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.

Download: http://bit.ly/2jcHCpp

Abstract: Collaborative localization is a special case for knowledge fusion where information is exchanged in order to attain improved global and local knowledge. We propose analytical as well as agent based simulation models for pedestrian dead reckoning (PDR) systems in agents collaborating to improve their location estimate by exchanging subjective position information when two agents are detected close to each other. The basis of improvement is the fact that two agents are at approximately the same position when they meet, and this can be used to update local position information. In analytical models we find that the localization error remains asymptotically finite in infinite systems or when there is at least one immobile agent (i.e. an agent with a zero localization error) in the system. In the agent model we tested finite systems under realistic (that is, inexact) meeting conditions and tested localization errors as function of several parameters. We found that a large finite system comprising hundreds of users is capable of collaborative localization with an essentially constant error under various conditions. The presented models can be used for predicting the improvement in localization that can be achieved by a collaboration among several mobile computers. Besides, our results can be considered as first steps toward a more general collaborative (incremental) form of knowledge fusion.

Citation: Kampis, G., Kantelhardt, J.W, Kloch, K., and Lukowicz, P. (2014): Analytical and Simulation Models for Collaborative Localization, J. Computational Science 6 (2015) 1–10.

Download: http://bit.ly/2jcMvP3

Abstract: Collaborative localization is the computation of improved spatial coordinates in mobile agents based on their physical meetings in a pedestrian dead reckoning (PDR) system. Upon meeting the agents can exchange information about their subjective position and update it based on a simple algorithm. We show in a simulation model that the localization error diverges unless this algorithm is introduced in which case it remains bounded. We consider collaborative localization as an example of broader incremental knowledge fusion and discuss its various implications such as the importance of well-informed agents.

Citation: Kampis, G. and Lukowicz, P. (2014): Collaborative Localization as a Paradigm for Incremental Knowledge Fusion, 5th IEEE CogInfoCom 2014 Conference

Download: http://bit.ly/2ilkZKT

This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: We study simulation models of spreading on peer-to-peer communication networks where any peer (or agent) can be the source of information, be it sensory recognition or contextual knowledge. In such a situation the value or quality of information is of key relevance. Questions of trust, provenance and the problem of the interaction pattern arise and are approached by three different algorithms in our paper: (i) “quantitative democracy”, where knowledge is averaged on a meeting (ii) “experience takes all”, where the more experienced (the teacher) overwrites all prior knowledge of the less experienced (the “student”), and (iii) “transitive experience” where not only information but also experience is handed over. We compare these different regimes and identify their tradeoffs.

Keywords: Trust, provenance, self-organization, emergence, collaborative information processing.

Citation: George Kampis and Paul Lukowicz. Collaborative Activity Recognition.

Download: http://bit.ly/1SQJhad

This book is about understanding, designing, controlling, and governing adaptive collective systems. It is intended for readers from master’s students to Ph.D. students, from engineers to decision makers, and anyone else who is interested in understanding how technologies are changing the way we think and live.

The authors are academics working in various areas of a new rising field: adaptive collective systems.

Stuart Anderson (The University of Edinburgh, United Kingdom | SmartSociety)
Nicolas Bredeche (Université Pierre et Marie Curie, France)
A.E. Eiben (VU University Amsterdam, Netherlands | FoCAS CA)
George Kampis (DFKI, Germany | Allow Ensembles and SmartSociety)
Maarten van Steen (VU University Amsterdam, Netherlands)

Book Sprint collaborative writing session facilitator: Adam Hyde
Editor: Sandra Sarala
Designer: Henrik van Leeuwen

View the FoCAS Book Sprint process with full details and photos here.

Download: http://bit.ly/1TZSwdE