things
13.3 Routing Protocols of IoUT
13.4 Machine Learning in IoUT
13.5 Performance Evaluation
13.6 Conclusion
References
14 Chest X-Ray for Pneumonia Detection
14.1 Introduction
14.2 Background
14.3 Research Methodology
14.4 Results and Discussion
14.5 Conclusion
Acknowledgment
References
Index
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 Bhubaneswar smart city structure.
Figure 1.2 Pune smart city overview.
Figure 1.3 Funds raised by government of India.
Figure 1.4 Physical infrastructure workflow.
Figure 1.5 Water supply chain in city structure.
Figure 1.6 Smart city control flow for command and control centers.
Chapter 2
Figure 2.1 System diagram of “isRice”.
Figure 2.2 Structure of RNN.
Figure 2.3 Output feedback for RNN.
Figure 2.4 Structure of a standard RNN cell.
Figure 2.5 Structure of a standard LSTM cell.
Figure 2.6 Chromosome structure.
Figure 2.7 One-point crossover.
Figure 2.8 Mutation: swap operator.
Figure 2.9 Business model of “isRice”.
Figure 2.10 “isRice” main interface.
Figure 2.11 Harvest prediction interface.
Figure 2.12 Demand prediction interface.
Figure 2.13 MAE for train set and test set: Paddy harvest prediction.
Figure 2.14 MAE for train set and test set: Rice demand prediction.
Figure 2.15 Number of generations vs. fitness value.
Figure 2.16 Time vs. number of consumer districts.
Chapter 3
Figure 3.1 Data collection and publishing model.
Figure 3.2 Classification accuracy.
Figure 3.3 Comparison of privacy preservation rate.
Figure 3.4 Data publishing security level.
Chapter 4
Figure 4.1 Domain technologies aiding Industry 4.0.
Figure 4.2 Industrial production monitoring system through IIoT.
Figure 4.3 Production unit in industries.
Figure 4.4 Programming chart of digital twin creation.
Figure 4.5 Digital twin design of production monitoring unit.
Figure 4.6 Network round trip delay time.
Chapter 5
Figure 5.1 Example of text CAPTCHA.
Figure 5.2 Image-based captcha.
Figure 5.3 Confusing character in a Google CAPTCHA.
Figure 5.4 Segmentation method based on individual character.
Figure 5.5 Segmented CAPTCHA image.
Figure 5.6 Graphical operation made CAPTCHA image.
Figure 5.7 Graphical sesign CAPTCHA in online application.
Chapter 6
Figure 6.1 CNN feature extraction structure diagram in deep learning.
Figure 6.2 (a) Input image; (b) Output image. (c) Input image; (d) Output image....
Figure 6.3 Smart IoT-enabled traffic signs recognizing with high accuracy using ...
Chapter 7
Figure 7.1 Evaluation structure of recommender system.
Figure 7.2 ROC-AUC curve.
Figure 7.3 Types of user study.
Figure 7.4 Basic structure of A/B test.
Figure 7.5 Process of data mining in RS.
Figure 7.6 Rating prediction through matrix factorization.
Figure 7.7 Process flow of offline evaluation.
Figure 7.8 Illustration of IBCF.
Figure 7.9 Performance evaluation of Random vs. SVD.
Figure 7.10 Performance evaluation of SVD vs. SVD++.
Figure 7.11 Novelty calculation of Random, SVD, and SVD++.
Chapter 8
Figure 8.1 Convolutional Neural Network (CNN).
Figure 8.2 Dataflow diagram.
Figure 8.3 Safety equipment detecting.
Figure 8.4 Detecting mask using deep learning.
Figure 8.5 Detecting body temperature using thermal sensor.
Figure 8.6 Raspberry Pi 3 connected with smart Locking door.
Chapter 9
Figure 9.1 Silhouette analysis on K-means clustering on sample data with n_clust...
Figure 9.2 City market hub marked in red and the market locations to deliver goo...
Figure 9.3 Assigned vehicle route for a key market hub.
Figure 9.4 Sample output for the depot (0).
Chapter 10
Figure 10.1 Fake news evaluation matrix.
Figure 10.2 Feature extraction.
Figure 10.3 Confusion matrix of logistic regression.
Figure 10.4 Confusion matrix of Naïve Bayes.
Figure 10.5 Confusion matrix for random forest classifier.
Figure 10.6 Confusion matrix for XGBoost algorithm.
Figure 10.7 Accuracy level of machine learning algorithms.
Figure 10.8 ROC curve of random forest for all four classes.
Chapter 11
Figure 11.1 The paradigm for ML on Big data (MLBiD).
Chapter 12
Figure 12.1 Internet of Underwater Things basic model.
Figure 12.2 Architecture of IoUT.
Figure 12.3 Applications of IoUT.
Figure 12.4 Average communication cost vs. node mobility.
Figure 12.5 Energy consumption vs number of nodes.
Chapter 13
Figure 13.1 Concept of IoUTs.
Figure 13.2 Concept and devices used in IoUT.
Figure 13.3 Different routing protocols in IoUT.
Figure 13.4 Multipoint relays in OLSR.
Figure 13.5 The relationship between S and L in GFGD.
Figure 13.6 A 3D logical grid view of EMGGR protocol.
Figure 13.7 The probability of ACK’s collision.
Figure 13.8 Operations in DRP.
Figure 13.9 Delivery ratio vs. number of nodes.
Figure 13.10 Energy consumption vs. number of nodes.
Chapter 14
Figure 14.1 (a) Pneumonia x-ray image, (b) Healthy x-ray image.
Figure 14.2 Xception network architecture.
Figure 14.3 (a) Model accuracy, (b) Model loss.
Figure 14.4 Confusion matrix.
List of Tables
Chapter 2
Table 2.1 Feasibility study summary.
Table 2.2 Harvest prediction: Raw data fields.
Table 2.3 Paddy harvest prediction - Data set.
Table 2.4 Demand predict: Raw data fields.
Table 2.5 Rice demand prediction: Data set.
Table 2.6 Mutation rate effect.
Table 2.7 Mutation probability effect.
Chapter 3
Table 3.1 Classification accuracy.
Chapter 5
Table 5.1 Text-based CAPTCHA used in commercial website.
Table 5.2 Breaking methodology and success rate of various sources.
Table 5.3 Pixel value changed entry.
Table 5.4 Look up table entry.
Chapter 7
Table 7.1 Offline evaluation metrics.
Table 7.2 Illustration of confusion matrix.
Table 7.3 Overview of additional metrics.
Table 7.4 Overview of filtering techniques.
Table 7.5 Overview of classifier algorithm.
Table 7.6 Overview of the explored dataset.
Table 7.7 System generated metric results using MovieLens with Random and SVD wh...
Table 7.8 System generated metric results using MovieLens with SVD and SVD++ whe...
Chapter 8
Table 8.1 Raspberry Pi history with version and configuration.
Chapter 9
Table 9.1 Phase 2: algorithm for key hub identification.
Table 9.2 Phase 3: algorithm for vehicle routing.
Chapter 13
Table 13.1 Comparison of various routing protocols for different quality paramet...
Chapter 14
Table 14.1 Dataset description.
Table 14.2 Model metric parameters.
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
Begin Reading
Index
End User License Agreement
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Next-Generation Computing and Communication Engineering
Series Editors: Dr. G. R. Kanagachidambaresan and Dr. Kolla Bhanu Prakash
Developments in artificial intelligence are made more challenging because the involvement of multi-domain technology creates new problems for researchers. Therefore, in order to help meet the challenge, this book series concentrates on next generation computing and communication methodologies involving smart and ambient environment design. It is an publishing platform for monographs, handbooks, and edited volumes on Industry 4.0, agriculture, smart city development, new computing and communication paradigms. Although the series mainly focuses on design, it also addresses analytics and investigation of industry-related real-time problems.
Publishers at Scrivener
Martin Scrivener ([email protected])Phillip Carmical ([email protected])
Machine Learning Paradigm for Internet of Things Applications
Edited by
Shalli Rani,
R. Maheswar
G. R. Kanagachidambaresan
Sachin Ahuja
and
Deepali Gupta
This edition first published 2022 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
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Preface
Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies and business people. The book addresses the problem and new algorithms, their accuracy and fitness ratio for existing real-time problems. Tapping into that data to extract useful information is a challenge that’s starting to be met using the pattern-matching abilities of ML, which is a subset of the field of artificial intelligence (AI). In order to provide a smarter environment, there needs to be implemented IoT devices with machine learning. Machine learning will allow these smart devices to be smarter in a literal sense. They can analyze the data generated by the connected devices and get an insight into human behavioral patterns. Hence, it would not be wrong to say that if the IoT is the digital nervous system, then ML acts as its medulla oblongata. Without implementing ML, it would really be difficult for smart devices and the IoT to make smart decisions in real-time, severely limiting their capabilities. This book provides the challenges and the solution in these areas.
This book provides the state-of-the-art applications of Machine Learning in IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’ and intelligent transportation systems. Readers will gain an insight into the integration of Machine Learning with IoT in various application domains.
Lastly, we would like to thanks all the authors who contributed whole heartedly in bringing their ideas and research in the form of chapters.
Shalli RaniR. MaheswarG. R. KanagachidambaresanSachin AhujaDeepali GuptaJanuary 2022
1Machine Learning Concept–Based IoT Platforms for Smart Cities’ Implementation and Requirements
M. Saravanan1*, J. Ajayan2, R. Maheswar3, Eswaran Parthasarathy4 and K. Sumathi5
1Sri Eshwar College of Engineering, Coimbatore, Tamilnadu, India
2SR University Warangal, Telangana, India
3School of EEE, VIT Bhopal University, Bhopal, India
4SRM Institute of Science and Technology, Chennai, India
5Sri Krishna College of Technology, Coimbatore, India
Abstract
In developing countries, smart cities are a challenge due to the exponential rise in population. With the rise in demand and availability for goods and facilities, it is now one of the world's most dynamic networks. Intelligent machines are crucial in the construction of critical infrastructure and smart cities in this new age. The increase in population has created new opportunities for smart city management and administration. In the smart city model, information and communication technology (ICT) plays a vital role in policy formulation, decision-making, implementation, and, finally, effective resource allocation. The study's key objective is to explore the role of artificial intelligence, machine learning, and deep reinforcement learning in the evolution of cities. Rapid advancements in computing and hardware, as well as high-speed internet connectivity, have enabled large amounts of data to be transmitted into the physical world.
Keywords: Smart city, process management, sewage treatment plan (STP), neural networks, control centers, cloud storage
1.1 Introduction
The idea of smart cities is the concept applied to the programs that uses the digital and the ICT-based innovation to increase the urban infrastructure quality and create the new economic and the prospect in the cities, and more is focused in the need of gaining the cost of the smart cities that are the distributed through all sectors with in the society emergence of the smart city projects around the world, such as analyzing the distributional impact of the individuals of the earth and the locations. The concept of smart city in the technical manner which will lead to debate the smart city varies across the countries according to the geopolitics; it implies more advanced and the necessary need to develop the city to both economically stable and more pollution-free concept. Initiatives that use the digital innovation with properly document are commitment of smart cities to enhancing the people’s lives while providing the sectoral and the multi-sectoral solutions to some of the most common urban challenges; stack-holders’ involvement in the local government and the strategic collaborations to improve the public engagement is maximized in private sectors positions in decision-making, and other benefits of the public access experimentation on open data with the interstate connectivity combined with the public and private people collaboration. Different regions of the world managed to establish their own smart city architecture in different manners also with approach of same belief [1]. The operable concept is complex for new setup process of the related to the increase in population to contribute in the development of technology with the social and political and the economy growth. The data that generated smart city concept are included in the networking application to monitor the application of various constrains like water monitoring and environment monitoring. Urban local bodies in particular for management service providers would be a crucial factor in evaluating the progress of smart cities mainly in India. Implementation approach will be consulted with pervious established architecture already present in various region of the globe. The well-developed cities like Singapore and Dubai UAE have the well-integrated business models, and the creative local collaborations will resolve the problems to get faced in India in nearly future [1]. In order to manage the data intelligently, IoT requires data to either represent improved customer services or optimize the effectiveness of the IoT system. In this way, applications should be able to access raw data across the network from different resources and evaluate this data to extract information.
Figure 1.1 Bhubaneswar smart city structure.
1.2 Smart City Structure in India
1.2.1 Bhubaneswar City
In India, Bhubaneswar has the best infrastructural setup of smart city project. It is the city where center of economic and having more religious importance in Eastern part of India. Consistently, this city has proved its efficiency in assessment among top smart city around the globe. It plays vital role in digital communication with advanced technologies. Figure 1.1 shows the Bhubaneswar smart city structure. This project included with construction engineering and green and park areas with road and development accessibility and slum accommodation.
1.2.1.1 Specifications
For government entities smart city specifications are, technology for the traffic, parking, emergency response, and emergency control, digitalized payment services via command payment methods schema capital of business planning and e-governance in this smart project [2].
1.2.1.2 Healthcare and Mobility Services
The smart city’s primary focus is more on the child and elderly friendly option. Most of the homeless camp, however, defecate in the open. In an integrated safe urban transport scheme, several positive measures have been taken, including low carbon mobility program, and the e-rickshaws are introduced to reduce the carbon emission in environment and also to control the pollution-free society [2]. It is still in the planning stage, and a variety of commuters are debating that it is continuous to have the poor transport facilities.
1.2.1.3 Productivity
Few centers for the skill development and the microbusiness incubators have also been developed. Most of these projects are small. Despite of that nearly 85 lakhs are unemployed in the year 2018, the rate of unemployment has soared to 6.77 from the past year percent of 4.7. In the first quarter of 2018, this state has ranked as the 7th among the state in India. In Bhubaneswar, there are 565 buses are linking the 67 wards with the help of the IT-backend support options the e-mobility attempt to update and develop the service under the Atal mission.
1.2.2 Smart City in Pune
Vision of smart city in Pune is to redesign its streets and roads and its equal for all people. Pune Smart City overview is shown in Figure 1.2. Design of the city is based upon the universal accessibility for the elderly and physically challenged and increased focus on the pedestrians, modern world infrastructure through the creation of appropriate arrangements for underground utilities [3]. Allocation is mainly to motorized traffic, continuous excavation of roads, and weak pedestrian crossing for layout facilities.
Figure 1.2 Pune smart city overview.
1.2.2.1 Specifications
This city has been developed to create an overall master plan based on a patented econometric model that will make Pune fit for the future up to 2030 comprehensive infrastructure specifications that have been completed for the next 5 years. It aims at a comprehensive range of urban options, including job opportunities creation, socio-economic growth, and beyond infrastructure and habitability [4].
1.2.2.2 Transport and Mobility
Real monitoring system of the live ongoing buses in the city is to track the location of different locations. Smart bus stops with the public information systems. This live tracking of the buses is availed through the mobile app by the people in this eco system. Around 319 signals are present in the city where the pedestrian right get the way for the emergency response system [4]. Also, advanced traffic management system by using the CCTV and the mobile GPS-based traffic system analysis is similar to Google live traffic system and intelligent road asset management system to help all.
1.2.2.3 Water and Sewage Management
New advanced technologies for water management are introduced in the smart bulk meters with the SCADA, for the commercial establishment; it used for the domestic households through the campaign along with a revised telescopic traffic.
1.3 Status of Smart Cities in India
According to the report the government of India has planned to launch 100 smart city missions (SCMs). These cities are able to provide decent roads, to build housing for everyone in the city, and also to create green spaces. Five years back, a substantial portion of the capital earmarked was no spent. A single network is yet to be completed by many smart cities. Actually, the project initial proposed for smart city was around 5,151 projects but only 3,629 have been actively pursued. In those number, only 25% of the projects are only have been completed [6]. But in the terms of value, the proportion of work done is just 11% of the total.
1.3.1 Funding Process by Government
Over 5 years, the central government has allocated Rs 48,000 crore to the mission. That amounts to an average of Rs 96 crore per city per year, maybe enough in many cities to create a sewage drain. An equivalent amount would have to be contributed by the states and urban local bodies of amount 96 crore. The city administration had to raise the remainder of the necessary financing through a host of sources-public-private partnerships, grants, resource monetization, and the likes. While renowned planners have created the smart city ideas, with the financial arrangements planned out in advance, most urban local authorities are struggling to raise the funds needed. While several bodies have raised concerns that the financing of the central government is insufficient, the government itself is not sympathetic [5, 6] and funds raised by government of India as shown in Figure 1.3. That any of the 30 cities will have no trouble collecting funds because they have A++ credit scores.
Pune is an smart example that has successfully launched a municipal bond, documenting its own process and replicating the success of the other cities.
The source of funds may vary in different countries; the sources of the smart city projects are provided by government and the private organizations; they are state government and the urban local bodies and central government. Public-private partnership organizations, convergence with the other government mission resources, and also load providers are all contributing in this mission progress. Analysts think that national transformative projects such as the Smart Cities Mission will take time to implement in a vast country like India. The mission is also suffering from the lack of urban planners.
Figure 1.3 Funds raised by government of India.
1.4 Analysis of Smart City Setup
The vision of a community and the priorities of people form an important aspect of the planning of smart cities. Since each city has distinct strength and the disadvantages, it is possible that their respective approaches to creating a smart city will vary. Here are some attempt to analyze the possible variation of the city setup by economical-based setup architecture [6]. Cities can be turned smart with any mixture of different smart components. A city does not need to be branded as smart for all the components. The number of smart components depends on the cost and available technologies.
1.4.1 Physical Infrastructure-Based
Digital innovations, in terms of physical technology, a smart city, transform into improved public facilities for people and better resource use while reducing environmental impacts. A city that integrates physical infrastructure, IT infrastructure, social infrastructure, and business infrastructure with a view to exploiting the city’s collective intellect. Technologies for embedded sensing allow data collection and analysis in real time [7]. This data is then presented to infrastructure companies as meaningful and accurate information, allowing them to make more sophisticated decisions [8]. AI learning is introduced in the infrastructure-based architectural-based systems which have more result in the accuracy and the beneficial purposes. For a deeper understanding of the usage of resources, AI may use accurate, robust, and practical knowledge obtained and processed by smart infrastructure. A change in urban planning and development leads to a more efficient and secure infrastructure that is better tailored to the needs of people. The data collection are carried through the process by the collecting the individual responses.
1.4.2 Social Infrastructure-Based
The standardization position involves many facets of the smart city’s architecture, organization, and functioning. Indian national smart city has principles that govern the unified criteria for radically new possibilities of centralized urban process management. The article describes the social infrastructure roles and tasks of single-industry cities, which should be taken into account in the introduction of the smart city framework. The Figure 1.4 shows the physical infrastructure workflow. The selected fields of operation set out in the smart city concept are closely linked to the growth of single-industry social infrastructure [8–10]. The dynamic system of social engineering that lead to enhancement of quality of life through the use of innovative decision-making technology through the economic and the eco-friendly of the life systems.
Figure 1.4 Physical infrastructure workflow.
This entire infrastructure aims system of objects essential for the promotion of human activity, communications, as well as businesses, organizations, and organizations, delivering social and household services to the community, management bodies, and workers whose operations are structured to meet the social needs of people in conjunction with the quality of life indicators created [11]. Certain areas should be covered in the social engineering process like the electricity supply with the higher energy and sustainable solid waste management robust connectivity and digitalization.
1.4.3 Urban Mobility
Mobility system encompasses a variety of operating technology used for the purpose of transportation system and also in management system, including the payment facilities, monitoring remote display devices which are used to track and maintain the traffic conditions along transport routes [12].
1.4.4 Solid Waste Management System
This scheme allows for the systematic storage of sewage in well-designed sewers that are delivered to the Sewage Treatment Plant (STP) to be handled there in such a manner that the effluent follows the parameters specified by India’s Central Pollution Control Board. For horticulture, road-side drainage, road sweeping, and irrigation, the treated water can be recycled.
1.4.5 Economical-Based Infrastructure
Innovation-driven and university-supported economy focuses on cutting-edge innovation, not just for technology, industry, and business but also for architecture, planning, growth, and the cultural heritage. Cities are a prosperous location, but their prosperity depends on their population size and other factors. In the last two decades, urban India has developed at an exponential pace [14]. An optimistic estimation of India’s population growth indicates that the total population is projected to hit around 1.5 billion by 2031, with an increased urban population of about 600 million, or about 40%, by 2031.
India has the large economic growth development in the world. Unfortunately, economic data is not calculated for urban agglomerations, but rather for the district administrative unit, which has no association with the border of urban agglomerations. In India, the extent of urbanization of the different states and union territories varies widely. The increased population base of cities resulted in higher demand for manufacturing goods and commodities. This was the case of cities and towns which grew in Europe in the 19th century in the industrial belts and regions. More often, a polycentric, nature-based and people-friendly urban structure was invented when center city regions became congested with growing population and increasing industrial emissions [14, 15].
1.4.6 Infrastructure-Based Development
Spending on infrastructure is crucial not only for the development of India and for sustaining the region’s fight against poverty but also for laying the foundation for stronger future economic growth. The 11th Plan emphasized the importance of investment in infrastructure to achieve a sustainable and inclusive increase in GDP of 9% to 10% over the next decade. The growth of infrastructure is a core focus of the 11th Five Year Plan of the Government of India (2007–2012). In 2010, the nation initiated 94 new projects and saw an investment of US$71.9 billion in 2010, a rise of 85% from 2009. The investment is the highest amount witnessed by any developed nation in the entire 1990–2010 period in any given year [16]. In 2010, India alone accounted for 43% of the overall expenditure in private ventures in developing countries.
Figure 1.5 Water supply chain in city structure.
1.4.7 Water Supply System
The consistency of the groups of organic surface and groundwater, known as raw water, will also not fulfil the quality requirements of domestic and industrial consumers. In such cases, water treatment is required prior to its use. Water, typically via a network of storage tanks and drains, can be collected and circulated throughout the metropolitan environment until handled. Figure 1.5 shows the water supply chain in city structure.
1.4.8 Sewage Networking
The concentrations of municipal sewers and their amounts of pollutants differ over a typical day of a typical week and over the course of a year. The conditions of flow may differ from free surface to supercharged flow, from constant to turbulent flow [18], and from static to non-uniform flow that varies rapidly or gradually.
1.5 Ideal Planning for the Sewage Networking Systems
1.5.1 Availability and Ideal Consumption of Resources
An equal and responsible distribution of services, including water and power, will be a smart city is most prominent feature one, which often requires access to proper sanitation and the disposal of solid waste. In order to ensure availability for future generations, smart cities must ensure proximity to services while placing a focus on the conscientious consumption of natural resources.
1.5.2 Anticipating Future Demand
India has becoming the most populated country around the world in the near decade. So, urbanization is expected to grow to 50% by 2030. Therefore, urban planning agencies need to consider potential demands to control and track the use of energy in today’s society. In industry and workplaces, we witness routine sanitization campaigns, daily sweeping in households, and intensified handwashing. It is estimated that a family of five needs 100 to 200 liters of water per day just to wash their hands. This would result in the development of about 200 liters of wastewater each day that would raise water demand and waste water generation from human habitation by 20% to 25%.
The aim of the architecture is to provide numerous APIs as well as visual web services with public smart city information via data [13]. In this particular instance, the system design can make it easy to transmit sensor data to a back-end system and be incorporated into the “standard” city monitoring system.
1.5.3 Transporting Networks to Facilitate
Multiple major companies, such as OLA, Uber, and the car manufacturers, are increasingly developing autonomous vehicles. For self-parking vehicles, the Indian Department of Transportation has just paved the way. This are projected to be on the market and generally available as early as 2020, likely with significant market shares. More users in the city nowadays are using the private transport more than the public transportation such that it has some effects in the public transportation and lead to more pollution around the economical city [16]. They should encourage the public mode of transportation to others and to help the environment.
It is possible that traffic control in a smart city would be drastically different. Future methods would be collaborative, unlike the individual driver-focused current solution, where the aim is to maximize flow in a road system. This could include a drop in waiting times for traffic lights and average delay, a decrease in mean cumulative travel time, or an increase in overall highway productivity. Traffic management now also uses traffic light networks that track road traffic with timers and sensors [17]. Efforts are being made to develop software that can forecast traffic flows, a smart trip simulation system built on the neural network that can simulate speed profile conditions with a high degree of accuracy at various sensor locations.
1.5.4 Control Centers for Governing the City
Recognizing these threats and prospects, the government of India initiated the 100 Smart Cities Mission in June 2015. Almost 100 smart cities have been established since the mission was launched and cities have begun to implement public infrastructure and ICT initiatives according to mission guidelines. Cities have conceptualized projects that enable them to do more, increase their organizational effectiveness, and provide residents with timely and reliable services.
1.5.5 Integrated Command and Control Center
The Integrated Command and Control Center (ICCC) serves as the “Nerve Center” for Operations Administration, Day-to-Day Exception, and Crisis Management. It also provides insights through the analysis of diverse aggregated data sets to produce information for better planning and policy making. The ICCC is intended to aggregate information through various applications and sensors distributed across the region and then provide actionable information with sufficient representation for decision-making. Although few cities have begun implementing ICCC with necessary software, networks, and sensors under the Smart Cities Mission, they are at different stages of maturity as far as informed decision-making is concerned [19]. As these ICCCs are introduced, it is imperative to assess the sophistication of productivity using a common methodology across the world ensuring that improvements made by cities can provide sufficient benefits for cities and people in the future.
While few cities have begun to deploy ICCCs with the necessary software, networks, and sensors under the Smart Cities Mission, they are at different stages when it comes to informed decision-making. As these ICCCs are introduced, it is imperative to assess the sophistication of productivity using a common methodology across the world ensuring that improvements made by cities can provide sufficient benefits for cities and people in the future. The purpose of this evaluation system is to provide communities with a do-it-yourself toolkit to measure the maturity and efficacy of the Centralized Command and Control Center in municipal operations management, day-to-day emergency management, crisis management, preparation, and policy-making.
It is envisaged that the ICCCs would be the brain of metropolitan service, exception handling, and crisis management [19, 20]. Figure 1.6 shows the smart city control flow for command and control centers. Sensors and edge devices can collect and produce real-time data from different services such as water, waste management, electricity, accessibility, the urban environment, education, health, and safety.
Figure 1.6 Smart city control flow for command and control centers.
The ICCC used to the following:
Enhanced understanding of circumstances by providing information through sensor deployment across the city for civic officials through urban functions.
Standardizing urban response protocol by developing modern protocols for repeated incidents, complaints, and requirement scenarios.
Strengthen cooperation inside and beyond various agencies local urban bodies and municipal authorities.
Institutionalization of daily activities decision-making guided by evidence and in the case of a crises around the city level—from the owners to the city managers.
Engaging on-site service workers in dealing with social concerns and residents’ complaints.
1.6 Heritage of Culture Based on Modern Advancement
India’s growth mechanism has been affected, in part, by the transnationalization of capital within the global economy, which has enabled the deployment of capital and labor within India by both foreign financing institutions (e.g., the World Bank) and private multinational companies (e.g., Union Carbide). In order to sustain capital-intensive modes of industrial and agricultural production, the Indian economy depends on foreign technology and finance [23]. As a result, the Indian state has accrued a huge foreign debt with both the US and the USSR and has encouraged a phase of growth that has a significant effect on the relationship within its borders between the state and the different indigenous cultures.
The state is not an individual fact, of course. It is composed of organizations tied, in turn, to the international economy. Therefore, amid some external financial and technical dependency, the dominant classes of India’s state capitalist system, namely, the bureaucratic elite and the governing alliance of the national bourgeoisie (large private enterprise), the army, wealthy peasant farmers, small traders, and money lenders, are steering indigenous production in India.
Western models of production, growth, and transformation, which in part view rural development as an issue of sectoral development based on an industrial urban economy, have profoundly shaped the ideological paradigm of development embraced by the state. India has been subject to a modernization phase that has already evolved in the West due to its reliance on international technologies and finance and its acceptance of western growth models.
An unjust cultural exchange that emphasizes Western traditions and devalues indigenous forms of knowledge has preceded the unequal economic exchange that occurs between industrialized capitalist states and developing nations. In the emphasis put on modernity within the development phase in India, this Western bias is evident [24]; it equates modern scientific rationality and technology with an effective process of development and devalues non-modern societies and their conventional information structures.
1.7 Funding and Business Models to Leverage
The business model is a very new term, and even though it is commonly debated, there is a lack of a common description. A business model defines the reasoning for creating, providing, and capturing value (economic, social, cultural, and other sources of value) through an entity. A business model concerns “the design of goods, facilities, and knowledge flows”, one of the most commonly known concepts derives from. This definition considers players, functions, market potential, and revenue streams. Four elements and positions, the meaning proposal, are in the middle of the business model structure or “canvas”. While multiple value ideas could be put forward, business models can be ranked in five different trends according to the following:
Business models unbundling, which could be used by organizations carrying out these three basic business types: customer relations; product innovation and infrastructure enterprises (e.g., private banking).
The long tail business model where an organization is seeking to sell less for more. This paradigm can be solved by selling a diverse variety of specialty items, each of which sells relatively infrequently (i.e., LEGO).
Multi-sided networks, which put together two or more separate but interdependent classes of consumers (i.e., video console manufacturers).
Free market model consistently rewards at least one large consumer group from a free-of-charge deal (i.e., mobile phone operators).
1.7.1 Fundings
Web-based market models match the trends described above. These findings suggest that the open pattern “conquers” web-based models, though there are still unbundling instances. Except in web-based situations, contemporary business models remain and the city acts as a direct information and service provider to its residents and businesses, on the other side, published on different smart city market models. While market models are not to be followed in public institutions (i.e., Masdar and Gdansk) [24], even in these ways the municipality uses smart cities to draw tourists, inhabitants, and investment. These studies also named members to two contemporary business model classes:
E-Service market model.
Openness in ownership of the private enterprise and the ICT network.
A specific provider (or stakeholder groups) was treated as provided in each service category. The network owner creates value for people and businesses. A significant result of this assignment process is the appointment of business model trends in cases that have no relevant network-related business models. This is fair because all of these municipal types need different resources (networks and grids, sensors, etc.). The unbundled trend is still in effect even though these facilities are leased for service provision. When the IoT is used as the main resource that results in the IoT market models involved, circumstances change [24, 25]. In the above-mentioned situations, though, cities have still not capitalized IoT, which helps start-ups and other vendors to build value.
1.8 Community-Based Development
1.8.1 Smart Medical Care
This approach aim, however, is focused on the individual experiences of professionals and/or neighborhood associations, to recognize the health concerns of the whole community. The value of such an approach is both cheap and constant, but it lacks the rigors of more rigorous quantitative methods and less likely to detect latent challenges within the group. In comparison, the practice of formal group consensus methods will address this role more thoroughly and rigorously in order to create consensus strategies so as to avoid narrowing the number of possible problems to consider, as is the tendency of various quantitative approaches.
Using data, however, the data must be extrapolitanized from wide region information in order to recognize urban health issues. The validity of the method ultimately relies on the amount of burden the wide region has taken on the society [21]. By using secondary data, such as vital statistics and census data, more comprehensive research is difficult for the practice as general problems are established.
Tendency, though to rely on some health conditions, may miss a significant issue merely because it was not part of the dataset. For example, an epidemiological analysis of diastolic blood pressure within the population may produce advanced data on distribution, correlates, and hypertension determinants. At the cost of a larger data collection, though, the information in the hypertension set is collected. The use of these data to classify the health issues of the population may also make it easier for the profession to ignore some (maybe more critical) problems of health.
1.8.2 Smart Safety for The IT
A smart public safety infrastructure is being built to provide the public with a better atmosphere for ordinary people. This system is complicated, distributed over all campuses of the University. It consists of a smart cameras tracking system, a backroom system with workflow engine and a smart-phone device within the context of a collaboration concept. The intelligent cameras are deployed and this last year the smartphone device and the back-office device with acceptable results are used. The smartphone app is the user entry point for documenting many problems pertaining to security and campus management which are instantly forwarded to the responsibilities team when they are taken directly in the event of security or join an integrated working flow engine when running the campus [21]. This paper reveals the framework for achieving a more intelligent climate for public protection, specifics of operation, and statistical evidence obtained by the system.
1.8.3 IoT Communication Interface With ML
The preparation and preparing of information for such interactions is a critical activity. To respond to this issue, various types of data processing, such as edge analytics, stream analysis, and database IoT analysis, must be applied [22]. Computing frameworks play an vital role in connecting the server with neighboring computer structures and frameworks that depend on the location and the processing server where the data is processed. Architecture is basically classified into several categories for the networking and filter data for data centers.
Edge Computing: This approach to computation allows data to initially be stored on edge computers. Edge devices cannot be linked continuously to the network, so a backup of the master data/reference data is required for offline processing.
Cloud Computing: This approach and the design has high latency and high load balance, which means that this architecture is not ideal for the processing of IoT data since it can work for other processing at high speeds.
There are several other type of cloud computing services like Iaas, Paas, and Saas. These are equipped with the data transmission via API or several other SDK kit for the user interface.
