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Blockchain is a technology making the shared registry concept from distributed systems a reality for a number of application domains, from the cryptocurrency one to potentially any industrial system requiring decentralized, robust, trusted and automated decision making in a multistakeholder situation. Nevertheless, the actual advantages in using blockchain instead of any other traditional solution (such as centralized databases) are not completely understood to date, or at least there is a strong need for a vademecum guiding designers toward the right decision about when to adopt blockchain or not, which kind of blockchain better meets use-case requirements, and how to use it. In this article we aim at providing the community with such a vademecum, while giving a general presentation of blockchain that goes beyond its usage in Bitcoin and surveying a selection of the vast literature that emerged in the last few years. We draw the key requirements and their evolution when passing from permissionless to permissioned blockchains, presenting the differences between proposed and experimented consensus mechanisms, and describing existing blockchain platforms.

International audienceIn this paper, we propose a human-based model which builds a trust relationship between nodes in an ad hoc network. The trust is based on previous individual experiences and on the recommendations of others. We present the Recommendation Exchange Protocol (REP) which allows nodes to exchange recommendations about their neighbors. Our proposal does not require disseminating the trust information over the entire network. Instead, nodes only need to keep and exchange trust information about nodes within the radio range. Without the need for a global trust knowledge, our proposal scales well for large networks while still reducing the number of exchanged messages and therefore the energy consumption. In addition, we mitigate the effect of colluding attacks composed of liars in the network. A key concept we introduce is the relationship maturity, which allows nodes to improve the efficiency of the proposed model for mobile scenarios. We show the correctness of our model in a single-hop network through simulations. We also extend the analysis to mobile multihop networks, showing the benefits of the maturity relationship concept. We evaluate the impact of malicious nodes that send false recommendations to degrade the efficiency of the trust model. At last, we analyze the performance of the REP protocol and show its scalability. We show that our implementation of REP can significantly reduce the number messages

Nowadays, the huge worldwide mobile-phone penetration is increasingly turning the mobile network into a gigantic ubiquitous sensing platform, enabling large-scale analysis and applications. Recently, mobile data-based research reached important conclusions about various aspects of human mobility patterns. But how accurately do these conclusions reflect the reality? To evaluate the difference between reality and approximation methods, we study in this paper the error between real human trajectory and the one obtained through mobile phone data using different interpolation methods (linear, cubic, nearest interpolations) taking into consideration mobility parameters. Moreover, we evaluate the error between real and estimated load using the proposed interpolation methods. From extensive evaluations based on real cellular network activity data of the state of Massachusetts, we show that, with respect to human trajectories, the linear interpolation offers the best estimation for sedentary people while the cubic one for commuters. Another important experimental finding is that trajectory estimation methods show different error regimes whether used within or outside the "territory" of the user defined by the radius of gyration. Regarding the load estimation error, we show that by using linear and cubic interpolation methods, we can find the positions of the most crowded regions ("hotspots") with a median error lower than 7%.