Results 71 to 80 of about 1,414,604 (271)
A Survey of Sparse Mobile Crowdsensing: Developments and Opportunities
IEEE Open Journal of the Computer Society, 2022 Sparse mobile crowdsensing (SMCS) has emerged as a promising sensing paradigm for urban sensing, leveraging the spatial and temporal correlation among data sensed in distinct sub-areas to cut sensing expenses dramatically. It intelligently selects only a
Shiting Zhao +5 more
doaj +1 more source
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
, 2015 Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an unprecedented scale ...
Belkin, Mikhail +4 more
core +1 more source
Towards a Data‐Driven Digital Twin AI‐Based Architecture for Self‐Driving Vehicles
IET Intelligent Transport Systems, Volume 19, Issue 1, January/December 2025.ABSTRACT Recent advancements on digital technologies, particularly artificial intelligence, have been resulted into remarkable transformations in automobile industry. One of these technologies is artificial intelligence (AI). AI plays a key role in the development of autonomous vehicles. In this paper, the role of AI in autonomous vehicle (AV) platform
Parinaz Babaei +3 more
wiley +1 more source
CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing
, 2017 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.
Jin, Haiming, Nahrstedt, Klara, Su, Lu
core +1 more source
Ensemble Transformer–Based Detection of Fake and AI–Generated News
Applied Computational Intelligence and Soft Computing, Volume 2025, Issue 1, 2025.The proliferation of fake online and AI–generated news content poses a significant threat to information integrity. This work leverages advanced natural language processing, machine learning, and deep learning algorithms to effectively detect fake and AI–generated content.
Md. Ishraquzzaman +4 more
wiley +1 more source
MAIM: A Novel Incentive Mechanism Based on Multi-Attribute User Selection in Mobile Crowdsensing
IEEE Access, 2018 In the user selection phase of mobile crowdsensing, most existing incentive mechanisms focus on either single-attribute selection or random selection, which possibly lead to serious consequences such as low user enthusiasm, decreased task completion rate,
Jinbo Xiong +5 more
doaj +1 more source
CSWA: Aggregation-Free Spatial-Temporal Community Sensing
, 2017 In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea of the target area, without ...
Bian, Jiang +3 more
core +1 more source
International Journal of Intelligent Systems, Volume 2025, Issue 1, 2025.Ambient temperature estimation plays a vital role in various domains, including environmental monitoring, smart cities, and energy‐efficient systems. Traditional sensor‐based methods suffer from high deployment costs and limited scalability, while centralized machine learning approaches raise significant privacy concerns.
Saeid Zareie +3 more
wiley +1 more source
Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy
IEEE Access, 2018 In recent years, the widespread prevalence of smart devices has created a new class of mobile Internet of Thing applications. Called mobile crowdsensing, these techniques use workers with mobile devices to collect data and send it to task requester for ...
Mengmeng Yang +3 more
doaj +1 more source
Applied Sciences, 2022 Mobile crowdsensing (MCS) has recently emerged as an urban-sensing paradigm that takes advantage of smartphone sensing capabilities and user mobility.
Dat Van Anh Duong, Seokhoon Yoon
doaj +1 more source