# Personal Dynamic Cost-Aware Sensing for Latent Context Detection

## TL;DR

This paper introduces a dynamic, cost-aware sensing approach for mobile devices that balances energy consumption and context accuracy using machine learning and optimization techniques.

## Contribution

It presents a novel method that adaptively determines sensor sampling policies based on context, predicted information loss, and sampling costs, improving over static approaches.

## Key findings

- Outperforms static sensing methods in energy efficiency and accuracy.
- Balances information loss and energy consumption effectively.
- Demonstrates superiority over state-of-the-art dynamic sensing methods.

## Abstract

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.