… points through mobile sensing. This study investigated three mobile sensing strategies to …
This paper builds upon previous mobile sensing studies to investigate the impact of sensing …

… a machine-learning-based mobile air pollution sensing … a combination of fixed and mobile
AQ sensors, we perform spatial … with ubiquitous sensing that relies primarily on fixed sensors. …

… models using machine learning techniques and data obtained from smartphone sensors
including … Consideration of multiple sensors is beneficial in that even without using GPS, the …

… In Section II, we discuss the system model for mobile spectrum sensing. In Section III, we
develop a Bayesian learning model to capture spatial-temporal correlation in the data. …

… if machine learning models can be trained and validated across multiple mobile sensing
longitudinal … We leveraged data from two longitudinal mobile sensing studies: a clinical study of …

… [137] developed a software accelerator for accelerating the execution of deep learning
models within mobile devices. We present a survey of some mobile machine learning …

… Although a growing literature examines detection of an increasingly broad range of behavioral
markers using mobile phone sensors, we describe the work on detection of sleep, social …

… reality of sensing apps. A strong candidate for such fundamental advances in how mobile
sensor data is processed is deep learning; an emerging area of machine learning that has …

… The rest of this paper is organized as follows: In Section II, we review earlier work on hazardous
spot detection with mobile sensing and cooperative frameworks under wireless networks…

… but denser measurements taken by mobile sensors carried by concerned citizens and … a
machine learning model that combines sparse fixedstation data with dense mobile sensor data …