Couplable Components for Data Processing in Mobile Sensing Campaigns Daniel Maya-Zapata, Iván R. Félix, Luis A. Castro ( ✉ ) , Luis-Felipe Rodríguez, and Manuel Domitsu Sonora Institute of Technology (ITSON), Ciudad Obregon, Mexico daniel_maya94@hotmail.com, rogelio.felix@gmail.com, luis.castro@acm.org, {luis.rodriguez,manuel.domitsu}@itson.edu.mx Abstract. In mobile sensing, modern phones allow scientists obtain the infor‐ mation about the participants and their surroundings. At times, obtaining raw sensor data from mobile devices demands their collection through sensing campaigns. Often, processing these data requires data processing components in the mobile device. Some of the data processing components pertain to mathe‐ matical functions that can be reused to form other functions. These types of func‐ tions are usually crafted at a design stage by the programmers. In this work, we present a novel way in which components can be coupled at the design of the sensing campaign, without the need to redeploy the app. That is, scientists can couple two existing data processing components into a new, high-level compo‐ nent. The results of this paper can facilitate code re-use, code maintenance, and flexibility to a mobile sensing campaign. Keywords: Mobile phone sensing · Sensing platform · Data processing components · Couplable components 1 Introduction When doing research, acquiring data from experiments or fieldwork could take a consid‐ erable amount of effort depending on what is needed. Mobile phones have become one of the preferred tools to collect data about users’ behavior and their surroundings through sensing campaigns. A sensing campaign is a planned enterprise for collecting data from end users, typically through a research protocol. A sensing campaign defines what data are to be captured and when. That is, what sensors are needed, how and when these sensors will be used, and if there are going to be some processing of the collected data in the mobile device. Mobile sensing has been used in different domains. For instance in health, they have promoted wellbeing [1, 2], and monitored the quality of the sleep [3]; in psychology to gather personality traits [4]; in urbanity to help create maps of noise pollution [4], map potholes or problematic traffic areas [5], and finally, in human computer interaction to create better ways to interact with the smartphone [6, 7]. Most of the works mentioned earlier usually construct a mobile software tool to fulfill the sensing campaign to be © Springer International Publishing AG 2017 S.F. Ochoa et al. (Eds.): UCAmI 2017, LNCS 10586, pp. 299–304, 2017. DOI: 10.1007/978-3-319-67585-5_31