Machine learning and cognitive computing for mobile communications and wireless networks

Machine learning and cognitive computing for mobile communications and wireless networks /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Singh, Krishna Kant (Telecommunications professor) (Editor), Singh, Akansha (Editor), Cengiz, Korhan (Editor), Le, Dac-Nhuong (Editor)
Format: Electronic eBook
Language:English
Published: Hoboken, NJ : John Wiley & Sons, Inc., 2020.
Subjects:
Mobile computing > Technological innovations.
Machine learning.
Soft computing.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • Machine generated contents note: 1. Machine Learning Architecture and Framework / Ajay Shankar Singh
  • 1.1. Introduction
  • 1.2. Machine Learning Algorithms
  • 1.2.1. Regression
  • 1.2.2. Linear Regression
  • 1.2.3. Support Vector Machine
  • 1.2.4. Linear Classifiers
  • 1.2.5. SVM Applications
  • 1.2.6. Naive Bayes Classification
  • 1.2.7. Random Forest
  • 1.2.8. K-Nearest Neighbor (KNN)
  • 1.2.9. Principal Component Analysis (PCA)
  • 1.2.10. K-Means Clustering
  • 1.3. Business Use Cases
  • 1.4. ML Architecture Data Acquisition
  • 1.5. Latest Application of Machine Learning
  • 1.5.1. Image Identification
  • 1.5.2. Sentiment Analysis
  • 1.5.3. News Classification
  • 1.5.4. Spam Filtering and Email Classification
  • 1.5.5. Speech Recognition
  • 1.5.6. Detection of Cyber Crime
  • 1.5.7. Classification
  • 1.5.8. Author Identification and Prediction
  • 1.5.9. Services of Social Media
  • 1.5.10. Medical Services
  • 1.5.11. Recommendation for Products and Services
  • 1.5.11.1. Machine Learning in Education
  • 1.5.11.2. Machine Learning in Search Engine
  • 1.5.11.3. Machine Learning in Digital Marketing
  • 1.5.11.4. Machine Learning in Healthcare
  • 1.6. Future of Machine Learning
  • 1.7. Conclusion
  • References
  • 2. Cognitive Computing: Architecture, Technologies and Intelligent Applications / Akansha Singh
  • 2.1. Introduction
  • 2.1. Components of a Cognitive Computing System
  • 2.3. Subjective Computing Versus Computerized Reasoning
  • 2.4. Cognitive Architectures
  • 2.5. Subjective Architectures and HCI
  • 2.6. Cognitive Design and Evaluation
  • 2.6.1. Architectures Conceived in the 1940s Can't Handle the Data of 2020
  • 2.7. Cognitive Technology Mines Wealth in Masses of Information
  • 2.7.1. Technology Is Only as Strong as Its Flexible, Secure Foundation
  • 2.8. Cognitive Computing: Overview
  • 2.9. Future of Cognitive Computing
  • References
  • 3. Deep Reinforcement Learning for Wireless Network / Krishna Kant Singh
  • 3.1. Introduction
  • 3.2. Related Work
  • 3.3. Machine Learning to Deep Learning
  • 3.3.1. Advance Machine Learning Techniques
  • 3.3.1.1. Deep Learning
  • 3.3.2. Deep Reinforcement Learning (DRL)
  • 3.3.2.1. Q-Learning
  • 3.3.2.2. Multi-Armed Bandit Learning (MABL)
  • 3.3.2.3. Actor-Critic Learning (ACL)
  • 3.3.2.4. Joint Utility and Strategy Estimation Based Learning
  • 3.4. Applications of Machine Learning Models in Wireless Communication
  • 3.4.1. Regression, KNN and SVM Models for Wireless
  • 3.4.2. Bayesian Learning for Cognitive Radio
  • 3.4.3. Deep Learning in Wireless Network
  • 3.4.4. Deep Reinforcement Learning in Wireless Network
  • 3.4.5. Traffic Engineering and Routing
  • 3.4.6. Resource Sharing and Scheduling
  • 3.4.7. Power Control and Data Collection
  • 3.5. Conclusion
  • References
  • 4. Cognitive Computing for Smart Communication / Aman Jatain
  • 4.1. Introduction
  • 4.2. Cognitive Computing Evolution
  • 4.3. Characteristics of Cognitive Computing
  • 4.4. Basic Architecture
  • 4.4.1. Cognitive Computing and Communication
  • 4.5. Resource Management Based on Cognitive Radios
  • 4.6. Designing 5G Smart Communication with Cognitive Computing and AI
  • 4.6.1. Physical Layer Design Based on Reinforcement Learning
  • 4.7. Advanced Wireless Signal Processing Based on Deep Learning
  • 4.7.1. Modulation
  • 4.7.2. Deep Learning for Channel Decoding
  • 4.7.3. Detection Using Deep Learning
  • 4.8. Applications of Cognition-Based Wireless Communication
  • 4.8.1. Smart Surveillance Networks for Public Safety
  • 4.8.2. Cognitive Health Care Systems
  • 4.9. Conclusion
  • References
  • 5. Spectrum Sensing and Allocation Schemes for Cognitive Radio / Rajeev Agrawal
  • 5.1. Foundation and Principle of Cognitive Radio
  • 5.2. Spectrum Sensing for Cognitive Radio Networks
  • 5.3. Classification of Spectrum Sensing Techniques
  • 5.4. Energy Detection
  • 5.5. Matched Filter Detection
  • 5.6. Cyclo-Stationary Detection
  • 5.7. Euclidean Distance-Based Detection
  • 5.8. Spectrum Allocation for Cognitive Radio Networks
  • 5.9. Challenges in Spectrum Allocation
  • 5.9.1. Spectrum and Network Heterogeneity
  • 5.9.2. Issues and Challenges
  • 5.10. Future Scope in Spectrum Allocation
  • References
  • 6. Significance of Wireless Technology in Internet of Things (IoT) / K. K. Mishra
  • 6.1. Introduction
  • 6.1.1. Internet of Things: A Historical Background 13
  • 6.1.2. Internet of Things: Overview, Definition, and Understanding
  • 6.1.3. Internet of Things: Existing and Future Scopes
  • 6.2. Overview of the Hardware Components of IoT
  • 6.2.1. IoT Hardware Components: Development Boards/Platforms
  • 6.2.1.1. Arduino
  • 6.2.1.2. Raspberry Pi
  • 6.2.1.3. BeagleBone
  • 6.2.2. IoT Hardware Components: Transducer
  • 6.2.2.1. Sensors
  • 6.2.2.2. Actuators
  • 6.3. Wireless Technology in IoT
  • 6.3.1. Topology
  • 6.3.1.1. Mesh Topology
  • 6.3.1.2. Star Topology
  • 6.3.1.3. Point-to-Point Topology
  • 6.3.2. IoT Networks
  • 6.3.2.1. Nano Network
  • 6.3.2.2. Near-Field Communication (NFC) Network
  • 6.3.2.3. Body Area Network (BAN)
  • 6.3.2.4. Personal Area Network (PAN)
  • 6.3.2.5. Local Area Network (LAN)
  • 6.3.2.6. Campus/Corporate Area Network (CAN)
  • 6.3.2.7. Metropolitan Area Network (MAN)
  • 6.3.2.8. Wide Area Network (WAN)
  • 6.3.3. IoT Connections
  • 6.3.3.1. Device-to-Device (D2D)/Machine-to-Machine (M2M)
  • 6.3.3.2. Machine-to-Gateway/Router (M2G/R)
  • 6.3.3.3. Gateway/Router-to-Data System (G/R2DS)
  • 6.3.3.4. Data System to Data System (DS2DS)
  • 6.3.4. IoT Protocols/Standards
  • 6.3.4.1. Network Protocols for IoT
  • 6.3.4.2. Data Protocols for IoT
  • 6.4. Conclusion
  • References
  • 7. Architectures and Protocols for Next-Generation Cognitive Networking / R. Karthika
  • 7.1. Introduction
  • 7.1.1. Primary Network (Licensed Network)
  • 7.1.2. CR Network (Unlicensed Network)
  • 7.2. Cognitive Radio Network Technologies and Applications
  • 7.2.1. Classes of CR
  • 7.2.2. Next Generation (xG) of CR Applications
  • 7.3. Cognitive Computing: Architecture, Technologies, and Intelligent Applications
  • 7.3.1. CR Physical Architecture
  • 7.4. Functionalities of CR in NeXt Generation (xG) Networks
  • 7.5. Spectrum Sensing
  • 7.5.1. Spectrum Decision
  • 7.5.2. Spectrum Mobility
  • 7.5.3. CR Network Functions
  • 7.6. Cognitive Computing for Smart Communications
  • 7.6.1. CR Technologies
  • 7.7. Spectrum Allocation in Cognitive Radio
  • 7.8. Cooperative and Cognitive Network
  • 7.8.1. Cooperative Centralized Coordinated
  • 7.8.2. Cooperative Decentralized (Distributed) Coordinated and Uncoordinated
  • References
  • 8. Analysis of Peak-to-Average Power Ratio in OFDM Systems Using Cognitive Radio Technology / Vetrivelan Ponnusamy
  • 8.1. Introduction
  • 8.2. OFDM Systems
  • 8.3. Peak-to-Average Power Ratio
  • 8.4. Cognitive Radio (CR)
  • 8.5. Related Works
  • 8.6. Neural Network System Model
  • 8.7. Complexity Examination
  • 8.8. PAPR and BER Examination
  • 8.9. Performance Evaluation
  • 8.10. Results and Discussions
  • 8.11. Conclusion
  • References
  • 9. Threshold-Based Optimization Energy-Efficient Routing Technique in Heterogeneous Wireless Sensor Networks / Samayveer Singh
  • 9.1. Introduction
  • 9.2. Literature Review
  • 9.3. System Model
  • 9.3.1. Four-Level Heterogeneous Network Model
  • 9.3.2. Energy Dissipation Radio Model
  • 9.4. Proposed Work
  • 9.4.1. Optimum Cluster Head Election of the Proposed Protocol
  • 9.4.2. Information Congregation and Communication Process Based on Chaining System for Intra and Inter-Cluster Communication
  • 9.4.3. Complete Working Process of the Proposed Method
  • 9.5. Simulation Results and Discussions
  • 9.5.1. Network Lifetime and Stability Period
  • 9.5.2. Network Outstanding Energy
  • 9.5.3. Throughput
  • 9.5.4. Comparative Analysis of the Level-4 Network Protocols
  • 9.6. Conclusion
  • References
  • 10. Efficacy of Big Data Application in Smart Cities / Pranati Rakshit
  • 10.1. Introduction
  • 10.1.1. Characteristics of Big Data
  • 10.1.1.1. Velocity
  • 10.1.1.2. Volume
  • 10.1.1.3. Value
  • 10.1.1.4. Variety
  • 10.1.1.5. Veracity
  • 10.1.2. Definition of Smart Cities
  • 10.2. Types of Data in Big Data
  • 10.2.1. Structured Data
  • 10.2.2. Unstructured Data
  • 10.2.3. Semi-Structured Data
  • 10.3. Big Data Technologies
  • 10.3.1. Apache Hadoop
  • 10.3.2. HDFS
  • 10.3.3. Spark
  • 10.3.4. Microsoft HDInsight
  • 10.3.5. NoSQL
  • 10.3.6. Hive
  • 10.3.7. Sqoop
  • 10.3.8. R
  • 10.3.9. Data Lakes
  • 10.4. Data Source for Big Data
  • 10.4.1. Media
  • 10.4.2. Cloud
  • 10.4.3. Web
  • 10.4.4. IOT
  • 10.4.5. Databases as a Big Data Source
  • 10.4.6. Hidden Big Data Sources
  • 10.4.6.1. Email
  • 10.4.6.2. Social Media
  • 10.4.6.3. Open Data
  • 10.4.6.4. Sensor Data
  • 10.4.7. Application-Oriented Big Data Source for a Smart City
  • 10.4.7.1. Healthcare
  • 10.4.7.2. Transportation
  • 10.4.7.3. Education
  • 10.5. Components of a Smart City
  • 10.5.1. Smart Infrastructure
  • 10.5.1.1. Intelligent Lighting
  • 10.5.1.2. Modern Parking Systems
  • 10.5.1.3. Associated Charging Points
  • 10.5.2. Smart Buildings and Belongings
  • 10.5.2.1. Safety and Security Systems
  • 10.5.2.2. Smart Sprinkler Systems for Gardens
  • 10.5.2.3. Smart Heating and Ventilation
  • 10.5.3. Smart Industrial Environment
  • 10.5.4. Smart City Services
  • 10.5.4.1. Smart Stalls
  • Contents note continued: 10.5.4.2. Monitoring of Risky Areas
  • 10.5.4.3. Public Safety
  • 10.5.4.4. Fire/Explosion Management
  • 10.5.4.5. Automatic Health-Care Delivery
  • 10.5.5. Smart Energy Management
  • 10.5.5.1. Smart Grid
  • 10.5.5.2. Intelligent Meters
  • 10.5.6. Smart Water Management
  • 10.5.7. Smart Waste Management
  • 10.6. Challenge and Solution of Big Data for Smart City
  • 10.6.1. Challenge in Big Data for Smart City
  • 10.6.1.1. Data Integration
  • 10.6.1.2. Security and Privacy
  • 10.6.1.3. Data Analytics
  • 10.6.2. Solution of Challenge Smart City
  • 10.6.2.1. Conquering Difficulties with Enactment
  • 10.6.2.2. Making People Smarter---Education for Everyone
  • 10.7. Conclusion
  • References.

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