Abstract: We propose a novel approach for Ad-Hoc WiFi based distribution of information within large crowds of mobile users. The work is motivated by civil protection scenarios where infrastructure based communication often breaks down in cases of emergency. We follow a basic opportunistic networking approach by making use of the smartphones’ built-in WiFi hotspot functionality which in combination with the devices switching between access point and client modes facilitates the propagation of messages on a multi-hop basis. We make three contributions with respect to previous work on this topic. First, we empirically determine core boundary conditions given by the performance of modern smartphones. To maximize system performance under such circumstances we propose novel heuristics for a mode switching strategy based on client mobility instead of random strategies that have mainly been utilized so far. Finally, we compare its performance to a random role switching strategy in a large-scale simulation based on a real dataset consisting of movement traces from 28’000 people during a three day festival in Zurich. Within the simulation we investigate the influence of various parameters on the system’s behavior.

Citation: Franke, T., Negele, S., Kampis, G. and Lukowicz, P. (2015): Leveraging Human Mobility in Smartphone Based Ad-Hoc Information Distribution in Crowd Management Scenarios, submitted to MobiSys 2015.

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Abstract: In this work, we show how a smart-phone worn unobtrusively in a nurses coat pocket can be used to document the patient care activities performed during a regular morning routine. The main contribution is to show how, taking into account certain domain specific boundary conditions, a single sensor node worn in such an (from the sensing point of view) unfavorable location can still recognize complex, sometimes subtle activities. We evaluate our approach in a large real life dataset from day to day hospital operation. In total, 4 runs of patient care per day were collected for 14 days at a geriatric ward and annotated in high detail by following the performing nurses for the entire duration. This amounts to over 800 hours of sensor data including acceleration, gyroscope, compass, wifi and sound annotated with groundtruth at less than 1min resolution.

Citation: Gernot Bahle, Agnes Gruenerbl, Enrico Bignotti, Mattia Zeni, Fausto Giunchiglia and Paul Lukowicz (2014): “Recognizing Hospital Care Activities with a Pocket Worn Smartphone”, 6th International Conference on Mobile Computing, Applications and Services (MobiCASE 2014)

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