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Big Data Analytics for Internet of Things Big Data Analytics for Internet of Things Edited by Tausifa Jan Saleem National Institute of Technology Srinagar, India Mohammad Ahsan Chishti Central University of Kashmir Ganderbal, Kashmir, India This edition first published 2021 © 2021 John Wiley & Sons, Inc 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 The right of Tausifa Jan Saleem and Mohammad Ahsan Chishti to be identified as the author(s) of the editorial material in this work has been asserted in accordance with law Registered Office John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA Editorial Officesw 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 Wiley also publishes its books in a variety of electronic formats and by print-on-demand Some content that appears in standard print versions of this book may not be available in other formats 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 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 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 Library of Congress Cataloging-in-Publication Data Names: Saleem, Tausifa Jan, editor | Chishti, Mohammad Ahsan, editor Title: Big data analytics for Internet of things / edited by Tausifa Jan   Saleem, Mohammad Ahsan Chishti Description: First edition | Hoboken, NJ : Wiley, 2021 | Includes   bibliographical references and index Identifiers: LCCN 2020049761 (print) | LCCN 2020049762 (ebook) | ISBN   9781119740759 (hardback) | ISBN 9781119740766 (adobe pdf) | ISBN   9781119740773 (epub) Subjects: LCSH: Big data | Internet of things Classification: LCC QA76.9.B45 B4995 2021 (print) | LCC QA76.9.B45   (ebook) | DDC 005.7–dc23 LC record available at https://lccn.loc.gov/2020049761 LC ebook record available at https://lccn.loc.gov/2020049762 Cover Design: Wiley Cover Image: © Blue Planet Studio/iStock/Getty Images Plus/Getty Images Set in 9.5/12.5pt STIXTwoText by SPi Global, Pondicherry, India 10  9  8  7  6  5  4  3  2  v Contents List of Contributors  xv List of Abbreviations  xix Big Data Analytics for the Internet of Things: An Overview  Tausifa Jan Saleem and Mohammad Ahsan Chishti Data, Analytics and Interoperability Between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3)  Shoumen Palit Austin Datta, Tausifa Jan Saleem, Molood Barati, María Victoria López López, Marie-Laure Furgala, Diana C Vanegas, Gérald Santucci, Pramod P Khargonekar, and Eric S McLamore ­Context  ­Models in the Background  12 ­Problem Space: Are We Asking the Correct Questions?  14 Solutions Approach: The Elusive Quest to Build Bridges Between Data and Decisions  15 ­Avoid This Space: The Deception Space  17 ­Explore the Solution Space: Necessary to Ask Questions That May Not Have Answers, Yet  17 ­Solution Economy: Will We Ever Get There?  19 ­Is This Faux Naïveté in Its Purest Distillate?  21 ­Reality Check: Data Fusion  22 “Double A” Perspective of Data and Tools vs The Hypothetical Porous Pareto (80/20) Partition  28 Conundrums  29 ­Stigma of Partition vs Astigmatism of Vision  38 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI  40 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 vi Contents 2.14 2.15 2.16 3.1 3.2 3.2.1 3.2.1.1 3.2.1.2 3.2.1.3 3.2.2 3.2.2.1 3.2.2.2 3.2.3 4.1 4.2 4.2.1 4.2.2 4.3 4.3.1 4.3.1.1 4.3.1.2 4.3.1.3 4.3.1.4 4.3.2 4.3.2.1 4.3.2.2 4.3.2.3 4.3.3 4.4 4.4.1 4.4.2 4.4.3 I­ n Service of Society  50 ­Data Science in Service of Society: Knowledge and Performance from PEAS  52 Temporary Conclusion  60 Acknowledgements  63 References  63 Machine Learning Techniques for IoT Data Analytics  89 Nailah Afshan and Ranjeet Kumar Rout ­Introduction  89 ­Taxonomy of Machine Learning Techniques  94 Supervised ML Algorithm  95 Classification  96 Regression Analysis  98 Classification and Regression Tasks  99 Unsupervised Machine Learning Algorithms  103 Clustering  103 Feature Extraction  106 Conclusion  107 References  107 IoT Data Analytics Using Cloud Computing  115 Anjum Sheikh, Sunil Kumar, and Asha Ambhaikar ­Introduction  115 ­IoT Data Analytics  117 Process of IoT Analytics  117 Types of Analytics  118 ­Cloud Computing for IoT  118 Deployment Models for Cloud  120 Private Cloud  120 Public Cloud  120 Hybrid Cloud  121 Community Cloud  121 Service Models for Cloud Computing  122 Software as a Service (SaaS)  122 Platform as a Service (PaaS)  122 Infrastructure as a Service (IaaS)  122 Data Analytics on Cloud  123 ­Cloud-Based IoT Data Analytics Platform  123 Atos Codex  125 AWS IoT  125 IBM Watson IoT  126 Contents 4.4.4 4.4.5 4.4.6 4.5 4.5.1 4.5.2 4.6 4.7 Hitachi Vantara Pentaho, Lumada  127 Microsoft Azure IoT  128 Oracle IoT Cloud Services  129 ­Machine Learning for IoT Analytics in Cloud  132 ML Algorithms for Data Analytics  132 Types of Predictions Supported by ML and Cloud  136 ­Challenges for Analytics Using Cloud  137 ­Conclusion  139 References  139 Deep Learning Architectures for IoT Data Analytics  143 Snowber Mushtaq and Omkar Singh ­Introduction  143 Types of Learning Algorithms  146 Supervised Learning  146 Unsupervised Learning  146 Semi-Supervised Learning  146 Reinforcement Learning  146 Steps Involved in Solving a Problem  146 Basic Terminology  147 Training Process  147 Modeling in Data Science  147 Generative  148 Discriminative  148 Why DL and IoT?  148 ­DL Architectures  149 Restricted Boltzmann Machine  149 Training Boltzmann Machine  150 Applications of RBM  151 Deep Belief Networks (DBN)  151 Training DBN  152 Applications of DBN  153 Autoencoders  153 Training of AE  153 Applications of AE  154 Convolutional Neural Networks (CNN)  154 Layers of CNN  155 Activation Functions Used in CNN  156 Applications of CNN  158 Generative Adversarial Network (GANs)  158 Training of GANs  158 Variants of GANs  159 5.1 5.1.1 5.1.1.1 5.1.1.2 5.1.1.3 5.1.1.4 5.1.2 5.1.2.1 5.1.2.2 5.1.3 5.1.3.1 5.1.3.2 5.1.4 5.2 5.2.1 5.2.1.1 5.2.1.2 5.2.2 5.2.2.1 5.2.2.2 5.2.3 5.2.3.1 5.2.3.2 5.2.4 5.2.4.1 5.2.4.2 5.2.4.3 5.2.5 5.2.5.1 5.2.5.2 vii viii Contents 5.2.5.3 5.2.6 5.2.6.1 5.2.6.2 5.2.7 5.2.7.1 5.2.7.2 5.3 Applications of GANs  159 Recurrent Neural Networks (RNN)  159 Training of RNN  160 Applications of RNN  161 Long Short-Term Memory (LSTM)  161 Training of LSTM  161 Applications of LSTM  162 ­Conclusion  162 References  163 Adding Personal Touches to IoT: A User-Centric IoT Architecture  167 Sarabjeet Kaur Kochhar ­Introduction  167 ­Enabling Technologies for BDA of IoT Systems  169 ­Personalizing the IoT  171 Personalization for Business  172 Personalization for Marketing  172 Personalization for Product Improvement and Service Optimization  173 Personalization for Automated Recommendations  174 Personalization for Improved User Experience  174 ­Related Work  175 ­User Sensitized IoT Architecture  176 ­The Tweaked Data Layer  178 ­The Personalization Layer  180 The Characterization Engine  180 The Sentiment Analyzer  182 ­Concerns and Future Directions  183 ­Conclusions  184 References  185 6.1 6.2 6.3 6.3.1 6.3.2 6.3.3 6.3.4 6.3.5 6.4 6.5 6.6 6.7 6.7.1 6.7.2 6.8 6.9 7.1 7.2 7.3 7.3.1 7.3.2 7.3.3 7.3.4 Smart Cities and the Internet of Things  187 Hemant Garg, Sushil Gupta, and Basant Garg ­Introduction  187 ­Development of Smart Cities and the IoT  188 ­The Combination of the IoT with Development of City Architecture to Form Smart Cities  189 Unification of the IoT  190 Security of Smart Cities  190 Management of Water and Related Amenities  190 Power Distribution and Management  191   ­Reference 10 Hoberg, G and Phillips, G (2010) Product market synergies and competition in mergers and acquisitions: a text-based analysis The Review of Financial Studies 23 (10): 3773–3811 11 Antweiler, W and Frank, M.Z (2004) Is all that talk just noise? 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Evidence from China Physica A: Statistical Mechanics and its Applications 452: 151–156 33 Choi, T.M and Lambert, J.H (2017) Advances in risk analysis with big data Risk Analysis 37 (8): 1435–1442 34 Cerchiello, P and Giudici, P (2016) Big data analysis for financial risk management Journal of Big Data (1): 18 35 Côrte-Real, N., Ruivo, P., Oliveira, T., and Popovič, A (2019) Unlocking the drivers of big data analytics value in firms Journal of Business Research 97: 160–173 36 Fanning, K and Grant, R (2013) Big data: implications for financial managers Journal of Corporate Accounting & Finance 24 (5): 23–30 37 Pejić Bach, M., Krstić, Ž., Seljan, S., and Turulja, L (2019) Text mining for big data analysis in financial sector: a literature review Sustainability 11 (5): 1277 38 Pérez-Martín, A., Pérez-Torregrosa, A., and Vaca, M (2018) Big Data techniques to measure credit banking risk in home equity loans Journal of Business Research 89: 448–454 39 Blackburn, M., Alexander, J., Legan, J.D., and Klabjan, D (2017) Big data and the future of R&D management: the rise of big data and big data analytics will have significant implications for R&D and innovation management in the next decade Research-Technology Management 60 (5): 43–51 40 Tian, X., Han, R., Wang, L et al (2015) Latency critical big data computing in finance The Journal of Finance and Data Science (1): 33–41 41 Xie, P., Zou, C., and Liu, H (2016) The fundamentals of internet finance and its policy implications in China China Economic Journal (3): 240–252 42 Blocher, J., Cooper, R., Seddon, J., & Van Vliet, B (2018) Phantom liquidity and high-frequency quoting Journal of Trading, Vol 11, No 3, 6–15 43 Preda, A (2007a) The sociological approach to financial markets Journal of Economic Surveys 21 (3): 506–533   ­Reference 44 Zaloom, C (2003) Ambiguous numbers: trading technologies and interpretation in financial markets American Ethnologist 30 (2): 258–272 45 Yang, D., Chen, P., Shi, F., and Wen, C (2018) Internet finance: its uncertain legal foundations and the role of big data in its development Emerging Markets Finance and Trade 54 (4): 721–732 46 Glancy, F.H and Yadav, S.B (2011) A computational model for financial reporting fraud detection Decision Support Systems 50 (3): 595–601 47 Ngai, E.W., Hu, Y., Wong, Y.H et al (2011) The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature Decision Support Systems 50 (3): 559–569 48 Hajizadeh, E., Ardakani, H.D., and Shahrabi, J (2010) Application of data mining techniques in stock markets: a survey Journal of Economics and International Finance (7): 109 49 Sun, Y., Shi, Y., and Zhang, Z (2019) Finance Big Data: management, analysis, and applications International Journal of Electronic Commerce 23: 50 Hennessy, C.A and Whited, T.M (2007) How costly is external financing? Evidence from a structural estimation The Journal of Finance 62 (4): 1705–1745 51 Fang, L and Peress, J (2009) Media coverage and the cross-section of stock returns The Journal of Finance 64 (5): 2023–2052 52 Gomes, J.F (2001) Financing investment American Economic Review 91 (5): 1263–1285 53 Tumarkin, R and Whitelaw, R.F (2001) News or noise? Internet postings and stock prices Financial Analysts Journal 57 (3): 41–51 54 Cao, M., Chychyla, R., and Stewart, T (2015) Big Data analytics in financial statement audits Accounting Horizons 29 (2): 423–429 55 Subrahmanyam, A (2019) Big data in finance: evidence and challenges Borsa Istanbul Review 19: 283–287 365 367 Index a analysis, IoT  117 Apache HBase  222 Apache Spark  221–222 Apache Storm  222 artificial intelligence (AI)  43, 48, 50, 144 artificial neural network (ANN)  48, 49, 106 artificial reasoning tools (ART)  48–50 association rule mining  324 Atos Codex  125 attribute‐based encryption (ABE)  274 autoencoders (AE)  153–154 automatic license plate recognition (ALPR)  232–233 AWS IoT analytics  125–126, 130 b backpropagation through time (BPTT)  160 bagging see bootstrap aggregating BERT  45–46 big data  data journalism BBC big data  340, 342 guardian data blog  342–343 Indian scenario  345–346 Internet of Things  346–347 media impact on  347–348 wikileaks  344 World Economic Forum  344–345 finance financial markets  358–359 financial services  359–360 internet finance  359 other financial issues  360 big data analytics (BDA)  351 business intelligence analytics  322–323 challenges in  324–325 IoT systems  169–171 massive analytics  323 memory‐level analytics systems  322 methods  324 off‐line analytics systems  322 personalization  168–169 real‐time analytical systems  322 binary prediction technique  136–137 bootstrap aggregating  102–103 business intelligence (BI) analytics  322–323 business, personalization for  172 Big Data Analytics for Internet of Things, First Edition Edited by Tausifa Jan Saleem and Mohammad Ahsan Chishti © 2021 John Wiley & Sons, Inc Published 2021 by John Wiley & Sons, Inc 368 Index c canonical correlation analysis (CCA)  107 canonical hyperplane  100 category prediction technique  137 Character Generator Protocol (CHARGEN)  296 city architecture, smart cities city assets and human resources  192 environmental pollution management  192 power distribution and management  191 revenue collection and administration  191–192 security of  190 unification of the IoT  190 water and related amenities  190–191 classification and regression trees (CART)  101–102 cloud‐based defense framework  310 cloud‐based integrated water management system data distribution, Wi‐Fi IOT communicator app  261 experimental setup module  259–260 flow rate vs bill generated  258 literature survey  248–250 six‐tier data framework contact unit (FC‐37)  253 GSM‐based ARM and control system  253 internet of things communicator (ESP8266)  253 methodology  253–256 primary components  251 proposed algorithm  256–257 time vs water flow rate  258 water report of both house  262 cloud‐based IoT data analytics platform Atos Codex  125 AWS IoT  125–126, 130 Hitachi Vantara Pentaho, Lumada  127–128, 131 IBM Watson IoT  126–127 Microsoft Azure IoT  128–129, 134 Oracle IoT cloud services  129, 132, 135 cloud‐based platforms, PMU  223–224 cloud computing analytics challenges  137–139 analytics types  118 benefits of  116, 119 data analytics on  123 DDoS attacks  293 application level attacks, 296m 297 community cloud  292–293 hybrid cloud  293 infrastructure level attacks  294–296 private cloud  292–294 probable impact  297–298 public cloud  290, 292–294 taxonomy  291 deployment models for community cloud  121 hybrid cloud  121 private cloud  120 public cloud  120–121 infrastructure storage  119 IoT analytics process  117–118 machine learning binary prediction  136–137 category prediction  137 for data analytics  132, 133, 135 value prediction  137 service models for  122–123 cloud node  292 Cloud Service Provider (CSP)  297 Cluster Communication Protocol (CCP)  301 Index cognition  43 collection of data  117 communication channel, data security  276–277 community cloud  121, 292–293 conditional restricted Boltzmann machine (CRBM)  151 control station  209 ConvNet see convolutional neural networks (CNN) convolutional neural network (CNN)  233–234 activation functions  156–157 applications of  158 convolution layer  155 fully connected layer  156 pooling layer  155–156 ReLU  156, 157 sigmoid function  156, 157 Tanh()  156, 157 core vector machine (CVM)  212 COVID‐19 pandemic  40 Crossfire attack  296 cybernetics  9, 13 cyberphysical systems (CPS)  12 cybersecurity  216–217 d data analytics cloud computing  123 ML algorithms, cloud computing  132, 133, 135 data and/or information‐informed decision support (DIDAS)  55–56 database management system (DBMS)  15–16 data democratization  28 data fusion  22–26 challenges  326 for IoT security  327–329 levels of  326–327 mathematical methods for  326–327 opportunities provided by  326 data‐informed decision support (DIDAS)  22 data journalism accessing data for  337–338 big data BBC big data  340, 342 guardian data blog  342–343 Indian scenario  345–346 Internet of Things  346–347 media impact on  347–348 wikileaks  344 World Economic Forum  344–345 data analytics  338–340 defined  333 next big thing  334–336 overview  336–337 data mining techniques  218 data models  15–17 data privacy  279–280 data science model  147–148 data security  121 application domain authentication  272–274 authorization  274 depletion of resources  274–275 establishment of trust  275 architectural domain  275–276 challenges  267–268 common attacks  271–272 communication channel  276–277 confidentiality, integrity, and authentication  278–279 data privacy  279–280 interface layer  271 IoT  266–267 network layer  269–271 research directions  280 sensing layer  268–269 datawrapper  340 369 370 Index DDoS attack mitigation big data analytics  305–306 divide and conquer  300 dynamic resource allocation  302–303 dynamic resource pricing  301 intelligent fast‐flux swarm network  301 proactive approach  298 push‐back  299 random flow network modeling  300 reactive approach  299 roaming honeypot  302 router throttling  299 SDN‐based DDoS defense  303–305 self‐cleansing intrusion tolerance  300–301 target defense moving  302 DDoS attacks see distributed denial of service (DDoS) attacks decisions, IoT  117 decision support systems (DSS)  13 deep belief networks (DBN)  151–153 deep learning (DL)  44, 48 autoencoders  153–154 convolutional neural networks activation functions  156–157 applications of  158 layers of  155–156 data science model  147–148 deep belief networks  151–153 deep neural networks  144–145 generative adversarial network  158–159 IoT  148–149 learning algorithms types  146–147 long short‐term memory  161–162 recurrent neural networks  159–161 restricted Boltzmann machine  149–151 solve a problem  146–147 deep neural learning  144–145 deep neural networks  144–145 delivering services  139 density‐based spatial clustering of applications with noise (DBSCAN)  104, 133 deployment cloud computing models community cloud  121 hybrid cloud  121 private cloud  120 public cloud  120–121 descriptive analytics  118 Device Provisioning System (DPS)  129 device‐to‐device communication  143 diagnostic analytics  118 digital transformation  55, 60 discriminative restricted Boltzmann machine (DRBM)  151 distributed denial of service (DDoS) attacks challenges and issues  309–310 cloud deployment models application level attacks  296 community cloud  292–293 hybrid cloud  293 infrastructure level attacks  294–296 private cloud  292–294 probable impact  297–298 public cloud  290, 292–294 taxonomy  291 contribution  288–290 define  285 future work  312 generic framework  310–311 intrusion scenario in 2020  286 mitigation approaches (see DDoS attack mitigation) organization  290 research on  287–288 selected approaches  307–308 statistics on 2020  286 Index “dollar‐sign‐dangling”  Domain Name Service (DNS)  295 domain‐specific denominator models (DSDM)  16 DoS attack, IoT  271 dynamic resource allocation strategy  302–303 Dynamic State Estimation (DSE)  213–214 e elliptic curve cryptography (ECC)  273–274 Elliptic Curve Diffie–Hellman (ECDH)  276 Elliptic Curve Digital Signature Algorithm (ECDSA)  276 encoding‐transformation‐decoding network  235 energy efficiency  200 Environmental Impact Assessment (EIA)  197 environmental pollution management  192 environmental sustainability  199–202 f feed forward neural networks (FFNN)  105–106 finance, big data article identification and selection  353–354 articles published year wise  355–356 big data analytics  351 citation analysis  356–357 content analysis financial markets  358–359 financial services  359–360 internet finance  359 other financial issues  360 journal of publication  356 methodology  353 research employed method  354–355 future research  362 literature review  361 systematic literature review  353 financial markets, big data  358–359 financial services, big data  359–360 flexibility of business  139 Forecast Aided State Estimation  213–214 g Gartner’s Hype Cycle  92–93 generation of data  117 generative adversarial network (GANs)  158–159 global positioning system (GPS)  209 GSM ARM and water flow sensor  257 based water meter  256 h Hadoop  220–221 HELLO flood attacks, IoT  272 heterogeneous measurement integration  215–216 Hitachi Vantara Pentaho, Lumada  127–128, 131 HTTP flood attacks  296 hybrid cloud  121, 123, 293 hybrid restricted Boltzmann machine (HRBM)  151 i IBM Watson IoT  126–127 ICMP flood  296 information age  14 information technology (IT)  14–15 Infrastructure as a Service (IaaS)  122–124 371 372 Index intelligent autonomous vehicles  232 intelligent enterprise bounding box, license plate  233–234 data role  236 invariances  237 mean intersection over union  240 model framework  234–236 number of classes  238 reducing number of features  237 segmentation objective  234 smart city, big data analytics  240–244 Softmax loss model  239–240 spatial invariances  234 synthesizing samples  236–237 intelligent fast‐flux swarm network  301 interface layer, data security  271 internet finance, big data  359 infrastructure  204 and LLN  270 Internet of Robotic Things (IoRT)  232 Internet of Things (IoT) aspect of  cybernetics  defined  goal of  principles of  8–11 trillion market  interoperability, IOT artificial intelligence  17 big data  concept of  8–12 conundrums  29–38 data fusion  22–26 data‐informed decision support  22 data models  15–18 economy  19–21 information technology  14–15 models  12–14 partition vs astigmatism of vision  38–40 PEAS  52–60 security mandates  17, 19 service of society  50–51 small data  28–29 intrusion responsive autonomic system (IRAS)  305 IoT see Internet of Things (IoT) big data analytics  323 data characteristics  92 data security  266–267 security classification  273 security layered architecture  268 j journalism see data journalism k key performance indicators (KPI)  10, 37 k‐means clustering technique  103–104 k nearest neighbors (KNN)  96, 133–134 knowledge graph networks (KGN)  56 knowledge graphs (KG)  56–59 knowledge‐informed decision support (KIDS)  55 l labeled property graph (LPG)  25 linear regression  98–99 long short‐term memory (LSTM)  161–162 loss of main (LOM) detection  214 Lumada IoT  127–128, 131 m machine‐generated data  231 machine learning (ML)  17 cloud computing anomaly detection  135 binary prediction  136–137 category prediction  137 Index classification  132–133 clustering  133 feature extraction  133, 135 regression  133 value prediction  137 Gartner’s Hype Cycle  92–93 knowledge hierarchy/pyramid  90–91 methods  233 supervised algorithm bootstrap aggregating  102–103 classification and regression trees  101–102 k nearest neighbors  96 linear regression  98–99 Naïve Bayes classifier  96–98 random forest  102 support vector machine  99–101 taxonomy of  94–95 unsupervised algorithm canonical correlation analysis  107 DBSCAN  104 k‐means clustering  103–104 multilayer perceptrons  105–106 neural networks  104–105 principal component analysis  106–107 Malware as a Service  303 masked authenticated messaging (MAM)  22 massive analytics  323 mean intersection over union (M‐IoU)  240 memory‐level analytics systems  322 message passing neural network (MPNN)  48–49 metropolis  231 micro‐electro‐mechanical systems (MEMS)  89 Microsoft Azure IoT  128–129, 134 mode of disturbance (MOD)  212 multilayer perceptrons (MLP)  105–106 n Naïve Bayes  96–98, 133–134 network layer, data security  269–271 Network Time Protocol (NTP)  296 neural networks  43, 104–105 o off‐line analytics systems  322 Oracle IoT cloud services  129, 132, 135 oscillation detection  215 p pandas  340 pay‐a‐penny‐per‐use (PAPPU)  20 pay‐a‐price‐per‐unit (PAPPU)  20 PEAS  24, 52–60 “penny”  20–21 personalization for automated recommendations  174 BDA  169–171 for business  172 concerns and future directions  183–184 defined  168 disadvantage of  184 IoT systems  171 layer characterization engine  180–181 sentiment analyzer  182–183 for marketing  172 product improvement and service optimization  173–174 related work  175–176 tweaked data layer  178–179 user‐centric IoT architecture  176–178 user experience  174–175 personalize  174 phasor data concentrator (PDC)  209 373 374 Index phasor measurement unit (PMU) data processing Apache HBase  222 Apache Spark  221–222 Apache Storm  222 cloud‐based platforms  223–224 Hadoop  220–221 driven applications data quality and security  216–217 heterogeneous measurement integration  215–216 utilization and analytics  217–218 variety and interoperability  216 visualization of data  218–219 volume and velocity  216 thirty measurements  210 Platform as a Service (PaaS)  122, 124 PMU see phasor measurement unit (PMU) power generation system  209 prediction, IoT  117 predictive analytics  118, 324 prescriptive analytics  118 principal component analysis (PCA)  106–107 privacy, IoT  324–325 private cloud computing  120 DDoS attacks  292–293 vs public cloud  293–294 processing of data  138 programming languages  340 public cloud computing  120–121 DDoS attacks  290, 292 vs private cloud  293–294 Python  340 q quality of data  138 r radio frequency identification (RFID)  random flow network modeling  300 random forest  102 real‐time analytical systems  322 recurrent neural networks (RNN)  159–161 regiopolis  231 reinforcement learning (RL)  49, 146 Relational DataBase Management System (RDBMS)  215–216 ReLU functions  156, 157 resource description framework (RDF)  24–25 restricted Boltzmann machine (RBM) applications of  151 defined  149 training of  150–151 return on investment (ROI)  roadmap background  197–198 big data in sustainability  198–199 environmental sustainability  199–202 high hardware and software cost  204 internet infrastructure  204 less qualified workforce  204–205 proposed  202–204 roaming honeypot  302 robust Boltzmann machine (RoBM)  151 router throttling model  299–300 s SARA see Sense, Analyze, Respond, Actuate (SARA) SDN‐based DDoS defense  303–305 security of data  138–139 selective forwarding attack, IoT  272 self‐cleansing intrusion tolerance (SCIT)  300–301 Index semi‐supervised learning algorithms  146 Sense, Analyze, Respond, Actuate (SARA)  8, 10 sensing layer data security  268–269 vulnerabilities  269 sensor‐based economy  22 sentiment analyzer, personalization  182–183 service cloud computing models  122–124 service level agreement (SLA)  120 sigmoid function  156, 157 Simple Network Management Protocol (SNMP)  296 Simple Object Access Protocol (SOAP)  295 Simple Service Discovery Protocol (SSDP)  295–296 Sinkhole attack, IoT  272 six‐tier data, water management system contact unit (FC‐37)  253 GSM‐based ARM and control system  253 internet of things communicator (ESP8266)  253 methodology  253–256 primary components  251 proposed algorithm  256–257 smart cities air pollution hotspots in Dublin  243 city architecture to city assets and human resources  192 environmental pollution management  192 power distribution and management  191 revenue collection and administration  191–192 security of  190 unification of the IoT  190 water and related amenities  190–191 clustering results, traffic in Dublin  243 development of  188–189 forwarder and Indexer architecture  241 Splunk front end and back end  242 wearable tech  193–194 Smart Water Management (SWM)  249 smart water meter  252 SNAPS  23, 55, 61 Softmax loss model  239–240 Software as a Service (SaaS)  122, 124 software‐defined IoT model (SDIoT)  276 software‐defined networking (SDN)  232 Spatial MapReduce  242 storage of data  137–138 supervised learning algorithms  146 supervised machine learning algorithm bootstrap aggregating  102–103 classification and regression trees  101–102 k nearest neighbors  96 linear regression  98–99 Naïve Bayes classifier  96–98 random forest  102 support vector machine  99–101 support vector regression  101 supervisory control and data acquisition (SCADA) systems  209 support vector machine (SVMs)  99–101 support vector regression (SVR)  101, 133 375 376 Index sustainability, big data  198–199 synchrophasor data management application types fault detection  214 loss of main detection  214 multiple event detection  213 oscillation detection  215 out of step splitting protection  213 state estimation  213–214 topology update detection  214 transient stability  212–213 voltage stability analysis  211–212 PMU‐data processing Apache HBase  222 Apache Spark  221–222 Apache Storm  222 cloud‐based platforms  223–224 Hadoop  220–221 PMU‐driven applications data quality and security  216–217 heterogeneous measurement integration  215–216 utilization and analytics  217–218 variety and interoperability  216 visualization of data  218–219 volume and velocity  216 t Tanh() functions  156, 157 target defense moving  302 TCP SYN flood  296 Temporal MapReduce  242 tensor processing unit (TPU)  44 topology update detection  214 transient stability  212–213 tweaked data layer  178–179 u UDP flood  296 unsupervised learning algorithms  146 canonical correlation analysis  107 DBSCAN  104 k‐means clustering  103–104 multilayer perceptrons  105–106 neural networks  104–105 principal component analysis  106–107 v value prediction technique  137 variational auto‐encoder (VAE)  234 visualization of data  218–219 voltage stability analysis  211–212 w water management system data distribution, Wi‐Fi IOT communicator app  261 experimental setup module  259–260 flow rate vs bill generated  258 literature survey  248–250 six‐tier data framework contact unit (FC‐37)  253 GSM‐based ARM and control system  253 internet of things communicator (ESP8266)  253 methodology  253–256 primary components  251 proposed algorithm  256–257 water report of both house  262 wide area monitoring system (WAMS)  209 Wikileaks, big data  344 witch attack, IoT  272 World Economic Forum (WEF)  344–345 WILEY END USER LICENSE AGREEMENT Go to www.wiley.com/go/eula to access Wiley’s ebook EULA ... Big Data Analytics for? ?Internet of? ?Things Big Data Analytics for Internet of Things Edited by Tausifa Jan Saleem National Institute of Technology Srinagar, India Mohammad Ahsan Chishti Central... directions of work in Big Data Analytics of IoT systems The seventh chapter entitled “Smart Cities and the Internet of Things? ?? investigates the development of smart cities from a perspective of the IoT... Better Decisions using Data Fusion” gives an idea of the problems that arise in the Big Data Analytics for? ?the Internet of? ?Things defense related IoT -big data analytics with special attention to