book

Big Data Analytics for Internet of Things

by Tausifa Jan Saleem, Mohammad Ahsan Chishti

April 2021

Beginner to intermediate

400 pages

11h 49m

English

Wiley

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2.1 Context2.2 Models in the Background2.3 Problem Space: Are We Asking the Correct Questions?2.4 Solutions Approach: The Elusive Quest to Build Bridges Between Data and Decisions2.5 Avoid This Space: The Deception Space2.6 Explore the Solution Space: Necessary to Ask Questions That May Not Have Answers, Yet2.7 Solution Economy: Will We Ever Get There?2.8 Is This Faux Naïveté in Its Purest Distillate?2.9 Reality Check: Data Fusion2.10 “Double A” Perspective of Data and Tools vs. The Hypothetical Porous Pareto (80/20) Partition2.11 Conundrums2.12 Stigma of Partition vs. Astigmatism of Vision2.13 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI2.14 In Service of Society2.15 Data Science in Service of Society: Knowledge and Performance from PEAS2.16 Temporary ConclusionReferences
3.1 Introduction3.2 Taxonomy of Machine Learning TechniquesReferences
4.1 Introduction4.2 IoT Data Analytics4.3 Cloud Computing for IoT4.4 Cloud‐Based IoT Data Analytics Platform4.5 Machine Learning for IoT Analytics in Cloud4.6 Challenges for Analytics Using Cloud4.7 ConclusionReferences
5.1 Introduction5.2 DL Architectures5.3 ConclusionReferences

6.1 Introduction6.2 Enabling Technologies for BDA of IoT Systems6.3 Personalizing the IoT6.4 Related Work6.5 User Sensitized IoT Architecture6.6 The Tweaked Data Layer6.7 The Personalization Layer6.8 Concerns and Future Directions6.9 ConclusionsReferences
7.1 Introduction7.2 Development of Smart Cities and the IoT7.3 The Combination of the IoT with Development of City Architecture to Form Smart Cities7.4 How Future Smart Cities Can Improve Their Utilization of the Internet of All Things, with Examples7.5 ConclusionReferences
8.1 Background and Motivation8.2 Execution of the Study8.3 Proposed Roadmap8.4 Identification and Prioritizing the Barriers in the Process8.5 Conclusion and DiscussionReferences
9.1 Introduction9.2 Applications of Synchrophasor Data9.3 Utility Big Data Issues Related to PMU‐Driven Applications9.4 Big Data Analytics Platforms for PMU Data Processing9.5 ConclusionsReferences
10.1 Introduction10.2 Fully Convolutional Deep Neural Network for Autonomous Vehicle Identification10.3 Experimental Setup and Results10.4 Practical Implementation of Enterprise‐Level Big Data Analytics for Smart City10.5 ConclusionReferences
11.1 Introduction11.2 Literature Survey11.3 Proposed Six‐Tier Data Framework11.4 Implementation and Result Analysis11.5 ConclusionReferences
12.1 Introduction12.2 IoT: Brief Introduction12.3 IoT Security Classification12.4 Security in IoT Data12.5 ConclusionReferences
13.1 Introduction13.2 Cloud and DDoS Attack13.3 Mitigation Approaches13.4 Challenges and Issues with Recommendations13.5 A Generic Framework13.6 Conclusion and Future WorkReferences
14.1 Introduction14.2 Analysis of Big Data14.3 Data Fusion14.4 Data Fusion for IoT Security14.5 ConclusionReferences
15.1 IntroductionReferences
16.1 Introduction16.2 Methodology16.3 Article Identification and Selection16.4 Description and Classification of Literature16.5 Content and Citation Analysis of Articles16.6 Reporting of Findings and Research GapsReferences

Content preview from Big Data Analytics for Internet of Things

List of Abbreviations

AI: artificial intelligence
ALPR: Automatic License Plate Recognition
ANN: artificial neural network
AWS: Amazon web services
BDA: big data analytics
CBSP: cloud‐based security provider
CCP: Cluster Communication Protocol
CHARGEN: Character Generator Protocol
CNN: convolutional neural network
CRF: conditional random field
CSP: cloud service provider
CVM: core vector machine
DBSCAN: density‐based spatial clustering of applications
DDoS: distributed denial of service
DNS: Domain Name System
DoS: denial of service
E‐DoS: economic denial of sustainability
FCNN: fully convolutional neural network
HTTP: Hyper‐Text Transfer Protocol
IaaS: infrastructure as a service
ICMP: Internet Control Message Protocol
IDS: Intrusion Detection System
IoRT: internet of robotic things
IoT: Internet of Things
IP: Internet Protocol
IPS: Intrusion Prevention Systems
IRAS: Intrusion Responsive Autonomic System
IWD: intelligent water drop
KDD: Knowledge Discovery in Databases
KNN: K‐nearest neighbor
LAN: local area networks
LAND: local area network denial
M‐IoU: mean intersection over union
ML: machine learning
NaaS: networking as a service
NTP: Network Time Protocol
OCSVM: one class support vector machine
PaaS: platform as a service
PCA: principal component analysis
PDC: Phasor Data Concentrator
PMU: phasor measurement unit
POS: part of speech
QoS: quality of service
RDBMS: Relational DataBase Management System
RNN: recurrent neural network
SaaS: software as a service
SCADA

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Publisher Resources

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