Zobrazeno 1 - 10
of 1 018
pro vyhledávání: '"mobile crowd sensing"'
Publikováno v:
ICT Express, Vol 10, Iss 1, Pp 111-117 (2024)
Internet of Things (IoT) is getting growing interest to offer great opportunities in combination with Mobile Crowd Sensing for real-time applications. Existing approaches motivate mobile workers (MWs) for approaching the distant locations to receive
Externí odkaz:
https://doaj.org/article/f8e652e38169427ba4f87897dad7ca02
Publikováno v:
IEEE Access, Vol 12, Pp 84387-84400 (2024)
With the rapid development of mobile networks and widespread use of mobile devices, there is an increasing focus on assigning location-based tasks to mobile users in the context of Mobile Crowd Sensing (MCS). One of the primary challenges in MCS is t
Externí odkaz:
https://doaj.org/article/6a7099094e0d4b84a7314587b398538e
Publikováno v:
Mathematical Biosciences and Engineering, Vol 20, Iss 7, Pp 11998-12023 (2023)
Sparse mobile crowd sensing saves perception cost by recruiting a small number of users to perceive data from a small number of sub-regions, and then inferring data from the remaining sub-regions. The data collected by different people on their respe
Externí odkaz:
https://doaj.org/article/e53b7975816346ce927a52c79b723b2a
Publikováno v:
Sensors, Vol 24, Iss 7, p 2353 (2024)
Mobile crowdsensing (MCS) systems rely on the collective contribution of sensor data from numerous mobile devices carried by participants. However, the open and participatory nature of MCS renders these systems vulnerable to adversarial attacks or da
Externí odkaz:
https://doaj.org/article/c293ce0d3a7e41f89e77a29c78862f6d
Publikováno v:
Intelligent Systems with Applications, Vol 20, Iss , Pp 200291- (2023)
This paper introduces a comprehensive strategy for heterogeneously allocating tasks, aiming to optimize mobile crowd sensing through the use of fuzzy logic and thus achieving superior coverage quality. We employed a deep learning method to address th
Externí odkaz:
https://doaj.org/article/4f8b4d48085d4c3bbd53980a29121bf4
Publikováno v:
IEEE Access, Vol 11, Pp 140325-140347 (2023)
Due to the growing capabilities of mobile phones and devices, mobile crowd sensing (MCS) is rapidly gaining popularity among researchers in different fields, given its ability to collect data at scale and low cost. MCS is particularly important in th
Externí odkaz:
https://doaj.org/article/fbb477f5628d4e4a977d7634f05a53af
Publikováno v:
IEEE Access, Vol 11, Pp 134074-134086 (2023)
Due to the widespread use of mobile intelligent terminal devices, Mobile Crowd Sensing (MCS) applications have gained significant research attention. However, ensuring users privacy remains a critical challenge, as it can hinder users’ willingness
Externí odkaz:
https://doaj.org/article/7309f8f4bcbd40588b95333f17975921
Autor:
Quan T. Ngo, Seokhoon Yoon
Publikováno v:
IEEE Access, Vol 11, Pp 92353-92364 (2023)
Opportunistic worker (OW) selection is a challenging problem in mobile crowd sensing (MCS), where tasks are assigned to individuals to be completed seamlessly during their daily routines without any deviation from their usual routes. In this paper, w
Externí odkaz:
https://doaj.org/article/cda9ec0650074e70aa254e59c18fcf78
Publikováno v:
IEEE Access, Vol 11, Pp 13349-13369 (2023)
Pedestrian-based mobile sensing enables a large number of urban-centric use cases in the areas of intelligent mobility, smart city, and crowd management. With increasing standardization in Vehicle-to-Everything (V2X) communication to increase localiz
Externí odkaz:
https://doaj.org/article/2a5c47f48901428db8d5bf07b7dc25e8
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