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024 7 _a10.1007/978-981-99-6921-0
_2doi
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072 7 _aCOM051460
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082 0 4 _a004.167
_223
100 1 _aLi, Youqi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aIncentive Mechanism for Mobile Crowdsensing
_h[electronic resource] :
_bA Game-theoretic Approach /
_cby Youqi Li, Fan Li, Song Yang, Chuan Zhang.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXI, 129 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 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _aChapter 1: A Brief Introduction -- Chapter 2: Long-term Incentive Mechanism for Mobile Crowdsensing -- Chapter 3: Fair Incentive Mechanism for Mobile Crowdsensing -- Chapter 4: Collaborative Incentive Mechanism for Mobile Crowdsensing -- Chapter 5: Coopetition-aware Incentive Mechanism for Mobile Crowdsensing -- Chapter 6: Summary.
520 _aMobile crowdsensing (MCS) is emerging as a novel sensing paradigm in the Internet of Things (IoTs) due to the proliferation of smart devices (e.g., smartphones, wearable devices) in people’s daily lives. These ubiquitous devices provide an opportunity to harness the wisdom of crowds by recruiting mobile users to collectively perform sensing tasks, which largely collect data about a wide range of human activities and the surrounding environment. However, users suffer from resource consumption such as battery, processing power, and storage, which discourages users’ participation. To ensure the participation rate, it is necessary to employ an incentive mechanism to compensate users’ costs such that users are willing to take part in crowdsensing. This book sheds light on the design of incentive mechanisms for MCS in the context of game theory. Particularly, this book presents several game-theoretic models for MCS in different scenarios. In Chapter 1, the authors present an overview of MCS and state the significance of incentive mechanism for MCS. Then, in Chapter 2, 3, 4, and 5, the authors propose a long-term incentive mechanism, a fair incentive mechanism, a collaborative incentive mechanism, and a coopetition-aware incentive mechanism for MCS, respectively. Finally, Chapter 6 summarizes this book and point out the future directions. This book is of particular interest to the readers and researchers in the field of IoT research, especially in the interdisciplinary field of network economics and IoT.
650 0 _aMobile computing.
650 0 _aCooperating objects (Computer systems).
650 0 _aData mining.
650 0 _aComputer science
_xMathematics.
650 0 _aMathematical statistics.
650 0 _aAlgorithms.
650 0 _aComputer science.
650 1 4 _aMobile Computing.
650 2 4 _aCyber-Physical Systems.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aDesign and Analysis of Algorithms.
650 2 4 _aTheory and Algorithms for Application Domains.
700 1 _aLi, Fan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aYang, Song.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZhang, Chuan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819969203
776 0 8 _iPrinted edition:
_z9789819969227
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-981-99-6921-0
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c187229
_d187229