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020 _a9789811990069
_9978-981-19-9006-9
024 7 _a10.1007/978-981-19-9006-9
_2doi
050 4 _aQA76.59
072 7 _aUMS
_2bicssc
072 7 _aCOM051460
_2bisacsh
072 7 _aUMS
_2thema
082 0 4 _a004.167
_223
100 1 _aXiang, Chaocan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMulti-dimensional Urban Sensing Using Crowdsensing Data
_h[electronic resource] /
_cby Chaocan Xiang, Panlong Yang, Fu Xiao, Xiaochen Fan.
250 _a1st ed. 2023.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2023.
300 _aXIV, 200 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aData Analytics,
_x2520-1867
505 0 _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.
520 _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.
650 0 _aMobile computing.
650 0 _aComputer networks .
650 0 _aCloud Computing.
650 1 4 _aMobile Computing.
650 2 4 _aComputer Communication Networks.
650 2 4 _aCloud Computing.
700 1 _aYang, Panlong.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aXiao, Fu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aFan, Xiaochen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811990052
776 0 8 _iPrinted edition:
_z9789811990076
776 0 8 _iPrinted edition:
_z9789811990083
830 0 _aData Analytics,
_x2520-1867
856 4 0 _uhttps://doi.org/10.1007/978-981-19-9006-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c177834
_d177834