Smart Grids for Smart Cities, Volume 2 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 444

Veröffentlichungsjahr: 2023

Beschreibung

SMART GRIDS for SMART CITIES Written and edited by a team of experts in the field, this second volume in a two-volume set focuses on an interdisciplinary perspective on the financial, environmental, and other benefits of smart grid technologies and solutions for smart cities. This second volume in this groundbreaking two-volume set continues the authors' and editors' mission to present the concepts and best practices of smart grids and how they can be utilized within the framework of a technological tapestry to create smart cities. Continuing to go through the challenges and their practical solutions, this second volume includes chapters on waste management, e-waste, automotive and transportation engineering, and how internet-of-things can be utilized within these "smart" technologies, and many others. Like its predecessor, this exciting new volume covers all of these technologies, including the basic concepts and the problems and solutions involved with practical applications in the real world. Whether for the veteran engineer or scientist, the student, or a manager or other technician working in the field, this volume is a must-have for any library.

Leseprobe

Table of Contents

Cover

Series Page

Title Page

Copyright Page

Preface

21 Smart Child Tracking System

21.1. Introduction

21.2. System Modeling

21.3. Hardware Design

21.4. Results and Discussion

21.5. Conclusion

References

22 Smart Vehicular Parking Systems for Open Parking Lots

22.1. Introduction

22.2. Description of Smart Parking System

22.3. Circuit Diagram

22.4. Block Diagram

22.5. Working Principle

22.6. Results and Inference

22.7. Conclusion

Future Scope

Bibliography

23 Two Efficient Approaches to Building a Recommendation Engine for Movies Based on Collaborative Filtering on User Ratings

23.1. Introduction

23.2. Approach 1: Model-Based Collaborative Filtering

23.3. Approach 2: Graph-Based Collaborative Filtering

23.4. Conclusion

References

24 Design and Construction of Unbiased Digital Dice

24.1. Introduction

24.2. Description

24.3. Circuit Diagram and Components

24.4. Working Principle

24.5. Conclusion

Bibliography

25 Review on Utilizing E-Waste in Concrete

25.1. Introduction

25.2. Methodology

25.3. Composition of E-Waste

25.4. Process of Export

25.5. Impact of E-Waste on Environment and Human Health

25.6. Techniques - 4R Approach

25.7. E-Waste in Concrete

25.8. Strength Analysis

25.9. Conclusion

References

26 Smart Trash Can

26.1. Introduction

26.2. Literature Survey

26.3. The Proposed System

26.4. Hardware Design

26.5. Design and Implementation of Software

26.6. Results

26.7. Conclusion

References

27 Voltage Fluctuation Control Analysis of Induction Motor Drives in Textile Mill Using Phasor Measurement Unit

27.1. Introduction

27.2. Existing System

27.3. Proposed System

27.4. Experimental Analysis

27.5. Experimental Results

27.6. Conclusion

Appendix

References

28 Smart Cities and Buildings

28.1. Introduction

28.2. Components of Smart City

28.3. Conclusion

References

29 Minimizing the Roundness Variation in Automobile Brake Drum by Using Taguchi Technique

29.1. Introduction

29.2. Methodology with Taguchi Technique for Minimum Roundness of Varies

29.3 Experimental Conditions

29.4. Control Factors and Levels

29.5. Selection of Array Size

29.6. Experimental Conditions and Calculations of S/N Ratio

29.7. Pareto Diagram for Out-of-Roundness

29.8. Response Table of Process Parameter

29.9. Conclusion

References

30 Analysis of Developments on Mechanical Properties on Aluminum Alloys

30.1. Introduction

30.2. Literature Review

30.3. Conclusion

References

31 Study of Electromagnetic Field in Induction Motor Using Ansys Maxwell

31.1. Introduction

31.2. Mathematical Modeling

31.3. Methodology

31.4. Simulation Result

31.5. Limitations

31.6. Future Scope

31.7. Conclusion

References

32 A New Method of Sensor-Less Speed Vector Control of Asynchronous Motor Drive in Model-Reference Adaptive System

32.1. Introduction

32.2 Adaptive Control with Reference Model System (Stationary Frame)

32.3. Modelling of Asynchronous Motor Drive in Stationary Reference Frame

32.4. Simulation Diagram

32.5. Simulation Results

32.6. Conclusion

References

33 LabVIEW-Based Speed-Sensorless Field-Oriented Control of Induction Motor Drive

33.1. Introduction

33.2. Induction Motor Model

33.3. Natural Observer

33.4. Simulation Results

33.5. Experimental Results and Discussions

33.6. Conclusions

References

34 IoT-Based Automatic Entry Check in COVID-19 Pandemic

34.1. Introduction

34.2. Related Works

34.3. Objectives

34.4. Proposed Model

34.5. Implementation

34.6. Results and Discussion

34.7. Conclusion and Future Work

References

35 Smart Power Strip for Household Power Outlet Control and Energy Conservation Using IoT

35.1. Introduction

35.2. Methodology

35.3. Results and Discussion

35.4. Conclusion

References

36 Review of Solar Luminescence-Based OFID for Internet of Things Application

36.1. Introduction

36.2. OWC for IoT

36.3. Optical Frequency Identification (OFID)

36.4. Prototype and Setup

36.5. Conclusion

References

37 IoT-Based Substation Monitoring and Controlling

37.1. Introduction

37.2. Block Diagram

37.3. Connection and Working

37.4. Result and Discussion

37.5. Result of GSM Module

37.6. Conclusion

References

38 Agricultural Advancement Using IoT

38.1. Introduction

38.2. Proposed System

38.3. Sensor System

38.4. Methodology

38.5. Hardware of the Proposed System

38.6. Results and Discussion

38.7. Conclusion

References

39 Smart Microgrid in Hospital Community to Enhance Public Health

39.1. Introduction

39.2. Hospital Struggling in Poor Backup Generation

39.3. Microgrid – The Future of Smart Grid and Reduce Power Shedding in Hospitals

39.4. Necessity of Microgrid in Hospital Network

39.5. Smart Grid-Digital Technology in Electric Grid

39.6. Big Data Analytics Reduces the Challenges in Microgrid

39.7. Case Study: Hospitals Poor Backup System Failures Causing Deaths in Recent Years

39.8. Conclusion

References

40 IoT-Based Smart Waste Management System

40.1. Introduction

40.2. Design of Smart Dustbins

40.3. Hardware Components

40.4. Working

40.5. Results and Discussion

40.6. Conclusion

References

41 Case Study: Smart City Prospects for Economic Growth and Policies for Land Use

41.1. Introduction

41.2. Data

41.3. Analysis

41.4. Results: Combined Model

41.5. Conclusions

References

42 Case Study: International Policy Effectiveness and Conservation Way Towards Smart Cities

42.1. Policy Effectiveness in Conservation

42.2. Case Studies of Land Use Policy Effectiveness

42.3. Scenarios

42.4. Scenario Interpretation

42.5. The Policy Processes

42.6. Conclusions

42.7. Epilogue

References

43 CNTFET-Based Gas Sensor with a Novel and Safe Testing Chamber Design

43.1. Introduction

43.2. Novel Gas Chamber Design

43.3. CNTFET-Based Gas Sensor

43.4. Conclusion

Acknowledgment

References

About the Editors

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 22

Table 22.1 Components assigned to ports.

Table 22.2 Hardware.

Chapter 23

Table 23.1 Item-USER association matrix before and after applying ALS algori...

Table 23.2 MOVIES details (First 5 MovieIds).

Table 23.3 Movies details (Last 5 MovieIds).

Table 23.4 User ratings (For MovieId 4: Waiting to Exhale (1995)).

Table 23.5 The ratings of the userId: 672 are added to the dataset.

Table 23.6 The ratings FOR 10 movies by new user: 672.

Table 23.7 The top 10 recommendations for the user sorted by ratings predict...

Table 23.8 The userId and Jaccard Index of the top 25 nearest neighbors for ...

Table 23.9 Recommendations for the new userId 672.

Chapter 25

Table 25.1 Components of E-Waste, its composition and processing.

Table 25.2 The effects of E-wastes on human health.

Chapter 27

Table 27.1 Switching sequence.

Chapter 29

Table 29.1 Experimental condition.

Table 29.2 Control factors and levels.

Table 29.3 Experimental values.

Table 29.4 S/N ratio for out-of-roundness.

Table 29.5 Pareto ANNOVA for out-of-roundness.

Table 29.6 Average effects of process parameters.

Table 29.7 Confirmation experiment.

Chapter 31

Table 31.1 Induction motor parameter.

Table 31.2 Rated Performance parameters.

Table 31.3 Performance parameter at no-load operation.

Table 31.4 Rated electric data.

Chapter 32

Table 32.1 Specifications of asynchronous motor drive.

Table 32.2 Gain value of speed control in asynchronous motor drive.

Chapter 33

Table 33.1 Rating of induction motor with parameters.

Chapter 41

Table 41.1 Descriptive statistics of five variables related to human wellbei...

Table 41.2 Effects of 8 covariates on 5 metrics of human wellbeing in the Ad...

List of Illustrations

Chapter 21

Figure 21.1 Block diagram of the proposed tracking system.

Figure 21.2 Flowchart of the child tracking system.

Figure 21.3 Circuit diagram of the pressure plate.

Figure 21.4 Circuit diagram of beam breaker sensor.

Figure 21.5 Hardware model of child tracking system.

Figure 21.6 GSM message sent to parents for pressure mat triggered.

Figure 21.7 GSM message sent to parents for sensor beam triggered.

Chapter 22

Figure 22.1 Circuit diagram.

Figure 22.2 Block diagram.

Figure 22.3 Working of ultrasonic sensor.

Figure 22.4 Implementation of parking sensor.

Figure 22.5 Hardware setup.

Figure 22.6 Simulation.

Chapter 23

Figure 23.1 Difference between user-based and item-based filtering.

Figure 23.2 Approach 1.

Figure 23.3 Memory based approach.

Figure 23.4 Cypher query to load the data from the ratings.csv file.

Figure 23.5 The graph shows 75 nodes and 76 relationships where the yellow a...

Figure 23.6 Jaccard Index(J).

Figure 23.7 Query to find the top 10 recommendations.

Chapter 24

Figure 24.1 Circuit diagram.

Figure 24.2 Simulation diagram.

Figure 24.3 Block diagram.

Figure 24.4 Physical model.

Chapter 25

Figure 25.1 Growth of E-waste in India.

Figure 25.2 Difference of electronic & electrical waste.

Figure 25.3 Symbols of 4Rs.

Chapter 26

Figure 26.1 System block diagram.

Figure 26.2 System flow chart.

Figure 26.3 Implemented system.

Figure 26.4 Arduino output for percentage filling.

Figure 26.5 Output from Python shell.

Figure 26.6 My SQL output.

Figure 26.7 Webpage output.

Chapter 27

Figure 27.1 Block diagram of the proposed system.

Figure 27.2 A block diagram of PMU.

Figure 27.3 AC to DC converter for PMU.

Figure 27.4 Output of inverter to each Induction Motor.

Figure 27.5 Phase current of motor.

Figure 27.6 FFT analysis of seven-level inverter.

Chapter 28

Figure 28.1 Smart lighting system [4].

Figure 28.2 Smart parking management system [4].

Figure 28.3 Smart heating and ventilation system [4].

Chapter 29

Figure 29.1 Experimental value for S/N ratio with L

9

orthogonal array.

Figure 29.2 Experimental value for S/N ratio for out-of-roundness.

Figure 29.3 Pareto diagram for out-of-roundness.

Chapter 30

Figure 30.1 Microstructures of (a) 7085, (b) 7085 Y, (c) 7085 Er, (d) 7085 S...

Figure 30.2 Investigation procedure.

Figure 30.3 Schematic of sample for observation of cooling rate and gray reg...

Figure 30.4 Impact on Z parameter.

Figure 30.5 High magnetic field system (schematic diagram).

Figure 30.6 Alloy-7085 (DSC curve) homogenized at various situation.

Figure 30.7 Untreated raw AA6061 aluminum sample.

Figure 30.8 5083 failed air samples showing fracture surfaces and bond surfa...

Chapter 31

Figure 31.1 Magnetic field density at no-load condition.

Figure 31.2 Distribution of magnetic field density at rated load condition....

Figure 31.3 Plot showing winding current.

Figure 31.4 Torque under transient state.

Figure 31.5 Mutual current and induced voltage.

Figure 31.6 Magnetic field density at rated load condition.

Figure 31.7 Magnetic field density at no-load condition.

Chapter 32

Figure 32.1 Vector control of IM drive.

Figure 32.2 Schematic flow of sensor-less speed vector control of asynchrono...

Figure 32.3 Block diagram: Model Reference Adaptive System (MRAS).

Figure 32.4 Simulation diagram for model reference adaptive system (MRAS).

Figure 32.5 Simulation diagram of asynchronous motor drive.

Figure 32.6 Sensor-less speed of asynchronous motor drive in vector componen...

Figure 32.7 (a): Current vs. time step signal of speed control loop. (b): Th...

Figure 32.8 (a): The rotor reference (ω

r

*) vs actual speed (ω

r

). (b): The ro...

Figure 32.9 (a): The rotor reference speed (ω

r

*) vs. Actual speed (ω

r

). (b):...

Figure 32.10 (a): The rotor reference speed (ω

r

*) vs. Actual speed (ω

r

). (b)...

Chapter 33

Figure 33.1 Front panel view for speed and torque waveforms.

Figure 33.2 Block diagram of a natural observer with adaptation.

Figure 33.3 Actual, estimated speed and error (simulation result).

Figure 33.4 Actual, estimated load torque and error (simulation result).

Figure 33.5 Block diagram codes with DAQ assistant.

Figure 33.6 Experimental set up.

Figure 33.7 Measured dq-axes stator currents and stator voltages (experiment...

Figure 33.8 Estimated speed and torque under no load (experimental result)....

Figure 33.9 Estimated dq-axes stator current (experimental result).

Figure 33.10 Estimated speed and torque under loaded condition (experimental...

Figure 33.11 Comparison of simulation and experimental results.

Figure 33.12 Actual speed waveform under closed loop operation.

Figure 33.13 Stator current waveform at no load and a load of 0.7 Nm.

Figure 33.14 Stator current waveform at a load of 0.9 Nm.

Chapter 34

Figure 34.1 Integration of violations checking activities.

Figure 34.2 Temperature monitoring.

Figure 34.3 Mask detection process.

Figure 34.4 Sanitization process.

Figure 34.5 (a). Temperature check using ESPP8266 [TinkerCAD]. (b). ThingSpe...

Figure 34.6 (a). Temperature check using LED [TinkerCAD]. (b). Conditions fo...

Figure 34.7 Automatic sanitizer dispenser [TinkerCAD].

Figure 34.8 Social distance checker.

Figure 34.9 Serial monitor indicating distance [ThingSpeak].

Figure 34.10 (a). Temperature set to 35°C. (b). Serial monitor of ThingSpeak...

Figure 34.11 (a). Temperature simulation for 38°C. (b). Temperature simulati...

Figure 34.12 ThingSpeak graph with temperature details.

Figure 34.13 Result showing “No Mask”.

Figure 34.14 Automatic hand sanitizer dispenser. (a). Green indication showi...

Figure 34.15 Red indication showing violation of social distancing.

Chapter 35

Figure 35.1 Functional block diagram of the proposed smart power strip.

Figure 35.2 Process flowchart of the proposed smart power strip.

Figure 35.3 Process flowchart of the proposed smart power strip (cont’d).

Figure 35.4 Simulation of the proposed smart power strip using Tinkercad.

Figure 35.5 Schematic diagram of bulb connection with four channel relay usi...

Figure 35.6 Monitoring power usage using Blynk app.

Figure 35.7 Final enclosure in wooden box.

Figure 35.8 Turning on mobile charger using Smart power strip.

Figure 35.9 Graphical representation of power using UBIdots.

Figure 35.10 SMS alert to limit the usage.

Chapter 36

Figure 36.1 p-n junction of solar cell [1].

Figure 36.2 Conceptual block diagram of the OFID system for optical wireless...

Chapter 37

Figure 37.1 Block diagram of proposed model.

Figure 37.2 Circuit diagram of power supply.

Figure 37.3 Sub devices used in the model.

Figure 37.4 Hardware model (off state).

Figure 37.5 Hardware model (on state).

Figure 37.6 Graph of transformer voltage under normal condition.

Figure 37.7 Numerical value of voltage.

Figure 37.8 Gauge value of voltage.

Figure 37.9 Lamp OFF under normal condition.

Figure 37.10 Graph of low voltage of transformer.

Figure 37.11 Numerical value of low voltage.

Figure 37.12 Gauge value of low voltage.

Figure 37.13 Lamp ON under low voltage condition.

Figure 37.14 Graph of level of oil of transformer.

Figure 37.15 Numerical value of oil level.

Figure 37.16 Gauge value of oil level.

Figure 37.17 Lamp OFF under normal range of oil level.

Figure 37.18 Graph of low level of oil.

Figure 37.19 Numerical value of low-level oil.

Figure 37.20 Gauge value of low-level oil.

Figure 37.21 Lamp ON under low-level condition.

Figure 37.22 Graph of load current under normal condition.

Figure 37.23 Numerical value of load current.

Figure 37.24 Gauge value of load current.

Figure 37.25 Lamp OFF under normal condition.

Figure 37.26 Graph of load current under series underload.

Figure 37.27 Numerical value of load current.

Figure 37.28 Gauge value of load current.

Figure 37.29 Lamp ON under series underload condition.

Figure 37.30 Graph of temperature of oil.

Figure 37.31 Numerical value of oil temperature.

Figure 37.32 Gauge value of oil temperature.

Figure 37.33 Lamp OFF under normal condition.

Figure 37.34 Graph of oil temperature under overheat condition.

Figure 37.35 Numerical value of oil temperature.

Figure 37.36 Gauge value of oil temperature.

Figure 37.37 Lamp ON under overheat condition.

Figure 37.38 SMS from GSM for low voltage and given instruction.

Figure 37.39 SMS from GSM for low oil level and given instruction.

Figure 37.40 SMS from GSM for high temperature and given instruction.

Figure 37.41 SMS from GSM for series overload and given instruction.

Chapter 38

Figure 38.1 Block diagram of the system.

Figure 38.2 Image of soil moisture sensor.

Figure 38.3 Image of humidity sensor.

Figure 38.4 Image of PIR sensor.

Figure 38.5 Image of LCD.

Figure 38.6 Image of speaker.

Figure 38.7 Image of relay.

Figure 38.8 Image of GSM.

Figure 38.9 Image of rain sensor.

Figure 38.10 Represents the flowchart of the system.

Figure 38.11 Represents the automatic irrigation.

Figure 38.12 Output of the proposed system.

Chapter 39

Figure 39.1 Microgrid in hospital community.

Figure 39.2 Renewable energy sources.

Chapter 40

Figure 40.1 Block diagram of smart dustbin.

Figure 40.2 Waste level measuring sensor.

Figure 40.3 Working of smart dustbin.

Figure 40.4 Assembly of smart dustbin.

Figure 40.5 Process flow of dustbin.

Chapter 41

Figure 41.1 Species richness in 61 townships in the Adirondack Park (ADK).

Figure 41.2 Species richness in 142 townships in the Upper Peninsula (UP).

Figure 41.3 Comparison of five wellbeing metrics between the Adirondack Park...

Figure 41.4 Effects of 8 covariates on 5 metrics of wellbeing in the Adirond...

Chapter 42

Figure 42.1 Scenario planning is an appropriate strategy when uncertainty is...

Figure 42.2 Method of producing four scenarios using two crossed drivers.

Figure 42.3 Four scenarios produced by drivers of affluence and environmenta...

Chapter 43

Figure 43.1 The atomic structure of (5, 0) carbon nanotube.

Figure 43.2 The volatile organic compounds and its concentration level in br...

Figure 43.3 The gaseous molecules of ammonia incident on CNT.

Figure 43.4 CAD design of chamber with measurements.

Figure 43.5 Prototype model of the chamber.

Figure 43.6 The customized gas chamber with pen-cap holder mechanism.

Figure 43.7 3D structure of CNTFET in COMSOL.

Figure 43.8 Mesh structure of CNTEFT.

Figure 43.9 Signed dopant concentration.

Figure 43.10 The process flow for the fabrication of CNTFET.

Figure 43.11 The characterization images of CNTFET.

Figure 43.12 Design flow of gas sensing process.

Guide

Cover Page

Series Page

Title Page

Copyright Page

Preface

Table of Contents

Begin Reading

Also of Interest

Index

Also of Interest

Wiley End User License Agreement

Pages

ii

iii

iv

xv

xvi

1

2

3

4

5

6

7

8

9

11

12

13

14

15

16

17

18

19

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

87

88

89

90

91

92

93

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

220

221

222

223

225

226

227

228

229

230

231

232

233

234

235

236

237

239

240

241

242

243

244

245

246

247

248

249

250

251

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

327

328

329

330

Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Smart Grids for Smart Cities Volume 2

Edited by

O.V. Gnana SwathikaK. KarthikeyanandSanjeevikumar Padmanaban

This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.

Library of Congress Cataloging-in-Publication Data

ISBN 9781394215874

Cover image: Pixabay.comCover design by Russell Richardson

Preface

What makes a regular electric grid a “smart” grid? It comes down to digital technologies that enable “two-way communication between the utility and its customers, and the sensing along the transmission lines,” according to SmartGrid.gov. Based on statistics and available research, even though IoT is the talk of the town, smart grids globally attract the largest investment venues in smart cities. Smart grids and city buildings that are connected in smart cities contribute to significant financial savings and contribute to improve the country economy globally. The smart grid evolves around its efficient portfolio in the forte of how and when to utilize electricity and other forms of energy. Smart Grids vastly involve IoT sensors and real-time communication features that contribute to control loads based on available supply and peak demand characteristics. Phenomenal research and deployment is witnessed in the area of smart meters enabled smart cities.

In the traditional electrical grid, “power flows in one direction — from centralized generation facilities, through transmission lines, and finally to the customer via distribution utilities.” The smart grid has a multitude of components, including controls, computers, automation, and new technologies and equipment working together. Also these technologies will work in conjunction with the electrical grid to respond digitally to our quickly changing electric demand.

The investment in smart grid technology also has certain challenges. Ideally the interconnected feature of smart grids is valuable but it tremendously increases their susceptibility to threats. Smart Grid stakeholders also agree on the fact that since numerous non-utility stakeholders and devices are connected to smart grids, even in the best conditions possible, the secure operations can no longer be guaranteed by a single organization or security department. It is crucial to make sure that smart grid is made secure wherein number of technologies are employed to increase the real-time situational awareness and the ability to support renewables and system automation to increase the reliability, efficiency and safety of the electric grid. Various secure communications solutions are available for public utilities to contribute to the newest smart grid applications including advanced metering infrastructure, distribution automation, voltage optimization and substation automation.

5 Salient Features:

Smart grids: Concepts, Challenges, Architecture, Standards, and Communication

Renewable Energy Systems (RES) enhanced smart grids

Smart Grid Applications and Benefits to Smart City

The synergy of Sustainability, ICT, and Urbanization in Smart Cities

Smart City: IoT, Cloud, Big Data Convergence and Wireless Networks

21Smart Child Tracking System

Vijayan Sumathi1*, Mohamed Abdullah. J.2, Rethinam Senthil3 and E. Prema4

1 Centre for Automation, School of Electrical Engineering, Vellore Institute of Technology, Chennai, India

2 School of Electrical Engineering, Vellore Institute of Technology, Chennai, India

3 Ege University, Bornova, Bornova/İzmir, Turkey

4 VIT School of Law, Vellore Institute of Technology, Chennai, India

Abstract

Recent advancements in embedded technologies have helped researchers develop various applications and systems with varying design requirements. This study focuses on safety and precautions for child monitoring systems. The main challenge of modern-day parenting is continuous monitoring of their children; it is rigorous and exhausting. A solution requires overseeing children when their parents are not in the children’s vicinity to avoid mishaps. When the parents do not directly supervise the child, the child’s movement inside the home is monitored through sensors interfaced with the microcontroller. The application of social network tools, in particular GSM, aids in providing tracking information of the child to its parents via SMS to their mobile phones. The developed tracking system is adaptable, affordable and easy to interface in real time.

Keywords: Global system for mobile communication, arduino microcontroller, tracking system, sensor network, child monitoring

21.1. Introduction

During the recent COVID-19 pandemic, many parents and caretakers were forced to isolate themselves and work from home. As a result, parenting and professional work together have become a burden, and continuous monitoring of the children’s activity becomes tiresome work. To work in peace, parents require a solution vigilant enough to track the whereabouts of their children and cautiously warn them.

The tracking system is vital in the modern day since it is not possible to provide constant observation. Tracking systems have been successfully deployed in many applications such as monitoring patients, elderly and tracking vehicles [1]. This research focuses on cost, reliability, flexibility, and robustness while using a microcontroller to design a workable solution.

The proposed system focuses on tracking the child’s movement inside the house. Unfortunately, good childcare cannot be substituted; it requires constant monitoring; the truth is that constant monitoring of children is not feasible always, especially with toddlers aged between 2-4 years, when they cannot recognize danger. The tracking system plays a vital role. The information about the child is sent to parents if they move beyond the safe zone at home. This project implements two sensors-pressure sensors and beam-breaker, and information is transmitted through the GSM modem [2]. Sensors are deployed in homes to provide information if the child is approaching a hazardous environment within the home. When the child enters the restricted region, an observant message is instantly sent to the parents’ mobile about the child’s current position. It is essential to develop a system that is adaptable and low-cost. The objective is to build an intelligent child tracking system that is easy to install and add affordable and socially beneficial functions. The system equipped with microcontroller uses sensors and a global system for mobile communication (GSM) [3, 4].

The proposed software-based method sends specialized requests to the GSM network providers to send messages to parents whenever the child enters an unsafe area. The system uses a similar communication process as used in common mobile phones provided with a SIM card. Since SMS technology is simple, inexpensive and convenient for short communications, it has become more prominent. GSM has good network coverage in most urban areas, and it supports the users to communicate by allowing them to send short text messages to each other at a minimal cost [5, 6]. The designed system is low-cost, reliable and can be easily installed in the home. The danger states taken into consideration are falling into a swimming pool, hiding behind a gas heater, etc. Furthermore, the proposed system will not necessarily require the child to wear any sensor device for monitoring.

21.2. System Modeling

The model of the proposed system given in Figure 21.1 consists of both hardware and software modules. The hardware design for the tracking system consists of a Pressure pad, Beam-breaker, Microcontroller and a GSM Modem. The tracking system will provide the accurate location of the child according to the triggered sensor location. The microcontroller unit plays a vital role in the tracking unit, by acquiring and processing the signals collected from all the sensors. First, the collected signal is transmitted through the GSM communication controller, and then the GSM network sends the message to the monitoring centre. Finally, the warning message is delivered to the cell number provided in the code [7, 8]. In order to integrate the hardware system with the GSM network, a supporting algorithm is required. The flowchart of the overall design is programmed using the ‘C’ language as given in Figure 21.2, and using compiler software; it is converted and uploaded to the Arduino microcontroller.

21.3. Hardware Design

The microcontroller used in this tracking system is Arduino Uno; it is based on the ATmega328 platform with RISC architecture. The Atmega328 has 32 KB of flash memory for data storage. It also has EEPROM of 1 KB and SRAM of 2 KB. UART TTL (5V) serial communication on digital pins 0 (RX) and 1 (TX) is available and GSM modem is used as user interface communication. As the system uses GSM mode for communication, even Internet outages will not affect the tracking system from sending messages. Moreover, short text communication is cost-efficient; it is effortless to choose a plan with low or zero cost SMS tariffs while purchasing a SIM card.

A piezoelectric plate is used as a pressure mat, and it is triggered from a single tap by feet; the model circuit for the pressure plate is given in Figure 21.3. If the area that needs to be covered is large, then a larger surfaced area pressure mat with several interconnected piezoelectric plates for sensing is required. By placing piezoelectric plates in parallel, we can create a pressure mat. Then, the LED status changes to high if the pressure sensor is triggered and a signal is sent to the microcontroller, followed by a message sent to the caregiver via GSM modem.

Figure 21.1 Block diagram of the proposed tracking system.

Figure 21.2 Flowchart of the child tracking system.

Figure 21.3 Circuit diagram of the pressure plate.

Figure 21.4 Circuit diagram of beam breaker sensor.

The model circuit diagram of the beam breaker sensor is given in Figure 21.4; this circuit requires a BC548 transistor, IR module, 470Nf capacitor, and a 470Ω resistor. The IR module is be placed at the entrance of an unsafe zone or near a hazardous region. If the child crosses the IR module, it sends a triggering signal to the microcontroller, and the LED glows to indicate the movement. The hardware implementation of the model innovative child tracking system using microcontroller and GSM module is given in Figure 21.5 [9, 10].

21.4. Results and Discussion

A cautionary short text message is sent to the parents’ mobile phone via GSM modem if one of the sensors is triggered. For example, assume that the child is walking across the kitchen area. When the child steps on the pressure mat, the sensor starts, and the high state of LED directs the GSM modem to transmit a SMS to the parents; the SMS conveyed by the GSM is given in Figure 21.6.

Figure 21.5 Hardware model of child tracking system.

Figure 21.6 GSM message sent to parents for pressure mat triggered.

Figure 21.7 GSM message sent to parents for sensor beam triggered.

If the child tries to enter an unsafe zone, then a beam of the sensor gets broken. It automatically senses the child’s current position and sends its data to the microcontroller, and the microcontroller transmits the data to the GSM modem. Finally, the message is forwarded to parents through the GSM, as given in Figure 21.7. There will be no transmission of the notice when none of the sensors is triggered.

21.5. Conclusion

This project implementation primarily focuses on monitoring a child’s position and then warning their parents about any critical situation. Its real-time capability assists parents in keeping their children safe from hazardous environments within the home. The flexibility of the designed system makes it easy to add new sensors to the existing system. The benefits of the tracking system include efficiency and affordable means of communication by use of SMS. Also, the system is robust and reliable and ensures easy installation. This pilot model system can be further extended and used in other applications for monitoring and tracking of even pets or any dependent who needs special supervision. And for future work, image sensors, cameras, GPS, and other sensors supported with provided data and images could be used to create a more reliable monitoring system. By using RFID tags [11, 12], multiple tracking of children in daycare or school is possible, and this can be extended to perform the same for all children in the school to monitor within the campus.

References

1. Punetha, Deepak; Mehta, Vartika (2014). “Protection of the child/elderly/disabled/pet by smart and intelligent GSM and GPS based automatic tracking and alert system”.

IEEE 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

, Delhi, India doi:10.1109/icacci.2014.6968490.

2. Khan Abid, Mishra Ravi, “GPS – GSM Based Tracking System”,

International Journal of Engineering Trends and Technology

, Volume 3, Issue 2, 2012.

3. Wang Xiaoli, Albert Kai-Sun Wong, and Yong ping Kong, “Mobility Tracking using GPS, Wi-Fi and Cell ID”. 27th

International Conference on Information Networking. Bali, Indonesia 2012.

4. Cassandra Dsouza, Dhanashree Rane, “Design of Child Security System”.

2018 3rd International Conference for Convergence in Technology (I2CT), Pune, India.

doi:10.1109/I2CT.2018.8529432.

5. Ramamurthy B., Bhargavi S., Shashi Kumar R., “Development of a Low-Cost GSM SMS-Based Humidity Remote Monitoring and Control system for Industrial Applications”.

International Journal of Advanced Computer Science and Applications

, Vol. 1, October 2010.

6. Al-Mazloum A., Omer E., Abdullah M. F. A., “GPS and SMS-Based Child Tracking System Using Smart Phone”.

International Journal of Electrical, Electronic Science and Engineering,

Vol. 7 No. 2, 2013.

7. Parvez M.Z., Ahmed K.Z., Mahfuz Q.R., Rahman M.S., “A theoretical model of GSM network-based vehicle tracking system”.

International Conference on Electrical and Computer Engineering (ICECE)

, 2010.

8. Liu Yanfei, “A Robotic Prototype System for Child Monitoring”.

International Journal of Robotics and Automation (IJRA)

, Volume 2, Issue 1, 2011.

9. Verma Pankaj, Bhatia J.S, “Design and Development of GPS-GSM Based Tracking System with Google Map-Based Monitoring”.

International Journal of Computer Science, Engineering and Applications (IJCSEA)

Vol. 3, No. 3, June 2013.

10. Sayad Tazeen, Upadhyay Jash, Naik Chetan, Mane Priyanka, “Parent Aid Mobile Application”.

International Journal on Recent and Innovation Trends in Computing and Communication

, Volume 2, Issue 1, 2014.

11. Ge, Xin; Gu, Runan; Lang, Yifan; Ding, Yinyue, “Design of handheld positioning tracker based on GPS/GSM”. IEEE

3rd Information Technology and Mechatronics Engineering Conference (ITOEC),

China 2017. doi:10.1109/ITOEC.2017.8122477.

12. Rengaraj Vinoth; Bijlani Kamal, “A study and implementation of Smart ID card with M-Learning and Child security”.

IEEE 2016 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

, Bangalore, India. doi:10.1109/ICATCCT.2016.7912013.

Note

*

Corresponding author

:

[email protected]

23Two Efficient Approaches to Building a Recommendation Engine for Movies Based on Collaborative Filtering on User Ratings

Aniket Biswal1* and Thirumurugan Krishnasamy2

1 School of Electrical and Electronics Engineering, Vellore Institute of Technology, Chennai, India

2 Department of Computer Science and Engineering, Kamaraj College of Engineering, Virudhunagar, India

Abstract

Recommendation systems have recently been an interest of research because of their ability to benefit both the businesses providing the products as well as the users using them. They are used in a variety of fields including entertainment, e-commerce, web pages, e-learning, etc. They help recognize the patterns which allow e-commerce giants like Amazon and Netflix to have competitive positions in their respective markets. This study aims to build a recommendation system based on collaborative filtering which can predict movies the users may like. The first approach focuses on using model-based collaborative filtering with ALS (Alternate Least Squaring) algorithm. The second approach uses a graph-based database Neo4j, which is the best NoSQL database suitable for such a study. The first approach can predict the movies a new user may like with a model having a regularization parameter as 0.18, rank as 13 and maximum iterations for ALS as 19. The model is hyper tuned using the RMSE (Root Mean Squared Error) as the error metric. This approach can overcome the problem of data sparsity and cold start using the ALS implementation of Apache Spark. The second approach uses a graph database along with the similarity metric as Jaccard Index to find out the top 25 nearest neighbors. It then ranks the movies a user may like based on the number of times the movie is rated. The top 10 recommendations made by the two approaches are illustrated and found to be meaningful.

Keywords: Recommendation systems, collaborative filtering, ALS, Apache Spark, graph database, Jaccard Index, Neo4j

23.1. Introduction

Recommender systems have recently been an interest of research because of their ability to benefit both the business providing the products as well as the users using them [1–5]. A good recommender system aims to be able to predict what a user might like, prefer to buy, or use. They are used in a variety of fields including entertainment, e-commerce, web pages, e-learning, etc. Recommenders help recognize the patterns which are abstruse to humans because of the huge amount of unstructured data that is generated by all the different types of user activities [6–12].

A recommendation engine can be broadly classified into three types:

Content-based Filtering

Collaborative Filtering

Hybrid Filtering

A content-based filtering engine is one in which the recommendations are highly dependent on the domain and emphasize more on the features and properties of the item, while a collaborative filtering technique is a domain-independent filtering technique where item-user preferences and ratings are used to make recommendations for the user. The hybrid filtering technique uses a combination of the two techniques. However, the scope of this paper is limited to implementing an efficient collaborative filtering engine. In collaborative filtering, the system filters out the items a user might not like based on the ratings or reactions provided by other similar users. It is one of the most commonly used techniques to build an intelligent recommender system. It forms the pith of many sophisticated recommender systems like Amazon, Netflix, and YouTube.

Further, there are two ways of implementing the CF technique as shown in Figure 23.1:

User-Based CF

Item-Based CF

Figure 23.1 Difference between user-based and item-based filtering.

In User-based CF, the algorithm tries to find the likeness of two users based on which items a user has bought or likes. It then picks up the most similar users and then recommends what these users have liked or purchased. In Item-based CF, the algorithm tries to find out the similarity between two items based on the rating given by the users.

In this paper, we use the MovieLens 100k dataset [8