You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
In the past few years, the use of smartphones has increased exponentially, and so have the capabilities of such devices. Together with an increase in raw processing power, modern smartphones are equipped with a wide variety of sensors and expose an extensive set of API (Accessible Programming Interface). These capabilities allow us to extract a wide spectrum of data that ranges from information about the environment (e.g., position, orientation) to user habits (e.g., which apps she uses and when), as well as about the status of the operating system itself (e.g., memory, network adapters). This data can be extremely valuable in many research fields such as user authentication, intrusion detection and detection of information leaks. For these reasons, researchers need to use a solid and reliable logging tool to collect data from mobile devices. In this paper, we first survey the existing logging tools available on the Android platform, comparing the features offered by different tools and their impact on the system, and highlighting some of their shortcomings. Then, we present DELTA - Data Extraction and Logging Tool for Android, which improves the existing Android logging solutions in terms of flexibility, fine-grained tuning capabilities, extensibility, and available set of logging features. We performed a full implementation of DELTA and we run a thorough evaluation on its performance. The results show that our tool has low impact on the performance of the system, on battery consumption, and on user experience. Finally, we make the DELTA source code and toolset available to the research community.

Cited Abstract(s):
[1]: We present ohmage, a mobile to web platform that records, analyzes, and visualizes data from both prompted experience samples entered by the user, as well as continuous streams of data passively collected from sensors onboard the mobile device. ohmage has been used in a number of research health studies. Key challenges in these deployments are engaging participants to sustain data collection in long-lived campaigns, conserving battery power, and extracting accurate inferences from the collected streams. To address these challenges, we have incorporated feedback from hundreds of behavioral and technology researchers, focus group participants, and end-users of the system in an iterative design process. We summarize this rich feedback, and present the resulting system.
An approach similar to is implemented by , presented by in [1] . It is an Android tool designed to present the user with interactive surveys and self-monitoring tasks depending on triggers such as time and location. This tool is also capable of automatically collecting sensor data from the device, such as accelerometer, GPS, WiFi, microphone audio recordings and cell towers logs. This data is meant to give context in order to better understand the user's behavior, activities and surroundings, and is uploaded to a central server for analysis. We did not include it in our comparison due to its limited scope when it comes to the number and variety of logged data sources. Ohmage is also a tool designed with user interaction in mind, while ours is a passive tool that does not interfere with the user's activities.