Eemil Lagerspetz

TL;DR: BeTelGeuse is an extensible data collection platform for mobile devices, which also automatically infers higher level context from sensor data and its impact on mobile phone performance is evaluated.

TL;DR: A research vision of real-time massive scale air quality sensing that integrates tens of thousands or even millions of air quality sensors to monitor air quality at fine spatial and temporal resolution is presented.

TL;DR: This paper presents the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices, and indicates that the application set does serve as an inexpensive method for identifying the pool of devices on which more expensive monitoring and analysis mechanisms should be deployed.

TL;DR: The rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques is surveyed and open research challenges are identified and present directions for future research.

TL;DR: In this article, the authors present the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices and find that the malware infection rate in Android devices estimated using two malware datasets (0.28% and 0.26%), though small, are significantly higher than the previous independent estimate.