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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Industry 5.0 Transformation Applications
- Sprache: Englisch
This book covers a wide range of advanced techniques and approaches for designing and implementing computationally intelligent methods in different application domains which is of great use to not only researchers but also academicians and industry experts.
Optimized Computational Intelligence (OCI) is a new, cutting-edge, and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, biologically-inspired computation, software engineering, AI, cybernetics, cognitive science, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. OCI aims to apply modern computationally intelligent methods to generate optimum outcomes in various application domains. This book presents the latest technologies-driven material to explore optimized various computational intelligence domains.
- includes real-life case studies highlighting different advanced technologies in computational intelligence;
- provides a unique compendium of current and emerging hybrid intelligence paradigms for advanced informatics;
- reflects the diversity, complexity, and depth and breadth of this critical bio-inspired domain;
- offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in dealing with cognitive informatics challenges;
- presents a variety of intelligent and optimized techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional data analytics research in intelligent decision-making system dynamics;
- includes architectural models and applications-based augmented solutions for optimized computational intelligence.
Audience
The book will interest a range of engineers and researchers in information technology, computer science, and artificial intelligence working in the interdisciplinary field of computational intelligence.
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Seitenzahl: 510
Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
1 Emergence of Advanced Computational Intelligence Coupled with Smart Environment
1.1 Introduction
1.2 Background Works
1.3 Integrated Smart Environment
1.4 Proposed Models for Smart Intelligent Environment
1.5 IoT Architecture
1.6 Smart Environment and Advanced Computational Intelligence
1.7 Advanced Computational Intelligences: Possible Uses in Smart Environment
1.8 Conclusion
References
2 Machine Learning-Enabled Integrated Information Platform for Educational Universities
2.1 Introduction
2.2 Cloud-Based Web Application for University
2.3 Integrated Information Platform of Indian Universities Using Machine Learning
2.4 Applications Used to Designed This Web Platform
2.5 Analysis Result
Conclusion
References
3 False Data Injection Attack Detection Using Machine Learning in Industrial Internet of Things
3.1 Introduction
3.2 Literature Review
3.3 Technical Methodology
3.4 Proposed Model for Detecting False Data and its Correction
3.5 Complexity Analysis of Proposed Model
3.6 Advantages of the Model
3.7 Future Scope and Limitations of the Proposed Model
3.8 Conclusion
References
4 Fake News Detection: Restricting Spreading of Misinformation Using Machine Learning
4.1 Introduction
4.2 Scope of False News Detection
4.3 Main Highlights of the Analysis
4.4 A Novel Model for False News Detection
4.5 Literature Review
4.6 Results and Analysis
4.7 Conclusion
References
5 Adaptability, Flexibility, and Accessibility Through Telemedicine
5.1 Introduction
5.2 Related Works
5.3 Proposed Model for Remote Health Monitoring System
5.4 Benefits of the Proposed Model
5.5 Constraints of the Proposed Model
5.6 Conclusion
5.7 Future Works
References
6 Crop Prediction by Implementing Machine Learning in an IoT-Based System
6.1 Introduction
6.2 Literature Review
6.3 Proposed Model for Crop Prediction
6.4 Results and Analysis
6.5 Challenges Faced
6.6 Advantages of the Proposed Model
6.7 Disadvantages of the Proposed Model
6.8 Conclusion
References
7 Relevance of Smart Management of Road Traffic System Using Advanced Intelligence
7.1 Introduction
7.2 Related Works
7.3 Proposed Model of Traffic Management System
7.4 Role of AI in Traffic Management
7.5 Conclusion and Future Works
References
8 Visualization of Textual Corpora Using Social Network Analysis
8.1 Introduction
8.2 Related Literature
8.3 Proposed Method
8.4 Implementation and Results
8.5 Conclusion and Future Work
References
9 Autonomous Intelligent Vehicles: Impact, Current Market, Future Trends, Challenges, and Limitations
9.1 Introduction
9.2 The Global Impact of the AV Industry
9.3 Role of Machine Learning in Autonomous Vehicles
9.4 Significance of the AV Industry in Various Sectors
9.5 Current Market and Future Trends in AV Industry
9.6 Challenges and Limitations
9.7 Conclusion
References
10 Role of Smart and Predictive Healthcare in Modern Society
10.1 Introduction
10.2 Healthcare System
10.3 Role of Predictive Analytics in Healthcare
10.4 Application of IoT in Healthcare
10.5 IoT Based Healthcare Management Framework
10.6 Future Recommendations for Research
10.7 Conclusion
References
11 An Analytical Study on Depression Detection Using Machine Learning
11.1 Introduction
11.2 Literature Survey
11.3 Proposed System
Challenges of Machine Learning in Depression Detection
11.5 Conclusion and Future Work
References
12 Revolutionizing Healthcare: Empowering Faster Treatment with IoT-Powered Smart Healthcare
12.1 Introduction
12.2 Scope/Motivation
12.3 Literature Survey
12.4 Smart Technology
12.5 Methods and Materials
12.6 Result
12.7 Conclusion
References
13 Machine Learning Algorithms for Initial Diagnosis of Parkinson’s Disease
13.1 Overview of Parkinson’s Disease
13.2 Scope
13.3 Related Works
13.4 Comparative Analysis of Parkinson’s Disease
13.5 Pros and Cons Using ML Algorithms
13.6 Conclusion and Future Works
13.7 Bibliography
References
14 Towards a Sustainable Future: Harnessing the Power of Computational Intelligence to Track Climate Change
14.1 Introduction
14.2 Artificial Intelligence and Climate Change Adaptation
14.3 Related Works
14.4 Comparative Analysis of Technological Frameworks to Handle Climate Crisis
14.5 Future Scope of Climatic Crisis Handling with AI
14.6 Conclusion
References
15 Impact of Computational Intelligence and Modeling in Tackling Weather Fluctuation
15.1 Introduction
15.2 Objective
15.3 Causes of Climate Crisis
15.4 Significance of AI and Modeling on Climate Crisis
15.5 Plastic Waste Detection Model
15.6 Forest Fire Prediction Models Using AI
15.7 Results
15.8 Conclusion
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 4
Table 4.1 Result comparison.
Chapter 6
Table 6.1 Top crops suitable for the derived location and soil type.
Chapter 9
Table 9.1 Five levels of autonomy for autonomous vehicles.
Chapter 13
Table 13.1 ML models employed in the cataloguing group.
Table 13.2 Conveying UPDRS tags constructed on tremor and speech features.
Chapter 14
Table 14.1 Shows data on meteorological droughts in various regions using Rand...
Table 14.2 Well-received methods for a variety of real world usage.
List of Illustrations
Chapter 1
Figure 1.1 Integrated smart environment using cloud based application.
Figure 1.2 Smart environment based IoT system in smart cities, smart homes, an...
Figure 1.3 Block diagram of garbage monitoring system.
Figure 1.4 Proposed accident detection system model.
Figure 1.5 Block diagram from proposed model.
Figure 1.6 Block diagram from the proposed IoT based smart home system.
Figure 1.7 Block diagram of weather monitoring system using Raspberry Pi.
Figure 1.8 System’s integral design architecture.
Figure 1.9 System architecture for noise pollution monitoring system.
Figure 1.10 System architecture for forest fire detection.
Figure 1.11 IoT architecture.
Chapter 2
Figure 2.1 Education system using cloud computing.
Figure 2.2 Cloud computing system working.
Figure 2.3 SQL performance.
Figure 2.4 Cloud database.
Figure 2.5 Services of education cloud.
Figure 2.6 Home page.
Figure 2.7 Sign up page.
Figure 2.8 Login page.
Figure 2.9 Explore university.
Figure 2.10 Statewise College.
Figure 2.11 College list.
Figure 2.12 College details.
Figure 2.13 College details.
Figure 2.14 Recommended comparison.
Figure 2.15 Comparison details of recommended colleges.
Figure 2.16 Manually comparison.
Chapter 3
Figure 3.1 The general block diagram of IIoT.
Figure 3.2 AE architecture with one hidden layer.
Figure 3.3 Flowchart of working of AE and DAE in detecting and correcting of d...
Chapter 4
Figure 4.1 Fake news detection model.
Figure 4.2 Fake news model overview.
Figure 4.3 Use of Naïve Bayes classifier.
Figure 4.4 Illustration of the hyper-plane classifying the data-set into two g...
Figure 4.5 Flow chart – classifier training.
Figure 4.6 Working of the proposed model.
Chapter 5
Figure 5.1 Overview of telemedicine using IoT.
Figure 5.2 The connection between doctors and patients.
Figure 5.3 Proposed model for health monitoring system.
Figure 5.4 ESP32.
Figure 5.5 MAX30100.
Figure 5.6 Various aspects of telemedicine.
Figure 5.7 Challenges in telemedicine.
Figure 5.8 Factors influencing telemedicine adoption.
Chapter 6
Figure 6.1 Accessing crop data using IoT on mobile device.
Figure 6.2 Proposed model for crop prediction.
Figure 6.3 Dataset to recommend crops.
Figure 6.4 Correlation matrix between all parameters related to crops.
Figure 6.5 Crop-wise environment requirements.
Figure 6.6 Crop-wise NPK requirements.
Figure 6.7 Distribution of data of phosphorus over the range of its values.
Figure 6.8 Distribution of data of potassium over the range of its values.
Figure 6.9 Distribution of data of nitrogen over the range of its values.
Figure 6.10 Distribution of data of temperature over the range of its values.
Figure 6.11 Distribution of data of rainfall over the range of its values.
Figure 6.12 Distribution of data of pH over the range of its values.
Figure 6.13 Random forest classifier’s confusion matrix.
Figure 6.14 Accuracy of different Algorithms based on results of training and ...
Figure 6.15 Accuracy of the top 7 models with hyperparameter tuning.
Chapter 7
Figure 7.1 Rural and Urban population growth from 1950 to 2050.
Figure 7.2 Layered architecture of traffic lighting system.
Figure 7.3 Representation of a single unit of traffic lighting system.
Figure 7.4 Roadside setup of traffic lighting system.
Figure 7.5 Flowchart of smart management system.
Figure 7.6 Vehicle theft detection system using IoT.
Chapter 8
Figure 8.1 Classification of networks.
Figure 8.2 Visualizing a network.
Figure 8.3 Snapshots of a graph to explain the community life cycle.
Figure 8.4 The color of the circle at
t
+1 denotes the color assigned to the co...
Figure 8.5 Splitting and shrinking of communities.
Figure 8.6 GCCs of sub-graphs
g
1
,
g
2
, and
g
3
.
Figure 8.7 GCC of sub-graphs
g
17
,
g
18
, and
g
19
.
Figure 8.8 Mapping communities based Score on Score Values of (
g
1
,
g
2
) and
g
2
,
Figure 8.9 Mapping communities based onValues of (
g
17
,
g
18) and (
g
18,
g
19).
Figure 8.10 Character network of Chapter 1, 2 and 3 of HPPA.
Figure 8.11 Character network of Chapter 17, 18, and 19 of HPPA.
Chapter 9
Figure 9.1 The global autonomous vehicle market [5].
Figure 9.2 The system created by Berkeley engineers applies machine learning t...
Figure 9.3 Trust and safety are among the top priorities for autonomous vehicl...
Figure 9.4 The autonomous vehicle revolution for our health and well-being [12...
Chapter 10
Figure 10.1 IoT-based healthcare management framework.
Figure 10.2 Comparison of various IoT technologies.
Chapter 11
Figure 11.1 Proposed methodology workflow model.
Figure 11.2 Computational implementation framework steps of the model.
Figure 11.3 A sample demonstration of decision tree.
Figure 11.4 A sample demonstration of the working of KNN algorithm.
Figure 11.5 A sample demonstration of the working of random forest algorithm.
Chapter 12
Figure 12.1 Implementation of IoT in smart healthcare.
Figure 12.2 Proposed model of smart healthcare.
Figure 12.3 Heart rate sensor.
Figure 12.4 Light detector.
Figure 12.5 Blood pressure sensor.
Figure 12.6 Body temperature sensor.
Figure 12.7 Accelerometer.
Figure 12.8 Gyroscope.
Figure 12.9 Magnetometer.
Figure 12.10 Magnetic declination.
Figure 12.11 Barometric pressure sensor.
Figure 12.12 Oximetry sensor.
Figure 12.13 Low absorption and high absorption of infrared lights.
Figure 12.14 Working of bioimpedance sensor.
Figure 12.15 Rate of the heartbeat.
Figure 12.16 Pressure rate.
Figure 12.17 Raw data collected from the accelerometer and gyroscope.
Figure 12.18 Data collected by barometric sensors.
Chapter 13
Figure 13.1 A schematic depicting an area of the brain damaged by the disease.
Figure 13.2 Signs of Parkinson’s sickness.
Figure 13.3 Results of bradykinesia.
Figure 13.4 Results of SST.
Figure 13.5 Results of DST.
Figure 13.6 Results of STCP.
Figure 13.7 Results of voice impairment.
Figure 13.8 Result of voice impairment.
Chapter 14
Figure 14.1 The workflow diagram.
Figure 14.2 (a) Model performance of three ML techniques for 1-, 3-, 6-, 9-, a...
Figure 14.3 Shows the correlation between actual 12-month SPI values and those...
Figure 14.4 The evaluation of drought monitoring maps (2013 and 2014) took pla...
Figure 14.5 Flow chart of the process used in the study.
Figure 14.6 CO
2
solubility obtained experimentally towards predictions of deve...
Figure 14.7 Bar graph showing a comparison between the four models - CO
2
,H
2
0 a...
Figure 14.8 Bar graph showing a comparison between the four models - CO
2
,H20 a...
Figure 14.9 Bar graph showing a comparison between the four models - CO
2
,H20 a...
Figure 14.10 Bar graph showing a comparison between the four models - CO
2
+wate...
Chapter 15
Figure 15.1 Forest Fire prediction system.
Figure 15.2 Pie chart of global greenhouse gas emission by economic sector.
Figure 15.3 Impact on health due to climate change.
Figure 15.4 CNN flowchart.
Figure 15.5 Plastic waste detection using machine learning algorithm.
Figure 15.6 Testing and training accuracy.
Figure 15.7 Google maps showing wildfires.
Figure 15.8 Accuracy of different ML algorithms.
Figure 15.9 Training and validation accuracy of different algorithms.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Industry 5.0 Transformation Applications
Series Editors: Dr. S. Balamurugan and Dr. Sheng-Lung Peng
Scope: The increase in technological advancements in the areas of artificial intelligence (AI), machine learning (ML) and data analytics has led to the next industrial revolution “Industry 5.0”. The transformation to Industry 5.0 collaborates human intelligence with machines to customize efficient solutions. This book series aims to cover various subjects under promising application areas of Industry 5.0 such as smart manufacturing, green ecology, digital medicine, supply chain management, smart textiles, intelligent traffic, innovation ecosystem, cloud manufacturing, digital marketing, real-time productivity optimization, augmented reality and virtual reality, smart energy consumption, predictive maintenance, smart additive manufacturing, hyper customization and cyber physical cognitive systems. The book series will also cover titles supporting technologies for promoting potential applications of Industry 5.0, such as collaborative robots (Cobots), edge computing, Internet of Everything, big data analytics, digital twins, 6G and beyond, blockchain, quantum computing and hyper intelligent networks.
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Optimized Computational Intelligence Driven Decision-Making
Theory, Application and Challenges
Edited by
Hrudaya Kumar Tripathy
Sushruta Mishra
Minakhi Rout
S. Balamurugan
and
Samaresh Mishra
This edition first published 2024 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© 2024 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 978-1-394-24253-5
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
Optimized Computational Intelligence (OCI) is a new, cutting-edge, and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, biologically-inspired computation, software engineering, AI, cybernetics, cognitive science, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. OCI aims to apply modern computationally intelligent methods to generate optimum outcomes in various application domains. This book presents the latest technologies-driven material to explore optimized various computational intelligence domains.
To begin, the first chapter discusses the emergence of computational intelligence in smart sensory settings. Chapter 2 deals with the capabilities of advanced machine intelligence in educational domains. The third chapter addresses the issue of recognizing false data injection attacks with a machine learning approach in the industrial IoT sector.
Chapter 4 discusses the analysis of fake news by using modern intelligence-based approaches to prevent misinformation propagation. The fifth chapter addresses the challenges and issues of telemedicine by applying computational intelligence techniques. Chapter 6 demonstrates how to detect and predict crop suitability by deploying machine intelligence in smart sensory settings. The seventh chapter explains the significance of using advanced intelligence methods for smart, IoT-based regulation of road traffic. The eighth chapter presents a succinct analysis of text-based corpora using social network analysis.
Chapter 9 highlights the growing role of autonomous intelligent vehicles, the challenging issues related to them, and their futuristic trends. The tenth chapter discusses the impact of smart predictive analytics in healthcare within a modern urban society. Chapter 11 show how to use advanced predictive analytics to assess depression in the modern world. The twelfth chapter discusses current scenarios that demonstrate IoT-enabled healthcare standards with revolutionized guidelines.
Chapter 13 presents a detailed analysis of Parkinson’s disease risk factors and explains how to apply machine learning in detection and treatment. Chapter 14 discusses the capability of computational intelligence to monitor climatic variations that are taking place in today’s world. The final chapter presents a deep analysis on the relevance of using computational intelligence to address weather fluctuation.
We are deeply grateful to everyone who helped with this book and greatly appreciate the dedicated support and valuable assistance rendered by Martin Scrivener and the Scrivener Publishing team during its publication.
1Emergence of Advanced Computational Intelligence Coupled with Smart Environment
Risha Rani* and Tirtha Deb
Kalinga Institute of Industrial Technology, Deemed to be University, Bhubaneswar, Odisha, India
Abstract
In this paper we have tried to work out various models which may elevate the lifestyle of us humans by using the technology of present day IoT system concept making it possible to make things or our daily requirement smart or very easily available. As for the models, we have thoroughly studied and discussed how we would build smart homes using various sub disciplines such as building garbage monitoring systems and a system in which we detect any accident. We also went through the whole idea of the present day healthcare system and we built a model for a smart healthcare system and how we can build a weather monitoring system, an Air Pollution monitoring system. Considering the extra need for the noise surrounding the environment, we have proposed a model for Noise Pollution monitoring system. We have proposed a forest fire detection system model. We also have tried to bring the knowledge of advanced computational intelligence and artificial intelligence to our work as we believe the huge significance of advanced computational intelligence and AI inside the development of smart green terrain is a manner to attain clever green frugality.
Keywords: Advanced computational intelligence, scalability, security, connectivity, analyzing and integration
1.1 Introduction
The things which we can hardly imagine are encouraged and supported by new opportunities that the IoTs is reinventing. The Internet of Things is changing our physical world to grow. Through the Internet of Things, the devices that are connected through the internet are designed to become specific, customized, and intelligent to fully fill our unique and day-to-day necessity and requirements. The word smart stands for making Specific, Measurable, Achievable, Relevant and Time-bound objects and the word environment means the surroundings. A smart environment is therefore said to be the ability to acquire knowledge and apply it to modify the needs of its residents to improve their occurrence with that environment. By using various wireless technologies we can enhance the functional capabilities of smart devices. According to reports from Cisco, 50 billion objects and devices will be connected to the Internet 2020. More than 99% of things available in the world today still remain unconnected. According to a Navigant research report, the number of smart meters installed worldwide will increase to 1.1 billion by 2023. [3] Automotive News reports that worldwide the number of Internet-connected cars will increase from 23 million in 2013 to 152 million in 2024. [5] The significant growth such as forecasting shows that the Internet of Things will become a modern society to conceive a concept of a smart environment. For the integration of IoT devices along with the smart environments, several research efforts have been made with the smart environments. The possibilities of smart objects from the combination of IoT with a smart environment are expanded by allowing remote locations of the environment to be monitored by the users. Primarily based on the software requirements, IoT can be included into various smart environments. Work on intelligent IoT-based environments can generally be divided into the areas: smart cities, smart houses, and smart health. An IoT-based system consists of objects, sensor devices, and a computing and processing unit that can be located in the cloud, a decision-making and action-invoking system. IoT things and devices play a vital role in interaction and communication through data exchange. They respond to actual events in the physical world and also have the effect of triggering processes that trigger various actions and services with or without human intervention. With the enabling current era technologies like advanced computational intelligence, the world is becoming a supremely computerized environment. These enabling technologies are planned for a smart environment focusing on restful life to live in. The digital transformations experts collaborating with the application tests providers lead in the progressed conclusion of the new innovative automation and the Internet of Things. The world in the near future will utilize the knowledge of such specialists for the remodeling of the various cities. The smart environment is characterized by complex systems which require balance between transparency and context awareness. The architecture of such a system responds to the demand space and the incorporation of modular and the design of such architecture are flexible and responsible for the right time production of appropriate services.
Main contribution of the paper:
How energy and resource management techniques are building up the nation and how more swiftly it can be utilized
Human computer interaction and building more sophisticated software and algorithms for a smart environment
Predictive maintenance of the society with the proper system using new technologies
1.2 Background Works
In 2017, Kanishka Majumdar Devices for Integrated Circuit (DevIC), 23–24 March 2017, Kalyani, India. “Development Board”, has exact attributes like the microcontrollers in the trade but has some additional features i.e., twice the figures of input and output pins adaptable with Arduino IDE also it is very cost efficient. [1] Soil & Surrounding Testing Module by K. P. Keyur and M. P. Sunil, “Internet of Things-IOT”. A farmer can easily be benefited by this model as it is an automated analysis producer of soil moisture, pH, temperature, humidity, etc. It can advise regarding the sprout growth and the amount of further fertilizers needed. LCD screens will be of great use here. Sensors like moisture sensor, pH sensors, humidity sensors, etc., are used. [12] Water Pump ON/OFF via Phone Call by M. Fahim and A. Sillitti, “Anomaly detection, analysis and prediction techniques in IoT environment: A systematic literature review,” IEEE Access. It provides an important feature of turning the water pump on or off by using a mere phone call option. It reduces a great deal of labor. [13] Solar Tracking System (Renewable Power Supply) by K. P. Keyur and M. P. Sunil, “Internet of Things-IOT”. Solar tracker is built on the idea that solar can provide an alternative of renewable energy fit for the respective farmland. It acts as an automatic single axis solar tracker. Sensors such as the LDR are used here. [6] Electronic Scarecrow by H. Haddad Pajouh, A. Dehghantanha, R. Khayami, and K.-K.-R. Choo. This model basically acts as a scarecrow which can be used to keep the harmful pests off the land. Sensors such as PIRs are being used here [11].
1.3 Integrated Smart Environment
The Internet of Things (IoT) is coming with new technologies to improve human capabilities in this modern world. These means or new technologies promise a high quality of life and professional efficiency; however, with each new advancement in IoT synthesis and human augmentation technologies coming, the challenges of the IoT go far beyond that. The integrated intelligent environment is designed with various applications such as intelligent home systems, intelligent health care, intelligent transportation, intelligent agricultural systems, intelligent electronic management system, intelligent weather monitoring system, intelligent education system, etc. IoT objects and things are connected with RFID tags and sensors that are already in various business applications of the smart environment. RFID tags that utilize intelligent barcodes to identify any item use high-frequency technology in which radio waves transmit data from the tag to a reader that acts as a translator to a computer program. In Figure 1.1, the data of an integrated smart environment are stored in cloud based applications consisting of sensors and connecting IoT devices. It makes it much easier to send and analyze the given data and predict outcomes [8].
Figure 1.1 Integrated smart environment using cloud based application.
1.4 Proposed Models for Smart Intelligent Environment
Figure 1.2 denotes the sensors enabled smart environment for smart cities and homes along with smart healthcare service.
1.4.1 Smart Cities
Smart cities are technologically upgraded urban areas which use smart things, sensors, and electronic methods to collect various data and make life easier. The various functions performed by smart things can be traffic management, multi-city connectivity, pollution control, and smart lighting. The main purpose of smart cities is to make things easier and make us look at things with a new perspective. The Internet of Things makes an impact on various things such as every daily reliable life activity and also to any human’s complex emotions. The Internet of things makes benefits for each day to us and the environment. Smart city can device itself as an assistant for anyone’s daily schedule telling him/her to get up, make coffee or have dinner and go up to remind the person for dinner. It can detect the health condition if there is a problem or any underlying disease.
Figure 1.2 Smart environment based IoT system in smart cities, smart homes, and smart healthcare.
1.4.1.1 Garbage Monitoring System
Our proposed model gives an answer to the various hygiene problems we face in our daily life. Our model basically includes three subjects: 1. Smart trash, 2. Correlate, 3. Notify.
In Figure 1.3, the basic structure of our proposed model is shown. We have used different types of sensors with connecting them together to an LCD display for a better understanding of the situation. The basket contains a sensor and an ultrasonic sensor. The sensor we connected will bring in the information about the level of waste. With the help of Arduino the Wi-Fi which is fitted inside the bin will inform the authority when the bin overflows. We will receive a notification from the web server via coding through inside the Arduino. The collected details are then displayed on the LCD in the corresponding area in control [10].
1.4.1.2 Accident Sensing System
This proposed model informs us about the events or possibilities of accidents caused by concussion of a gas vehicle. This project contains three subjects: 1. Accident sensing, 2. Correlate, 3. Notify.
Figure 1.3 Block diagram of garbage monitoring system.
Figure 1.4 Proposed accident detection system model.
In Figure 1.4, the basic structure of the proposed Accident Sensing System is shown. The model will require power supply for the different utilities such as the sensors and we can have a clear visualization of the situation through the LCD display. This model contains two sensors, an accelerometer, and a vibration sensor. These sensors have some ability to alert a person to a possible occurrence. Wi-Fi located inside the module informs the appropriate person about the accident via Arduino. The coding programmed inside the Arduino helps the web server in sending the respective notification. The individual in charge will take action on the accident. If the casualty is not solved, the Arduino located on the module will generate constant information about the possibilities of an accident to the associated individual until the problem is solved. A crash sensing system is considered to be an economical and better way to maintain a safe environment without vehicle collision accidents [16].
1.4.2 Smart Healthcare
The Smart Healthcare system is mainly focused on the vision to provide the best healthcare to all the people across the globe. This system is done in a more than gentle and economical way. Henceforth, if you want to boost the efficiency of the healthcare system and patient care system one should focus on improving the healthcare monitoring equipment. In monitoring patients, medical fields are facing generally two problems; firstly the need that is to be present at the patient’s bedside for providing the health care and caregiving, and secondly, the patients are attached to large machines and confined to a bed. The problem of providing flexible and friendly patient care, the solution was given to develop bio instrumentation and telecommunication technologies. With the help of these technologies it has become possible to design home vital signs monitoring systems to display, collect, record and transfer physical data from the body of humans to any further location. There are many reasons that motivate doing work like making healthcare accessible to all public who do not have ingress to healthcare providers and for going hospitals there is no availability of public transportations; giving care to those patients who require more time to heal and more care; avoiding in the delay of delivering the medical kit to patients for health care providers, specifically in the event of accidents or emergencies; and reducing manual patient data entry, allowing healthcare staff to effectively monitor their patients [18].
In Figure 1.5, we show how in a smart healthcare system we can use many different types of sensors and using new technologies we can infer data and collectively enhance the diagnosis of many people at the same time. Our proposed model is an automated system which senses the patient’s heart rate, blood pressure or the body temperature. The features can be expanded to predict the patient’s possible chronic disease or other health parameters and other various symptoms. In Level 1, the various data we collect from the IoT devices will be gathered, organized, and stored on a server. Various sensors are acquired here, for example, sensors for BP, heart rate, body motion, etc. Since the output maximum times are given as an analog output, we first have to convert the analog values into digital form using a converter IC so that raspberry pi can use it. Further, the raspberry pi with the help of its Linux OS installed, converts the data to a python code which will update the database at specific or required time periods. At Level 2, with the help of filtering, classifying, and categorizing the helpful data is obtained from the stored data. This relevant data is solely about the patient’s real-time health information and his/her symptoms. This information will further help in predicting or diagnosing the patient. This helps the system be more self-sufficient and efficient. In phase 3, analysis/prediction is performed, techniques of data mining are utilized to understand the problem, its nature and type. i.e., the disease characteristics. Artificial Intelligence can be integrated to make the system more proficient [9].
Figure 1.5 Block diagram from proposed model.
1.4.3 Smart Homes
Nowadays, smart homes are considered some of the important applications for IoT based environments. A crucial feature involving smart homes is automation. What the goal we try to achieve here is to reduce human efforts as much as possible. At the present time, remote control systems are of great importance. The important advantage we get for using IoT in smart homes is the remote control of every device in the home. Home automation architecture can differ depending on the protocols and hardware utilized by the very system. Further following, the main services fetched out in the field of home automation are analyzed and, based on the analysis, a comparison of IoT architectures is made. There are certain advantages of using wireless technologies which cannot be established using traditional wired networks. We often call a smart home a home automation system, which uses the brand new technology to make household activities easier.
In Figure 1.6, we see how different sensors which are very justifiable to a home being secure and efficient connected to each other with the help of present technologies and thus providing a means of smart home. The proposed home automation system model includes a server, actuators, sensors and microcontrollers. The back end server will be set up to control and monitor the sensor devices. The proposed smart home system is going to be remotely controlled by wireless technology communication devices such as smartphones, cards, and other wireless devices remotely over the Internet. The room temperature could be remotely controlled, controlled, automatic fan on and off, automatic lights on and off, automatic gas leakage detected by sensors, air conditioning system, etc. are automatically controlled and controlled by the home automation system. Designed without the help of any human interaction with the home automation system monitor as well as gas leak control, fan on/off system, lights on/off system, room temperature, and humidity level control and monitoring through IoT communication device. The Node MCU is the primary need of this system and performs numerous procedures for the home appliance system. The Node MCU secures, interfaces with numerous sensors and collects real-time information for a home automation system. These contain two node MCUs. Node MCU (Node Micro Controller Unit) is an open source containing software and hardware that built a much cheaper system designed on a chip known as ESP8266. In particular, the home automation system remotely manages home appliances to make them convenient for people. This system includes for warning of any violation of safety assurance and violation of harmful events certainly will not happen in the home. A system linked to the Buzzer Alarm system can alert a person in the home with an acoustic signal to signal any problem. And there is also an alert SMS to the user’s mobile phone or an email that can be sent to the affected user for home security alerts.
Figure 1.6 Block diagram from the proposed IoT based smart home system.
1.4.3.1 Weather Monitoring IoT-Based System
Nowadays, the technologies and innovations are focused to control and monitor the various devices wirelessly across the Internet, so in order to be a medium of communication between the communicating devices, the Internet comes into play. These technologies are mainly focused in managing and monitoring the various objects. In order to detect weather conditions, whether the prescribed parameter levels are exceeded and to collect the data for research purposes an effective monitoring system comes namely, weather monitoring system. Multiple instruments namely thermometers, barometers, wind vanes, rain gauges, etc. are used in weather stations inserted inside the weather monitoring system used to detect changes existing in the weather and the climatic conditions. Databases are used to store and the instruments that have been used, use simple analog calculations that are recorded later on physically. The radio stations and news stations collect this information lately and thus a weather report has been made. The data has been collected using a number of different connected sensors including humidity, pressure, temperature, and so forth and the statistics has been dispatched to cloud applications in order to supply them. The cloud applications then analyze and visualize the data that has been collected. These apps then send the weather alert to the users that have been logged in. The device named AirPi is used to detect air quality and weather and is able to record and upload the information on humidity, air pressure, light levels, temperature, UV levels, carbon monoxide, nitrogen dioxide, and smoke level on the Internet [2]. The proposed system is an advanced weather monitor solution that uses IoT to easily make actual time data available on a very wide scale. The changes made by the system deals with weather and climate are monitored as follows:
Figure 1.7 Block diagram of weather monitoring system using Raspberry Pi.
DHT11 sensors are used in monitoring humidity and temperature.
An anemometer measures wind speed and directions with LDR which keeps track of Light intensity.
GY8511 solar sensor measures UV radiation and MQ7 measures the Carbon monoxide in the air.
Hygrometer measures soil moisture.
Level sensors measure ultrasonic rainwater.
Raindrop sensor to detect rain or snow.
Mainly two devices namely, Dark Sky and Raspberry Pi are also used, which is an open source IoT source. Dark Sky is for storing and retrieving data. It is an open source Internet of Things (IoT) is an open source API using HTTP over the Internet or over a local network. We connect it using a Raspberry pi. In Figure 1.7 we use Raspberry Pi, which is an inexpensive mini sized credit card computer that fits into a computer monitor or TV and uses a standard keyboard and mouse.
1.4.3.2 Air Pollution Monitoring IoT-Based System
Air pollution contributes to a severe problem that adversely affects living organisms. It creates the major real concerns of the globe. Air pollution is a major global concern consisting of multinational companies, administration and broadcasting. Even some use of essential resources at an amount faster than nature’s capacity to regenerate can cause pollution of plants, water and air. In addition to human activities, there are several irregular characteristic cycles that further lead to the release of dangerous things. In addition to artificial activities, nature’s calamities can lead to air contamination. The Internet of Things (IoT) has become a primary conveying trends of recent times. Using this idea, it is foreseeable to secure innumerable intelligent embedded objects with low consumption among everyone and to the Internet. The ubiquitous existence of numerous wireless technologies for example, tags, RFID (Radio Frequency Identification), actuators, sensors, mobile phones form the foundation of the IoT concept. An IoT-based air pollution sensing and system design can detect dangerous gas discharge from industries and vehicles utilizing gas and weather sensors. Collected information can conceivably be analyzed to create informed conclusions regarding the pollution control application.
In Figure 1.8, we showcase what type of sensors our proposed system will use to detect the level of noise and pollution in the air. It has features of data transmission and perception so as to act to the benefit of the user.
The air pollution detection and forecasting design we proposed in this paper proposed a decent quick fix to the complication of air pollution. Utilization of some sensors makes sure that the monitoring accuracy is appropriate, also lessens monitoring costs, and fabricates the monitoring data in the monitoring area more organized and clean. The huge aggregate of field data given by the front-end sensor network builds big data analysis in the background application layer more directly and efficiently, and gives a factual and good decision-making foundation for emergency response after a pollution accident occurs.
Figure 1.8 System’s integral design architecture.
1.4.3.3 Noise Pollution Monitoring IoT-Based System
The pollution’s growth is expanding nowadays with a certain number of factors which affects the environment and results in the dropping of bio degradation. The rapid increases of industrialization and urbanization may have led this to happen. This leads to population degradation and also affects the people’s health by directly affecting people’s health in one way or another. To manage a positive future and healthy lifestyle for everyone, examining air quality and noise levels are required. A great decrease is seen in the industrial and infrastructure plants and their dispatch, which has caused various ecological problems such as air pollution, water pollution, noise pollution, climate change, atmospheric dissimilarities, defects that has an environmental effect on the requirement of anatomically adaptable, efficient, an affordable and smart monitoring system. The air quality and sound pollution monitoring enables us to monitor and control the actual time air peculiarities as well as sound pollution in specialized areas through our latest IoT device automations. Noise maps for cities are created by monitoring the noise pollution of the cities. To manipulate noise stages near residential areas, schools and playgrounds, it helps policy makers by formulating policies. Various noise monitoring systems are used which are located in various parts of the cities. The cloud or servers used the collected noise level data from stations for further data aggregation and to create noise maps.
In Figure 1.9, the proposed system manages different types of sensors for detecting noise pollution in the environment. Power supply is the main key to generate these sensors. We also use buzzers for any hazard that might occur. This model controls the problem of the greatly polluted field, which is the main problem, and aids the newest automation and sufficiently supports the idea of the good life. The contamination level used to be checked by the community through their cell phones using the applications. In this, we have focused on the efficient way to standardize the atmosphere and an efficient, cost-effective equipped model to be presented. The proposed model effectively describes the functionality of the other modules. The proposed model describes the functionality of the other modules. The IoT idea has proven itself in practice for managing certain criteria. This also gives the criteria and actuator data to the cloud. For later reviews these information are useful and also shared between various operators. These automations intensify to control various aspects of nature, such as aerial properties and the problem of sound properties is highlighted. By the use of different technological trends the plan of IoT is to spread happiness for the various communities.
Figure 1.9 System architecture for noise pollution monitoring system.
1.4.3.4 Forest Fire Detection IoT-Based System
The use of detectors serving as a data collection center Temperature detectors and bank detectors are used, which should be set up in specific chambers in order to maintain a view of the entire timber area, with the ultimate goal of distinguishing the original disturbing temperature and carbon dioxide gas (CO2) position [4]. These detectors will shoot a point to the microcontroller. Each describes transformations on Earth and naturally replies in the event of an extremity. New technologies in programmed launch widgets use cameras and PC computations to explore apparent honey impacts and development in a way that other discovery widgets can’t. A number of end detectors are to be used on a case-by-case basis, which should be set up on specific wards in order to maintain a view of the entire timber region. Information gathering using IC installed in Arduino transmitter circuit – The IC ATMega 328-p (micro regulator) bedded in the Arduino stage shown in the transmitter circuit acquires the data found and collected by the detectors [7]. At this point, the regulator plays back the exertion to it and passes it to the transmitter to transmit the data to the receiving station. Transmission of information by transmitter – after entering the data from the regulator, the transmitter sends the data to a certain scope where the receiving station is extended to be used. The micro regulator is the central element of the device circuit; it governs and certifies the operation of the entire circuit, then for this situation the transmitter circuit event of data by the receiving station. After entering the data from the transmitter circuit, the receiver transmits the data to the control IC of the connected Arduino unto installed in the entering circuit in the computer frame, which makes it possible that the regulator performs altered conduct to control the temperature position and CO2 position for honey identification. Display of temperature situations and CO2 position on point runners is available through a private system. At the moment the temperature and CO2 position information is ready in the IC circuits of the receiver node Mcu, which is altered with colorful library rudiments of the Ethernet guard interface, so that it’s possible to produce a runner. Fire Security System using the node Mcu Ethernet guard R3( assembled) which allows the NodeMcu board to connect to the web. It relies on the Wiznet W5100 Ethernet chip( datasheet). Wiznet W5100 provides a system( IP) mound equipped for both TCP and UDP. Arduino Ethernet Shield 2 will connect your knot Mcu to the web in negligible twinkles [4]. Simply plug this module into the NodeMcu board, connect it to your system using RJ45 connection with benefits like:
Association speed 10/100Mb
Ethernet Controller W5500 with internal 32K support
Association with esp8266 on SPI harborage
Working voltage 5V (handed from the NodeMcu board)
In Figure 1.10 shows addition, a warning circuit was created to encourage the Fire security platoon to find the vulnerable section at the foremost occasion. This fire protection circuit will sound an alarm just when the temperature situations exceed the preset value.
Figure 1.10 System architecture for forest fire detection.
1.5 IoT Architecture
IoT Architecture is the combination of different types of sensors, protocols, actuators and many layers that form the basic foundation of IoT networking systems. The layers in the IoT architecture allows us to properly administer, which is by good evaluation, monitoring the quality of the system. The four layers are namely : Perception Layer, Network layer, Session layer, Application layer. In Figure 1.11, As we see that each layer has different functionalities and functions which are required in the IoT architecture. Every layer differs in functionalities in terms of different services and technologies. Any possible threat to any layer in the IoT architecture may completely redirect the functionality of the layer.
Figure 1.11 IoT architecture.
1.5.1 Perception Layer
In IoT architecture, the sensor layer is named as the perception layer. The sensor provides some special technical support and receives the data from the environment. The sensing layer uses RFID sensors and readers that have definite memory, ineffective less power consumption, and definite computing capabilities, making them unprotected. The involved devices used to capture the information through the attached sensors into it. In order to track the locations for structural applications over the network GPS is inserted inside the layer. For short distance and local communication, the combination of IoT nodes is done using the perception layer. The data is first collected, observed then processed and then transmits the information stored in the network layer. Intimidation in the perception layer focuses primarily on information gathering activities via sensors. Various security risks are associated in this layer. RFID, sensors and smart implanted modulations are liable to various harms at this layer [14]. Here are some secured procedures and the available answers to mitigate the risks:
1.5.1.1 Privacy and Verification
Aggression may include replay attack or spoofing or even eavesdropping. These attacks pose some serious security risks in the sensor network authentication and secrecy may be disturbed. Solution – Making reliable cryptographic solutions which are built to be robust and efficient in many terms.
1.5.1.2 Network Availability
Attacks include Denial of Services (DoS), which is caused when there is a threat to the sensor networks. The reason is because the sensor network is divided into layers and all sensors are vulnerable to this kind of attack. Solution – Path-based DoS (PDoS) is a solution which reduces the attack.
1.5.1.3 Service Integrity
There may be instances when the attacker may trick the network into accepting incorrect data. Solution – Good reliable and robust cryptographic solution is sure to minimize the misleading of the networks by the attackers.
1.5.1.4 Jamming
Jamming is also a Denial of Service attack in which the communication operations related to the reader and the tag is paralyzed thus affecting the air interface. For example, communication is disturbed when the nodes in between are occupied in the communication channel and this is called Signal jamming. Solution - Primary sensing of jamming gadgets is an optimal solution to steer clear of such attacks.
1.5.1.5 Eavesdropping
The attacker secretly watches the communication between the tag and the reader in the search for personal details. The attacker can use the RFID feature to get the information about the user’s passwords and confidential information. Solution – A possible solution to such attacks is data encryption or restricting the space between the tag and the reader [15].
1.5.1.6 Replay Attack
In this attack, the invader or the attacker compares the authentication sequences by breaking the transmission connecting the tag and the reader and a duplicate tag. This type of attack affects RFID tags and air interfaces. Solution - Data encryption and token security can lessen this attack.
1.5.1.7 Man-in-the-Middle (MITM) Attack
MITM refers to the man in the middle attack that occurs during the communication of the data between two users communicating directly. MITM attacks are usually known as Interception and decryption. In Interception the attackers get the full visibility access of the online data transmission. Through malicious Wi-Fi hotspots which are available publicly, free attackers intercept the user .In decryption there are several methods involved namely HTTPS spoofing, SSL beast, SSL hijacking, SSL stripping used to decrypt the two way SSL traffic. By avoiding Wi-Fi connections which are not secured by the passwords we can prevent MITM attacks. By practicing activities such as immediately turning out from secured applications and during sensitive data transactions avoiding the use of public networks we can prevent the Man in the middle attack.
1.5.1.8 Denial of Service (DoS)
To prevent end users from accessing other networking devices by using a blocking tag to stimulate the multiple tags and resulting in the cause of DoS. The user tries to validate the non-existing tags which causes Denial of Service (DoS). The authenticated users can minimize the use of blocking attacks by detecting before the blocking device.
1.5.1.9 Tag Cloning
Through an illegal approach the attacker creates a duplicate tag basically on the real tag. RFID tags and air interfaces of the tag cloning are mostly affected which causes the problem related to financially in the applications. In order to prevent the user from this attack, users can access token authentication. The methods namely Synchronized Secrets Methods which is used to detect tag cloning attacks and provides secured manner of cloning. It is used to detect various tags which have their own identical IDs [19].
1.5.1.10 Take Off i.e. Spoofing
The spoofing attacks enables attackers to access the user’s system exchanges of the data or spread malware. It makes changes in the original appearing sources and falsifies it which pertains to cybersecurity. Some of its examples are Email spoofing, Website or/URL spoofing.
1.5.1.11 Device Tampering
It also refers to Node captain because it enables the attacker to change the sensor node to its malevolent node. It gains complete access over the captured node. It makes the device initiate inappropriate actions.
1.5.1.12 Outage of Nodes
In this attack, the attacker damages the network by physically or logically enabling the stoppage of functionality. Node outage attacks are one of the hazardous attacks in WSNs.
1.5.1.13 Leakage of Information
This is a type of passive evasion. When there is random behavior the attacker gains access to some private important information to which authorization is not allowed.
1.5.2 Network Layer
The main role of the network layer is data routing and transmission between different IoT hubs and various other IoT devices. It basically acts as the central nervous system in the architecture of IoT. Network layer works for the data initial processing and transmission of information. Modern wireless technologies are used here, for example, Bluetooth, Wi-Fi, LTE. The attacks involved in this layer can practically damage the network’s capability of communication between different IoT devices. Some of the threats are mentioned below:
1.5.2.1 Selective Forwarding
In this attack, the intruder selectively advances some of the personal information and discards them. The malicious node forwards the leftover traffic to show its malfeasance. Neglect and Greed is an alike type of attack, where an overthrown node jumps over routing some data haphazardly. A similar type of attack is one called Neglect and Greed, where a subverted node skips routing some messages randomly.
1.5.2.2 Sybil Attack
It is when the striker has numerous malicious places to attack. The malicious node or the device illegitimately holds several identities on the network. The attacks affect the fault-tolerant schemes. Solution - Individual node cites and the location of nodes can be tracked using distributed hash tables.
1.5.2.3 Sinkhole/Black Hole Attack
A Sinkhole or Black hole attack is described by strong competition for resources between nodes which are near to the of the attacker’s node due to limited channel access and limited bandwidth. What happens after is that the network becomes congested and the energy which is consumed by the nodes increases.
1.5.2.4 Wormhole
It is a very acute and challenging attack that can cause failure of location dependent protocols. A kind of DoS attack where information bits in the network are moved from their previous spots over a low-latency link. Security - The authentication protection process is proposed and is known as the “Markle tree authentication” method.
1.5.2.5 Attacks of Hello Flood
Instigation of lofty congestion in a detection network by flooding the path with tremendous amounts of unwanted requests is known as Hello-Flood attack. An attack in which a dangerous node sends an unnecessary message to which the attacker responds, producing congestion.
1.5.3 Support Layer
Action of gathering important information or data, and data processing and identification of the physical world is done here. In bulk data processing, smart tackling of dangerous data is very crucial. Intelligent detection of malicious information is a very difficult job. The support layer is occupied with storing of the data or the information, access to cloud services for efficient use of network advancements, and data analysis to indicate accurate facts. The attackers primarily focus on attacks in the support layer in order to target the data storage technologies. A number of the maximum common attacks assaults of this IoT software layer are described underneath:
1.5.3.1 Data Tampering
The data tampering takes place when the benefits of an insider are taken away by manipulating the data either for their own benefits or for the benefit of any third party. Protection – Data authentication must be provided so that insiders cannot easily make changes to the data.
1.5.3.2 Unauthorized Access
The system data can be corrupted as an attacker sneaks into the system. The denial of access to related IoT devices and its services are considered under such attacks. For the prevention of such attacks there must be a proper security mechanism.
1.5.3.3 DoS Attack
In this layer of the IoT architecture, the possible effects of such DoS attacks are attacks such as system shutdown, which may damage the system or lead to system unavailability.
1.5.4 Application Layer
Application layer plays its role in conditioning services to various industries like Smart e-health, smart government, smart city, etc. The security requirements in the application layer vary from application to application. The one most main feature of Application layer is data sharing, which also gives rise to several problems like the need for data privacy, authentication, integrity, confidentiality, etc. The main threats related to this layer are underneath:
