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Decentralized Movement Analysis

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Computational Movement Analysis

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

Abstract

This chapter investigates the implications of decentralized spatial computing for Computational Movement Analysis (CMA). As more and more moving objects are permanently connected to some communication network and to each other, movement analysis is no longer limited to desktop computers collecting movement data first and then analyzing it. By contrast, networked and communicating agents start analyzing information about their movement in a decentralized but collaborative way. This chapter illustrates decentralized spatial analysis concepts for the CMA tasks of monitoring network flow in transportation systems, movement pattern mining, point clustering, and privacy-aware location-based services.

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Notes

  1. 1.

    This book is solely about the analysis of movement data. Even though the distinction between data capture and data analysis gets occasionally a bit blurred in this chapter, there are important aspects of wireless sensor networks involving movement that are not covered in this chapter. For example, target tracking, that is in essence the capturing of raw positional data of moving objects, is not covered. Also information routing, another wireless sensor network classic, contributes to setting up and maintaining the network infrastructure, but is not considered analysis. Readers interested in such issues are referred to the introductory text on wireless sensor networks in Zhao and Guibas (2004).

  2. 2.

    Recall the SNAP and SPAN ontologies (Grenon and Smith 2004). Endurants or continuants are things that endure through time, e.g. a moving object, this printed book (SNAP ontology). Perdurants or occurrents by contrast are things that occur in time, e.g. the reader reading this book (SPAN ontology).

  3. 3.

    In Both et al. (P19. 2013) “fish” is used as a shorthand for moving objects because the work was initiated in response to a set of problems coming out of a river health monitoring system deployed in the Murray River, Australia, tracking real fish with RF transmitters and riverside cordons (Koehn et al. 2008).

  4. 4.

    Think of Pac-Man moving through his maze and eating away the pellets.

  5. 5.

    Clearly, in most current ICT applications the system provider maintains a detailed log of the whereabouts and activities of its customers, but from a conceptual point of view underlining the argument of the mobility privacy opportunity such an omniscient system provider database is not a necessity.

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Authors and Affiliations

  1. Institute of Natural Resource Sciences, Zurich University of Applied Sciences, Wädenswil, Switzerland

    Patrick Laube

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Laube, P. (2014). Decentralized Movement Analysis. In: Computational Movement Analysis. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-10268-9_4

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  • DOI: https://doi.org/10.1007/978-3-319-10268-9_4

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  • Print ISBN: 978-3-319-10267-2

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