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008			230323s2023 si | o |||| 0|eng d
020			_a9789811990069
024	7		_a10.1007/978-981-19-9006-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.59 _b2023 EB
100	1		_aXiang, Chaocan _eautor _0(orcid)0000-0002-1473-6006 _1https://orcid.org/0000-0002-1473-6006 _4http://id.loc.gov/vocabulary/relators/aut _9689526
245	1	0	_aMulti-dimensional Urban Sensing Using Crowdsensing Data _cby Chaocan Xiang, Panlong Yang, Fu Xiao, Xiaochen Fan
250			_a1st ed 2023
264		1	_aSingapore _bSpringer Nature _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_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.
988			_aSpringer_Computer_2023
650		7	_2embne _9476230 _aInformática móvil
700	1		_9689892 _aYang, Panlong _eautor
700	1		_9689893 _aXiao, Fu _eautor
700	1		_9689894 _aFan, Xiaochen _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-9006-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _ek _zSI