LoRa is a low-power wide-area network solution that is recently gaining popularity in the context of the Internet of Things due to its ability to handle massive number of devices. One of the main challenges faced by LoRa implementations is the allocation of Spreading Factors to the devices. While the assignment of these parameters is virtually simple to execute, scalability and complexity issues hint at its implementation through a game theoretic approach. This would offer the advantage of being readily implementable in vast networks of devices with limited hardware capabilities. Hence, we formulate the SF allocation problem as a Bayesian game, of which we compute the Bayesian Nash equilibria. We also implement the procedure in the ns-3 network simulator and evaluate the resulting performance, showing that our approach is scalable and robust, and also offers room for improvement with respect to existing approaches.

We study seven fitness trackers and their associated smartphone apps from a wide variety of manufacturers, and record who they are talking to. Our results suggest that some of them communicate with unexpected third parties, including social networks, advertisement websites, weather services, and various external APIs. This implies that such unanticipated third-parties may glean personal information of users.

We propose a data-driven secure wireless communication scheme, in which the goal is to transmit a signal to a legitimate receiver with minimal distortion, while keeping some information about the signal private from an eavesdropping adversary. When the data distribution is known, the optimal trade-off between the reconstruction quality at the legitimate receiver and the leakage to the adversary can be characterised in the information theoretic asymptotic limit. In this paper, we assume that we do not know the data distribution, but instead have access to a dataset, and we are interested in the finite blocklength regime rather than the asymptotic limits. We propose a data-driven adversarially trained deep joint source-channel coding architecture, and demonstrate through experiments with CIFAR-10 dataset that it is possible to transmit to the legitimate receiver with minimal end-to-end distortion while concealing information on the image class from the adversary.

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Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral and psychological patterns due to challenges in collecting and releasing such datasets, including waning user engagement or privacy considerations. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n = 71 participants. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71 M rows of data. The participants contributed their data through validated surveys, ecological momentary assessments, and a Fitbit Sense smartwatch and consented to make these data available to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data will open novel research opportunities and potential applications in multiple disciplines.

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