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001461640 001__ 1461640
001461640 003__ OCoLC
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001461640 0247_ $$a10.1007/978-981-19-9006-9$$2doi
001461640 035__ $$aSP(OCoLC)1373984772
001461640 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dOCLCF
001461640 049__ $$aISEA
001461640 050_4 $$aQA76.59
001461640 08204 $$a004.167$$223/eng/20230329
001461640 24500 $$aMulti-dimensional urban sensing using crowdsensing data /$$cChaocan Xiang, Panlong Yang, Fu Xiao, Xiaochen Fan.
001461640 260__ $$aSingapore :$$bSpringer,$$c2023.
001461640 300__ $$a1 online resource (xiv, 200 pages) :$$billustrations (black and white).
001461640 4901_ $$aData analytics,$$x2520-1867
001461640 500__ $$aBaseline Methods and Evaluation Metrics
001461640 504__ $$aIncludes bibliographical references.
001461640 5050_ $$aIntro -- Preface -- Acknowledgments -- Contents -- Part I: How to Collect Crowdsensing Data (Multi-dimensional Fundamental Issues) -- Chapter 1: Incentivizing Platform-Users with Win-Win Effects -- 1.1 Introduction -- 1.2 Related Work -- 1.3 System Model and Problem Formulation -- 1.3.1 System Model -- 1.3.2 Example of Personalized Bidding Scenario -- 1.3.3 Problem Formalization -- 1.3.4 Analysis of Problem Complexity -- 1.4 Design of Picasso -- 1.4.1 Bid Description in 3-D Space -- PB Description in 3-D Space -- Formal Framework of Bid Description Using 3-D Expressive Space
001461640 5058_ $$aPB Description Method -- Theoretical Analysis -- 1.4.2 Task Allocation Based on Dependency Graph -- Construction of Task Dependency Graph -- PB Decomposition for Efficient Task Allocation -- Problem Transformation by Decomposing the Task Dependency Graph -- Task Allocation with Constant-Factor Approximation -- Theoretical Analysis -- PB Recombination for Strategy-Proof Payment -- Truthful Payment Scheme for Non-PB Based on Critical Prices -- Truthful Payment Scheme for PB Based on Graph Recombination -- Theoretical Analysis -- 1.5 Performance Evaluation of Picasso -- 1.5.1 Simulations
001461640 5058_ $$aSimulation Methodology and Settings -- Results -- 1.5.2 Trace-Driven Case Study of Gigwalk -- Evaluation Methodology and Settings -- Results -- 1.6 Discussion and Future Work -- 1.7 Conclusion -- References -- Chapter 2: Task Recommendation Based on Big Data Analysis -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Motivation -- 2.3.1 Crowdsourcing-Based User Studies -- 2.3.2 Large-Scale Dataset Collection and Analysis -- 2.4 LSTRec Design for MOVE-CS -- 2.4.1 Model Design -- 2.4.2 Research Problem and Challenge Analysis -- 2.5 Key Algorithm Design for LSTRec
001461640 5058_ $$aPick-Up Profit Heatmap Construction -- 2.5.2 Differentiation-Aware Sensing Reward Design -- 2.5.3 Submodularity-Based Task Recommendation Algorithm -- 2.6 Evaluation -- 2.6.1 Emulation Methodology and Settings -- 2.6.2 Results of Model Evaluation -- 2.6.3 Results of Algorithm Evaluation -- 2.7 Conclusion -- References -- Chapter 3: Data Transmission Empowered by Edge Computing -- 3.1 Introduction -- 3.2 Related Work -- 3.3 Motivation -- 3.3.1 Uncovering Missing Data Issue in Large-Scale ITSs -- 3.3.2 Experimental Explorations of Spatio-Temporal Correlations on Traffic Data
001461640 5058_ $$a3.3.3 Implementation Dilemmas for Large-Scale Traffic Recovery -- 3.4 System Model and Problem Formulation -- 3.4.1 System Model of Edge Computing -- 3.4.2 Problem Formalization -- 3.5 System Design -- 3.5.1 Sub-optimal Deployment of Edge Nodes -- Problem Reformulation -- Local Search-Based Suboptimal Deployment -- 3.5.2 Accurate Traffic Data Recovery Based on Low-Rank Theory -- Experimental Analysis of Low-Rank -- Accurate Traffic Recovery Based on Low-Rank Theory -- 3.6 Traces-Based Evaluations -- 3.6.1 Experimental Methodology and Settings -- Dataset and Experimental Methodology
001461640 506__ $$aAccess limited to authorized users.
001461640 520__ $$aIn smart cities, the indispensable devices used in peoples daily lives, such as smartphones, smartwatches, vehicles, and smart buildings, are equipped with more and more sensors. For example, most smartphones now have cameras, GPS, acceleration and light sensors. Leveraging the massive sensing data produced by users common devices for large-scale, fine-grained sensing in smart cities is referred to as the urban crowdsensing. It can enable applications that are beneficial to a broad range of urban services, including traffic, wireless communication service (4G/5G), and environmental protection. In this book, we provide an overview of our recent research progress on urban crowdsensing. Unlike the extant literature, we focus on multi-dimensional urban sensing using crowdsensing data. Specifically, the book explores how to utilize crowdsensing to see smart cities in terms of three-dimensional fundamental issues, including how to incentivize users participation, how to recommend tasks, and how to transmit the massive sensing data. We propose a number of mechanisms and algorithms to address these important issues, which are key to utilizing the crowdsensing data for realizing urban applications. Moreover, we present how to exploit this available crowdsensing data to see smart cities through three-dimensional applications, including urban pollution monitoring, traffic volume prediction, and urban airborne sensing. More importantly, this book explores using buildings sensing data for urban traffic sensing, thus establishing connections between smart buildings and intelligent transportation. Given its scope, the book will be of particular interest to researchers, students, practicing professionals, and urban planners. Furthermore, it can serve as a primer, introducing beginners to mobile crowdsensing in smart cities and helping them understand how to collect and exploit crowdsensing data for various urban applications.
001461640 588__ $$aDescription based on print version record.
001461640 650_0 $$aMobile computing.
001461640 650_0 $$aElectronic data processing.
001461640 650_0 $$aRemote sensing.
001461640 650_0 $$aSmart cities.
001461640 655_0 $$aElectronic books.
001461640 7001_ $$aXiang, Chaocan,$$eeditor.
001461640 7001_ $$aYang, Panlong,$$eeditor.
001461640 7001_ $$aXiao, Fu,$$eeditor.
001461640 7001_ $$aFan, Xiaochen,$$eeditor.
001461640 77608 $$iPrint version:$$aXiang, Chaocan$$tMulti-Dimensional Urban Sensing Using Crowdsensing Data$$dSingapore : Springer Singapore Pte. Limited,c2023$$z9789811990052
001461640 830_0 $$aData analytics.$$x2520-1867
001461640 852__ $$bebk
001461640 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-9006-9$$zOnline Access$$91397441.1
001461640 909CO $$ooai:library.usi.edu:1461640$$pGLOBAL_SET
001461640 980__ $$aBIB
001461640 980__ $$aEBOOK
001461640 982__ $$aEbook
001461640 983__ $$aOnline
001461640 994__ $$a92$$bISE