Encountering Mobile Data Dynamics in Heterogeneous Wireless Networks

✍ Scribed by Jie Wang, Wenye Wang, Xiaogang Wang

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 187
• Edition: 2024
• Category: Library

✦ Synopsis

This book offers a systematic view of data services in heterogeneous wireless networks, from the perspectives of cause, governing rules, and impact of data’s mobility on such networks. Specifically, it covers application requirements break-down, network modeling, performance analysis and evaluation by examining mobile data dynamics that are particularly important to data service provisioning. Additionally, application prospects such as information dissemination, fog computing, Internet-of-Things and dynamic spectrum access are discussed on the basis of these dynamics. Theoretic analysis, example illustrations, and algorithms are also presented to provide a concise coverage of this important area of networking.

Mobile data dynamics refers to the stochastic processes of information, geographical coverage and spectrum, which accompanies the movements of data across wireless networks. Owing to the challenge raised by a high level of network heterogeneity, and the innate requirement on scalability, knowledge on the evolution of mobile data dynamics is essential to the design and deployment of emerging data services and applications, such as Internet-of-Things, data/task offloading and edge-based machine learning/inference.

This book is designed for researchers and advanced-level students in the field of wireless networking and edge computing, who seek to understand the models and evolutions of mobile data dynamics for future edge applications. Practitioners, who specialize in the design, operation and maintenance of edge computing systems will also want to purchase this book as a reference.

✦ Table of Contents

Preface
Acknowledgment
Contents
Acronyms
1 Introduction
1.1 Motivation
1.1.1 Data Is Alive and Mobile
1.1.2 Mobile Data Dynamics in Heterogeneous WirelessNetworks
1.1.2.1 Information Dynamics: The Driving Force of Mobile Data
1.1.2.2 Coverage Dynamics: The Whereabouts of Mobile Data
1.1.2.3 Spectrum Dynamics: Impact of Mobile Data in the Spectrum Domain
1.1.2.4 Network-Data Interaction: Governing Rules of Mobile Data
1.2 Contents of This Book
1.2.1 Information Dynamics: When Data Start and Stop Moving
1.2.2 Coverage Dynamics: Where Data Are duringDissemination
1.2.3 Governing Rules: How Data Move during Offloading
1.2.4 Spectrum Dynamics: What Observable Impact Mobile Data Cause
1.3 Organization of This Book
References
2 Information Dynamics: Modeling and Analysis of Conflicting Information Propagation in a Finite Time Horizon
2.1 Motivation and Related Work
2.1.1 Motivating Examples in Different Systems
2.1.1.1 Rumor vs. Truth in OSN
2.1.1.2 Advanced Product vs. Outdated Product in Word-of-Mouth Networks
2.1.1.3 Malware vs. Security Patch in Institutional Computer Networks and Faults vs. Restoration Commands in IoT
2.1.2 Related Work
2.1.3 Our Approach and Contributions
2.2 Preliminaries and Problem Statement
2.2.1 Conflicting Information Pair: Virus x and Antidote ax
2.2.2 Network G(V,E)
2.2.3 Epidemic Propagation Process
2.2.3.1 State Transitions
2.2.3.2 Propagation Rules
2.2.4 Problem Formulation
2.3 Lifetime of the Undesired Information in Networks with Simple Topologies
2.3.1 Bounds for Complete Networks Kn
2.3.2 Bounds for Star Networks Sn
2.3.3 Numerical Simulation and Discussion
2.4 Lifetime of the Undesired Information in Networks with Arbitrary Topologies
2.4.1 Bounds by Considering the Edge-Expansion Property
2.4.2 Bounds by Considering Vertex Eccentricity
2.4.3 Validation in Synthetic and Real-World Networks
2.5 Divide-and-Conquer: Leveraging Topology to Control Undesired Information
2.5.1 Topology-Based Antidote Distribution
2.5.2 Ideal Antidote Distribution Policy
2.5.3 Practical Approaches
2.5.4 Numerical Results and Discussion
2.6 Dynamics in Motion: Estimating the Number of Information Adopters at Time t
2.6.1 Temporal Dependence
2.6.2 Spatial Dependence
2.6.3 Expected Infection Count E(I(t)) and Cured CountE(C(t))
2.7 Summary
References
3 Coverage Dynamics: Modeling and Analysis of Data Coverage in Heterogeneous Edge Networks
3.1 Motivation and Related Work
3.1.1 Motivation
3.1.2 Related Work
3.1.3 Our Approach and Contributions
3.2 Problem Formulation
3.2.1 Scope of the Where' Problem 3.2.2 Entity Model 3.2.2.1 Attributes of Entity e 3.2.2.2 Actions 3.2.3 Data Coverage and Coverage Dynamics 3.3 Representing Coverage Dynamics with Graph Signals 3.3.1 A Location-Centric Measure: Data-Strength 3.3.2 State Transitions of a Single Entity 3.3.3 Evolution of the Dynamics via Data-Strength Vector st 3.3.3.1 User Mobility Component 3.3.3.2 Data Dissemination Component 3.3.4 Preliminaries on Graph Signal Processing (GSP) 3.3.4.1 Graph Fourier Transform (GFT) and Spectrum of a Graph Signal 3.3.4.2 Time-Vertex Process and Stationarity 3.4 Information from a Snapshot 3.4.1 A Simple Homogeneous Scenario 3.4.2 Impact of Mobility 3.4.2.1 Weighted Adjacency Matrix Wt"0365Wt of G 3.4.2.2 Mobility Dependence Index (MDI) 3.4.2.3 Simulation Configuration 3.4.2.4 Observations 3.5 Summary References 4 Governing Rules: Modeling and Analysis of Task Offloading Processes in the Fog 4.1 Motivation and Related Work 4.1.1 Fog Emerges on the Edge: Remedy or Resource Drain? 4.1.2 Related Work 4.1.3 Our Approach and Contributions 4.2 System Model and Problem Formulation 4.2.1 Network Model 4.2.1.1 Node Model 4.2.1.2 Communication Model 4.2.2 Task Model 4.2.3 Performance Metrics through Data Movements 4.3 How Data Move: The Gravity Model for Task Offloading 4.3.1 An Offloading Procedure Under Gravity Rule 4.3.2 Typical and Generic Gravity Rules 4.3.2.1 Distance-Oriented Gravity Rule 4.3.2.2 Delay-Oriented Gravity Rule 4.3.2.3 Energy-Oriented Gravity Rule 4.3.2.4 Generic Form of the Gravity Function 4.3.3 Offloading Probability Under the Generic Gravity Rule 4.4 Bounds and Scaling Laws of Performances 4.4.1 Expected Queuing Delay E(TQ|nε) 4.4.2 Device Effort and Network Effort 4.4.2.1 Distance-Oriented Offloading 4.4.2.2 Delay-Oriented Offloading 4.5 Numerical Results and Discussions 4.5.1 Describing an Offloading Scheme with the Gravity Model 4.5.2 Comparison with Existing Offloading Schemes 4.5.2.1 Single Criterion 4.5.2.2 Multiple Criteria 4.5.3 Gravity Model as an Offloading Scheme 4.6 Summary References 5 Spectrum Dynamics: Modeling, Analysis, and Design of Spectrum Activity Surveillance in DSA-Enabled Systems 5.1 Motivation and Related Work 5.1.1 Motivation 5.1.2 Related Work 5.1.3 Our Approach and Contributions 5.2 Problem Formulation 5.2.1 System Model 5.2.1.1 Spectra of Interest S 5.2.1.2 The Spectra-Location Space X 5.2.1.3 Surveillance Model 5.2.1.4 Exploit Model 5.2.1.5 Switching Model 5.2.2 Performance Metrics 5.2.3 SAS Strategy Design Problem 5.3 A Two-Step Solution 5.3.1 Space Tessellation: Reducing the Solution Space 5.3.1.1 Solution to the Space-Tessellation Problem 5.3.1.2 Exploit Patterns of Culprits in Assignment Space V 5.3.2 Graph Walk: A Chain of Switching Actions 5.3.2.1 Range Aspect (Switching Capacity) 5.3.2.2 Time Aspect (Switching Rates) 5.3.2.3 A Graph Walk on Composite Graph (GM, GR) 5.4 Deterministic SAS Strategies for Dedicated Monitors 5.4.1 Low Cost Deterministic Strategies fS 5.4.2 Detection Time of the Deterministic Strategy fS 5.4.2.1 Detecting Persistent Culprits Rp 5.4.2.2 Detecting an Adversary Culprit Ra 5.5 Patching theWandering Hole': Randomized Strategies
5.5.1 Randomized SAS Strategies
5.5.2 Coverage Time of the Two Randomized Strategies fI and fD
5.5.2.1 Coverage Time of the I-Strategy TIm
5.5.2.2 Coverage Time of the D-Strategy TDm
5.5.2.3 Numerical Validation
5.5.3 Bounded Detection Time of Adversarial Culprits
5.6 SAS with Limited Switching Capacities
5.6.1 Performance Analysis Through Regular Graph Approximation
5.6.1.1 Coverage Time TrMm
5.6.1.2 Detection Time τR(rR, rM)
5.6.2 Gap Between (GM,GR) and Approximation (GrM, GrR)
5.6.3 Numerical Results
5.7 Summary
References
6 Conclusion and Future Directions
6.1 Recap of Key Findings on Mobile Data Dynamics
6.2 Emerging Trends on Edge Intelligence
References