(Ebook PDF) Cognitive Sensors and Iot Architecture Deployment and Data Delivery 1st edition by Fadi Al Turjman 1351595563 9781351595568 full chapters

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ISBN-10 ‏ : ‎1351595563 

ISBN-13 ‏ : ‎ 9781351595568

Author :  Fadi Al-Turjman 

This book presents the Cognitive Information Centric Sensor Network (CICSN) framework for the IoT. This framework is built on top of cognitive nodes, capable of knowledge representation, learning, and reasoning, along with an information-centric approach for data delivery. It also discusses the most appropriate deployment strategy for these cognitive nodes under realistic assumptions that cares about the Quality of Information (QoI). In addition, it uses a QoI aware data delivery strategy, with Analytic Hierarchy Process (AHP) as the reasoning technique to identify data delivery paths that dynamically adapt to changing network conditions and user requirements.

Cognitive Sensors and Iot: Architecture, Deployment, and Data Delivery 1st Table of contents:

Chapter 1 Introduction to Cognition in IoT
1.1 Contributions
1.2 Book Outline
Chapter 2 Information-Centric Sensor Networks for Cognitive IoTAn Overview
2.1 Introduction
2.2 WSN Design Issues in IoT Applications
2.2.1 User Expectations from the Network
2.2.1.1 Multi-User Application Platform Support
2.2.1.2 User Requirement—Aware Request Classification
2.2.1.3 Internet Access Interface between User and WSN
2.2.2 Adaptations at the Network Level
2.2.2.1 Energy Considerations and Resource Management
2.2.2.2 Query Dissemination and Data Delivery
2.2.3 Summary of WSN Design Change Requirements
2.3 Artificial Intelligence and Learning in WSN
2.3.1 Computational Intelligence
2.3.2 Machine Learning
2.3.2.1 Model-Based Reinforcement Learning
2.3.2.2 Model-Free Reinforcement Learning
2.3.2.3 Support Vector Machines
2.3.3 Cognitive Framework and Multiagent Systems
2.3.4 Context Aware Computing
2.4 A Hybrid Solution Platform: Learning in ICSN
2.4.1 What Should the ICSN Learn?
2.4.2 Choosing the Learning Strategy
2.4.3 Heuristically Accelerated Reinforcement Learning
2.4.4 Learning in the ICSN Framework
2.5 Use-Case and Performance Evaluation
2.5.1 Simulation Setup
2.5.2 Simulation Results and Analysis
2.6 Conclusion and Open Issues
Acknowledgment
References
Chapter 3 Cognitive-Node Architecture and a Deployment Strategy for the Future Sensor Networks
3.1 Introduction
3.2 Related Work
3.3 System Models
3.3.1 Network Model
3.3.2 Energy Consumption Model
3.3.3 Communication Model
3.3.4 Operational Cost Model
3.3.5 Problem Definition
3.4 A Cognitive Deployment Strategy for ICSN
3.4.1 Learning
3.4.2 Knowledge-Representation and Reasoning
3.5 Simulation Results and Discussions
3.5.1 The Quality of Information (QoI) Attributes
3.5.1.1 Node Reliability (NR) at the Transmitting Node
3.5.1.2 Instantaneous Throughput (IT) at the Receiving Node
3.5.1.3 Observed Latency (OL) at the Receiving Node
3.5.2 Simulation Setup
3.5.3 Simulation Results
3.6 Conclusions
Acknowledgment
References
Chapter 4 Quantifying Connectivity in Wireless Sensor Networks with Grid-Based Deployments
4.1 Introduction
4.2 Related Work
4.3 System Models
4.3.1 Network Model
4.3.2 Communication Model
4.3.3 Placement Uncertainty Models
4.3.3.1 Bounded Uniform Errors
4.3.3.2 Unbounded Normal Errors
4.4 Quantifying the Grid Connectivity
4.4.1 Generic Approach
4.4.2 Theoretical Analysis
4.5 Discussion and Numerical Results
4.5.1 Grid Connectivity with Bounded Uniform Errors
4.5.2 Grid Connectivity with Unbounded Normal Errors
4.6 Conclusion
Acknowledgment
References
Chapter 5 A Data Delivery Framework for Cognitive Information-Centric Sensor Networks in Smart Outdoor Monitoring
5.1 Introduction
5.2 Related Work
5.2.1 Information-Centric Networks
5.2.2 Data-Centric Sensor Networks
5.2.3 Cognition in Communication Networks and Cognitive Sensor Networks
5.3 System Models
5.3.1 Quality of Information (QoI)
5.3.2 Network Lifetime
5.3.2.1 Application Traffic Profiles for Smart Outdoor Monitoring Applications
5.3.2.2 Network Architecture and Components
5.3.2.3 Node Functions
5.4 The COGNICENSE Framework
5.4.1 Knowledge Representation
5.4.2 Learning
5.4.3 Reasoning
5.4.3.1 The AHP Framework for Data Delivery Based on QoI Attributes
5.4.3.2 Node Mobility Support in the COGNICENSE Framework
5.4.3.3 Energy Considerations in the COGNICENSE Framework
5.5 Simulations and Results
5.5.1 Simulation Setup and Parameters
5.5.2 Simulations Showing the Impact of Network and Node Parameter Variations on the QoI Attributes
5.5.3 Comparative Evaluation of Data Delivery Protocols: AHPDD, HRBDD, and MDD
5.5.4 Use-Case Analysis of the Data Delivery Protocols Based on QoI Attribute Performance
5.6 Conclusions
References
Chapter 6 Cognitive Routing Protocol for Disaster-Inspired WSNs in the Internet of Things
6.1 Introduction
6.2 Related Work
6.3 System Model
6.3.1 Network Architecture
6.3.2 Lifetime of IoT Network
6.3.3 Energy Conservation and Dead Node Issue
6.3.4 Communication Model
6.3.4.1 Number of Hops without Delay Constraints
6.3.4.2 Number of Hops with Delay Constraints
6.4 Cognitive Energy-Efficient Approach (CEEA)
6.4.1 Learning
6.4.2 Reasoning
6.5 Performance Evaluation
6.5.1 Performance Metrics and Parameters
6.5.2 Experimental Setup
6.5.3 Simulation Results
6.6 Conclusion
References
Chapter 7 Price-Based Data Delivery Framework for Dynamic and Pervasive IoT
7.1 Introduction
7.2 Background
7.3 IoT System Model
7.3.1 IoT Model
7.3.2 IoT Node
7.3.2.1 Residual Energy and Power Model
7.3.2.2 Load and Buffer Space
7.3.2.3 Delay
7.3.2.4 Trust
7.3.3 Pricing Model
7.3.4 Communication Model
7.4 ARA Routing Approach
7.5 Use Case and Theoretical Analysis
7.6 Performance Evaluation
7.6.1 Simulation Setup and Baseline Approaches
7.6.2 Performance Parameters and Metrics
7.6.3 Simulation Results
7.7 Conclusion
References
Chapter 8 Fog-Based Caching and Learning for Information-Centric Networks
8.1 Introduction
8.2 Related Work
8.2.1 Location-Based Caching (LC)
8.2.2 Content-Based Caching (CC)
8.2.3 Node Functionality-Based Caching (FC)
8.3 System Models
8.3.1 ICSN Network Model
8.3.2 Delay Model
8.3.3 Age Model
8.3.4 Popularity of On-Demand Requests
8.3.5 Channel Communication Model
8.4 VoI Cache Replacement
8.4.1 Learning
8.4.2 Reasoning
8.5 Performance Evaluation
8.5.1 Performance Metrics
8.5.2 Simulation Parameters
8.5.3 Simulation and Results
8.6 Conclusions
References
Chapter 9 Conclusions and Future Directions
9.1 Summary of the Book
9.2 Future Directions

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