research-article

Static, Dynamic, and Adaptive Heterogeneity in Distributed Smart Camera Networks

Authors:

Peter R. Lewis,

Bernhard Rinner,

Xin YaoAuthors Info & Claims

ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 10, Issue 2

Article No.: 8, Pages 1 - 30

https://doi.org/10.1145/2764460

Published: 09 June 2015 Publication History

Abstract

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization.

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Index Terms

Static, Dynamic, and Adaptive Heterogeneity in Distributed Smart Camera Networks

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cover image ACM Transactions on Autonomous and Adaptive Systems

ACM Transactions on Autonomous and Adaptive Systems Volume 10, Issue 2

June 2015

175 pages

ISSN:1556-4665

EISSN:1556-4703

DOI:10.1145/2790463

Editors:
Manish Parashar
Rutgers University, USA
,
Franco Zambonelli
University of Modena e Reggio Emilia, Italy

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Copyright © 2015 ACM.

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Publication History

Published: 09 June 2015

Accepted: 01 April 2015

Revised: 01 November 2014

Received: 01 March 2014

Published in TAAS Volume 10, Issue 2

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https://doi.org/10.1109/ACSOS-C63493.2024.00038
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https://dl.acm.org/doi/10.1145/3524844.3528079
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