SenSocial

Abstract

Smartphone sensing enables inference of physical context, while online social networks (OSNs) allow mobile applications to harness users' interpersonal relationships. However, OSNs and smartphone sensing remain disconnected, since obstacles, including the synchronization of mobile sensing and OSN monitoring, inefficiency of smartphone sensors, and privacy concerns, stand in the way of merging the information from these two sources.In this paper we present the design, implementation and evaluation of SenSocial, a middleware that automates the process of obtaining and joining OSN and physical context data streams for the development of ubiquitous computing applications. SenSocial enables instantiation, management and aggregation of context streams from multiple remote devices. Through micro-benchmarks we show that SenSocial successfully and efficiently captures OSN and mobile sensed data streams. We developed two prototype applications in order to evaluate our middleware and we demonstrate that SenSocial significantly reduces the amount of programming effort needed for building social sensing applications.

Insights – Future Implications of Passive Smartphone Sensing in the Therapeutic Context

“…The smartphone is one possibility to collect behavioral data in everyday life [Alvarez-Lozano et al, 2014;Canzian and Musolesi, 2015;LiKamWa et al, 2013;Mehrotra et al, 2014;Saeb et al, 2015;Suhara et al, 2017]. Currently, around 36% of individuals all over the world and 81% of Germans own at least one smartphone, which is used by youths and young adults for an average of 162 min a day [Montag et al, 2015].…”

Crowdsensing solutions for urban pollution monitoring using smartphones

“…Our analysis has shown that most studied proposals have addressed energy efficiency issues, while only some of them have introduced mechanisms to mitigate security and privacy concerns. In fact, we find that only a few solutions [38,113,2,83,134] actually account for both privacy and energy efficiency issues. We now proceed to discuss these prominent solutions in more detail.…”

“…Figure 2.2 shows the number of crowdsensing-related proposal search on different impact journal and conference, and that mobile crowdsensing applications have experienced a significant increase in the last five years. Specifically, researchers have focused their efforts on various areas including environment monitoring [105,108,113,145], transportation and urban sensing [6,26,67,75,117], healthcare [54,78,100,128,155], social issues [21,49,83,92,130], and others [38,97,103,135,146]. The different crowdsensing proposals available are characterized by having different designs, and involve different architectural levels.…”

“…SenSocial [83] has a module for privacy management control which allows managing policies regarding the type and level of granularity of sensed data, deciding what will be stored and made available to the different middleware components. SenSocial uses filtering rules for maintaining energy efficiency, thereby restricting transmissions only to those cases passing the set of defined rules.…”

“…In particular, CAROM [146] uses a plugin where, among other functionalities, it incorporates Open Mobile Miner (WMO), which is an open-source solution that allows performing data analysis on the mobile terminal. Similarly, SenSocial [83] uses a plugin providing an agent able to retrieve data from both Facebook and Twitter, and its process is based on joining Online Social Networks (OSNs) that provide a physical context data stream. In addition, we found that few solutions include a broker functionality.…”