LEADER 04152nam 2200553 450 001 996546827503316 005 20230730235825.0 010 $a981-19-9006-9 024 7 $a10.1007/978-981-19-9006-9 035 $a(MiAaPQ)EBC7219076 035 $a(Au-PeEL)EBL7219076 035 $a(OCoLC)1373984772 035 $a(DE-He213)978-981-19-9006-9 035 $a(PPN)269096582 035 $a(EXLCZ)9926309469900041 100 $a20230730d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMulti-dimensional urban sensing using crowdsensing data /$fChaocan Xiang [and three others] 205 $a1st ed. 2023. 210 1$aSingapore :$cSpringer, Springer Nature Singapore Pte Ltd,$d[2023] 210 4$dİ2023 215 $a1 online resource (204 pages) 225 1 $aData Analytics,$x2520-1867 311 08$aPrint version: Xiang, Chaocan Multi-Dimensional Urban Sensing Using Crowdsensing Data Singapore : Springer Singapore Pte. Limited,c2023 9789811990052 320 $aIncludes bibliographical references. 327 $aChapter 1. Incentivizing Platform-users with Win-Win Effects -- Chapter 2. Task recommendation Based on Big Data Analysis -- Chapter 3. Data Transmission Empowered by Edge Computing -- Chapter 4 Environmental Protection Application---Urban Pollution Monitoring.-Chapter 5. Urban Traffic Application---Traffic Volume Prediction -- Chapter 6. Airborne Sensing Application---Reusing Delivery Drones -- Chapter 7. Open Issues and Conclusions. 330 $aIn smart cities, the indispensable devices used in people?s daily lives, such as smartphones, smartwatches, vehicles, and smart buildings, are equipped with more and more sensors. For example, most smartphones now have cameras, GPS, acceleration and light sensors. Leveraging the massive sensing data produced by users? common devices for large-scale, fine-grained sensing in smart cities is referred to as the urban crowdsensing. It can enable applications that are beneficial to a broad range of urban services, including traffic, wireless communication service (4G/5G), and environmental protection. In this book, we provide an overview of our recent research progress on urban crowdsensing. Unlike the extant literature, we focus on multi-dimensional urban sensing using crowdsensing data. Specifically, the book explores how to utilize crowdsensing to see smart cities in terms of three-dimensional fundamental issues, including how to incentivize users? participation, how to recommend tasks, and how to transmit the massive sensing data. We propose a number of mechanisms and algorithms to address these important issues, which are key to utilizing the crowdsensing data for realizing urban applications. Moreover, we present how to exploit this available crowdsensing data to see smart cities through three-dimensional applications, including urban pollution monitoring, traffic volume prediction, and urban airborne sensing. More importantly, this book explores using buildings? sensing data for urban traffic sensing, thus establishing connections between smart buildings and intelligent transportation. Given its scope, the book will be of particular interest to researchers, students, practicing professionals, and urban planners. Furthermore, it can serve as a primer, introducing beginners to mobile crowdsensing in smart cities and helping them understand how to collect and exploit crowdsensing data for various urban applications. 410 0$aData Analytics,$x2520-1867 606 $aElectronic data processing 606 $aMobile computing 606 $aRemote sensing 606 $aSmart cities 615 0$aElectronic data processing. 615 0$aMobile computing. 615 0$aRemote sensing. 615 0$aSmart cities. 676 $a004 700 $aXiang$b Chaocan$01349063 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996546827503316 996 $aMulti-Dimensional Urban Sensing Using Crowdsensing Data$93087012 997 $aUNISA