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Data-Centric Mobile Crowdsensing [PDF]
IEEE Transactions on Mobile Computing, 2018 Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages the diverse embedded sensors in massive mobile devices. A key objective in MCS is to efficiently schedule mobile users to perform multiple sensing tasks. Prior work mainly focused on interactions between the task-layer and the user-layer, without considering tasks' similar data ...
Changkun Jiang +3 more
openaire +2 more sources
FairCs—Blockchain-Based Fair Crowdsensing Scheme using Trusted Execution Environment
Sensors, 2020 Crowdsensing applications provide platforms for sharing sensing data collected by mobile devices. A blockchain system has the potential to replace a traditional centralized trusted third party for crowdsensing services to perform operations that involve ...
Yihuai Liang, Yan Li, Byeong-Seok Shin
doaj +1 more source
A Novel Quality and Reliability-Based Approach for Participants’ Selection in Mobile Crowdsensing
IEEE Access, 2019 With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events.
May El Barachi +3 more
doaj +1 more source
Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
Future Internet, 2020 Recently, crowdsensing, which can provide various sensing services using consumer mobile devices, is attracting considerable attention. The success of these services depends on active user participation and, thus, a proper incentive mechanism is ...
Mihui Kim, Junhyeok Yun
doaj +1 more source
A people-oriented paradigm for smart cities [PDF]
, 2017 Most works in the literature agree on considering the Internet of Things (IoT) as the base technology to collect information related to smart cities.
A Sheth +12 more
core +1 more source
Federated Crowdsensing: Framework and Challenges
CoRR, 2020 Crowdsensing is a promising sensing paradigm for smart city applications (e.g., traffic and environment monitoring) with the prevalence of smart mobile devices and advanced network infrastructure. Meanwhile, as tasks are performed by individuals, privacy protection is one of the key issues in crowdsensing systems.
Leye Wang, Han Yu 0001, Xiao Han 0001
openaire +2 more sources
Frontiers in Aging Neuroscience, 2017 For understanding the heterogeneity of tinnitus, large samples are required. However, investigations on how samples recruited by different methods differ from each other are lacking.
Thomas Probst +12 more
doaj +1 more source
A Socially-Aware Incentive Mechanism for Mobile Crowdsensing Service Market
, 2018 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.
Luo, Jun +4 more
core +1 more source
Human in the Loop: Distributed Deep Model for Mobile Crowdsensing [PDF]
, 2018 With the proliferation of mobile devices, crowdsensing has become an appealing technique to collect and process big data. Meanwhile, the rise of fifth generation wireless systems, especially the new cellular base stations with computing ability, brings ...
DONG Mianxiong, LI Liangzhi, OTA Kaoru
core +2 more sources
When Crowdsensing Meets Federated Learning: Privacy-Preserving Mobile Crowdsensing System
CoRR, 2021 Mobile crowdsensing (MCS) is an emerging sensing data collection pattern with scalability, low deployment cost, and distributed characteristics. Traditional MCS systems suffer from privacy concerns and fair reward distribution. Moreover, existing privacy-preserving MCS solutions usually focus on the privacy protection of data collection rather than ...
Bowen Zhao 0001 +2 more
openaire +2 more sources