Learning to be different: heterogeneity and efficiency in distributed smart camera networks

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Abstract

In this paper we study the self-organising behaviour of smart camera networks which use market-based handover of object tracking responsibilities to achieve an efficient allocation of objects to cameras. Specifically, we compare previously known homogeneous configurations, when all cameras use the same marketing strategy, with heterogeneous configurations, when each camera makes use of its own, possibly different marketing strategy. Our first contribution is to establish that such heterogeneity of marketing strategies can lead to system wide outcomes which are Pareto superior when compared to those possible in homogeneous configurations. However, since the particular configuration required to lead to Pareto efficiency in a given scenario will not be known in advance, our second contribution is to show how online learning of marketing strategies at the individual camera level can lead to high performing heterogeneous configurations from the system point of view, extending the Pareto front when compared to the homogeneous case. Our third contribution is to show that in many cases, the dynamic behaviour resulting from online learning leads to global outcomes which extend the Pareto front even when compared to static heterogeneous configurations. Our evaluation considers results obtained from an open source simulation package as well as data from a network of real cameras.

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  • Learning to be different: heterogeneity and efficiency in distributed smart camera

    Rights statement: © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Publication date2013
Publication titleSASO: 2013 IEEE 7th international conference on Self-Adaptive and Self-Organizing Systems
PublisherIEEE
Pages209-218
Number of pages10
ISBN (Print)978-0-7695-5129-6
Original languageEnglish
Event7th International Conference on Self-Adaptive and Self-Organizing Systems - Philadelphia, PA, United States

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Name
ISSN (Print)1949-3673

Conference

Conference7th International Conference on Self-Adaptive and Self-Organizing Systems
Abbreviated titleSASO 2013
CountryUnited States
CityPhiladelphia, PA
Period9/09/1313/09/13

Bibliographic note

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    Keywords

  • distributed smart cameras, heterogeneity, learning, self-organisation, variation

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