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Record Nr. |
UNINA9910874672503321 |
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Autore |
Wang Jie |
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Titolo |
Encountering Mobile Data Dynamics in Heterogeneous Wireless Networks |
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Pubbl/distr/stampa |
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Cham : , : Springer, , 2024 |
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©2024 |
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ISBN |
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9783031629068 |
9783031629051 |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (187 pages) |
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Altri autori (Persone) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Intro -- 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-- 2.1.1 Motivating Examples in Different Systems -- 2.1.1.1 Rumor vs. Truth in OSN -- 2.1.1.2 Advanced Product vs. Outdated Product in Word-of-Mouth Networks -- 2.1.1.3 Malware vs. Security Patch in Institutional Computer Networks and Faults vs. Restoration Commands in IoT -- 2.1.2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 |
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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 |
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-- 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 the `Wandering 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. |
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