This paper reports our experience with crowd monitoring technologies in the challenging real-world conditions of a modern, open-space museum. We seized the opportunity to use the NEMO science center as a testbed, and studied the effectiveness of neighborhood discovery and density estimation algorithms in a network formed by visitors wearing bracelets emitting RF beacons. The diverse set of conditions (flash crowds in open spaces vs. single person booths) revealed three interesting findings: (i) state-of-the-art density estimation fails in 80% of the cases, (ii) RSS-based classifiers fail too, because their underlying assumptions do not hold in many scenarios, and (iii) neighborhood discovery can obtain exact information in an energy-efficient way, provided that static and mobile nodes are differentiated to filter out “passers by” clobbering the true popularity of an exhibit. The overall lesson from the experiment is that today’s algorithms are quite far from the ideal of monitoring popularity in a privacy-preserving and energy-efficient way with minimal infrastructure across the set of heterogeneous conditions encountered in practice.@en

A large fraction of indoor localization methods rely on anchor nodes that periodically transmit their coordinates using radio signals. Mobile nodes then use the received information to decode their own locations. For all these methods to work, the underlying assumption is that anchors should send their beacons at different times, i.e. the beacons should not collide. We propose a radically new approach for indoor localization: to overlap the transmissions of beacons (synchronized collisions). Our collision-based method leverages the capture effect, which states that when several radio signals collide, only the strongest (nearest) signal is detected. Compared to the state of the art, our simple change of perspective -from non-colliding to colliding beacons- provides two important advantages. First, the lifetime of the mobile nodes can be increased by three orders of magnitude (from days to years). Second, our method is more resilient to external interfering sources, such as WiFi stations. In this work-in-progress, we (i) provide a preliminary evaluation of our prototype, and (ii) describe the challenges that we are currently working on to produce a fully-fleshed commercial system. While indoor localization is a very active research area, to the best of our knowledge, we are the first ones to evaluate a collision-based approach.

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