  In the past decade, the usage of mobile devices has gone far beyond simple
activities like calling and texting. Today, smartphones contain multiple
embedded sensors and are able to collect useful sensing data about the user and
infer the user's context. The more frequent the sensing, the more accurate the
context. However, continuous sensing results in huge energy consumption,
decreasing the battery's lifetime. We propose a novel approach for cost-aware
sensing when performing continuous latent context detection. The suggested
method dynamically determines user's sensors sampling policy based on three
factors: (1) User's last known context; (2) Predicted information loss using
KL-Divergence; and (3) Sensors' sampling costs. The objective function aims at
minimizing both sampling cost and information loss. The method is based on
various machine learning techniques including autoencoder neural networks for
latent context detection, linear regression for information loss prediction,
and convex optimization for determining the optimal sampling policy. To
evaluate the suggested method, we performed a series of tests on real-world
data recorded at a high-frequency rate; the data was collected from six mobile
phone sensors of twenty users over the course of a week. Results show that by
applying a dynamic sampling policy, our method naturally balances information
loss and energy consumption and outperforms the static approach.% We compared
the performance of our method with another state of the art dynamic sampling
method and demonstrate its consistent superiority in various measures. %Our
methods outperformed, and were able to improve we achieved better results in
either sampling cost or information loss, and in some cases we improved both.
