LEADER 05562nam 2200517 450 001 9910510534803321 005 20231113205638.0 010 $a3-030-88743-X 035 $a(MiAaPQ)EBC6812224 035 $a(Au-PeEL)EBL6812224 035 $a(CKB)19919404400041 035 $a(OCoLC)1287131500 035 $a(PPN)258841575 035 $a(EXLCZ)9919919404400041 100 $a20220819d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aConnectivity and edge computing in IoT $ecustomized designs and AI-based solutions /$fJie Gao, Mushu Li, Weihua Zhuang 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (177 pages) 225 1 $aWireless networks (Springer (Firm)) 311 08$aPrint version: Gao, Jie Connectivity and Edge Computing in IoT: Customized Designs and AI-Based Solutions Cham : Springer International Publishing AG,c2022 9783030887421 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Acknowledgements -- Contents -- Acronyms -- 1 Introduction -- 1.1 The Era of Internet of Things -- 1.2 Connectivity in IoT -- 1.3 Edge Computing in IoT -- 1.4 AI in IoT -- 1.5 Scope and Organization of This Book -- References -- 2 Industrial Internet of Things: Smart Factory -- 2.1 Industrial IoT Networks -- 2.2 Connectivity Requirements of Smart Factory -- 2.2.1 Application-Specific Requirements -- 2.2.2 Related Standards -- 2.2.3 Potential Non-Link-Layer Solutions -- 2.2.4 Link-Layer Solutions: Recent Research Efforts -- 2.3 Protocol Design for Smart Factory -- 2.3.1 Networking Scenario -- 2.3.2 Mini-Slot Based Carrier Sensing (MsCS) -- 2.3.3 Synchronization Sensing (SyncCS) -- 2.3.4 Differentiated Assignment Cycles -- 2.3.5 Superimposed Mini-slot Assignment (SMsA) -- 2.3.6 Downlink Control -- 2.4 Performance Analysis -- 2.4.1 Delay Performance with No Buffer -- 2.4.2 Delay Performance with Buffer -- 2.4.3 Slot Idle Probability -- 2.4.4 Impact of SyncCS -- 2.4.5 Impact of SMsA -- 2.5 Scheduling and AI-Assisted Protocol Parameter Selection -- 2.5.1 Background -- 2.5.2 The Considered Scheduling Problem -- 2.5.3 Device Assignment -- 2.5.4 AI-Assisted Protocol Parameter Selection -- 2.6 Numerical Results -- 2.6.1 Mini-Slot Delay with MsCS, SyncCS, and SMsA -- 2.6.2 Performance of the Device Assignment Algorithms -- 2.6.3 DNN-Assisted Scheduling -- 2.7 Summary -- References -- 3 UAV-Assisted Edge Computing: Rural IoT Applications -- 3.1 Background on UAV-Assisted Edge Computing -- 3.2 Connectivity Requirements of UAV-Assisted MEC for Rural IoT -- 3.2.1 Network Constraints -- 3.2.2 State-of-the-Art Solutions -- 3.3 Multi-Resource Allocation for UAV-Assisted Edge Computing -- 3.3.1 Network Model -- 3.3.2 Communication Model -- 3.3.3 Computing Model -- 3.3.4 Energy Consumption Model -- 3.3.5 Problem Formulation. 327 $a3.3.6 Optimization Algorithm for UAV-Assisted EdgeComputing -- 3.3.7 Proactive Trajectory Design Based on Spatial Distribution Estimation -- 3.4 Numerical Results -- 3.5 Summary -- References -- 4 Collaborative Computing for Internet of Vehicles -- 4.1 Background on Internet of Vehicles -- 4.2 Connectivity Challenges for MEC -- 4.2.1 Server Selection for Computing Offloading -- 4.2.2 Service Migration -- 4.2.3 Cooperative Computing -- 4.3 Computing Task Partition and Scheduling for Edge Computing -- 4.3.1 Collaborative Edge Computing Framework -- 4.3.2 Service Delay -- 4.3.3 Service Failure Penalty -- 4.3.4 Problem Formulation -- 4.3.5 Task Partition and Scheduling -- 4.4 AI-Assisted Collaborative Computing Approach -- 4.5 Performance Evaluation -- 4.5.1 Task Partition and Scheduling Algorithm -- 4.5.2 AI-Based Collaborative Computing Approach -- 4.6 Summary -- References -- 5 Edge-Assisted Mobile VR -- 5.1 Background on Mobile Virtual Reality -- 5.2 Caching and Computing Requirements of Mobile VR -- 5.2.1 Mobile VR Video Formats -- 5.2.2 Edge Caching for Mobile VR -- 5.2.3 Edge Computing for Mobile VR -- 5.3 Mobile VR Video Caching and Delivery Model -- 5.3.1 Network Model -- 5.3.2 Content Distribution Model -- 5.3.3 Content Popularity Model -- 5.3.4 Research Objective -- 5.4 Content Caching for Mobile VR -- 5.4.1 Adaptive Field-of-View Video Chunks -- 5.4.1.1 Extended FoV -- 5.4.1.2 Content Types -- 5.4.1.3 Rules for Content Distribution -- 5.4.2 Content Placement on an Edge Cache -- 5.4.3 Placement Scheme for Video Chunks in a VS -- 5.4.4 Placement Scheme for Video Chunks of Multiple VSs -- 5.4.5 Numerical Results -- 5.5 AI-Assisted Mobile VR Video Delivery -- 5.5.1 Content Distribution -- 5.5.2 Intelligent Content Distribution Framework -- 5.5.3 WI-based Delivery Scheduling -- 5.5.4 Reinforcement Learning Assisted Content Distribution. 327 $a5.5.5 Neural Network Structure -- 5.5.6 Numerical Results -- 5.6 Summary -- References -- 6 Conclusions -- 6.1 Summary of the Research -- 6.2 Discussion of Future Directions -- Index. 410 0$aWireless networks (Springer (Firm)) 606 $aInternet of things 606 $aEdge computing 615 0$aInternet of things. 615 0$aEdge computing. 676 $a004.678 700 $aGao$b Jie$f1985-$01433842 702 $aLi$b Mushu 702 $aZhuang$b Weihua 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910510534803321 996 $aConnectivity and edge computing in IoT$93584339 997 $aUNINA