Visualizing, clustering, and predicting the behavior of museum visitors

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Fine-arts museums design exhibitions to educate, inform and entertain visitors. Existing work leverages technology to engage, guide and interact with the visitors, neglecting the need of museum staff to understand the response of the visitors. Surveys and expensive observational studies are currently the only available data source to evaluate visitor behavior, with limits of scale and bias. In this paper, we explore the use of data provided by low-cost mobile and fixed proximity sensors to understand the behavior of museum visitors. We present visualizations of visitor behavior, and apply both clustering and prediction techniques to the collected data to show that group behavior can be identified and leveraged to support the work of museum staff.

Science Direct | Paper

Authors: Claudio Martella, Armando Miraglia, Jeana Frost, Marco CattaniMaarten van Steen
Research group:  Large-scale Distributed Systems group, VU University Amsterdam and Department of Communication Sciences at VU University Amsterdam and Embedded Software group, Delft University of Technology
Journal: Pervasive and Mobile Computing
Year: 2016

Demo: Gondola – a Parametric Robot Infrastructure for Repeatable Mobile Experiments

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When deploying a testbed infrastructure for Wireless Sensor Networks (WSNs), one of the most challenging features is to provide repeatable mobility. Wheeled robots, usually employed for such tasks, strive to adapt to the wide range of environments where WSNs are deployed, from chaotic office spaces to neatly raked potato fields. For this reason, wheeled robots often require an expensive customization step in order to adapt, for example, their localization and navigation systems to the specific environment. To avoid this issue, we present Gondola, a parametric robot infrastructure based on pulling wires, rather than wheels. Gondola avoids the most common problems of wheeled robots and easily adapts to many WSNs’ scenarios. Different from existing solutions, Gondola can easily move in 3-dimensional space, with no need of a complex localization system and with an accuracy that is comparable to o-the-shelf traditional robots.

Acm Digital Library | Paper  | Code and schematics on GitHub

Authors: Marco Cattani and Ioannis Protonotarios
Research groups:  Embedded Software group, Delft University of Technology
Conference: 14th ACM Conf. on Embedded Networked Sensor Systems (SenSys)
Year: 2016

PhD thesis: Opportunistic Communication in Extreme Wireless Sensor Networks

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Sensor networks can nowadays deliver 99.9% of their data with duty cycles below 1%. This remarkable performance is, however, dependent on some important underlying assumptions: low traffic rates, medium size densities and static nodes.
In this thesis, we investigate the performance of these same resource-constrained devices, but under scenarios that present extreme conditions: high traffic rates, high densities and mobility: the so-called Extreme Wireless Sensor Networks (EWSNs).

Full textPropositions

Staffetta: Smart Duty-Cycling for Opportunistic Data Collection

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Recent opportunistic routing protocols address the problem of efficient data collection in dynamic wireless sensor networks, where the radio is duty cycled to save energy and
the topology changes due to link dynamics and node mobility.
Unlike traditional approaches that maintain a routing structure (e.g., a tree), opportunistic schemes forward packets to the first neighbor that wakes up, thereby reducing latency and energy consumption and increasing the robustness to network dynamics.
Notwithstanding the many advantages, this paper argues that existing opportunistic protocols do not fully exploit the synergy between duty cycling and routing in that all nodes wake up with the same fixed frequency. Instead, we present Staffetta, a duty-cycle scheduling mechanism that dynamically adjusts each node’s wake-up frequency in a distributed fashion, such that nodes closer to the sink are more active than nodes at the edge of the network. The resulting activity gradient effectively biases the forwarding choices of opportunistic mechanisms towards the sink, as the time to find an awake neighbor is shorter in the direction of the sink. We implement Staffetta in Contiki, and evaluate it in combination with three different opportunistic routing mechanisms
on two testbeds. Our results show that, across the board, Staffetta reduces packet latency by an order of magnitude while halving the energy consumption at minimal overhead.
In some cases the impact is even more profound. Compared to using ORW alone, adding Staffetta can lead to network lifetimes that are six times longer and end-to-end packet deliveries that are 450 times faster. In other words, by smart duty cycling, Staffetta improves opportunistic data collection basically for free.

Acm Digital Library | Paper | Code on GitHub | Presentation

Authors: Marco Cattani and Andreas Loukas and Marco Zimmerling and Marco A. Zuniga and Koen G. Langendoen
Research groups:  Embedded Software group, Delft University of Technology and  Networked Embedded Systems Group, TU Dresden
Conference: 14th ACM Conf. on Embedded Networked Sensor Systems (SenSys)
Year: 2016