Details

<p>List of Contributors xv</p> <p>List of Abbreviations xix</p> <p><b>1 Big Data Analytics for the Internet of Things: An Overview 1<br /></b><i>Tausifa Jan Saleem and Mohammad Ahsan Chishti</i></p> <p><b>2 Data, Analytics and Interoperability Between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3) 7<br /></b><i>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</i></p> <p>2.1 Context 8</p> <p>2.2 Models in the Background 12</p> <p>2.3 Problem Space: Are We Asking the Correct Questions? 14</p> <p>2.4 Solutions Approach: The Elusive Quest to Build Bridges Between Data and Decisions 15</p> <p>2.5 Avoid This Space: The Deception Space 17</p> <p>2.6 Explore the Solution Space: Necessary to Ask Questions That May Not Have Answers, Yet 17</p> <p>2.7 Solution Economy: Will We Ever Get There? 19</p> <p>2.8 Is This Faux Naïveté in Its Purest Distillate? 21</p> <p>2.9 Reality Check: Data Fusion 22</p> <p>2.10 “Double A” Perspective of Data and Tools vs. The Hypothetical Porous Pareto (80/20) Partition 28</p> <p>2.11 Conundrums 29</p> <p>2.12 Stigma of Partition vs. Astigmatism of Vision 38</p> <p>2.13 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI 40</p> <p>2.14 In Service of Society 50</p> <p>2.15 Data Science in Service of Society: Knowledge and Performance from PEAS 52</p> <p>2.16 Temporary Conclusion 60</p> <p>Acknowledgements 63</p> <p>References 63</p> <p><b>3 Machine Learning Techniques for IoT Data Analytics 89<br /></b><i>Nailah Afshan and Ranjeet Kumar Rout</i></p> <p>3.1 Introduction 89</p> <p>3.2 Taxonomy of Machine Learning Techniques 94</p> <p>3.2.1 Supervised ML Algorithm 95</p> <p>3.2.1.1 Classification 96</p> <p>3.2.1.2 Regression Analysis 98</p> <p>3.2.1.3 Classification and Regression Tasks 99</p> <p>3.2.2 Unsupervised Machine Learning Algorithms 103</p> <p>3.2.2.1 Clustering 103</p> <p>3.2.2.2 Feature Extraction 106</p> <p>3.2.3 Conclusion 107</p> <p>References 107</p> <p><b>4 IoT Data Analytics Using Cloud Computing 115<br /></b><i>Anjum Sheikh, Sunil Kumar, and Asha Ambhaikar</i></p> <p>4.1 Introduction 115</p> <p>4.2 IoT Data Analytics 117</p> <p>4.2.1 Process of IoT Analytics 117</p> <p>4.2.2 Types of Analytics 118</p> <p>4.3 Cloud Computing for IoT 118</p> <p>4.3.1 Deployment Models for Cloud 120</p> <p>4.3.1.1 Private Cloud 120</p> <p>4.3.1.2 Public Cloud 120</p> <p>4.3.1.3 Hybrid Cloud 121</p> <p>4.3.1.4 Community Cloud 121</p> <p>4.3.2 Service Models for Cloud Computing 122</p> <p>4.3.2.1 Software as a Service (SaaS) 122</p> <p>4.3.2.2 Platform as a Service (PaaS) 122</p> <p>4.3.2.3 Infrastructure as a Service (IaaS) 122</p> <p>4.3.3 Data Analytics on Cloud 123</p> <p>4.4 Cloud-Based IoT Data Analytics Platform 123</p> <p>4.4.1 Atos Codex 125</p> <p>4.4.2 AWS IoT 125</p> <p>4.4.3 IBM Watson IoT 126</p> <p>4.4.4 Hitachi Vantara Pentaho, Lumada 127</p> <p>4.4.5 Microsoft Azure IoT 128</p> <p>4.4.6 Oracle IoT Cloud Services 129</p> <p>4.5 Machine Learning for IoT Analytics in Cloud 132</p> <p>4.5.1 ML Algorithms for Data Analytics 132</p> <p>4.5.2 Types of Predictions Supported by ML and Cloud 136</p> <p>4.6 Challenges for Analytics Using Cloud 137</p> <p>4.7 Conclusion 139</p> <p>References 139</p> <p><b>5 Deep Learning Architectures for IoT Data Analytics 143<br /></b><i>Snowber Mushtaq and Omkar Singh</i></p> <p>5.1 Introduction 143</p> <p>5.1.1 Types of Learning Algorithms 146</p> <p>5.1.1.1 Supervised Learning 146</p> <p>5.1.1.2 Unsupervised Learning 146</p> <p>5.1.1.3 Semi-Supervised Learning 146</p> <p>5.1.1.4 Reinforcement Learning 146</p> <p>5.1.2 Steps Involved in Solving a Problem 146</p> <p>5.1.2.1 Basic Terminology 147</p> <p>5.1.2.2 Training Process 147</p> <p>5.1.3 Modeling in Data Science 147</p> <p>5.1.3.1 Generative 148</p> <p>5.1.3.2 Discriminative 148</p> <p>5.1.4 Why DL and IoT? 148</p> <p>5.2 DL Architectures 149</p> <p>5.2.1 Restricted Boltzmann Machine 149</p> <p>5.2.1.1 Training Boltzmann Machine 150</p> <p>5.2.1.2 Applications of RBM 151</p> <p>5.2.2 Deep Belief Networks (DBN) 151</p> <p>5.2.2.1 Training DBN 152</p> <p>5.2.2.2 Applications of DBN 153</p> <p>5.2.3 Autoencoders 153</p> <p>5.2.3.1 Training of AE 153</p> <p>5.2.3.2 Applications of AE 154</p> <p>5.2.4 Convolutional Neural Networks (CNN) 154</p> <p>5.2.4.1 Layers of CNN 155</p> <p>5.2.4.2 Activation Functions Used in CNN 156</p> <p>5.2.4.3 Applications of CNN 158</p> <p>5.2.5 Generative Adversarial Network (GANs) 158</p> <p>5.2.5.1 Training of GANs 158</p> <p>5.2.5.2 Variants of GANs 159</p> <p>5.2.5.3 Applications of GANs 159</p> <p>5.2.6 Recurrent Neural Networks (RNN) 159</p> <p>5.2.6.1 Training of RNN 160</p> <p>5.2.6.2 Applications of RNN 161</p> <p>5.2.7 Long Short-Term Memory (LSTM) 161</p> <p>5.2.7.1 Training of LSTM 161</p> <p>5.2.7.2 Applications of LSTM 162</p> <p>5.3 Conclusion 162</p> <p>References 163</p> <p><b>6 Adding Personal Touches to IoT: A User-Centric IoT Architecture 167<br /></b><i>Sarabjeet Kaur Kochhar</i></p> <p>6.1 Introduction 167</p> <p>6.2 Enabling Technologies for BDA of IoT Systems 169</p> <p>6.3 Personalizing the IoT 171</p> <p>6.3.1 Personalization for Business 172</p> <p>6.3.2 Personalization for Marketing 172</p> <p>6.3.3 Personalization for Product Improvement and Service Optimization 173</p> <p>6.3.4 Personalization for Automated Recommendations 174</p> <p>6.3.5 Personalization for Improved User Experience 174</p> <p>6.4 Related Work 175</p> <p>6.5 User Sensitized IoT Architecture 176</p> <p>6.6 The Tweaked Data Layer 178</p> <p>6.7 The Personalization Layer 180</p> <p>6.7.1 The Characterization Engine 180</p> <p>6.7.2 The Sentiment Analyzer 182</p> <p>6.8 Concerns and Future Directions 183</p> <p>6.9 Conclusions 184</p> <p>References 185</p> <p><b>7 Smart Cities and the Internet of Things 187<br /></b><i>Hemant Garg, Sushil Gupta, and Basant Garg</i></p> <p>7.1 Introduction 187</p> <p>7.2 Development of Smart Cities and the IoT 188</p> <p>7.3 The Combination of the IoT with Development of City Architecture to Form Smart Cities 189</p> <p>7.3.1 Unification of the IoT 190</p> <p>7.3.2 Security of Smart Cities 190</p> <p>7.3.3 Management of Water and Related Amenities 190</p> <p>7.3.4 Power Distribution and Management 191</p> <p>7.3.5 Revenue Collection and Administration 191</p> <p>7.3.6 Management of City Assets and Human Resources 192</p> <p>7.3.7 Environmental Pollution Management 192</p> <p>7.4 How Future Smart Cities Can Improve Their Utilization of the Internet of All Things, with Examples 193</p> <p>7.5 Conclusion 194</p> <p>References 195</p> <p><b>8 A Roadmap for Application of IoT-Generated Big Data in Environmental Sustainability 197<br /></b><i>Ankur Kashyap</i></p> <p>8.1 Background and Motivation 197</p> <p>8.2 Execution of the Study 198</p> <p>8.2.1 Role of Big Data in Sustainability 198</p> <p>8.2.2 Present Status and Future Possibilities of IoT in Environmental Sustainability 199</p> <p>8.3 Proposed Roadmap 202</p> <p>8.4 Identification and Prioritizing the Barriers in the Process 204</p> <p>8.4.1 Internet Infrastructure 204</p> <p>8.4.2 High Hardware and Software Cost 204</p> <p>8.4.3 Less Qualified Workforce 204</p> <p>8.5 Conclusion and Discussion 205</p> <p>References 205</p> <p><b>9 Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids 209<br /></b><i>C.M. Thasnimol and R. Rajathy</i></p> <p>9.1 Introduction 209</p> <p>9.2 Applications of Synchrophasor Data 210</p> <p>9.2.1 Voltage Stability Analysis 211</p> <p>9.2.2 Transient Stability 212</p> <p>9.2.3 Out of Step Splitting Protection 213</p> <p>9.2.4 Multiple Event Detection 213</p> <p>9.2.5 State Estimation 213</p> <p>9.2.6 Fault Detection 214</p> <p>9.2.7 Loss of Main (LOM) Detection 214</p> <p>9.2.8 Topology Update Detection 214</p> <p>9.2.9 Oscillation Detection 215</p> <p>9.3 Utility Big Data Issues Related to PMU-Driven Applications 215</p> <p>9.3.1 Heterogeneous Measurement Integration 215</p> <p>9.3.2 Variety and Interoperability 216</p> <p>9.3.3 Volume and Velocity 216</p> <p>9.3.4 Data Quality and Security 216</p> <p>9.3.5 Utilization and Analytics 217</p> <p>9.3.6 Visualization of Data 218</p> <p>9.4 Big Data Analytics Platforms for PMU Data Processing 219</p> <p>9.4.1 Hadoop 220</p> <p>9.4.2 Apache Spark 221</p> <p>9.4.3 Apache HBase 222</p> <p>9.4.4 Apache Storm 222</p> <p>9.4.5 Cloud-Based Platforms 223</p> <p>9.5 Conclusions 224</p> <p>References 224</p> <p><b>10 Intelligent Enterprise-Level Big Data Analytics for Modeling and Management in Smart Internet of Roads 231<br /></b><i>Amin Fadaeddini, Babak Majidi, and Mohammad Eshghi</i></p> <p>10.1 Introduction 231</p> <p>10.2 Fully Convolutional Deep Neural Network for Autonomous Vehicle Identification 233</p> <p>10.2.1 Detection of the Bounding Box of the License Plate 233</p> <p>10.2.2 Segmentation Objective 234</p> <p>10.2.3 Spatial Invariances 234</p> <p>10.2.4 Model Framework 234</p> <p>10.2.4.1 Increasing the Layer of Transformation 234</p> <p>10.2.4.2 Data Format of Sample Images 235</p> <p>10.2.4.3 Applying Batch Normalization 236</p> <p>10.2.4.4 Network Architecture 236</p> <p>10.2.5 Role of Data 236</p> <p>10.2.6 Synthesizing Samples 236</p> <p>10.2.7 Invariances 237</p> <p>10.2.8 Reducing Number of Features 237</p> <p>10.2.9 Choosing Number of Classes 238</p> <p>10.3 Experimental Setup and Results 239</p> <p>10.3.1 Sparse Softmax Loss 239</p> <p>10.3.2 Mean Intersection Over Union 240</p> <p>10.4 Practical Implementation of Enterprise-Level Big Data Analytics for Smart City 240</p> <p>10.5 Conclusion 244</p> <p>References 244</p> <p><b>11 Predictive Analysis of Intelligent Sensing and Cloud-Based Integrated Water Management System 247<br /></b><i>Tanuja Patgar and Ripal Patel</i></p> <p>11.1 Introduction 247</p> <p>11.2 Literature Survey 248</p> <p>11.3 Proposed Six-Tier Data Framework 250</p> <p>11.3.1 Primary Components 251</p> <p>11.3.2 Contact Unit (FC-37) 253</p> <p>11.3.3 Internet of Things Communicator (ESP8266) 253</p> <p>11.3.4 GSM-Based ARM and Control System 253</p> <p>11.3.5 Methodology 253</p> <p>11.3.6 Proposed Algorithm 256</p> <p>11.4 Implementation and Result Analysis 257</p> <p>11.4.1 Water Report for Home 1 and Home 2 Modules 263</p> <p>11.5 Conclusion 263</p> <p>References 263</p> <p><b>12 Data Security in the Internet of Things: Challenges and Opportunities 265<br /></b><i>Shashwati Banerjea, Shashank Srivastava, and Sachin Kumar</i></p> <p>12.1 Introduction 265</p> <p>12.2 IoT: Brief Introduction 266</p> <p>12.2.1 Challenges in a Secure IoT 267</p> <p>12.2.2 Security Requirements in IoT Architecture 268</p> <p>12.2.2.1 Sensing Layer 268</p> <p>12.2.2.2 Network Layer 269</p> <p>12.2.2.3 Interface Layer 271</p> <p>12.2.3 Common Attacks in IoT 271</p> <p>12.3 IoT Security Classification 272</p> <p>12.3.1 Application Domain 272</p> <p>12.3.1.1 Authentication 272</p> <p>12.3.1.2 Authorization 274</p> <p>12.3.1.3 Depletion of Resources 274</p> <p>12.3.1.4 Establishment of Trust 275</p> <p>12.3.2 Architectural Domain 275</p> <p>12.3.2.1 Authentication in IoT Architecture 275</p> <p>12.3.2.2 Authorization in IoT Architecture 276</p> <p>12.3.3 Communication Channel 276</p> <p>12.4 Security in IoT Data 277</p> <p>12.4.1 IoT Data Security: Requirements 277</p> <p>12.4.1.1 Data: Confidentiality, Integrity, and Authentication 278</p> <p>12.4.1.2 Data Privacy 279</p> <p>12.4.2 IoT Data Security: Research Directions 280</p> <p>12.5 Conclusion 280</p> <p>References 281</p> <p><b>13 DDoS Attacks: Tools, Mitigation Approaches, and Probable Impact on Private Cloud Environment 285<br /></b><i>R. K. Deka, D. K. Bhattacharyya, and J. K. Kalita</i></p> <p>13.1 Introduction 285</p> <p>13.1.1 State of the Art 287</p> <p>13.1.2 Contribution 288</p> <p>13.1.3 Organization 290</p> <p>13.2 Cloud and DDoS Attack 290</p> <p>13.2.1 Cloud Deployment Models 290</p> <p>13.2.1.1 Differences Between Private Cloud and Public Cloud 293</p> <p>13.2.2 DDoS Attacks 294</p> <p>13.2.2.1 Attacks on Infrastructure Level 294</p> <p>13.2.2.2 Attacks on Application Level 296</p> <p>13.2.3 DoS/DDoS Attack on Cloud: Probable Impact 297</p> <p>13.3 Mitigation Approaches 298</p> <p>13.3.1 Discussion 309</p> <p>13.4 Challenges and Issues with Recommendations 309</p> <p>13.5 A Generic Framework 310</p> <p>13.6 Conclusion and Future Work 312</p> <p>References 312</p> <p><b>14 Securing the Defense Data for Making Better Decisions Using Data Fusion 321<br /></b><i>Syed Rameem Zahra</i></p> <p>14.1 Introduction 321</p> <p>14.2 Analysis of Big Data 322</p> <p>14.2.1 Existing IoT Big Data Analytics Systems 322</p> <p>14.2.2 Big Data Analytical Methods 324</p> <p>14.2.3 Challenges in IoT Big Data Analytics 324</p> <p>14.3 Data Fusion 325</p> <p>14.3.1 Opportunities Provided by Data Fusion 326</p> <p>14.3.2 Data Fusion Challenges 326</p> <p>14.3.3 Stages at Which Data Fusion Can Happen 326</p> <p>14.3.4 Mathematical Methods for Data Fusion 326</p> <p>14.4 Data Fusion for IoT Security 327</p> <p>14.4.1 Defense Use Case 329</p> <p>14.5 Conclusion 329</p> <p>References 330</p> <p><b>15 New Age Journalism and Big Data (Understanding Big Data and Its Influence on Journalism) 333<br /></b><i>Asif Khan and Heeba Din</i></p> <p>15.1 Introduction 333</p> <p>15.1.1 Big Data Journalism: The Next Big Thing 334</p> <p>15.1.2 All About Data 336</p> <p>15.1.3 Accessing Data for Journalism 337</p> <p>15.1.4 Data Analytics: Tools for Journalists 338</p> <p>15.1.5 Case Studies – Big Data 340</p> <p>15.1.5.1 BBC Big Data 340</p> <p>15.1.5.2 The Guardian Data Blog 342</p> <p>15.1.5.3 Wikileaks 344</p> <p>15.1.5.4 World Economic Forum 344</p> <p>15.1.6 Big Data – Indian Scenario 345</p> <p>15.1.7 Internet of Things and Journalism 346</p> <p>15.1.8 Impact on Media/Journalism 347</p> <p>References 348</p> <p><b>16 Two Decades of Big Data in Finance: Systematic Literature Review and Future Research Agenda 351<br /></b><i>Nufazil Altaf</i></p> <p>16.1 Introduction 351</p> <p>16.2 Methodology 353</p> <p>16.3 Article Identification and Selection 353</p> <p>16.4 Description and Classification of Literature 354</p> <p>16.4.1 Research Method Employed 354</p> <p>16.4.2 Articles Published Year Wise 355</p> <p>16.4.3 Journal of Publication 356</p> <p>16.5 Content and Citation Analysis of Articles 356</p> <p>16.5.1 Citation Analysis 356</p> <p>16.5.2 Content Analysis 357</p> <p>16.5.2.1 Big Data in Financial Markets 358</p> <p>16.5.2.2 Big Data in Internet Finance 359</p> <p>16.5.2.3 Big Data in Financial Services 359</p> <p>16.5.2.4 Big Data and Other Financial Issues 360</p> <p>16.6 Reporting of Findings and Research Gaps 360</p> <p>16.6.1 Findings from the Literature Review 361</p> <p>16.6.1.1 Lack of Symmetry 361</p> <p>16.6.1.2 Dominance of Research on Financial Markets, Internet Finance, and Financial Services 361</p> <p>16.6.1.3 Dominance of Empirical Research 361</p> <p>16.6.2 Directions for Future Research 362</p> <p>References 362</p> <p>Index 367</p>

<p><b>Tausifa Jan Saleem</b> is currently pursuing her Doctor of Philosophy (Ph.D) from National Institute of Technology Srinagar, India. She has received the Bachelor of Technology (B. Tech.) degree in Information Technology (IT) from National Institute of Technology Srinagar, India and the M.Tech. degree in Computer Science from University of Jammu, India. She has published more than 10 research articles in reputed journals (indexed by Scopus and SCI) and conferences (indexed by Scopus). Her research areas of interest include Internet of Things, Data Analytics, Machine Learning, and Deep Learning.</p><p><b>Mohammad Ahsan Chishti, Ph.D,</b> is Dean at the School of Engineering & Technology and Associate Professor in the Department of Information Technology at the Central University of Kashmir. He has published over 100 scholarly papers and holds 12 patents. He is the recipient of “<i>Young Engineers Award 2015-2016</i>” from IEI and “<i>Young Scientist Award 2009-2010</i>” from the government of Jammu and Kashmir. He is a Senior Member of the IEEE, MIEI, MCSI & MIETE.</p>

<p><b>Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field</b></p><p><i>Big Data Analytics for Internet of Things</i> delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security.</p><p>The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems.</p><p>With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, <i>Big Data Analytics for Internet of Things</i> also offers readers:</p><ul><li>A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications</li><li>An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc.</li><li>A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics</li><li>A treatment of machine learning techniques for IoT data analytics</li></ul><p>Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, <i>Big Data Analytics for Internet of Things</i> will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.</p>

US