Biblio

Bluetooth Low Energy is a fast growing protocol which has gained wide acceptance during last years. Key features for this growth are its high data rate and its ultra low energy consumption, making it the perfect candidate for piconets. However, the lack of expandability without serious impact on its energy consumption profile, prevents its adoption on more complex systems which depend on long network lifetime. Thus, a lot of academic research has been focused on the solution of BLE expandability problem and BLE mesh has been introduced on the latest Bluetooth version. In our point of view, most of the related work cannot be efficiently implemented in networks which are mostly comprised of constrained-resource nodes. Thus, we propose a new energy efficient tree algorithm for BLE static constrained-resources networks, which achieves a longer network lifetime by both reducing as much as possible the number of needed connection events and balancing the energy dissipation in the network.

Nowadays we are witnessing an unprecedented evolution in how we gather and process information. Technological advances in mobile devices as well as ubiquitous wireless connectivity have brought about new information processing paradigms and opportunities for virtually all kinds of scientific and business activity. These new paradigms rest on three pillars: i) numerous powerful portable devices operated by human intelligence, ubiquitous in space and available, most of the time, ii) unlimited environment sensing capabilities of the devices, and iii) fast networks connecting the devices to Internet information processing platforms and services. These pillars implement the concepts of crowdsourcing and collective intelligence. These concepts describe online services that are based on the massive participation of users and the capabilities of their devices.in order to produce results and information which are "more than the sum of the part". The EU project Privacy Flag relies exactly on these two concepts in order to mobilize roaming citizens to contribute, through crowdsourcing, information about risky applications and dangerous web sites whose processing may produce emergent threat patterns, not evident in the contributed information alone, reelecting a collective intelligence action. Crowdsourcing and collective intelligence, in this context, has numerous advantages, such as raising privacy-awareness among people. In this paper we summarize our work in this project and describe the capabilities and functionalities of the Privacy Flag Platform.