A. Nordio

F. Paonessa, G. Virone, S. Kianoush et al.

This paper empirically investigates the body induced electromagnetic (EM) effects, namely the human body blockage, by conducting indoor measurement campaigns in the unexplored sub-THz W-band (75-110 GHz) and G-band (170-260 GHz). The proposed analysis focuses on both the alterations of channel frequency response induced by body presence, fully or partially obstructing the line-of-sight (LoS) between transmitter and recevier, as well as on the channel impulse response (CIR) for selected movements of the target, i.e. crossing the LoS of the radio link. Modelling of large scale parameters is also presented using a phantom body object. The proposed study has applications in device-free radio localization and radio frequency (RF) sensing scenarios where the EM radiation or environmental radio signals are collected and processed to detect and locate people without requiring them to wear any electronic devices. Although preliminary, the study reveals that discrimination of the blockage micro-movements is possible, achieving higher precision compared to classical RF sensing and localization using cm-scale wavelengths (2.4-6GHz bands).

A. Tarable, A. Nordio, E. Leonardi et al.

This paper presents the first systematic investigation of the potential performance gains for crowd work systems, deriving from available information at the requester about individual worker reputation. In particular, we first formalize the optimal task assignment problem when workers' reputation estimates are available, as the maximization of a monotone (submodular) function subject to Matroid constraints. Then, being the optimal problem NP-hard, we propose a simple but efficient greedy heuristic task allocation algorithm. We also propose a simple "maximum a-posteriori" decision rule and a decision algorithm based on message passing. Finally, we test and compare different solutions, showing that system performance can greatly benefit from information about workers' reputation. Our main findings are that: i) even largely inaccurate estimates of workers' reputation can be effectively exploited in the task assignment to greatly improve system performance; ii) the performance of the maximum a-posteriori decision rule quickly degrades as worker reputation estimates become inaccurate; iii) when workers' reputation estimates are significantly inaccurate, the best performance can be obtained by combining our proposed task assignment algorithm with the message-passing decision algorithm.