Abstract
Continuously gathering data from wireless sensor network is one of crucial issue to be resolved. In the problem, there are multiple sensors to be transmitted, however, their data distributions are unknown at the starting point and to know such distributions we should try to gather data from them, and the resources to be used for it is also limited. The problem is often called Budget-Limited Multi-Armed Bandit problem, and several approaches have been proposed. However, often a wireless sensor network has a number of nodes to be retrieved so that it is difficult to try the all nodes to gather their potential possibilities because of very limited budgets, i.e., limited electricity power or limited bandwidth of the network. In this paper, we present an improved BLMAB algorithm that is more suitable for highly budget-constrained situation. The proposed approach can effectively limit sources to be retrieved when a relatively hard budget-limitation has applied. We conduct its experiments on a simulation environment to evaluate the potential performance of the approach.
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Kadono, Y., Fukuta, N. (2014). LAKUBE: An Improved Multi-Armed Bandit Algorithm for Strongly Budget-Constrained Conditions on Collecting Large-Scale Sensor Network Data. In: Pham, DN., Park, SB. (eds) PRICAI 2014: Trends in Artificial Intelligence. PRICAI 2014. Lecture Notes in Computer Science(), vol 8862. Springer, Cham. https://doi.org/10.1007/978-3-319-13560-1_94
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DOI: https://doi.org/10.1007/978-3-319-13560-1_94
Publisher Name: Springer, Cham
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