Zobrazeno 1 - 10
of 1 774
pro vyhledávání: '"crowd sensing"'
Publikováno v:
Digital Communications and Networks, Vol 10, Iss 3, Pp 597-608 (2024)
The past decades have witnessed a wide application of federated learning in crowd sensing, to handle the numerous data collected by the sensors and provide the users with precise and customized services. Meanwhile, how to protect the private informat
Externí odkaz:
https://doaj.org/article/c4368bc380a743dabafcdf320f23dc27
Publikováno v:
Digital Communications and Networks, Vol 10, Iss 3, Pp 645-654 (2024)
The maturity of 5G technology has enabled crowd-sensing services to collect multimedia data over wireless network, so it has promoted the applications of crowd-sensing services in different fields, but also brings more privacy security challenges, th
Externí odkaz:
https://doaj.org/article/c38c72140c524c71a0157871b3506217
Publikováno v:
In Internet of Things January 2025 29
Autor:
Yang, Guangrong a, He, An a, ⁎, Wu, Guangwei a, Zhao, Jianing a, Zhang, Jinhuan b, Liu, Anfeng b
Publikováno v:
In Computer Networks February 2025 257
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:
Big Data Mining and Analytics, Vol 6, Iss 4, Pp 391-403 (2023)
With the rapid development of mobile devices, aggregation security and efficiency topics are more important than past in crowd sensing. When collecting large-scale vehicle-provided data, the data transmitted via autonomous networks are publicly acces
Externí odkaz:
https://doaj.org/article/b522007bc50d401aa40d61b47327440d
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:
Applied Sciences, Vol 14, Iss 11, p 4788 (2024)
In the process of completing large-scale and fine-grained sensing tasks for the new generation of crowd-sensing systems, the role of analysis, reasoning, and decision making based on artificial intelligence has become indispensable. Mobile crowd sens
Externí odkaz:
https://doaj.org/article/3358fc845586405580c21ed3f3db63e4
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