UID:
almahu_9949384725202882
Format:
1 online resource (466 pages) :
,
illustrations
ISBN:
1498724183
,
9781498724180
,
9781523113606
,
152311360X
,
9781351636988
,
1351636987
,
9781351646512
,
1351646516
Content:
"D2D-based proximity service is a very hot topic with great commercial potential from an application standpoint. Unlike existing books which focus on D2D communications technologies, this book fills a gap by summarizing and analyzing the latest applications and research results in academic, industrial fields, and standardization. The authors present the architecture, fundamental issues, and applications in a D2D networking environment from both application and interdisciplinary points of view."--Provided by publisher.
Note:
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Authors -- PART I: ARCHITECTURE -- CHAPTER 1: DEVICE-TO-DEVICE COMMUNICATIONS TECHNOLOGIES -- 1.1 Introduction -- 1.2 Unlicensed D2D Communications -- 1.2.1 A Technical Overview on Wi-Fi Direct -- 1.2.2 Formations of Unlicensed Device-to-Device Communication -- 1.2.2.1 Autonomous Device-to-Device Communication -- 1.2.2.2 Network-Assisted Device-to-Device Communication (Controlled) -- 1.2.3 Key Problems -- 1.2.3.1 Issues in Autonomous Device-to-Device Communication -- 1.2.3.2 Network-Assisted Device-to-Device Communication -- 1.3 Licensed Device-to-Device Communication -- 1.3.1 LTE Direct Architecture -- 1.3.2 Underlay and Overlay Device-to-Device Communication -- 1.3.3 Key Challenges -- 1.3.3.1 Resource Allocation -- 1.3.3.2 Transmission Mode Selection -- 1.4 Conclusion -- References -- CHAPTER 2: PEER AND SERVICE DISCOVERY IN PROXIMITY SERVICE -- 2.1 Introduction -- 2.2 Peer and Service Discovery without Network Assistance -- 2.2.1 Peer Discovery -- 2.2.1.1 Main Challenges -- 2.2.1.2 Solution to the Challenges -- 2.2.1.3 Combination of Wi-Fi and Bluetooth -- 2.2.2 Service Discovery without Network Assistance -- 2.2.2.1 Supported Types of Service in Wi-Fi Direct -- 2.2.2.2 Service Discovery Entity in Wi-Fi Direct -- 2.2.2.3 Service Discovery in Wi-Fi Direct Compared with Service Discovery in Bluetooth -- 2.2.2.4 Service Discovery in Wi-Fi Direct Compared with Service Discovery in LTE Direct -- 2.3 Peer and Service Discovery with Network Assistance -- 2.3.1 Peer Discovery -- 2.3.1.1 Direct Discovery with Network Assisted -- 2.3.1.2 Network-Dependent Peer Discovery Schemes -- 2.3.2 Service Discovery with Network Assistance -- 2.3.2.1 App and Service Registration.
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2.3.2.2 Approach for Network-Assisted Service Discovery -- 2.4 Conclusion -- References -- CHAPTER 3: MESSAGE FORWARDING STRATEGIES -- 3.1 Introduction -- 3.2 Taxonomy of Social Behavior-Based Forwarding Strategies -- 3.2.1 Location-Based Strategies -- 3.2.2 Encounter-Based Strategies -- 3.2.2.1 Social Property-Based Strategies -- 3.2.2.2 Community-Based Strategies -- 3.3 Typical Message-Forwarding Schemes for Proximity Service -- 3.3.1 Location-Based Message-Forwarding Schemes -- 3.3.2 Encounter-Based Forwarding Schemes -- 3.3.2.1 Social Property-Based Schemes -- 3.3.2.2 Community-Based Forwarding Scheme -- 3.3.3 Comparisons of Social-Based Forwarding Strategies in Mobile Social Networking in Proximity -- 3.4 Open Issues and Challenges -- 3.4.1 Internal Challenges -- 3.4.1.1 Bandwidth Constraint -- 3.4.1.2 Energy Constraint -- 3.4.1.3 Balance of Various Trade-Offs -- 3.4.2 External Challenges -- 3.4.2.1 Human Mobility Prediction -- 3.4.2.2 Dynamic Communities -- 3.4.2.3 Selfishness and Incentive Mechanisms -- 3.5 Several Typical Prototypes -- 3.5.1 Haggle -- 3.5.2 DMS: DTN-Based Mobile Social Network Application -- 3.6 Conclusion -- References -- CHAPTER 4: PROFILE MATCHING AND RECOMMENDATION SYSTEMS IN PROXIMITY SERVICE -- 4.1 Introduction -- 4.2 Profile-Matching Schemes Device-to-Device-Based Proximity Services -- 4.2.1 Bloom Filter-Based Profile Matching -- 4.2.1.1 Basic Concepts about Bloom Filters -- 4.2.1.2 Existing Bloom Filter-Based Profile-Matching Schemes -- 4.2.2 Privacy-Preserving Profile Matching -- 4.2.2.1 Protocol 1 for Level-I Privacy -- 4.2.2.2 Protocol 2 for Level-II Privacy -- 4.2.3 Light-Weighted Privacy-Preserving Profile Matching -- 4.2.3.1 System Architecture of LIP3 -- 4.2.3.2 The Proposed Protocols -- 4.3 Over-the-Top-Based Recommendation Systems.
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4.3.1 Existing Recommendation Techniques -- 4.3.1.1 Content-Based Filtering Algorithms -- 4.3.1.2 Collaborative Filtering Recommendation Algorithm -- 4.3.1.3 Improvement to Recommendation Systems -- 4.3.2 Location Recommendation Systems in Location-Based Social Networks -- 4.4 Conclusion -- References -- CHAPTER 5: PROXIMITY-SERVICE APPLICATION DEVELOPMENT FRAMEWORK -- 5.1 Introduction -- 5.2 Overview of Proximity Service (Basic Concept and Development View Point) -- 5.2.1 Over-the-Top and Device-to-Device Development Pattern -- 5.2.2 Typical Scenarios of Proximity Services -- 5.2.2.1 Public Safety Communication -- 5.2.2.2 Discovery Mode -- 5.2.3 Two Different Viewpoints for ProSe Development -- 5.3 From ProSe Application Designers' View: Components, Requirements, and Existing Works -- 5.3.1 Requirements in Developing ProSe Applications -- 5.3.1.1 Physical Layer -- 5.3.1.2 Context Layer -- 5.3.1.3 Application Layer -- 5.3.1.4 Crosslayer -- 5.3.2 Comparison of Existing ProSe Applications and Solutions -- 5.4 From ProSe Development Framework Builder View: Benefits and Challenges -- 5.4.1 Benefits of Introducing Development Framework -- 5.4.2 Challenges in Building ProSe Application Development Framework -- 5.5 Summary of Existing ProSe Application Development Frameworks -- 5.5.1 Commotion Wireless Project -- 5.5.2 Serval Project -- 5.5.3 AllJoyn -- 5.5.4 Proxima -- 5.5.5 Crossroads -- 5.5.6 USABle -- 5.5.7 CAMEO -- 5.6 Conclusion -- References -- PART II: FUNDAMENTAL ISSUES -- CHAPTER 6: SMARTPHONE-BASED HUMAN ACTIVITY RECOGNITION -- 6.1 Introduction -- 6.2 Background -- 6.2.1 Common Sensors Embedded in Smartphones -- 6.2.1.1 Accelerometer -- 6.2.1.2 Compass Sensor -- 6.2.1.3 Gyroscope -- 6.2.2 Activities -- 6.3 Core Techniques -- 6.3.1 Raw Data Collection and Preprocessing -- 6.3.1.1 Types of Errors.
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6.3.1.2 Techniques to Address Error -- 6.3.2 Feature Computation -- 6.3.2.1 Time-Domain Features -- 6.3.2.2 Frequency-Domain Features -- 6.3.2.3 Discrete-Domain Features -- 6.3.3 Classification Methods -- 6.3.3.1 Performance Metrics for Activity Recognition -- 6.3.3.2 Taxonomy of Classifiers -- 6.3.3.3 Decision Tree -- 6.3.3.4 KNN -- 6.3.3.5 HMM -- 6.3.3.6 SVM -- 6.3.4 Data Analysis Tools -- 6.4 Application -- 6.4.1 HAR for End Users -- 6.4.1.1 Daily Life Monitoring -- 6.4.1.2 Health Care -- 6.4.2 HAR for the Third Parties -- 6.4.2.1 Corporate Management and Accounting -- 6.4.2.2 Targeted Advertising -- 6.4.3 Applications for Crowds and Groups -- 6.4.3.1 Activity-Based Social Networking -- 6.4.3.2 Activity-Based Crowdsourcing -- 6.4.4 Vehicle-Related Applications -- 6.5 Challenges and Open Issues -- 6.5.1 Energy Consumption and Battery Life -- 6.5.2 Quality of Smartphone Sensors -- 6.5.3 Burden -- 6.5.4 Smartphone Placement and Use Habits -- 6.5.5 Security and Privacy Threats -- 6.6 Conclusion -- References -- CHAPTER 7: INDOOR LOCALIZATION AND TRACKING SYSTEMS -- 7.1 Introduction -- 7.2 Background -- 7.2.1 Performance Metrics -- 7.2.2 Positioning Principles -- 7.2.2.1 Triangulation -- 7.2.2.2 Proximity -- 7.2.2.3 Scene Analysis -- 7.2.2.4 Pedestrian Dead Reckoning -- 7.3 Wireless Technologies Used in Indoor Localization -- 7.3.1 Existing Wireless Technologies for Indoor Localization -- 7.3.2 Wi-Fi Fingerprint-Based Indoor Localization -- 7.3.2.1 Basic Concepts about Wi-Fi Fingerprint -- 7.3.2.2 Improvements to Traditional Wi-Fi Fingerprint-Based Systems -- 7.3.3 Pedestrian Dead Reckoning-Based Indoor Localization -- 7.3.3.1 Step Detection -- 7.3.3.2 Step Length Estimation -- 7.3.3.3 Heading Estimation.
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7.3.4 Comparison of Wi-Fi Fingerprint and PDR-Based Indoor Localizations -- 7.4 Improvements to PDR and Wi-Fi Fingerprint-Based Indoor Localization Schemes -- 7.5 Conclusion -- References -- CHAPTER 8: MOBILE CROWDSOURCING WITH BUILT-IN INCENTIVES -- 8.1 Introduction -- 8.2 MCS Framework and Typical Applications -- 8.2.1 MCS Enabling Modes -- 8.2.2 Comparison among Typical MCS Applications -- 8.2.2.1 Direct Mode -- 8.2.2.2 WoM Mode -- 8.3 Generic Framework -- 8.3.1 A Generic Mobile-Crowdsourcing Framework -- 8.3.2 Typical Workflow of MCS Applications -- 8.4 Key Challenges -- 8.4.1 Task Design and Allocation -- 8.4.2 Incentives -- 8.4.3 Security and Privacy -- 8.4.3.1 General Privacy and Security Threats in Mobile Crowdsourcing -- 8.4.3.2 Countermeasures to Address Privacy and Security Threats -- 8.4.4 Quality Control -- 8.4.4.1 The Malicious Behavior in the Crowd -- 8.4.4.2 Ensuring Trustworthiness of Data -- 8.5 Quality-Aware Incentive -- 8.5.1 Detailed Operation of QuaCentive Scheme -- 8.5.1.1 Selecting Participants Based on Their Reputation Ranking and Bids (Step 3) -- 8.5.1.2 Rewarding Contributors (Step 7) -- 8.5.1.3 Verifying Crowdsourcers' Bad Ratings on the Quality of the Sensed Contents (Step 8) -- 8.5.1.4 Updating Reputation Rankings of Participants and Crowdsourcers -- 8.5.2 Theoretical Analysis of QuaCentive -- 8.5.2.1 From Crowd Side: Individual Rationality, Incentive of Providing High-Quality Sensed Contents, and Truthful Bid -- 8.5.2.2 From Crowdsourcing Platform's Side: Condition of Being Profitable -- 8.5.2.3 From Crowdsourcer's Side: Truthful Feedback about Quality of Sensed Contents -- 8.5.2.4 Strategies to Deal to Whitewashing Users -- 8.6 Conclusion -- References.
Additional Edition:
Print version: ISBN 9781498724173
Language:
English
Keywords:
Electronic books.
;
Electronic books.
DOI:
10.1201/9781315120201
URL:
https://www.taylorfrancis.com/books/9781315120201
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