Learning to be Different: Heterogeneity and Efficiency in Distributed Smart Camera Networks

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.

[1]  Michael Sonnenschein,et al.  On the Influence of Inter-Agent Variation on Multi-Agent Algorithms Solving a Dynamic Task Allocation Problem under Uncertainty , 2012, 2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems.

[2]  Juan A. Rodríguez-Aguilar,et al.  Self-Configuring Sensors for Uncharted Environments , 2010, 2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[3]  Jürgen Schmidhuber,et al.  Algorithm portfolio selection as a bandit problem with unbounded losses , 2011, Annals of Mathematics and Artificial Intelligence.

[4]  Bernhard Rinner,et al.  Resource-Aware Coverage and Task Assignment in Visual Sensor Networks , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Annie S. Wu,et al.  On the Impact of Variation on Self-Organizing Systems , 2011, 2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems.

[6]  Igor Cavrak,et al.  Agent-based topology control for wireless sensor network applications , 2012, 2012 Proceedings of the 35th International Convention MIPRO.

[7]  Mehryar Mohri,et al.  Multi-armed Bandit Algorithms and Empirical Evaluation , 2005, ECML.

[8]  Xin Yao,et al.  Improved adaptivity and robustness in decentralised multi-camera networks , 2012, 2012 Sixth International Conference on Distributed Smart Cameras (ICDSC).

[9]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[10]  Xin Yao,et al.  Socio-economic vision graph generation and handover in distributed smart camera networks , 2014, TOSN.

[11]  Ali A. Minai,et al.  Self-Organization of Sensor Networks with Heterogeneous Connectivity , 2010 .

[12]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[13]  Pedro José Marrón,et al.  Generic role assignment for wireless sensor networks , 2004, EW 11.

[14]  Peter Auer,et al.  Finite-time Analysis of the Multiarmed Bandit Problem , 2002, Machine Learning.

[15]  Eduardo Freire Nakamura,et al.  A reactive role assignment for data routing in event-based wireless sensor networks , 2009, Comput. Networks.

[16]  R. Lyndon While,et al.  A faster algorithm for calculating hypervolume , 2006, IEEE Transactions on Evolutionary Computation.