LEADER 12067nam 2200565 450 001 9910830459503321 005 20231013234542.0 010 $a1-119-91144-3 010 $a1-119-91146-X 035 $a(MiAaPQ)EBC30764568 035 $a(Au-PeEL)EBL30764568 035 $a(EXLCZ)9928432842900041 100 $a20231013d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aPositioning and Location-Based Analytics in 5G and Beyond /$fedited by Stefania Bartoletti and Nicola Blefari Melazzi 205 $aFirst edition. 210 1$aHoboken, New Jersey :$cJohn Wiley & Sons, Inc.,$d[2024] 210 4$dİ2024 215 $a1 online resource (291 pages) 311 08$aPrint version: Bartoletti, Stefania Positioning and Location-Based Analytics in 5G and Beyond Newark : John Wiley & Sons, Incorporated,c2023 9781119911432 320 $aIncludes bibliographical references and index. 327 $aCover -- 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. 327 $a3.1 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. 327 $a4.3.2 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. 327 $a7.2 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. 327 $a8.2.1 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. 327 $a8.7.11 Management and Orchestration. 330 $a"Location information is a pivotal service of 5G-and-beyond cellular networks and will enable a plethora of new location-dependent use cases. Since Release 16, the 3rd Generation Partnership Program (3GPP) is enhancing 5G networks and devices with localization functionalities targeting a very high level of location. Besides the localization of users, there is a growing interest in location-based analytics - the analysis of the location and behavior of people and objects in public areas, roads, and buildings - through dedicated infrastructures or by relying on user devices. While closely related, location-based analytics are not a mere extension of user equipment localization, but rather a new paradigm that enables a large variety of scenarios and applications"--$cProvided by publisher. 606 $aWireless localization 606 $aLocation-based services 606 $a5G mobile communication systems 606 $aWireless communication systems 615 0$aWireless localization. 615 0$aLocation-based services. 615 0$a5G mobile communication systems. 615 0$aWireless communication systems. 676 $a621.38456 702 $aBartoletti$b Stefania 702 $aBlefari-Melazzi$b Nicola 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830459503321 996 $aPositioning and Location-Based Analytics in 5G and Beyond$94020852 997 $aUNINA LEADER 01039nam a2200301 i 4500 001 991003346089707536 005 20250414090122.0 008 010514s1990 it er 001 0dita d 020 $a884203553X 035 $ab11147696-39ule_inst 035 $aPARLA180117$9ExL 040 $aBibl. Dip.le Aggr. Studi Umanistici - Sez. Filosofia$bita$dSocioculturale Scs 041 0 $aita 082 04$a530.0924$223 100 1 $aMamiani, Maurizio$046238 245 10$aIntroduzione a Newton /$cdi Maurizio Mamiani 260 $aRoma ;$aBari :$bLaterza,$c1990 300 $a147 p. :$bill. ;$c18 cm 490 1 $aI filosofi ;$v52 600 14$aNewton, Isaac$xBiografie 600 14$aNewton, Isaac$xFisica 830 2$aI filosofi ;$v52 907 $a.b11147696$b21-09-06$c28-06-02 912 $a991003346089707536 945 $aLE005IF XVII F 23$g1$i2005000268526$lle005$o-$pE0.00$q-$rl$s-$t0$u1$v0$w1$x0$y.i11288656$z28-06-02 996 $aIntroduzione a Newton$9871245 997 $aUNISALENTO 998 $ale005$b01-01-01$cm$da$e-$fita$git$h0$i1