Abstract
New approaches to Quality-of-Service (QoS) routing in wireless sensor networks which use different forms of learning are the subject of this paper. The Cognitive Packet Network (CPN) algorithm uses smart packets for path discovery, together with reinforcement learning and neural networks, while Self-Selective Routing (SSR) is based on the “Ant Colony” paradigm which emulates the pheromone-based technique which ants use to mark paths and communicate information about paths between different insects of the same colony (Koenig et al. in Ann Math Artif Intell 31(1–4): 41–76, 2001). In this paper, we present first experimental results on a network test-bed to evaluate CPN’s ability to discover paths having the shortest delay, or shortest length. Then, we present small test-bed experiments and large-scale network simulations to evaluate the effectiveness of the SSR algorithm. Finally, the two approaches are compared with respect to their ability to adapt as network conditions change over time.
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Research was sponsored by US Army Research Laboratory and the UK Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The collaboration was also supported by the NSF Grant OISE-0334667. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the US Army Research Laboratory, the U.S. Government, the UK Ministry of Defence, or the UK Government. The US and UK Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
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Gelenbe, E., Liu, P., Szymanski, B.K. et al. Cognitive and self-selective routing for sensor networks. Comput Manag Sci 8, 237–258 (2011). https://doi.org/10.1007/s10287-009-0102-y
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DOI: https://doi.org/10.1007/s10287-009-0102-y