Quality of information in mobile crowdsensing: Survey and research challenges
Smartphones have become the most pervasive devices in people's lives and are clearly
transforming the way we live and perceive technology. Today's smartphones benefit from …
transforming the way we live and perceive technology. Today's smartphones benefit from …
Deepiot: Compressing deep neural network structures for sensing systems with a compressor-critic framework
Recent advances in deep learning motivate the use of deep neutral networks in sensing
applications, but their excessive resource needs on constrained embedded devices remain …
applications, but their excessive resource needs on constrained embedded devices remain …
[HTML][HTML] Veracity assessment of online data
Fake news, malicious rumors, fabricated reviews, generated images and videos, are today
spread at an unprecedented rate, making the task of manually assessing data veracity for …
spread at an unprecedented rate, making the task of manually assessing data veracity for …
A Survey on Truth Discovery: Concepts, Methods, Applications, and Opportunities
In the era of data information explosion, there are different observations on an object (eg, the
height of the Himalayas) from different sources on the web, social sensing, crowd sensing …
height of the Himalayas) from different sources on the web, social sensing, crowd sensing …
A lightweight privacy-preserving truth discovery framework for mobile crowd sensing systems
The recent proliferation of human-carried mobile devices has given rise to the mobile crowd
sensing (MCS) systems. However, the sensory data provided by the participating workers …
sensing (MCS) systems. However, the sensory data provided by the participating workers …
Theseus: Incentivizing truth discovery in mobile crowd sensing systems
The recent proliferation of human-carried mobile devices has given rise to mobile crowd
sensing (MCS) systems that outsource sensory data collection to the public crowd. In order …
sensing (MCS) systems that outsource sensory data collection to the public crowd. In order …
Sensegan: Enabling deep learning for internet of things with a semi-supervised framework
Recent proliferation of Internet of Things (IoT) devices with enhanced computing and
sensing capabilities has revolutionized our everyday life. The massive data from these …
sensing capabilities has revolutionized our everyday life. The massive data from these …
Sadeepsense: Self-attention deep learning framework for heterogeneous on-device sensors in internet of things applications
Deep neural networks are becoming increasingly popular in Internet of Things (IoT)
applications. Their capabilities of fusing multiple sensor inputs and extracting temporal …
applications. Their capabilities of fusing multiple sensor inputs and extracting temporal …
Rdeepsense: Reliable deep mobile computing models with uncertainty estimations
Recent advances in deep learning have led various applications to unprecedented
achievements, which could potentially bring higher intelligence to a broad spectrum of …
achievements, which could potentially bring higher intelligence to a broad spectrum of …
InPPTD: A lightweight incentive-based privacy-preserving truth discovery for crowdsensing systems
Recently, truth discovery in crowdsensing systems has received considerable attention with
its appealing features for extracting truthful information from multiple unreliable data …
its appealing features for extracting truthful information from multiple unreliable data …