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Economics of Peer-to-Peer Mobile Crowdsensing
2015 IEEE Global Communications Conference (GLOBECOM), 2014Mobile crowdsensing is a new sensing paradigm relying on computation and storage capabilities of mobile devices. However, traditional server-client mobile crowdsensing models suffer from a high operational cost on the server, and hence a poor scalability.
Changkun Jiang +3 more
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Investigating mobile crowdsensing application performance
Proceedings of the third ACM international symposium on Design and analysis of intelligent vehicular networks and applications, 2013Mobile Crowdsensing (MCS) is an emerging distributed paradigm lying at the intersection between the Internet of Things and the volunteer/crowd-based approach. MCS applications are usually deployed on contributing nodes such as smart devices and mobiles, equipped by sensing resources that sample the physical environment and provide the sensed data, once
DISTEFANO, SALVATORE +2 more
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When Mobile Crowdsensing Meets Privacy
IEEE Communications Magazine, 2019Mobile crowdsensing (MCS) has now become an effective paradigm to collect massive data for various sensing applications. However, the interactions between mobile users and the platform, and the data release to third parties, pose severe challenges of privacy leakage for MCS systems, such as the leakage of users' identities and locations.
Zhibo Wang 0001 +6 more
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QoS Assessment of Mobile Crowdsensing Services
Journal of Grid Computing, 2015© 2015, Springer Science+Business Media Dordrecht. The wide spreading of smart devices drives to develop distributed applications of increasing complexity, attracting efforts from both research and business communities. Recently, a new volunteer contribution paradigm based on participatory and opportunistic sensing is affirming in the Internet of ...
DISTEFANO, SALVATORE +2 more
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Preserving privacy in mobile crowdsensing
International Journal of Sensor Networks, 2022Bayan Hashr Alamri +4 more
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Mobile Crowdsensing with Mobile Agents: JAAMAS Extended Abstract
International Joint Conference on Autonomous Agents and Multiagent Systems, 2016Mobile crowdsensing applications can be designed as multi-agent systems with campaign-specific roles, realized as mobile agents, and role-based interactions. Mobile agents facilitate decentralized and autonomous crowdsesing campaign execution that takes into account dynamic resource availability in the system.
Teemu Leppänen +4 more
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Sensing Interpolation Strategies for a Mobile Crowdsensing Platform
2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), 2017Mobile Crowd Sensing (MCS) allows an efficient collection of heterogeneous data over large areas, leveraging on the cooperation of MCS subscribers that offer services on their smartphones to this purpose. However, the coverage that a MCS platform can provide for a given area depends on the availability of subscribers and on their mobility in that area.
Michele Girolami +4 more
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A Stack4Things-based platform for mobile crowdsensing services
2016 ITU Kaleidoscope: ICTs for a Sustainable World (ITU WT), 2016© 2016 International Telecommunication Union.As mobiles grow pervasive in people's lives and expand their reach, Mobile CrowdSensing (MCS) and similar paradigms are going to play an ever more prominent role. There is a pressing need then to ease developers and service providers in embracing the opportunity, and that means offering a platform for such ...
DISTEFANO, SALVATORE +4 more
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Smart Parking by Mobile Crowdsensing
International Journal of Smart Home, 2016An increasing number of mobile applications aim to realize “smart cities” by utilizing contributions from citizens armed with mobile devices like smartphones. However, there are few generally recognized guidelines for developing and deploying crowdsourcingbased solutions in mobile environments.
Xiao Chen, Nianzu Liu
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Offloading Surrogates Characterization via Mobile Crowdsensing
Proceedings of the First ACM Workshop on Mobile Crowdsensing Systems and Applications, 2017This paper uses data mining of a mobile crowdsensed dataset of passive WiFi scans to define attributes that can characterize a chaotic WiFi deployment with respect to offloading opportunities. Besides indicators of signal quality, we define indicators of contact windows and contact opportunities with an Access Point (AP). We apply k-means clustering to
Emanuel Lima +2 more
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