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EWIDS (Very large wireless sensor networks for well-being)


This Extreme Wireless Distributed Systems (EWiDS) project takes its inspiration from managing and controlling large crowds of people. Its uniqueness lies in the way observations take place. Traditionally, crowds are monitored through a few tens of cameras placed at strategic locations, leading to global, external observations. In this project we assume that thousands of sensing devices are present inside a crowd, leading to local, internal observations that are not geographically restricted to a specific instrumented area. Such a sensing device is typically a smartphone (now owned by many people) or a low-cost electronic badge. By harnessing devices people already own we would be able to monitor crowds without large infrastructure investments. Our approach however needs to overcome extreme networking conditions that have not been tackled before: large scalability, extreme high densities, mobility and real-time performance.

The scientific ICT challenge for EWiDS is to automatically extracting and analyzing the evolving structure of large groups of (autonomous) mobile entities, be they people as in crowds, tagged cargo as in logistics or bicycles in a city. By detecting which of sensor devices are close to each other, and when, we obtain a picture of the group from within the group; a picture that continuously changes as sensor nodes move: a proximity graph.  An important specific ICT challenge is transferring massively distributed continuous streams of low-level, highly faulty radio-based proximity detections from very simple sensor nodes to an external analysis system, transforming those streams into a highly reliable and continuously up-to-date view on dynamic proximity graphs.

Within the proximity graphs, scientific questions arise: Can we (automatically) discover lanes, clogging, or other structural properties? Is it possible to discover conversing groups or other socially related structures? And, given feedback from users, can we say anything about the mood in (parts of) a crowd?

Biggest results so far

Wireless crowd monitoring in Arnhem

Wifi-tuin Arnhem



Measuring crowd densities for safety and efficiency

View presentation by clicking on this image


Involved COMMIT/partners: Van Mierlo, VU, TUDelft.