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.

References

[1]

Gerrit Anders, Christian Hinrichs, Florian Siefert, Pascal Behrmann, Wolfgang Reif, and Michael Sonnenschein. 2012. On the influence of inter-agent variation on multi-agent algorithms solving a dynamic task allocation problem under uncertainty. In Proceedings of the 6th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO’12). IEEE Computer Society Press, Washington, DC, 29--38.

Digital Library

[2]

Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine Learning 47 (2002), 235--256.

Digital Library

[3]

Robert Axelrod. 1987. The evolution of strategies in the Iterated Prisoners Dilemma. In Genetic Algorithms and Simulated Annealing, Lawrence Davis (Ed.). Pittman, 32--41.

[4]

Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. 2008. Speeded-up robust features (SURF). Computer Vision and Image Understanding 110, 3 (2008), 346--359.

Digital Library

[5]

Ken Binmore. 2007. Game Theory: A Very Short Introduction. Oxford University Press.

[6]

Adam Campbell, Cortney Riggs, and Annie S. Wu. 2011. On the impact of variation on self-organizing systems. In Proceedings of the 5th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO’11). IEEE Computer Society Press, Washington, DC, 119--128.

Digital Library

[7]

Zhaolin Cheng, Dhanya Devarajan, and Richard J. Radke. 2007. Determining vision graphs for distributed camera networks using feature digests. EURASIP Journal on Applications in Signal Processing 2007, 1 (Jan. 2007), 220--220.

Digital Library

[8]

Carlos A. Coello Coello and Nareli Cruz Cortés. 2005. Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines 6, 2 (2005), 163--190.

Digital Library

[9]

Henry Detmold, Anton van den Hengel, Anthony Dick, Alex Cichowski, Rhys Hill, Ekim Kocadag, Katrina Falkner, and David S. Munro. 2007. Topology estimation for thousand-camera surveillance networks. In Proceedings of the ACM/IEEE International Conference on Distributed Smart Cameras. 195--202.

[10]

Giovanna Di Marzo Serugendo, Marie-Pierre Gleizes, and Anthony Karageorgos (Eds.). 2011. Self-Organising Software: From Natural to Artificial Adaptation. Springer.

Digital Library

[11]

Bernhard Dieber, Christian Micheloni, and Bernhard Rinner. 2011. Resource-aware coverage and task assignment in visual sensor networks. IEEE Transactions on Circuits and Systems for Video Technology 21, 10 (2011), 1424--1437.

Digital Library

[12]

Ugur Murat Erdem and Stan Sclaroff. 2005. Look there&excl; Predicting where to look for motion in an active camera network. In Proceedings of the IEEE Conference on Vision and Signal-Based Surveillance. 105--110.

[13]

Lukas Esterle, Peter R. Lewis, Bernhard Rinner, and Xin Yao. 2012. Improved adaptivity and robustness in decentralised multi-camera networks. In Proceedings of the 6th ACM/IEEE International Conference on Distributed Smart Cameras. IEEE Press, 1--6.

[14]

Lukas Esterle, Peter R. Lewis, Xin Yao, and Bernhard Rinner. 2014. Socio-economic vision graph generation and handover in distributed smart camera networks. ACM Transactions on Sensor Networks 10, 2, Article 20 (2014).

Digital Library

[15]

Matteo Gagliolo and Jürgen Schmidhuber. 2011. Algorithm portfolio selection as a bandit problem with unbounded losses. Annals of Mathematics and Artificial Intelligence 61, 2 (2011), 49--86.

Digital Library

[16]

Omar Javed, Zeeshan Rasheed, Khurram Shafique, and Mubarak Shah. 2003. Tracking across multiple cameras with disjoint views. In Proceedings of the 9th IEEE International Conference on Computer Vision - Volume 2 (ICCV’03). IEEE Computer Society, Washington, DC, 952--.

Digital Library

[17]

Peter R. Lewis, Lukas Esterle, Arjun Chandra, Bernhard Rinner, and Xin Yao. 2013. Learning to be different: Heterogeneity and efficiency in distributed smart camera networks. In Proceedings of the 7th IEEE Conference on Self-Adaptive and Self-Organizing Systems (SASO’13). IEEE Press, 209--218.

Digital Library

[18]

Peter R. Lewis, Paul Marrow, and Xin Yao. 2010. Resource allocation in decentralised computational systems: An evolutionary market based approach. Autonomous Agents and Multi-Agent Systems 21, 2 (2010), 143--171.

Digital Library

[19]

Yiming Li and Bir Bhanu. 2009. A comparison of techniques for camera selection and handoff in a video network. In Proceedings of the ACM/IEEE International Conference on Distributed Smart Cameras. 1--8.

[20]

Yiming Li and Bir Bhanu. 2011. Utility-based camera assignment in a video network: A game theoretic framework. IEEE Sensors Journal 11, 3 (2011), 676--687.

[21]

Dimitrios Makris, Tim Ellis, and James Black. 2004. Bridging the gaps between cameras. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 205--210.

Digital Library

[22]

Zehavit Mandel, Ilan Shimshoni, and Daniel Keren. 2007. Multi-camera topology recovery from coherent motion. In Proceedings of the ACM/IEEE International Conference on Distributed Smart Cameras. 243--250.

[23]

Kristof Van Moffaert, Tim Brys, Arjun Chandra, Lukas Esterle, Peter R. Lewis, and Ann Nowé. 2014. A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning. In Proceedings of the Annual International Joint Conference on Neural Networks (IJCNN’14). IEEE Press.

[24]

Birgit Möller, Thomas Plötz, and Gernot A. Fink. 2008. Calibration-free camera hand-over for fast and reliable person tracking in multi-camera setups. In Proceedings of the 19th International Conference on Pattern Recognition. 1--4.

[25]

Kazuyuki Morioka, Szilveszter Kovacs, Joo-Ho Lee, and Peter Korondi. 2010. A cooperative object tracking system with fuzzy-based adaptive camera selection. International Journal on Smart Sensing and Intelligent Control 3, 3 (2010), 338--358.

[26]

Eduardo F. Nakamura, Heitor S. Ramos, Leandro A. Villas, Horacio A. B. F. de Oliveira, Andre L. L. de Aquino, and Antonio A. F. Loureiro. 2009. A reactive role assignment for data routing in event-based wireless sensor networks. Computer Networks 53, 12 (2009), 1980--1996.

Digital Library

[27]

Georg Nebehay, Walter Chibamu, Peter R. Lewis, Arjun Chandra, Roman Pflugfelder, and Xin Yao. 2013. Can diversity amongst learners improve online object tracking? In Multiple Classifier Systems, Zhi-Hua Zhou, Fabio Roli, and Josef Kittler (Eds.). Lecture Notes in Computer Science, Vol. 7872. Springer, Berlin, 212--223.

[28]

Steve Phelps, Kai Cai, Peter McBurney, Jinzhong Niu, Simon Parsons, and Elizabeth Sklar. 2008. Auctions, evolution, and multi-agent learning. In Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning. Lecture Notes in Computer Science, Vol. 4865. Springer, 188--210.

Digital Library

[29]

Arun Prasath, Abhinay Venuturumilli, Aravind Ranganathan, and Ali A. Minai. 2009. Self-organization of sensor networks with heterogeneous connectivity. In Sensor Networks: Where Theory Meets Practice, Gianluigi Ferrari (Ed.). Springer, 39--59.

[30]

Markus Quaritsch, Markus Kreuzthaler, Bernhard Rinner, Horst Bischof, and Bernhard Strobl. 2007. Autonomous multicamera tracking on embedded smart cameras. EURASIP Journal on Embedded Systems Volume 2007 (2007), 10.

Digital Library

[31]

Faisal Qureshi and Demetri Terzopoulos. 2008. Multi-camera control through constraint satisfaction for persistent surveillance. In IEEE Conference on Vision and Signal-Based Surveillance. 211--218.

Digital Library

[32]

Dominik Rojković, Tihomir Crnic, and Igor Cavrak. 2012. Agent-based topology control for wireless sensor network applications. In MIPRO, 2012 Proceedings of the 35th International Convention. MIPRO, Rijeka, Croatia, 277--282.

[33]

Kay Römer, Christian Frank, Pedro José Marrón, and Christian Becker. 2004. Generic role assignment for wireless sensor networks. In Proceedings of the 11th ACM SIGOPS European Workshop. ACM, New York, NY.

Digital Library

[34]

Norman Salazar, Juan A. Rodriguez-Aguilar, and Josep Lluis Arcos. 2010. Self-configuring sensors for uncharted environments. In Proceedings of the 4th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO’10). IEEE Computer Society Press, Washington, DC, 134--143.

Digital Library

[35]

Richard S. Sutton and Andrew G. Barto. 1998. Reinforcement Learning: An Introduction. MIT Press.

Digital Library

[36]

Peter Vamplew, Richard Dazeley, Adam Berry, Rustam Issabekov, and Evan Dekker. 2011. Empirical evaluation methods for multiobjective reinforcement learning algorithms. Machine Learning 84, 1--2 (2011), 51--80.

Digital Library

[37]

Peter Vamplew, John Yearwood, Richard Dazeley, and Adam Berry. 2008. On the limitations of scalarisation for multi-objective reinforcement learning of pareto fronts. In Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence (AI’08). Springer-Verlag, Berlin, 372--378.

Digital Library

[38]

Kristof Van Moffaert, Madalina M. Drugan, and Ann Nowé. 2013a. Hypervolume-based multi-objective reinforcement learning. In Evolutionary Multi-Criterion Optimization, Robin C. Purshouse, Peter J. Fleming, Carlos M. Fonseca, Salvatore Greco, and Jane Shaw (Eds.). Lecture Notes in Computer Science, Vol. 7811. Springer, 352--366.

[39]

Kristof Van Moffaert, Madalina M. Drugan, and Ann Nowé. 2013b. Scalarized multi-objective reinforcement learning: Novel design techniques. In IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning (ADPRL’13). IEEE Press, 191--199.

[40]

Lyndon While, Phil Hingston, Luigi Barone, and Simon Huband. 2006. A faster algorithm for calculating hypervolume. IEEE Transactions on Evolutionary Computation 10, 1 (2006), 29--38.

Digital Library

[41]

Michael J. Wooldridge. 2001. Introduction to Multiagent Systems. John Wiley and Sons.

Digital Library

[42]

Qingfu Zhang, Aimin Zhou, and Yaochu Jin. 2008. RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation 12, 1 (Feb. 2008), 41--63.

Digital Library

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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://dl.acm.org/doi/10.1145/3524844.3528079
Dixon EAnderson JLazar A(2022)Understanding How Sensory Changes Experienced by Individuals with a Range of Age-Related Cognitive Changes Can Affect Technology UseACM Transactions on Accessible Computing10.1145/351190615:2(1-33)Online publication date: 19-May-2022
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