Decentralised control of adaptive sampling and routing in wireless visual sensor networks

ABSTRACT

The efficient management of the limited energy resources of a wireless visual sensor network is central to its successful operation. Within this context, this paper focuses on the inter-dependent adaptive sampling and routing actions of each node in order to maximise the information value of the data collected. Thus, we develop two optimal decentralised algorithms to solve this distributed constraint optimization problem. The first assumes fixed routing and works in tree-structured networks. The second works in networks with any topologies by using a flexible routing approach. The two algorithms represent a trade-off in optimality, communication cost, and processing time. In an empirical evaluation on loopy sensor networks, we show that the flexible routing algorithm is able to deliver approximately twice the quantity of information compared to the fixed routing algorithm, where an arbitrary choice of route is made. However, this gain comes at a considerable communication and computational cost (increasing both by a factor of 100 times).

REFERENCES

• 1: Michael Bramberger , Andreas Doblander , Arnold Maier , Bernhard Rinner , Helmut Schwabach, Distributed Embedded Smart Cameras for Surveillance Applications, Computer, v.39 n.2, p.68, February 2006 [doi>10.1109/MC.2006.55]
• 2: P. Sinha and A. A. Zoltners. The multiple-choice knapsack problem. Operations Research, 27(3):503--15, 1979.
• 3: T. Zahariadis and S. Voliotis. Open issues in wireless visual sensor networking. In Proceedings of the 6th EURASIP Conference on Speech and Image Processing, Multimedia Communications and Services, pages 335--338, 2007.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Algorithms, Performance

Keywords:
decentralised mechanism, information metric, inter-related adaptive sampling and routing algorithm