The paradox of wireless sensor networks (WSNs) is that the low-power, miniaturized sensors that can be deeply embedded in the physical world, are too resource-constrained to capture high frequency phenomena. In this paper, we propose RHA, a robust hybrid architecture for information processing to extend sensing capacity while conserving energy, storage and bandwidth. We evaluate RHA with two real world applications, bio-acoustic monitoring and spatial monitoring. RHA provides accurate signal reconstruction with significant down sampling compared to traditional multi-rate sampling, is robust to noise, enables fast triggering and load balancing across sensors.
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