# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 7 Conclusions and Future Work

Table 7: Comparison between MASU and other frameworks. perform distributed pervasive data sensing, both in an autonomous and collaborative way. The design of the framework includes the definition of different roles that can be played by a group of devices that are performing collaborative sensing activities. Developers of this type of applications can take advantage of this framework, by reusing these services and reducing thus the risks and effort involved in the development process. The framework was evaluated using both simulations and empirical tests involving a software prototype. Both types of evaluations provided various insights on the performance of the framework under diverse hardware, networking and mobility conditions. The simulation results demonstrated that the social nature of human interaction can benefit the performance of the framework, since people's mobility patterns contribute to have high availability of the information sensed by the mobile devices. In addition, the simulations helped us determine the costs of the collaborative sensing, in terms of network utilization. We also showed how Mobile Ad hoc Networks (MANETs) provide an interesting communication support that provides higher network connectivity due to their multi-hop features. Consequently, we demonstrated that MANETs can also contribute to the accessibility of the information sensed. Nevertheless, the simulations also revealed that MANETs have a slightly higher cost (in terms of number of control messages), but a considerably higher cost in terms of data messages due to the unicast nature of the communication, as well as due to the higher node connectivity. These facts could derive in scalability problems of the framework when using MANETs as communication support. Using the prototype, we evaluated the Sensing Tier of the framework. We showed the benefits of using the framework to perform collaborative sensing in terms of energy savings and social welfare in comparison to pure phone sensing, achieving a 43% energy savings for the overall system (Section 5.4.3). We also evaluated the costs associated with the use of the framework for collaborative sensing in terms of hardware resources of the participating devices. This evaluation considered some device heterogeneity since we used smartphone and tablets with diverse hardware specifications. Results from both types of evaluation methods provided empirical evidence on the usefulness of the proposed framework. These results provided valuable insights about the benefits of the proposed framework to support pervasive sensing in terms of resource optimization as well as about its limitations and associated costs. The obtained results in both evaluation experiences show that the services provided by the framework are suitable for supporting distributed pervasive data sensing, and also contributes to keep low the energy consumption of the mobile devices involved in the process. As a next step, we consider to develop a variety of these applications to assess more in-depth the impact of the MASU framework. This work has also been partially supported by Fondecyt (Chile), grant 1150252; by the Spanish Ministry of Science and Innovation (MCI) and FEDER funds of the EU under the contract TIN2013-47245-C2-1-R, by the European Community through the project Community Networks Testbed for the Future Internet (CONFINE): FP7-288535, and also by the Generalitat de Catalunya as a Consolidated Research Group 2014-SGR-881. The development and evaluation of the MASU framework were led by [PERSON], [PERSON] and [PERSON]. [PERSON], [PERSON] and [PERSON] evaluated the framework design and proposed improvements already implemented in the platform. Moreover, these last three authors supported the design of the MASU evaluation process, and the analysis and reporting of the results. The authors declare no conflict of interest.