Capturing high-frequency phenomena using a bandwidth-limited sensor network

Abstract

Small-form-factor, low-power wireless sensors-motes-are convenient to deploy, but lack the bandwidth to capture and transmit raw high-frequency data, such as human voices or neural signals, in real time. Local filtering can help, but we show that the right filter settings depend on changing ambient conditions and network effects such as congestion, which makes them dynamic and unpredictable. Mote collection systems for high-frequency data must support iteratively-tuned, deployment-specific filter settings as well as fast sampling.VANGO, our software system for high-frequency data collection, achieves these goals via integrated processing across network tiers. Bandwidth-limited sensor nodes reduce data in network but rely on microservers, which have greater computational capabilities and a wider scope of observation, to plan how. VANGO provides a cross-platform library for data transformation, measurement, and classification; a fast and low-jitter data acquisition system for motes; and a mechanism to control mote and microserver signal processing. With VANGO we have developed new applications: the first acoustic collection system for motes responsive to changing environmental conditions and user interests, and the first neural spike acquisition application capable of supporting a network of nodes.

EnviroMic: Towards Cooperative Storage and Retrieval in Audio Sensor Networks

“…To tackle the problem, EnviroMic mainly focus on reducing data redundancy. Obviously, other techniques including in-network filters [11] and data compression algorithms [26] can be easily integrated into EnviroMic to further reduce the data volume to be stored in network.…”

Efficient cross-correlation via sparse representation in sensor networks

“…Case-A -Outdoor, Very low multipath: A less frequently used urban walkway, and the weather being sunny with occasional mild breeze. [20][21][22][23][24][25][26][27][28][29][30] dB. For reasons that will be explained in the next subsection, we slightly modified the peak selection criteria of the detection algorithm to choose the tallest peak if there was no valid peak (6 standard deviation above the mean).…”

“…Fig. 6(a) for Case-A presents the most clear characterization by negating the effect of channel multipaths (though introducing an increased background noise level), where observations with a high SNR of [20][21][22][23][24][25][26][27][28][29][30] dB provide reliable range estimates by using only 15% projections while those having low SNR of [0-5) dB show confident result only with α = 0.30 (i.e., using more projections). Fig.…”

SenQ: An Embedded Query System for Streaming Data in Heterogeneous Interactive Wireless Sensor Networks

“…VanGo [8] similarly requires the use of micro-servers for adaptive processing. They take advantage of an ADC DMA capability to provide high rate sampling, and have a static processing chain compiled into the motes.…”

“…Instead of a static sequence (as in VanGo [8]), blocks are wired to a dynamic data flow coordinator. This provides more flexibility to the application, since each query has its own ordering of the processing chain.…”