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Crowd sensing is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices to collect data. To achieve good quality for crowd sensing, incentive mechanisms are indispensable to attract more participants. Most of existing…

计算机科学与博弈论 · 计算机科学 2014-05-01 Jiajun Sun

Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive mechanisms are…

计算机科学与博弈论 · 计算机科学 2014-03-24 Jiajun Sun

Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect and upload sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive…

网络与互联网体系结构 · 计算机科学 2014-12-25 Jiajun Sun

Crowd sensing is a new paradigm that leverages pervasive sensor-equipped mobile devices to provide sensing services like forensic analysis, documenting public spaces, and collaboratively constructing statistical models. Extensive user…

社会与信息网络 · 计算机科学 2015-05-26 Jiajun Sun

Vehicular mobile crowd sensing is a fast-emerging paradigm to collect data about the environment by mounting sensors on vehicles such as taxis. An important problem in vehicular crowd sensing is to design payment mechanisms to incentivize…

计算机科学与博弈论 · 计算机科学 2018-09-17 Susu Xu , Weiguang Mao , Yue Cao , Hae Young Noh , Nihar B. Shah

Recently, a novel class of incentive mechanisms is proposed to attract extensive users to truthfully participate in crowd sensing applications with a given budget constraint. The class mechanisms also bring good service quality for the…

计算机科学与博弈论 · 计算机科学 2014-10-21 Jiajun Sun

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware…

计算机科学与博弈论 · 计算机科学 2017-01-09 Haiming Jin , Lu Su , Klara Nahrstedt

Mobile Crowd Sensing (MCS) is a new paradigm which takes advantage of pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive mechanisms are…

计算机科学与博弈论 · 计算机科学 2014-04-10 Dong Zhao , Huadong Ma , Liang Liu

Mobile crowd sensing (MCS) is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices such as smartphones, music players, and in-vehicle sensors at the edge of the Internet, to collect data. The new paradigm will fuel…

网络与互联网体系结构 · 计算机科学 2014-10-01 Jiajun Sun

Networking on white spaces (i.e., locally unused spectrum) relies on active monitoring of spectrum usage. Spectrum databases based on empirical radio propagation models are widely adopted but shown to be error-prone, since they do not…

网络与互联网体系结构 · 计算机科学 2017-05-23 Xuhang Ying , Sumit Roy , Radha Poovendran

This paper evaluates the benefit of integrating vehicle-based mobile crowd-sensing tasks into the ride-hailing system through the collaboration between the data user and the ride-hailing platform. In such a system, the ride-hailing platform…

最优化与控制 · 数学 2024-07-02 Shenglin Liu , Qian Ge , Ke Han , Daisuke Fukuda , Takao Dantsuji

Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In…

计算机科学与博弈论 · 计算机科学 2018-08-14 Jiangtian Nie , Zehui Xiong , Dusit Niyato , Ping Wang , Jun Luo

Mobile Crowd Sensing (MCS) is the mechanism wherein people can contribute in data collection process using their own mobile devices which have sensing capabilities. Incentives are rewards that individuals get in exchange for data they…

计算机科学与博弈论 · 计算机科学 2025-07-11 Jowa Yangchin , Ningrinla Marchang

Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile crowdsensing system, it is paramount to incentivize smartphone users to provide…

网络与互联网体系结构 · 计算机科学 2018-05-01 Francesco Restuccia , Pierluca Ferraro , Simone Silvestri , Sajal K. Das , Giuseppe Lo Re

We consider a requester who acquires a set of data (e.g. images) that is not owned by one party. In order to collect as many data as possible, crowdsourcing mechanisms have been widely used to seek help from the crowd. However, existing…

计算机科学与博弈论 · 计算机科学 2020-02-28 Wen Zhang , Yao Zhang , Dengji Zhao

Crowdsensing, also known as participatory sensing, is a method of data collection that involves gathering information from a large number of common people (or individuals), often using mobile devices or other personal technologies. This…

计算机科学与博弈论 · 计算机科学 2024-05-17 Chattu Bhargavi , Vikash Kumar Singh

Mobile crowdsourced sensing (MCS) is a new paradigm which takes advantage of the pervasive smartphones to efficiently collect data, enabling numerous novel applications. To achieve good service quality for a MCS application, incentive…

计算机科学与博弈论 · 计算机科学 2013-06-25 Dong Zhao , Xiang-Yang Li , Huadong Ma

Currently, explosive increase of smartphones with powerful built-in sensors such as GPS, accelerometers, gyroscopes and cameras has made the design of crowdsensing applications possible, which create a new interface between human beings and…

计算机与社会 · 计算机科学 2019-01-04 Yufeng Zhan , Yuanqing Xia , Jiang Zhang , Ting Li , Yu Wang

Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces…

社会与信息网络 · 计算机科学 2016-11-15 Pin-Yu Chen , Shin-Ming Cheng , Pai-Shun Ting , Chia-Wei Lien , Fu-Jen Chu

Mobile Crowd Sensing (MCS) is a new paradigm of sensing, which can achieve a flexible and scalable sensing coverage with a low deployment cost, by employing mobile users/devices to perform sensing tasks. In this work, we propose a novel MCS…

计算机科学与博弈论 · 计算机科学 2017-08-29 Xiaoru Zhang , Lin Gao , Bin Cao , Zhang Li , Mengjing Wang
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