Digital Cities Roadmap E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
• Sprache: Englisch

Seitenzahl: 770

Veröffentlichungsjahr: 2021

Beschreibung

DIGITAL CITIES ROADMAP

This book details applications of technology to efficient digital city infrastructure and its planning, including smart buildings.

Rapid urbanization, demographic changes, environmental changes, and new technologies are changing the views of urban leaders on sustainability, as well as creating and providing public services to tackle these new dynamics. Sustainable development is an objective by which the processes of planning, implementing projects, and development is aimed at meeting the needs of modern communities without compromising the potential of future generations. The advent of Smart Cities is the answer to these problems.

Digital Cities Roadmap provides an in-depth analysis of design technologies that lay a solid foundation for sustainable buildings. The book also highlights smart automation technologies that help save energy, as well as various performance indicators needed to make construction easier. The book aims to create a strong research community, to have a deep understanding and the latest knowledge in the field of energy and comfort, to offer solid ideas in the nearby future for sustainable and resilient buildings. These buildings will help the city grow as a smart city. The smart city has also a focus on low energy consumption, renewable energy, and a small carbon footprint.

Audience

The information provided in this book will be of value to researchers, academicians and industry professionals interested in IoT-based architecture and sustainable buildings, energy efficiency and various tools and methods used to develop green technologies for construction in smart cities.

Leseprobe

Table of Contents

Cover

Title Page

Copyright

Preface

1 The Use of Machine Learning for Sustainable and Resilient Buildings

1.1 Introduction of ML Sustainable Resilient Building 2

1.2 Related Works

1.3 Machine Learning

1.4 What is Resilience?

1.5 Sustainability and Resilience of Engineered System 12

1.6 Community and Quantification Metrics, Resilience and Sustainability Objectives

1.7 Structure Engineering Dilemmas and Resilient Epcot 21

1.8 Development of Risk Informed Criteria for Building Design Hurricane Resilient on Building

1.9 Resilient Infrastructures Against Earthquake and Tsunami Multi-Hazard

1.10 Machine Learning With Smart Building

1.11 Conclusion and Future Research

References

2 Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicles (UAVs)

2.1 Introduction

2.2 Literature Review

2.3 Experimental Methods

2.4 Results

2.5 Conclusion and Future Work

References

3 Sustainable Infrastructure Theories and Models

3.1 Introduction to Data Fusion Approaches in Sustainable Infrastructure

3.2 Smart City Infrastructure Approaches

3.3 Theories and Models

3.4 Case Studies

3.5 Conclusion and Future Scope

References

4 Blockchain for Sustainable Smart Cities

4.1 Introduction

4.2 Smart City

4.3 Blockchain

4.4 Use Cases of Smart City Implementing Blockchain

4.5 Conclusion

References

5 Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework

5.1 Introduction

5.2 Related Works

5.3 Related E-Governance Frameworks

5.4 Proposed Smart Governance Framework

5.5 Results Discussion

References

6 Revolutionizing Geriatric Design in Developing Countries: IoT-Enabled Smart Home Design for the Elderly

6.1 Introduction to Geriatric Design

6.2 Background

6.3 Need for Smart Homes: An Assessment of Requirements for the Elderly-Activity Mapping

6.4 Schematic Design for a Nesting Home: IoT-Enabled Smart Home for Elderly People

6.5 Worldwide Elderly Smart Homes

6.6 Conclusion and Future Scope

References

7 Sustainable E-Infrastructure for Blockchain-Based Voting System

7.1 Introduction

7.2 Related Works

7.3 System Design

7.4 Experimentation

7.5 Findings & Results

7.6 Conclusion and Future Scope

References

8 Impact of IoT-Enabled Smart Cities: A Systematic Review and Challenges

8.1 Introduction

8.2 Recent Development in IoT Application for Modern City

8.3 Classification of IoT-Based Smart Cities

8.4 Impact of 5G Technology in IT, Big Data Analytics, and Cloud Computing

8.5 Research Advancement and Drawback on Smart Cities

8.6 Summary of Smart Cities and Future Research Challenges and Their Guidelines

8.7 Conclusion and Future Direction

References

9 Indoor Air Quality (IAQ) in Green Buildings, a Pre-Requisite to Human Health and Well-Being

9.1 Introduction

9.2 Pollutants Responsible for Poor IAQ

9.3 Health Impacts of Poor IAQ

9.4 Strategies to Maintain a Healthy Indoor Environment in Green Buildings

9.5 Conclusion and Future Scope

References

10 An Era of Internet of Things Leads to Smart Cities Initiatives Towards Urbanization

10.1 Introduction: Emergence of a Smart City Concept

10.2 Components of Smart City

10.3 Role of IoT in Smart Cities

10.4 Sectors, Services Related and Principal Issues for IoT Technologies

10.5 Impact of Smart Cities

10.6 Key Applications of IoT in Smart Cities

10.7 Challenges

10.8 Conclusion

References

11 Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure

11.1 Introduction

11.2 Smart City and IoT

11.3 Mobile Computing for Smart City

11.4 Smart City and its Applications

11.5 Smart Tourism in Smart City

11.6 Mobile Computing-Based Smart Tourism

11.7 Case Study: A Mobile Application for Trip Planner Task Scheduling in Smart City’s Sustainable Infrastructure

11.8 Experimentation and Results Discussion

11.9 Conclusion and Future Scope

References

12 Smart Health Monitoring for Elderly Care in Indoor Environments

12.1 Introduction

12.2 Sensors

12.3 Internet of Things and Connected Systems

12.4 Applications

12.5 Case Study

12.6 Conclusion

12.7 Discussion

References

13 A Comprehensive Study of IoT Security Risks in Building a Secure Smart City

13.1 Introduction

13.2 Related Works

13.3 Overview of IoT System in Smart Cities

13.4 IoT Security Prerequisite

13.5 IoT Security Areas

13.6 IoT Security Threats

13.7 Review of ML/DL Application in IoT Security

13.8 Challenges

13.9 Future Prospects

13.10 Conclusion

References

14 Role of Smart Buildings in Smart City—Components, Technolo Indicators, Challenges, Future Research Opportunities

14.1 Introduction

14.2 Literature Review

14.3 Components of Smart Cities

14.4 Characteristics of Smart Buildings

14.5 Supporting Technology

14.6 Key Performance Indicators of Smart City

14.7 Challenges While Working for Smart City

14.8 Future Research Opportunities in Smart City

14.9 Conclusion

References

15 Effects of Green Buildings on the Environment

15.1 Introduction

15.2 Sustainability and the Building Industry

15.3 Goals of Green Buildings

15.4 Impacts of Classical Buildings that Green Buildings Seek to Rectify

15.5 Green Buildings in India

15.6 Conclusion

References

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1 Natural hazard year wise in US.

Figure 1.2 Societal principle of resilience and sustainability.

Figure 1.3 Decision making resilience and sustainable development framework.

Figure 1.4 Bavarian decision analytics.

Figure 1.5 Framework system modeling.

Figure 1.6 Quantification of resilience.

Figure 1.7 Mapping of quantification of sustainability and resilience.

Figure 1.8 Techniques of quantification of sustainability and resilience [58].

Figure 1.9 Paradigm of damage of building.

Figure 1.10 Estimation of household dislocation.

Figure 1.11 Estimation of permanent residence.

Figure 1.12 Resilience seismic concept [58].

Figure 1.13 (a) Healthy population (b) Patients-days treatment.

Figure 1.14 (a) Building after earthquake, (b) Building two years after earthqua...

Figure 1.15 (a) Improve resilience structure, (b) Reduce probability structural ...

Figure 1.16.Building damage by earthquake [55].

Figure 1.17 Framework of upper de-aggregation (ULD).

Figure 1.18 Framework of critical system modeling.

Figure 1.19 Smart building appliances [52].

Figure 1.20 Smart Residential Building Connected Sensors and Actuators.

Figure 1.21 IoT smart resilience building architecture.

Figure 1.22 Smart building components.

Figure 1.23 Machine learning techniques.

Figure 1.24 ML tasks in SB Environment.

Figure 1.25 Framework ML concept in the SB context.

Figure 1.26 Taxonomy of SB Services.

Chapter 2

Figure 2.1 Block diagram of a sensor [10].

Figure 2.2 Blue color represents training data and red color test data.

Figure 2.3 Naïve Bayes prediction.

Figure 2.4 Forecasting with simple average.

Figure 2.5 Prediction based on moving average.

Figure 2.6 Simple Exponential Smoothing.

Figure 2.7 Holt’s Linear Trend.

Figure 2.8 Holt–Winters Method.

Figure 2.9 Auto Regressive Integrated Moving Average model.

Figure 2.10 Artificial neural network [67].

Figure 2.11 Artificial neural network [67].

Figure 2.12 Membership values.

Figure 2.13 The result when parameters values are high.

Figure 2.14 The results at average values of the parameters.

Figure 2.15 The results at low values of the parameters.

Figure 2.16 Information collected by UAV from smart building for processing.

Chapter 3

Figure 3.1 Internet of things landscape concerning data fusion.

Figure 3.2 Centralized architecture.

Figure 3.3 Decentralized architecture.

Figure 3.4 Distributed architecture.

Figure 3.5 Smart city infrastructure development framework.

Figure 3.6 Smart city monitoring center.

Figure 3.7 System use case diagram.

Figure 3.8 System flow chart explaining the working.

Figure 3.9 First page.

Figure 3.10 Firstcandidate and frequent list.

Figure 3.11 Second and third candidate and frequent list.

Figure 3.12 Input data from text file.

Figure 3.13 Display of data and euclidean distance matrix.

Figure 3.14 Final result.

Chapter 4

Figure 4.1 Domains of smart city [31].

Figure 4.2 Simplistic view of blockchain.

Figure 4.3 Electronic voting process [50].

Figure 4.4 The flow of dividend payment and tax refund application [61].

Figure 4.5 Blockchain and environment.

Figure 4.6 Smart living.

Chapter 5

Figure 5.1 Basic E-Governance model.

Figure 5.2 Platform types for Electronic Governance.

Figure 5.3 IoT and Cloud (IC) Platform.

Figure 5.4 Big Data & Cloud (BC) Platform.

Figure 5.5 CPS and Cloud (CC) Platform.

Figure 5.6 Ecosystem based data analytics framework [8].

Figure 5.7 Ecosystem-based data analytics framework [19].

Figure 5.8 Four stage model of E-Governance [3].

Figure 5.9 Smart city features/dimensions in India [61].

Figure 5.10 Proposed framework for smart governance applications.

Chapter 6

Figure 6.1 Framework for design of smart homes for the elderly.

Figure 6.2 Schematic design for Nesting Homes.

Figure 6.3 IoT-based provisions proposed in Nesting Homes.

Figure 6.4 Comprehensive health monitoring system integrated in nesting homes.

Chapter 7

Figure 7.1 Organizer use case diagram.

Figure 7.2 Candidate use case diagram.

Figure 7.3 Voter use case diagram.

Figure 7.4 Class diagram for E-voting system.

Figure 7.5 Home page.

Figure 7.6 Organizer registration.

Figure 7.7 Organizer registration.

Figure 7.8 Candidate registration.

Figure 7.9 Candidate dashboard.

Figure 7.10 LIVE election section of HomePage.

Figure 7.11 Election result.

Figure 7.12 Truffle environment.

Figure 7.13 Migration of our contacts to our test environment.

Figure 7.14 Transaction Logs of user Election Contract.

Figure 7.15 Transaction logs of admin contract.

Figure 7.16 Smart contract deployed.

Figure 7.17 Adding credentials.

Figure 7.18 Check Aadhaar.

Chapter 8

Figure 8.1 Comparability in the middle of the approximated global population and...

Figure 8.2 IoT-based interconnection.

Figure 8.3 Applications of Smart City.

Figure 8.4 Classification of IoT-Based Smart Cities.

Figure 8.5 IoT-Enabled Smart Cities concerning technologies in IoT Five-layer Ar...

Figure 8.6 IoT Five-layer architecture for Smart city applications.

Figure 8.7 The IoT Computing paradigm for Smart City Application.

Figure 8.8 Clever uses for deploying IoT architecture components on distinct con...

Figure 8.9 5-Layered IoT Architecture for Smart City Over the 5-Staged of Smart ...

Chapter 9

Figure 9.1 Common symptoms of VOC exposure.

Figure 9.2 Various strategies for maintaining healthy IAQ in Green Buildings.

Chapter 10

Figure 10.1 Components and themes of a Smart City.

Figure 10.2 Smart building.

Figure 10.3 Smart building framework.

Figure 10.4 Smart health care systems.

Figure 10.5 IoT framework.

Figure 10.6 Communication technologies.

Figure 10.7 Applications of IoT.

Figure 10.8 Smart city design challenges.

Chapter 11

Figure 11.1 Basic architecture for IoT-based Smart City and Smart Tourism.

Figure 11.2 Smart city and smart tourism components.

Figure 11.3 Basic flow of mobile application-based trip planner.

Figure 11.4 User interfaces for travel booking and task scheduling.

Chapter 12

Figure 12.1 Elderly living in a smart home independently and a happy care taker.

Figure 12.2 Elderly living in a home independently and a sad care taker.

Figure 12.3 Classification of sensors.

Figure 12.4 Smart health monitoring cycle.

Chapter 13

Figure 13.1 IoT for smart city.

Figure 13.2 Potential threats in Smart City.

Figure 13.3 ML/DL in securing Smart City.

Figure 13.4 KNN for IoT security.

Figure 13.5 CNN for IoT security.

Figure 13.6 Deep auto encoders working principle.

Figure 13.7 Illustration of RNN for IoT security.

Figure 13.8 Illustration of RBM working principle.

Figure 13.9 GAN working principles.

Figure 13.10 Implementation of ML/DL with other technologies.

Chapter 14

Figure 14.1 Components of Smart City.

Chapter 15

Figure 15.1 Schematic diagram of goals of green buildings.

Figure 15.2 Green building optimization (Source: https://blogs.umass.edu/natsci3...

Figure 15.3 Use of energy in commercial buildings and green buildings (Source: E...

Figure 15.4 Emission of GHG in building structure.

Figure 15.5 Comparison between construction materials used in green and conventi...

List of Tables

Chapter 1

Table 1.1 Report data of a survey.

Table 1.2 Planning for community resilience [55].

Table 1.3 Smart control devices [56].

Table 1.4 Difference Between Various Smart Control Devices in SB [56].

Table 1.5 Difference between of ML techniques.

Table 1.6 Difference between deep learning and machine learning tools [56].

Table 1.7 Difference between real time data analysis tools [56].

Table 1.8 Application of smart buildings [56].

Chapter 2

Table 2.1 Results of different techniques.

Table 2.2 Action taken for different range of values.

Table 2.3 Forecast accuracy of different variables.

Table 2.4 Features to be considered for fire detection.

Chapter 5

Table 5.1 E-Governance and Smart Cities Governance related work area dimensions.

Table 5.2 Stages of E-governance framework [60].

Table 5.3 Literacy rate comparison by UNESCO Data Source [62].

Table 5.4 Electronic governance, smart city governance evaluation indicators.

Chapter 6

Table 6.1 Elderly activity mapping source: authors (based on primary interviews)...

Table 6.2 Issues faced by the elderly in the Indian context.

Chapter 7

Table 7.1 Software requirements.

Table 7.2 Testing the application.

Table 7.3 Testing the application.

Table 7.4 Testing the application.

Chapter 8

Table 8.1 IoT applications for Smart City.

Table 8.2 A survey on communication protocols for fulfilment of IoT-enabled smar...

Table 8.3 Summary of Smart cities and future research challenges and their guide...

Chapter 9

Table 9.1 Sources of indoor air pollution.

Table 9.2 The relationship between energy-efficient strategies for Green Buildin...

Table 9.3 Strategies for improving IAQ in an energy-efficient manner.

Chapter 10

Table 10.1 Sectors, services and principal issues of IoT technologies.

Chapter 11

Table 11.1 Smart Tourism place and details.

Table 11.2 Comparative analysis of various trip planner mobile applications.

Chapter 13

Table 13.1 Passive threats.

Table 13.2 Active attacks.

Table 13.3 Summary of studies on ML.

Table 13.4 Summary of studies on DL.

Table 13.5 Datasets available for intrusion detection system.

Chapter 15

Table 15.1 An enumeration of major indoor air pollutants.

Table 15.2 Green buildings in India.

Guide

Cover

Table of Contents

Title Page

Copyright

Preface

Begin Reading

Index

End User License Agreement

Pages

v

ii

iii

iv

xix

xx

xxi

xxii

xxiii

xxiv

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

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

86

87

88

89

90

91

92

93

94

95

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

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

218

219

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

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

310

311

312

313

314

315

316

317

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

509

510

511

512

513

514

515

516

Scrivener Publishing

100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Advances in Learning Analytics for Intelligent Cloud-IoT Systems

Series Editor: Dr. Souvik Pal and Dr. Dac-Nhuong Le

The role of adaptation, learning analytics, computational Intelligence, and data analytics in the field of cloud-IoT systems is becoming increasingly essential and intertwined. The capability of an intelligent system depends on various self-decision-making algorithms in IoT devices. IoT-based smart systems generate a large amount of data (big data) that cannot be processed by traditional data processing algorithms and applications. Hence, this book series involves different computational methods incorporated within the system with the help of analytics reasoning and sense-making in big data, which is centered in the cloud and IoT-enabled environments. The series publishes volumes that are empirical studies, theoretical and numerical analysis, and novel research findings.

Submission to the series:

Please send proposals to Dr. Souvik Pal, Department of Computer Science and Engineering, Global Institute of Management and Technology, Krishna Nagar, West Bengal, India.

E-mail: [email protected]

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

Digital Cities Roadmap

IoT-Based Architecture and Sustainable Buildings

Edited by

Arun Solanki, Adarsh Kumar and Anand Nayyar

This edition first published 2021 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 © 2021 Scrivener Publishing LLC For 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 Headquarters

111 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 Warranty

While 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 merchantability 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 978-1-119-79159-1

Cover image: Pixabay.Com Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

Preface

Due to their continuously growing populations, cities are facing major challenges in providing conditions that will contribute to the development of a healthy sustainable environment. This population growth has increased resource requirements and the demand for large-scale waste management systems and other services. Therefore, the aim of sustainable development is to provide processes for the planning, implementation and development of projects to meet the needs of modern communities without compromising the potential of future generations. Sustainability always includes a balance of priorities in various areas, including economics, community needs and environmental quality, as well as justice, health and well-being, energy, water and material resources, and transportation needs. Also, since communication is of fundamental importance for both internet access and new digital services, an important starting point for smart cities is the introduction of public Wi-Fi.

Another point of significant concern that has contributed to the advent of sustainable smart cities is the energy crisis brought about by the global demand for limited natural resources, which are declining as demand grows. These natural resources are used by the industrial, transport, commercial, and residential sectors. Those living in residential areas use energy mostly for space heating, followed by electronics, lighting and other appliances, water heating, air conditioning, and cooling. Because the global residential sector consumes a significant amount of energy, which is equivalent to one-third of all available primary energy resources, it is necessary to reduce energy consumption by using sustainable buildings. A good management strategy must be expected to mitigate the dangerous consequences of rapid urbanization in modern society, the economy and the environment. Since sustainable smart cities include established structures, infrastructures, communities, institutions, and individuals, the proposed solution should be the result of real interdisciplinary discussions in a multicultural environment that encourages communication and has a real chance of succeeding.

This book provides readers with a platform through which they can simulate all of the requirements for the development of smart sustainable cities. It helps readers interact, brainstorm, and work on common problems or discuss proven solutions and models. Moreover, it also deals with energy consumption. Such energy consumption leads to a rapid depletion of energy resources, an increased need for building maintenance, an improvised comfortable lifestyle, and an increase in time spent on building construction. A sustainable building mainly refers to the renewable sources used for construction that help the structure withstand atmospheric changes. Currently, all countries are looking for ecological materials; that is, renewable plant materials such as straw and clay bricks, wood from forests certified for sustainable management, recycled materials, and other nontoxic, reusable and renewable products. For sustainable and durable construction, energy efficiency is an urgent problem, and researchers are currently actively involved in this area. This book provides an in-depth analysis of design technologies that lay a solid foundation for sustainable buildings. Smart automation technologies that help save energy are also highlighted, as well as various performance indicators needed to make construction easier. The aim of this book is to create a strong research community and to impart a deep understanding of the latest knowledge regarding the field of energy and comfort, along with offering solid ideas in the near future for sustainable buildings. These buildings will help cities grow into smart cities. Since the focus of smart cities is on low energy consumption, renewable energy, and a small carbon footprint, researchers must study optimization methods in order to find the optimal use of energy resources.

The book is organized as follows: Chapter 1, “The Use of Machine Learning for Sustainable and Resilient Buildings,” provides insights into intelligent resources, artificial learning and big data analytics. A detailed study of the field of intelligent architecture is presented, which focuses on the role of machine learning and large-scale data analytics technologies. Finally, some of the challenges and opportunities of applying machine learning in the built environment are discussed. Chapter 2, “Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicle (UAV),” discusses various time-series methods used to calculate the threshold value of the parameters in UAV-based data, including the Naive Bayes, simple average, moving average, simple exponential smoothing (SES), Holt’s linear, Holt-Winters, and autoregressive integrated moving average (ARIMA) methods. Since variation in the degree of value from the threshold range is helpful in predicting different actions, the vector autoregressive (VAR) method is also discussed, which is a multivariate time-series analysis used to calculate the threshold value that considers all the features at once along with their impact on each other.

Chapter 3, “Sustainable Infrastructure Theories and Models,” introduces the concepts of data fusion and data fusion approaches with respect to sustainable infrastructure. This work computes and explains various data fusion tools, techniques, and important methods of decentralized and distributed detection. Several smart city infrastructure approaches are highlighted along with the smart city components architecture. Chapter 4, “Blockchain for Sustainable Smart Cities,” explains how a sustainable smart city is a key solution for the large-scale urbanization of rural areas. However, urbanization poses a number of challenges for governments and city planners, including increased traffic congestion, reduction in quality health service provision, burden on civic facilities, and data management among others. Blockchain is integrated into smart city applications to improve the standard of living of citizens and overall management of the smart city. With the advantage of blockchain, a smart city can provide efficient and reliable services to people. Chapter 5, “Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework,” surveys and shows the research gaps in various E-governance services developed and implemented in India that are being initiated to achieve the Digital India program launched by the government of India with the help of information and communication technology (ICT). Furthermore, the architectural framework for smart governance-based services for smart cities in India based on transforming electronic governance to governance in a smart city is proposed.

Chapter 6, “Revolutionizing Geriatric Design in Developing Countries: IoT-Enabled Smart Home Design for the Elderly,” presents a study that emanated from concern for the growing population of the elderly in our cities who are forced to live alone without much assistance due to shrinking family size and intercity and international migration of their children in search of better job opportunities. The study looks at the middle-class to upper-middle-class elderly population aged 65 and above living in urban cities of India such as Bangalore. This group usually comes from a well-educated background with mid-level financial security. Chapter 7, “Sustainable E-Infrastructure for Blockchain-Based Voting System,” explores the block-chain technology used to implement an electronic voting system. E-voting can change the way in which we have voted for decades. The main feature of this system is that voters can cast their vote from anywhere in the world. As this voting process starts going digital and online, voters from outside the country can also vote from wherever they are, which can increase the total voting percentages tremendously. Chapter 8, “Impact of IoT Enabled for Smart Cities: A Systematic Review and Challenges,” discusses the way in which the IoT has influenced specific areas of our daily lives. Moreover, the reader will discover the fundamental options that come with smart cities and exactly why a contemporary community is given that name, along with some of its problems and solutions. Additionally, this particular chapter covers the role of 5G technologies in the IoT along with big data analysis. Finally, it includes the primary options that come with the Indian perspective of smart cities by 2030 to enhance the daily lives of humans, along with conceptual and block diagrams.

Chapter 9, “Indoor Air Quality (IAQ) in Green Buildings: A Prerequisite to Human Health and Well-Being,” examines why the IAQ inside buildings is one of the most important determining factors of human health as more than half of the air inhaled by a person during his/her lifetime is at home. Illnesses associated with environmental exposure often stem from indoor air exposure. Prominent air pollutants are found indoors, including volatile organic compounds (VOCs), particulate matter (PM), carbon monoxide (CO), lead (Pb), nitrogen oxides (NOx), and asbestos. Smart and sustainable approaches to green building construction should incorporate IAQ as a critical component of building design as the air quality is directly related to the inhabitants’ sound well-being. Chapter 10, “An Era of Internet of Things Leads to Smart Cities Initiatives towards Urbanization,” outlines the components of smart cities and IoT technologies used in smart cities for establishing relationships between industries and their services, and includes a table showing various sectors providing different services and related principal issues of IoT technologies. Finally, the challenges of smart cities, urbanization, and IoT are highlighted. The perceived concept of the smart city appears to initiate the new standards for urban city planning. Urban planners imagine the city of the future as smart and economical. This initiative will always remain critical for development and sustainability. Chapter 11, “Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure,” provides comprehensive, automatic task rescheduling for mobile application. This approach will enhance the growth of smart city workers’ planning and boost the growth of smart sustainable infrastructure. Here, a comparative study of existing mobile applications of task scheduling is also presented.

Chapter 12, “Smart Health Monitoring for Elder Care in Indoor Environments,” discusses the various technologies that are being used by researchers to measure indoor environmental quality, human health and well-being along with case studies and real-life examples. Technology plays a crucial role in supporting the self-sufficient living of the elderly and their caregivers. However, the environmental quality of the spaces they reside in affects their health. Real-world case studies and their results are discussed in subsequent sections. Finally, available tools and research to aid readers delve further into this vital application area are discussed. Chapter 13, “A Comprehensive Study of IoT Security Risks in Building a Secure Smart City,” presents a holistic review of ML/DL algorithms that can be deployed to improve security. The IoT is capable of assimilating a variety of heterogeneous end systems by facilitating seamless access and communication among an expansive range of devices, people and their environment, making it the key feature in developing the idea of smart cities. This chapter delineates the challenges related to the technology’s implementation and standardization. It briefly overviews existing IoT architectures and enabling technologies, and also explores the prospects of ML/DL methodologies that can be implemented on the IoT platform to maintain an admissible level of services, security and privacy issues, with the aim of enhancing the overall experience of smart cities.

Chapter 14, “Role of Smart Buildings in Smart City: Components, Technologies, Indicators, Challenges, and Future Research Opportunities,” presents various indicators, technologies, components, and features of smart buildings in any smart city. General architectures are subsequently discussed along with the various supporting technologies and requirements of smart buildings for smart cities. The chapter ends with a discussion of the different challenges followed by future research opportunities in the domain of smart buildings in a smart city. Chapter 15, “Effects of Green Buildings on the Environment,” discusses concerns related to rapidly increasing environmental and sustainability issues like urbanization, climate change, loss of biodiversity and degradation of resources, which highlight the need for advancements in housing. Green building is the theory, science and styling of buildings planned and constructed in accordance with a minimum impact on the surroundings by reducing utilization of water, energy, and disturbances in the surrounding environment in which the building is located. This contribution is an attempt to appraise the value of green buildings compared to standard buildings. An attempt is also made to illustrate the available good practices regarding green structures in India.

The information provided in this book will be an incentive to the researchers, academicians and industry professionals interested in IoT-based architecture and sustainable buildings. The book also provides a platform to exchange knowledge in the field of energy efficiency and various tools and methods used to develop green technologies for construction in smart cities.

We would like to express our sincere gratitude to the contributors to the book, who supported us with the contribution of their valuable work and dedication to make this book a resounding success. Last but not the least, we thank Scrivener Publishing and associated production editors for handling the project and making this book a reality.

The Editors

January 2021

1The Use of Machine Learning for Sustainable and Resilient Buildings

Kuldeep Singh Kaswan1* and Jagjit Singh Dhatterwal2

1School of Computing Science and Engineering, Galgotias University, Greater Noida, India

2Department of Computer Science & Applications, PDM University, Bahadurgarh, India

Abstract

The use of Artificial Intelligence to ensure that intelligent and resilient buildings are sustainably developed. The intelligence displayed in buildings by electronic devices and software operated systems is artificial intelligence which perceives the building environment and takes actions aimed at optimizing output in a given context or constraint. A complex, sensitive infrastructure that ensures efficient, cost-effective and environmentally acceptable conditions for every occupant by constantly communicating with its four basic elements: locations (components, frameworks, facilities); processes (automation, control systems), staff (services, users) and management (maintaining, performance) and processes (controlling, systems); and they separate current technologies into two major groups, occupantcentered and energy-centered facilities. The first level approaches that use ML for occupant dimensions, including (1) occupancy and identity estimations, (2) behavior recognition and (3) choice and enforcement estimates. The approach in the second-class category used ML to approximate energy or device-related aspects. It is divided into three categories, (1) estimating the energy profiling and demand, (2) profiling and detection of faults of devices, and (3) sensor inferiority. In this chapter, we focus on guided study, unrestricted learning and improving learning. The main variants, implications of specific parameter choices are explored and we generate standard algorithms. Finally, discuss some of the challenges and opportunities in the built environment to apply machine learning.

Keywords: Machine learning, big data analytics, Internet of Things, smart building, resilient building, sustainable building

1.1 Introduction of ML Sustainable Resilient Building

The hyperconnectivity generated by IoT will enhance the assurance of Smart Sustainable Resilient Building (SSRB) as all basic construction facilities and goods from your home electronics to your plant vessels have now been connected [1–5]. Nevertheless, this hyperconnectivity could hinder the control of SSRBs at the same time. In particular, massive quantities of streaming data are required from SSRB and its residents. The management of large data streams is becoming more and more relevant with ML, testing, compaction, learning and filtration technologies. In order to obtain a greater interpretation of human beings than their environment computers, the amount of sensory data obtained by sensors and devices needs to be processed by algorithms, converted into details and derived expertise [6–8]. This awareness can also contribute, and most significantly, innovative goods and services that change our lives drastically. For starters, smart meter readings may be used to help estimate and control power usage. To optimize this convenience, reduce expenses adapting to requirements of its residents, the SSRB requires sophisticated tools to understand, anticipate and make intelligent decisions. SSRB must also provide a variety of wearable sensor data linked to its patients and produce new remote sensors. SSRB algorithms include estimation, decision analysis, robots, smart devices, wireless sensor networks, interactive, web computing and cloud computing and include several other developments. Cognitive maintenance of offices is necessary in several SSRB programs for starters, fitness, safety, energy management, illumination, repair, the elderly and digital entertainment through these technologies.

1.2 Related Works

While several SB-focused survey papers have been released, none focuses on the role of data analysis and ML within SBs. All the relevant survey papers are comprehensively presented in Table 1.1.

Table 1.1 Report data of a survey.

Cite

Purpose

Limitations

Chan

et al.

[12]

A country and continent arranged project SH Review as well as the associated technologies for monitoring systems and assistive robotics.

It not emphasized on the importance of ML and big data analytics, it does not review and classify the papers according to the applications of SH

Alam

et al.

[13]

Research objectives and services-based review of SH projects; namely, comfort, healthcare, and security.

It not emphasized on the importance of ML and big data analytics for SB.

Lobaccaro

et al.

[14]

Review of existing software, hardware, and communications control systems for S.H and smart grid.

It not emphasized on the importance of ML and big data analytics. It also does not focus on reviewing and categorizing papers according to the applications of SH.

Pan

et al.

[15]

The energy efficiency and the vision of microgrids topics research review in SBs.

The emphasis of the paper is not the ML and big data analytics for SB services. It does not consist of the other applications of SB rather than energy efficiency.

Ni

et al.

[16]

Propose a classification of activities considered in SH for older peoples independent living, they also classify sensors and data processing techniques in SH.

Does not cover all the services in SH. It also does not categorize the research according to different ML model styles.

Rashidi and Mi-hailidis [17]

Review AAL technologies, tools, and techniques.

The paper focuses only on AAL in healthcare, and does not cover the other applications in SH or SB; in addition, there is no classifying of the researches according to ML model styles.

Peetoom

et al.

[18]

The monitoring technologies that detect ADL or significant events in SH based review.

Does not focus on the role of ML in SB.

Salih and Abraham [19]

The ambient intelligence assisted healthcare monitoring focuses only on AAL in healthcare, and does not cover the other applications in SH or SB in the review.

The challenges and the future research directions in the field not covered in the research.

Perera

et al.

[20]

Discuss and analyzed the works in context awareness from an IoT perspective

Not emphasized specifically on the SB domain and its application services.

Tsai

et al.

[21]

Data mining technologies for IoT applications data reviewed.

SB applications not emphasized.

Mahdavinejad

et al.

[22]

Discussed and analyzed some ML methods applied to IoT data by studying smart cities as a use case scenario.

Not concentrated on SB and its applications as a use case.

Chan et al. in 2008 [12] gave a description of intelligent home study. It even speaks about smart and friendly robotics. The article examines the nation and the continent’s smart home programs. Alam et al. [13] presented information on sensors, apps, algorithms and protocols of communication used in smart homes. The paper explores intelligent homes focused on their facilities and study aims: protection, fitness and comfort.

Lobaccaro et al. [14] shared the notion of a smart house but smart grid technology and address obstacles, advantages and potential developments of intelligent home technology. Pan et al. [15] analyzed the research of SBs with microgrids on efficient energy usage. The study explores subjects for analysis and latest developments in SBs and microgrid vision.

For multiple study articles research on making the autonomous lives of seniors for smart homes simpler has been checked. Ni et al. [16] have reported on sensing machine features including practices which can help elderly people reside peacefully in intelligent residences. Rashidi and Mihailidis provided a study on environmental assistance systems for elderly people [17]. Peetoom et al. [18] concentrated software tracking that understands householder existence, including reduced identification and changes of safety condition. Salih et al. [19] proposed a health-assisted urban knowledge report surveillance system identifying different methods included in current research literature, as well as connectivity and wireless sensor network technology.

1.3 Machine Learning

A brief list of the different algorithms for machine learning [49] in sustainable and resilient building is obtained below.

Decision Tree

—

Decision Tree is a supervised learning system used for classification or regression. A training model is built in Decision Tree Learning and the importance of the results is determined through the learning decision rules derived from the data attributes. In Big data there are many drawbacks to these decision tree algorithms. Firstly, if the data are very large, it is very time to build a decision tree. Secondly, there is no optimal solution to the distribution of data that contributes to higher communication cost.

Support Vector Machine (SVM)—Support Vector Machine is a supervised learning approach that can be used for either regression or classification. When used on big data, due to its high machine complexity, the SVM technique is not successful. The demand for measurement and storage is increased considerably for enormous amount of data.

K-Nearest Neighbor (KNN)

—

For regression and classification problems, K-Nearest Neighbor (KNN) algorithms are used. KNN approaches are using data and graded use similar steps to different data points. The information is reserved for the class with the closest neighbors. The value of k increases with the increase of the number of closest neighbors. KNN is not realistic on big data applications because of the high cost of calculation and memory.

Naive Bayes Classifier

—

For classification function Naive Bayes Classifier is commonly used. For any class or data point that belongs to a certain class, they define membership probabilities. The most probable class is the one with the highest likelihood. The efficiency of Naive Bayes is not possible in text classification tasks due to text redundant features and rough parameter estimation.

Neural Networks

—

A semi-supervised technique for classification and regression, the Neural networks. Neural Nets is a computing device consisting of highly interrelated processing elements that process data via their dynamic state response. Back Propagation is one of the best-known algorithms in the neural network. Neural networks have few challenges for big data with the growing scale of information. The huge quantity of information makes it difficult for the technique to maintain both reliability and efficiency and also increases the system operating load.

1.4 What is Resilience?

Over the last couple of decades, the concept of resilience has received increasing attention in several ways and is now viewed as a desirable feature of physical systems and communities. A popular feature in both meanings is that resilience “is the system’s capacity to tolerate external disturbance(s), adjust and rapidly return to the initial or a new stage,” and also offers a multi-disciplinary concept in resilience within the engineering sector [9–15].

Resilience can be described as an ability to reach a desired level of reliability or provide a desired level of service or features in the physical systems, Q, immediately after a risk arises.

1.4.1 Sustainability and Resiliency Conditions

Most societies choose to be resilient and sustainable [50]. When priorities and plans are formulated separately in order to enhance resilience and sustainability, there are strong risks that the targets may overlap and may also clash. This chapter looks at the principles of safe and durable cities, how increasing environmental and constructed environments and stressors will need different approaches and resources to improve stability and longevity for the environment.

When their resilience and sustainable strategies align themselves, the best results for communities occur. However, sustainable and resilient advancement must be accomplished before promising future generations are delivered. Challenges include reduction of impacts on environmental systems, management and the time it takes to change current practices and replace existing infrastructure with standard renewal rates. Nevertheless, while the governance potential and sustainability and adaptation strategies are open, intergenerational wealth is undermined by expectations that natural environments (our atmosphere, habitats, and climate) are secure and healthy [16–20]. Introduction to sustainability and the resilience of buildings, the dynamic nature of natural systems has not been fully understood through their intricate interrelationships across time and space and their preference for inclination points and threshold values. Many experts face the challenge of developing dangerous model infrastructure that does not involve potential improvements in risk magnitude or frequency, because scientific consensus is not yet formed on this topic. In fact, today’s construction methodology does not take into consideration the harm rates and related impacts on building operation recovery—a critical aspect of resilience.

1.4.2 Paradigm and Challenges of Sustainability and Resilience

A basic yet strong definition is sustainable development to ensuring that society “combines the present need without compromising potential generations’ capacity to fulfill their needs” (UN 1987). The groundbreaking Bruntland Commission study on sustainable growth presented this Sustainable development concept for the first time. With the implementation of the Sustainable Development Goals in 2015, sustainable development remains an international initiative which has motivated policy and individuals worldwide to alleviate some of the more drastic consequences of mankind on the global operation of the environment.

The idea of cohesive societies emerged concurrently. Application and special concept of resilience to a variety of subjects and dimensions, include psychology, economics, public safety, protection, business continuity, disaster preparation and reaction, risk reduction and ability of the building system (i.e. design, transport, services and other infrastructure) to physically resist and rapidly recover. In terms of populations and dangerous incidents, “the capacity to adjust and withstand and recover rapidly from damage” is specified (PPD-21 2013). The idea of building resilience and infrastructure systems, in order to minimize damage to the environment, restore and reconstruct expenses as well as economic impacts, is to be avoided until a certain point, then improve or recover over a certain period of time [21, 22, 26–28]. In reality there also are situations where the constructed system cannot avoid only threshold hazard in terms of the different facilities age and circumstances around a city. Throughout these situations, contingency preparation may be used to recognize performance gaps and transitional measures which would allow the society to continue to deliver services, if the building(s) or network system(s) impacted is not willing to do so. Such performance holes often present the possibility of beginning an innovative cycle to enhance construct environments efficiency.

Figure 1.1 Natural hazard year wise in US.

Natural disasters will affect societies by human loss, relocation, death, property harm and economic impacts. Such consequences and sluggish group rebounder may be amplificated by structural stressors like high unemployment, inadequate services or food shortages. The National Environmental Awareness Centers (NOAA, 2018a) report that 218 extreme weather events happened in the USA between 1980 and 2017 worth at least $1 billion. The degree to which societies have been affected and lost their work from natural disasters is seen in Figure 1.1. The enhancement of construction and infrastructure’s robust and sustainable efficiency will help cities escape major economic loss and long-term consequences.

1.4.3 Perspectives of Local Community

There are a number of communities in the United States which recover each year from a dangerous event. Over the last 50 years, an annual average of 40 declarations of presidential hazardous events has been issued (FEMA 2013). Hazardous results are first experienced and first handled in populations. While governments cannot eliminate natural threats, long-term planning and prioritized initiatives that are enforced over time will mitigate their effects. The level of recovery and the eventual outcome would rely on the scope and magnitude of the incident and on the action taken by government to mitigate harm, preserve properties, react in a timely and organized manner and restore government functionality within a given time period. Such activities collectively assess the strength of a group.

Resilience provides a holistic solution to risk handling catastrophic incidents, as well as environmental problems, through structures that allow new generations the same opportunities to prosper. Communities will move for a more socially and economically equitable and prosperous environment by resolving skill differences and essential threats through a systemic integrated and systematic strategy [29–35]. Approaches include: introduction into City planning and network projects with adaptability, resilience and regeneration, utilizing a framework methodology that tackles multi-scale connections and dependency and methodologies that resolve the complexity of the potential severity of hazards (Table 1.2).

Table 1.2 Planning for community resilience [55].

Planning steps

Key activities

1. Form a collaborative Planning Team

Identify resilience leader for the community

Identify team members, and their roles and responsibilities

Identify key public and private stakeholders for all phases of planning and implementation

2. Understand the situation

Social Dimensions

Identify and characterize functions and dependencies of social institutions, including business, industry, and financial systems, based on individual/social needs met by these institutions and social vulnerabilities

Identify how social functions are supported by the built environment

Identify key contacts and representatives for evaluation, coordination, and decision nuking activities

Built Environment

➢ Identify and characterize buildings and infrastructure systems, including condition, location, and dependencies between and among systems

➢ Identify key contacts/ representatives for evaluation, coordination, and decision-making activities

➢ Identify existing plans to be coordinated with the resilience plan

Link social functions to the supporting built environment

Define building clusters andsupporting built environment supporting infrastructure

3. Determine goals and objectives

Establish long-term community goals

Establish desired recovery performance goals for the built environment at the comma level based on social needs, and dependencies and cascading effects between systems

Define community hazards and levels

Determine anticipated performance during and after a hazards event to support social functions

Summarize the results

4. Plan development

Evaluate gaps between the desired and anticipated performance of the built environment to improve community resilience and summarize results

Identify solutions to address gaps including both administrative and construction options

Prioritize solutions and develop an implementation strategy

5. Plan preparation, review and approval

Document the community plan and implementation strategy

Obtain feedback and approval Mon stakeholders and community

Finalize and approve the planMon stakeholders and community

6. Plan implementation and maintenance

Execute approved administrative and construction solutions

Evaluate and update on a periodic basis

Modify short or long-term implementation strategy to achieve performance goals as needed

1.5 Sustainability and Resilience of Engineered System

The word “anthroposphere” has more and more been used by scientists to emphasize the impact of human existence in the new geological era. The accelerated demographic increase, technical advances and industrialization have reached a state in which the relations of human enterprises, the global environment of the world and the surroundings have a devastating effect on potential social changes at local level. The lack of natural capital, arable and inhabitable property, potable water and lifethreats in general, are increasingly impacting civilization-culminating in civil instability and migration. Human environmental emissions are widely accepted to adversely affect the earth’s geology and biosphere itself, thereby affecting the same living conditions which enable human civilization to be promoted in various ways, including global climate change. Regions and towns are not merely at danger, but are also a fact for millions. Environmental contamination, clean water and land, significant damage to the safety, well-being and livelihoods of current and future generations are a hazard. A global catastrophically danger must be taken seriously at all stages of society’s policy-making in the absence of sustainable social growth.

Earth structure and individual behavior on the functionalities of health organizations. It is therefore clear that the relation between sustainable growth and resilience is powerful and that the two concepts are essentially similar from two separate viewpoints, see also Figure 1.2.

Figure 1.2 Societal principle of resilience and sustainability.

1.5.1. Resilience and Sustainable Development Framework for Decision-Making

A program delegate must be developed to encourage the creation of decision-making resources for the resiliency, healthy community and to promote the rating of decision-making options in line with the information required, compatible with priorities and goals and conforming to potential requirements. The following introduces a structure representation paradigm, which fits closely Faber et al. [58].

• Analysis system representation of hierarchical decisionsIn order to help decisions about the management of processes, it is essential to create structure representations that regularly chart potential alternative options for decision-makers and the stakeholders involved in achieving their priorities. This assumes that the nature of the structures is decided by the policy makers, stakeholders and their choice, time-boundary and spatial limits, the functional features and functionality of the systems and their impact on system efficiency, and feasible and appropriate decision-making alternatives.

In other governance contexts, such as private organizations, or industrial practices, the overarching concept which underlies the hierarchical governance system seen in Figure 1.3 may be extended.

Theoretically, it is important for decisions to be rated in accordance with their anticipated worth (or benefit) in accordance with the Bavarian Decision Analytics and the axioms to be made in order to automate the design and/or the management of engineering systems subject to complexity and inadequate information in a normative decision sense.

The structure as outlined in Figure 1.4 incorporates not just threats in terms of potential negative value in various applicable indicators (e.g. negative in life, disruption to environmental values and financial losses) but also gains linked to decision-making options—the key goal of optimized structures—as opposed to more traditional risk-informed solutions to decision-making. The expansion supported the way Section 4 discusses durability and longevity as a framework for evaluation for stability outlined by Linkov et al. [59], thus accurately correcting typical risk modeling limitations. Specific decision alternatives to designed device architecture and management in accordance with the predicted utility benefit or any particular metrical requirements can be assessed and classified according to the device modeling paradigm as outlined in Figure 1.5.

Figure 1.3 Decision making resilience and sustainable development framework.

Figure 1.4 Bavarian decision analytics.

Figure 1.5 Framework system modeling.

1.5.2. Exposures and Disturbance Events

As seen in Figure 1.5, exposure incidents (disturbances) are considered to reflect, in theory, all future occurrences that may have implications. Resiliency, ecological models and analyses can include exposures.

Type-1 Hazards: The related threats are manageable in broad enough time and room, rendering their management far simpler. Geohazards such as earthquakes, flooding, waves, etc. are common manifestations of this form of hazards [37, 41, 43, 44].

Type-2 Hazards: They may be correlated with catastrophic combined effects on adequate time and space scales. Furthermore, their cumulative effects may cause the same characteristics as the hazards of type 3 to have more disastrous consequences. Typical cases include biological pollution, misuse of land, plant destruction, ineffective or poor management, insufficient financial planning, human mistakes, etc.

Type-3 Hazards: Very unusual and possibly catastrophic occurrences, also in broad sections of time and space, that are unforeseen and about which little evidence is practically available. The cumulative effects of type 2 hazards may be triggered. Examples include volcano eruptions, meteor collisions, solar storms of extreme severity, rapid temperature change as well as significant terrorist activities.

1.5.3 Quantification of Resilience

The literature includes a fairly wide number of ideas for modeling and quantifying network durability, e.g. Cimellaro et al. [60], Linkov et al. [59], Sharma et al. [61] and Tamvakis and Xenedis [62]. The proposed models are more commonly aimed at the short-term reflection of the system’s capacity to withstand and rebound from disruptions, without major output loss and without outside assistance, usually, the emphasis on the portrayal of resilience models.

For impact on service delivery of the stated perturbations and on recovery characteristics in relation to service grade recovered against period and overall service failure, see Figure 1.6.

Until recently only the modeling of processes to rebound from disruptions has been granted tacit attention. Neither the functional failure nor rather the production of capability that is critical to the productive, yet quick reorganization, change, yet recovery following disruptions and danger events will take account of processes flexibility providing a life cycle gain in the flexibility model described in Faber and Qin [57]. See Figure 1.6.

Figure 1.6 Quantification of resilience.

1.5.4 Quantification of Sustainability

Addressing biodiversity includes a shared analysis of the implications of inter-generational and intra-generational inequality on the environment, public safety and wellbeing, financial circumstances and extension of natural capital [45, 46, 48, 49]. In relation to the consequences currently discussed in resilience models, the emphasis is on whether changes on the ecosystem should be taken into consideration.