Abstract
This paper focuses on a novel software module that allows agents running on smart appliances to estimate their location in the physical environment thanks to an underlying ranging technology and a specific localization algorithm. The proposed module is an add-on of the AMUSE platform which allows agents to estimate their position in the physical environment and to have it readily available as a specific game element in the scope of location-aware games. The module first acquires range estimates between the appliance where the agent is running and the access points of the WiFi network, and then it properly processes such range estimates using a localization algorithm. In order to prove the validity of the proposed approach, we show experimental results obtained in an illustrative indoor scenario where four access points have been accurately positioned. The position estimates of the appliance are obtained by applying the Two-Stage Maximum-Likelihood localization algorithm to the range estimates from the four access points. According to the results presented in this paper, the proposed agent-based localization approach guarantees sufficiently accurate position estimates for many indoor applications.
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Bergenti, F., Monica, S. (2016). Location-Aware Social Gaming with AMUSE. In: Demazeau, Y., Ito, T., Bajo, J., Escalona, M. (eds) Advances in Practical Applications of Scalable Multi-agent Systems. The PAAMS Collection. PAAMS 2016. Lecture Notes in Computer Science(), vol 9662. Springer, Cham. https://doi.org/10.1007/978-3-319-39324-7_4
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DOI: https://doi.org/10.1007/978-3-319-39324-7_4
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