Chapter
3.4 Simulation Configurations
3.5 Experimental Results and Performance Evaluation
3.5.1 TCP Performance Analysis Under Bufferbloated Circumstance
3.5.1.1 TCP performance of ABRWDA in a bufferbloated circumstance
3.5.1.2 TCP performance of DRWA in a bufferbloatedcircumstance
3.5.1.3 TCP performance of vegas in a bufferbloated circumstance
3.5.2 The Effect on TCP Performance Caused by Parameters’ Selection
3.5.3 The Improvement in User Experiences
3.5.4 The Improvement of System Performance from Kalman Filter
Chapter 4 - Adaptive Monitoring for Mobile Networks in Challenging Environments
4.2 Background on Monitoring in Mobile Networks
4.2.4 Information Distribution
4.3 RelatedWork: Data Collection in Mobile Networks
4.5 CRATER: Design of an Adaptive Monitoring Solution
4.5.1 No-Sink Advertising
4.5.4 CRATER Cloud Component
4.6.1 Modeling of the Scenario and Evaluation Setup
4.6.2 System Parameter Configurations
4.6.4 Static Monitoring vs. CRATER
Chapter 5 - Inferring Network Topologies in MANETs: Application to Service Redeployment
5.4.1 Virtual Topology Construction
5.4.4 Inference of Nodes Physical Locations
5.5 Iterative Service Redeployment (iSP) Algorithm
5.5.1 Formalization of the Multiple Service Replicas Deployment Problem
5.6.1 First Set of Experiments
5.6.1.1 Single connected component
5.6.1.2 Multiple connected components
5.6.2 Second Set of Experiments
5.6.3 Third Set of Experiments
5.6.4 Fourth Set of Experiments
5.7 Discussion and Future Research Directions
Chapter 6 - Towards Unified Wireless Network: A Software Defined Architecture based on Network Virtualization and Distributed Mobility Management
6.2 Software-Defined Distributed Mobility Management
6.2.1 Distributed Mobility Management in Mobile Backhaul Network
6.2.2 Virtualized Core Network Architecture
6.2.3 Path Establishment and Packet Flow in the Core Network
6.2.3.1 Initial attachment and session establishment
6.2.3.2 Uplink (UE→PDN) packet flow
6.2.3.3 Downlink (PDN→UE) packet flow
6.2.3.4 Some key points of design
6.2.3.6 Multiple border router scenario
6.2.3.7 IP address assignment considerations
6.3 Analytical Modeling of Signaling Load on EPC
6.4 Experiments and Results
6.4.1 Test Bed Description
6.5 Extending the Design to Support Fixed WLAN Users
6.5.1 Network Architecture
6.5.1.1 Uplink (UE→PDN) packet flow
6.5.1.2 Downlink (PDN→UE) packet flow
Chapter 7 - Improving the Effectivenessof Data Transfers in MobileComputing Using LosslessCompression Utilities
7.2 Lossless Compression Utilities
7.4 Metrics and Experiments
7.5.2 Compression and Decompression Throughputs
7.5.4 Putting It All Together
Chapter 8 - Scheduling-Inspired Spectrum Assignment Algorithms for Mesh Elastic Optical Networks
8.2 SA in Mesh Networks: A Special Case of Multiprocessor Scheduling
8.2.1 Illustrative Example
8.3 Scheduling Algorithms for Spectrum Assignment in Mesh Networks
8.3.1 Scheduling Algorithm for Chain Networks
8.4.3 Running Time Scalability
Chapter 9 - Wideband Spectrum Sensing in Cognitive Radio Networks
9.2 Single-Band Spectrum Sensing Methods
9.2.2 Matched Filter Detection
9.2.3 Cyclostationary Feature Detection
9.3 Wideband or Subdivided Band Spectrum Sensing
9.3.1 Wavelet Transform (WT)
9.3.2 Signal Edge Detection Using DWT
9.3.3 Wideband Spectrum Sensing Using DWT
9.3.3.1 Spectrum sensing byWTMM
9.3.3.2 Spectrum sensing by WTMP and WTMS
9.4 Exponentially Moving Averaged Multiscale Summation (EMAMS)
9.4.1 Edge Detection through EMAMS
9.4.2 Adaptive Thresholds
9.5 Performance Evaluation of EMAMS
PART IV Pervasive Computing/Sensor Networks/IoT
Chapter 10 - Assessing Performance of Smart Grid Applications Using Co-simulation
10.2 Background and RelatedWork
10.3 Co-simulation Models and Scenarios
10.3.1 Power Grid and Communication Network Models
10.3.2 Co-simulation Scenarios
10.3.2.1 Smart Grid applications
10.3.2.2 Operation conditions
10.3.2.3 Co-simulation scenarios
10.4 Performance Evaluation
10.4.2 Energy Market: Market Clearing Price
10.4.3 Energy Market: Market Clearing Quantity
10.4.4 HVAC Population Statistics
10.5.1 Wireless Network Models
10.5.2 Evaluation Results
Chapter 11 - Tight Bounds on Localized Sensor Self-Deployment for Focused Coverage
11.1.1 Chapter Organization
11.3 Model and Preliminaries
11.6 Analysis of the TTGREEDY Algorithm
Chapter 12 - Toward Resident Behavior Prediction in Wireless Sensor Network-Based Smart Homes
12.5.1 Data Classification
12.5.2 Support Vector Machines
Chapter 13 - Mobile Node Scheduling in MANETs for Resource Assignment: From Hospital Assignment to Energy Charging
13.2 Target Problem and RelatedWorks
13.4 Method to Solve Multidimension Hospital Assignment
13.4.1 Cost Matrix Buildup
13.4.2 Hospital Assignment
13.4.3 Parameter Formulation
13.5 Experimental Evaluation and Scenario Overview
13.6 Charger Assignment in MANETs
13.6.1 Charger Assignment Problem in MANETs
13.6.1.1 Capacity of chargers
13.6.1.2 Effective charging distance
13.6.1.3 Mobility of chargers
13.6.1.4 Charging duration
13.6.1.5 Appearance of charging request
13.6.1.6 Local waiting queue
13.6.2 Bipartite Matching-Based Algorithm
13.6.3 Results Analysis and Discussion
PART V Multimedia Networks
Chapter 14 - User Experience Awareness Network Optimization for Video Streaming Based Applications
14.3 Multi-Layered User Utility Function
14.3.2 User Utility Function
14.4 Adaptive User Demand
14.4.2 User’s Desire for Better Quality
14.4.3 The Impact of Adaptive User Deman
14.4.4 The Ripple Effects of Active Users on Network
14.5.1 Admission Control Designed
14.6 Simulation and Discussion
Chapter 15 - METhoD: A Framework for the Emulation of a Delay-Tolerant Network Scenario for Media Content Distribution in Under-Served Regions
15.3 Delay-Tolerant Networking
15.4 DTN-Enabled Infostation
15.5 Cinema-in-a-Backpack Kit
15.6.2 Mobility Trace Processor
15.8.1 Experimental Setting
15.8.2 Emulation with a Single Movie
15.8.3 Emulation with Multiple Movies
PART VI Network Optimization
Chapter 16 - On the Routing of Kademlia-type Systems
16.2 Kademlia-type Systems
16.2.1 Introducing Kademlia
16.2.2 Analyzing P2P Routing
16.3.3 Distribution of Closest Contacts
16.4.2 Computation Complexity
16.4.3 Reducing the ID Space Size
16.5 Verification and Scalability
16.5.1 Model Verification
16.5.3 Real-World Measurements
Chapter 17 - Access Efficient Bloom Filters with TinySet
17.2 Background and RelatedWork
17.2.1 Bloom Filter Variants
17.2.2 Hash Table-Based Bloom Filters
17.3 TinySet: Dynamic Fingerprint Resizing
17.3.1 Motivation and Overview
17.3.2 Basic Block Structure
17.3.3 Variable Fingerprint Size
17.3.4 Two Fingerprint Sizes in One Block
17.3.6 Implicit Size Counters
17.3.7 Integration with TinyTable
17.4.2 Variable-Sized Fingerprints
17.4.3 Variable-Sized Fingerprint with Mod
17.5.2 Space/Accuracy Tradeoff
17.5.5 Integration with TinyTable
17.6 Conclusions and Discussion
Chapter 18 - Maximum Correntropy-Based Distributed Estimation of Adaptive Networks
18.2.1 Cooperative Strategies
18.2.3 Impulsive Noise Model
18.3 Derivation of Adaptive Networks under MCC
18.3.1 Incremental MCC Algorithm
18.3.2 Diffusion MCC Algorithms
Chapter 19 - InfoMax: ATransport-Layer Paradigm for the Age of Data Overload
19.2 Design and Implementation
19.2.1 The InfoMax Information Summarization Abstraction
19.2.2 Assumptions and Properties
19.2.3 The InfoMax Protocol
19.2.3.1 Producer and consumer APIs in NDN
19.2.3.2 Enforcing the InfoMax order
19.2.3.3 Handling dynamic updates
19.2.4 An Approximate Transmission Ordering Algorithm
19.2.5 Customizing InfoMax Order
19.3.1 Transmission Overhead
19.3.3 Shortest-Shared-Postfix-First Ordering
19.3.4 Customized Ordering
19.4 Application Examples
19.6 Conclusions and FutureWork
Chapter 20 - Improvement in Load Balancing Decision for Massively Multiplayer Online Game (MMOG) Servers Using Markov Chains
20.1.1 Hotspot Problem in MMOG
20.1.2 Load Balancing Approaches
20.1.3 Sharing of Outdated Information
20.1.4 Load Balancing Decision Affects Player Response Time
20.2 Minimizing the Impact of Outdated Information
20.2.1 Prediction Algorithm
20.2.2 Accuracy of Arrival (ˆλ) and Departure Rates (ˆμ) Estimates
20.2.3 Use Case Scenarios
20.3 MMOG Server Load Migration Affects User Experience
20.3.1 System Model and Impact of Migration Decision on a User Response Time
20.3.1.1 Impact of Migration on User Response Time
20.3.1.2 Simulation and Results
Advances in Computer Communications and Networks From Green, Mobile, Pervasive Networking to Big Data Computing
( River Publishers Series in Communications )
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