65 research outputs found
Experimental Evaluation of Empirical NB-IoT Propagation Modelling in a Deep-Indoor Scenario
Path-loss modelling in deep-indoor scenarios is a difficult task. On one
hand, the theoretical formulae solely dependent on transmitter-receiver
distance are too simple; on the other hand, discovering all significant factors
affecting the loss of signal power in a given situation may often be
infeasible. In this paper, we experimentally investigate the influence of
deep-indoor features such as indoor depth, indoor distance and distance to the
closest tunnel corridor and the effect on received power using NB-IoT. We
describe a measurement campaign performed in a system of long underground
tunnels, and we analyse linear regression models involving the engineered
features. We show that the current empirical models for NB-IoT signal
attenuation are inaccurate in a deep-indoor scenario. We observe that 1) indoor
distance and penetration depth do not explain the signal attenuation well and
increase the error of the prediction by 2-12 dB using existing models, and 2) a
promising feature of average distance to the nearest corridor is identified.Comment: 6 pages, 6 figures, submitted to Globecom2020 conference, Selected
Areas in Communications Symposium, Track on Internet of Things and Smart
Connected Communitie
The Privacy Dependency Thesis and Self-Defense
If I decide to disclose information about myself, this act can undermine other people’s ability to effectively conceal information about themselves. One case in point involves genetic information: if I share ‘my’ genetic information with others, I thereby also reveal genetic information about my biological relatives. Such dependencies are well-known in the privacy literature and are often referred to as ‘privacy dependencies’. Some take the existence of privacy dependencies to generate a moral duty to sometimes avoid sharing information about oneself. If true, we argue, then it is sometimes justified for others to impose harm on the person sharing the information to prevent them from doing so. This is a highly revisionary implication. Hence, one must either endorse a highly revisionary view on what one may do to protect one’s privacy, or one must reject the view that privacy dependencies can be used to justify a moral duty that constrains choices about sharing information about oneself
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