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In sparse wireless sensor networks, data collection is carried out through specialized mobile nodes that visit sensor nodes, gather data, and transport them to the sink node. Since visit times are typically unpredictable, one of the main challenges to be faced in this kind of networks is the energy-efficient discovery of mobile collector nodes by sensor nodes. In this paper, the authors propose an adaptive discovery algorithm that combines a learning-based approach with a hierarchical scheme. Thanks to its hybrid nature, the proposed algorithm is very flexible, as it can adapt to very different mobility patterns of the mobile collector node(s), ranging from deterministic to completely random mobility.
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