12067nam 2200565 450 991083045950332120231013234542.01-119-91144-31-119-91146-X(MiAaPQ)EBC30764568(Au-PeEL)EBL30764568(EXLCZ)992843284290004120231013d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierPositioning and Location-Based Analytics in 5G and Beyond /edited by Stefania Bartoletti and Nicola Blefari MelazziFirst edition.Hoboken, New Jersey :John Wiley & Sons, Inc.,[2024]©20241 online resource (291 pages)Print version: Bartoletti, Stefania Positioning and Location-Based Analytics in 5G and Beyond Newark : John Wiley & Sons, Incorporated,c2023 9781119911432 Includes bibliographical references and index.Cover -- Title Page -- Copyright -- Contents -- About the Editors -- List of Contributors -- Preface -- Acknowledgments -- List of Abbreviations -- Chapter 1 Introduction and Fundamentals -- 1.1 Introduction and Motivation -- 1.2 Use Cases, Verticals, and Applications -- 1.2.1 Emergency Calls -- 1.2.2 Public Safety and Natural Disasters -- 1.2.3 ITS and Autonomous Vehicles -- 1.2.4 IIoT, Construction Sites, and Mines -- 1.2.5 Commercial and Transport Hubs -- 1.2.6 Internet‐of‐Things -- 1.2.7 Education and Gaming -- 1.3 Fundamentals of Positioning and Navigation -- 1.3.1 Position‐Dependent Measurements -- 1.3.2 Positioning Methods -- 1.3.3 AI/ML for Positioning -- 1.4 Fundamentals of Location‐Based Analytics -- 1.5 Introduction to Architectural Principles -- 1.5.1 5G Architecture and Positioning -- 1.5.2 Location‐Based Analytics Platform -- 1.6 Book Outline -- References -- Part I Positioning Enablers -- Chapter 2 Positioning Methods -- 2.1 Positioning as Parameter Estimation -- 2.1.1 The Snapshot Positioning Problem -- 2.1.2 Fisher Information and Bounds -- 2.1.3 Tracking and Location‐Data Fusion -- 2.1.3.1 Practical Aspects -- 2.2 Device‐Based Radio Positioning -- 2.2.1 Theoretical Foundations -- 2.2.1.1 Signal Model -- 2.2.1.2 Equivalent Fisher Information Matrix -- 2.2.1.3 Interpretation -- 2.2.2 Signal Processing Techniques -- 2.2.3 Example Results of 5G‐Based Positioning in IIoT Scenarios -- 2.3 Device‐Free Radio Localization -- 2.3.1 Theoretical Foundations -- 2.3.1.1 Signal Model -- 2.3.1.2 EFIM for DFL -- 2.3.1.3 Interpretation -- 2.3.2 Signal Processing Techniques -- 2.3.3 Experimental Results on 5G‐Based DFL -- 2.4 AI/ML for Positioning -- 2.4.1 Fingerprinting Approach -- 2.4.2 Soft Information‐Based Approach -- 2.4.3 AI/ML to Mitigate Practical Impairments -- References -- Chapter 3 Standardization in 5G and 5G Advanced Positioning.3.1 Positioning Standardization Support Prior to 5G -- 3.1.1 GNSS and Real‐Time Kinematics (RTK) GNSS Positioning -- 3.1.2 WiFi/Bluetooth‐Based Positioning -- 3.1.3 Terrestrial Beacon System -- 3.1.4 Sensor Positioning -- 3.1.5 RAT‐Dependent Positioning Prior to 5G -- 3.1.5.1 Enhanced CID (eCID) -- 3.1.5.2 Observed Time‐Difference‐of‐Arrival (OTDoA) -- 3.1.5.3 Uplink Time‐Difference‐of‐Arrival (UTDoA) -- 3.1.6 Internet of Things (IoT) Positioning -- 3.1.7 Other Non‐3GPP Technologies -- 3.1.7.1 UWB -- 3.1.7.2 Fingerprinting -- 3.2 5G Positioning -- 3.2.1 5G Localization Architecture -- 3.2.2 Positioning Protocols -- 3.2.3 RAT‐Dependent NR Positioning Technologies -- 3.2.3.1 Downlink‐Based Solutions -- 3.2.3.2 Uplink‐Based Solutions -- 3.2.3.3 Downlink‐ and Uplink‐Based Solutions -- 3.2.4 Specific Positioning Signals -- 3.2.4.1 Downlink Positioning Reference Signal -- 3.2.4.2 Uplink Signal for Positioning -- 3.2.5 Positioning Measurements -- 3.3 Hybrid Positioning Technologies -- 3.3.1 Outdoor Fusion -- 3.3.2 Indoor Fusion -- 3.4 5G Advanced Positioning -- References -- Chapter 4 Enablers Toward 6G Positioning and Sensing -- 4.1 Integrated Sensing and Communication -- 4.1.1 ISAC Application: Joint Radar and Communication with Sidelink V2X -- 4.1.1.1 V2X and Its Sensing Potential -- 4.1.1.2 V2X Target Parameter Estimation and Signal Numerology -- 4.1.1.3 V2X Resource Allocation -- 4.1.2 ISAC Application: Human Activity Recognition and Person Identification -- 4.1.2.1 Beyond Positioning -- 4.1.2.2 System Aspects -- 4.1.2.3 Processing Chain (see Figure ) -- 4.2 Reconfigurable Intelligent Surfaces for Positioning and Sensing -- 4.2.1 RIS Enabling and Enhancing Positioning -- 4.2.1.1 RIS Enabling Positioning -- 4.2.1.2 RIS Enhancing Positioning -- 4.2.1.3 Use Cases -- 4.2.2 RIS for Sensing -- 4.3 Advanced Methods -- 4.3.1 Model‐Based Methods.4.3.2 AI‐Based Methods -- 4.3.2.1 Use Case -- References -- Chapter 5 Security, Integrity, and Privacy Aspects -- 5.1 Location Privacy -- 5.1.1 Overview on the Privacy Implication -- 5.1.2 Identification and Authentication in Cellular Networks -- 5.1.3 IMSI Catching Attack -- 5.1.4 Enhanced Privacy Protection in 5G Networks -- 5.1.5 Location Privacy Algorithms -- 5.1.6 Location Privacy Considered Model -- 5.1.7 Location Privacy Tested Approach -- 5.2 Location Security -- 5.2.1 Location Security in 4G/5G Networks -- 5.2.2 Threat Models and Bounds -- 5.2.2.1 Formal Model -- 5.2.2.2 Error Model for the Spoofing Attack -- 5.2.2.3 Threat Model Example Case Study: Range‐Based Localization Using RSSI -- 5.2.2.4 Error Bound Under Spoofing Attack -- 5.2.2.5 Case Study -- 5.3 3GPP Integrity Support -- References -- Part II Location‐based Analytics and New Services -- Chapter 6 Location and Analytics for Verticals -- 6.1 People‐Centric Analytics -- 6.1.1 Crowd Mobility Analytics -- 6.1.1.1 Introduction and Related Work -- 6.1.1.2 Example Experimental Results from Crowd Mobility Analytics: Group Inference -- 6.1.2 Flow Monitoring -- 6.1.2.1 Introduction and Related Work -- 6.1.2.2 Selected DL Approaches and Results for Trajectory Prediction -- 6.1.3 COVID-19 Contact Tracing -- 6.1.3.1 Introduction and Related Work -- 6.1.3.2 Selected Approach and Example Results from Contact Tracing -- 6.2 Localization in Road Safety Applications -- 6.2.1 Safety‐Critical Use Cases and 5G Position‐Related Requirements -- 6.2.1.1 Introduction and Related Work -- 6.2.1.2 Example Results for Safety‐Critical Use Cases -- 6.2.2 Upper Layers Architecture in ETSI ITS Standard -- 6.2.2.1 Introduction and Related Work -- 6.2.2.2 Example Results for ITS -- 6.2.3 5G Automotive Association (5GAA) Activities -- References -- Chapter 7 Location‐Aware Network Management -- 7.1 Introduction.7.2 Location‐Aware Cellular Network Planning -- 7.2.1 What Is the Cellular Network Planning? -- 7.2.2 Why Is Localization Important in the Planning Phase? -- 7.2.3 Location‐Aware Cellular Network Planning -- 7.2.4 Future Directions -- 7.3 Location‐Aware Network Optimization -- 7.3.1 What Is the Cellular Network Optimization? -- 7.3.2 Why Is Location Information Important in Optimization? -- 7.3.3 Hybrid Clustering‐Based Optimization of 5G Mobile Networks -- 7.3.3.1 Clustering Methods and Algorithmic Approach -- 7.3.3.2 Results and Conclusions -- 7.3.4 Location‐Aware Capacity and Coverage Optimization -- 7.3.4.1 Dual‐Connectivity Optimization -- 7.3.4.2 Results and Conclusions -- 7.3.5 SINR Prediction in Presence of Correlated Shadowing in Cellular Networks -- 7.3.5.1 SINR Prediction with Kriging -- 7.3.5.2 Results and Conclusions -- 7.3.5.3 Multi‐user (MU) Scheduling Enhancement with Geolocation Information and Radio Environment Maps (REMs) -- 7.3.5.4 Results and Conclusions -- 7.3.6 Social‐Aware Load Balancing System for Crowds in Cellular Networks -- 7.3.6.1 Social‐Aware Fuzzy Logic Controller (FLC) Power Traffic Sharing (PTS) Control -- 7.3.6.2 Results and Conclusions -- 7.3.7 Future Directions -- 7.4 Location‐Aware Network Failure Management -- 7.4.1 What Is the Cellular Network Failure Management? -- 7.4.2 Why Is Localization Important in Failure Management? -- 7.4.3 Contextualized Indicators -- 7.4.3.1 Contextualized Indicators -- 7.4.3.2 Results and Conclusions -- 7.4.4 Location‐Based Deep Learning Techniques for Network Analysis -- 7.4.4.1 Synthetic mages and Deep‐Learning Classification -- 7.4.4.2 Results and Conclusions -- References -- Part III Architectural Aspects for Localization and Analytics -- Chapter 8 Location‐Based Analytics as a Service -- 8.1 Motivation for a Dedicated Platform -- 8.2 Principles.8.2.1 Microservice Architectural Approach -- 8.2.2 Software Containerization -- 8.2.3 Mixed Kappa and Lambda Data Lake Approach -- 8.2.4 Designing an ML‐ and AI‐Aware Solution -- 8.2.5 Abstracting Computation Optimization Processes -- 8.2.6 Automating Dependency Resolution and Linking -- 8.2.7 Achieving Low Latency End‐to‐End -- 8.2.8 Decoupling Processing and API Access -- 8.2.9 Offering Dynamic Resource Allocation -- 8.2.10 Decoupling Services and Security -- 8.3 Platform System Overview -- 8.4 Platform System Blocks Description -- 8.4.1 API Blocks -- 8.4.2 Control Blocks -- 8.4.3 Core Blocks -- 8.4.4 Virtualization Management and Infrastructure Blocks -- 8.5 Functional Decomposition -- 8.5.1 Data Collection Functions -- 8.5.2 Persistence and Message Queue Functions -- 8.5.3 Positioning and Analytics Functions -- 8.5.3.1 Positioning Functions -- 8.5.3.2 Analytics Functions -- 8.5.4 Security and Privacy Functions -- 8.5.4.1 Security Functions -- 8.5.4.2 Privacy Functions -- 8.5.5 Analytics API Functions -- 8.5.6 Control Functions -- 8.5.7 Management, Orchestration, and Virtualization Functions -- 8.6 System Workflows and Data Schema Analysis -- 8.6.1 System Workflows -- 8.6.1.1 Service Activation -- 8.6.1.2 Service Consumption -- 8.6.1.3 Southbound Data Collection -- 8.6.1.4 Positioning and Analytics Service Operation -- 8.6.2 Applicable Data Schema -- 8.6.2.1 GeoJSON Data Format -- 8.6.2.2 JSON SQL Table Schema Format -- 8.6.2.3 3GPP Location Input Data -- 8.7 Platform Implementation: Available Technologies -- 8.7.1 Access Control Module -- 8.7.2 Service Discovery Module -- 8.7.3 API Gateway and Service Subscription Module -- 8.7.4 Data Operations Controller -- 8.7.5 ML Pipeline Controller -- 8.7.6 ML Model Repository -- 8.7.7 Data Collection Module -- 8.7.8 Data Persistence Module -- 8.7.9 Message Queue -- 8.7.10 Virtualization layer.8.7.11 Management and Orchestration."Location information is a pivotal service of 5G-and-beyond cellular networks and will enable a plethora of new location-dependent use cases. Since Release 16, the 3rd Generation Partnership Program (3GPP) is enhancing 5G networks and devices with localization functionalities targeting a very high level of location. Besides the localization of users, there is a growing interest in location-based analytics - the analysis of the location and behavior of people and objects in public areas, roads, and buildings - through dedicated infrastructures or by relying on user devices. While closely related, location-based analytics are not a mere extension of user equipment localization, but rather a new paradigm that enables a large variety of scenarios and applications"--Provided by publisher.Wireless localizationLocation-based services5G mobile communication systemsWireless communication systemsWireless localization.Location-based services.5G mobile communication systems.Wireless communication systems.621.38456Bartoletti StefaniaBlefari-Melazzi NicolaMiAaPQMiAaPQMiAaPQBOOK9910830459503321Positioning and Location-Based Analytics in 5G and Beyond4020852UNINA03161nam 2200721Ia 450 991096364590332120200520144314.09786612426889978128242688712824268859780226736570022673657110.7208/9780226736570(CKB)2550000000001597(EBL)471908(OCoLC)550640864(SSID)ssj0000336562(PQKBManifestationID)11929279(PQKBTitleCode)TC0000336562(PQKBWorkID)10282189(PQKB)11201787(MiAaPQ)EBC471908(DE-B1597)535762(OCoLC)748208889(DE-B1597)9780226736570(Au-PeEL)EBL471908(CaPaEBR)ebr10354022(CaONFJC)MIL242688(Perlego)1852824(EXLCZ)99255000000000159719890418d1967 uy 0engur||#||||||||txtrdacontentcrdamediacrrdacarrierThe deer and the tiger a study of wildlife in India /George B. Schaller1st ed.Chicago University of Chicago Press19671 online resource (370 pages) illustrations, mapsDescription based upon print version of record.9780226736310 0226736318 9780226736334 0226736334 Includes bibliography: p. 337-355.Frontmatter --Contents --Illustrations --Part I. Introduction, Study Locations, and Methods --Part II. The Hoofed Animals --Part III. The Predators --Appendix A --References Cited --Acknowledgments --IndexThe Deer and the Tiger is Schaller's detailed account of the ecology and behavior of Bengal tigers and four species of the hoofed mammals on which they prey, based on his observations in India's Kanha National Park. "This book is a treasure house of biological information and it is also a delight to read. . . . Excellent phoographs accompany the text."-Robert K. Enders, American Scientist "The one book that has been my greatest source of inspiration is The Deer and the Tiger by George Schaller, based on the first ever scientific field study of the tiger. . . . This book is written by a scientist, but speaks from the heart. . . . It reveals startling information on feeding habitats, territorial behaviour, and the nuances that make up the language of the forest; you become totally immersed in the world of the tiger. . . . For all of us who work in tiger conservation, this book is the bible."-Valmik Thapar, BBC WildlifeMammalsBehaviorTigerDeerIndiaMammalsBehavior.Tiger.Deer599.650954Schaller George B204039MiAaPQMiAaPQMiAaPQBOOK9910963645903321The deer and the tiger4365845UNINA