In this section
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
@article{Dekkers:2022:10.1016/j.inffus.2021.07.022,
author = {Dekkers, G and Rosas, F and van, Waterschoot T and Vanrumste, B and Karsmakers, P},
doi = {10.1016/j.inffus.2021.07.022},
journal = {Information Fusion},
pages = {196--210},
title = {Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities},
url = {http://dx.doi.org/10.1016/j.inffus.2021.07.022},
volume = {77},
year = {2022}
}
TY - JOUR
AB - For the past decades there has been a rising interest for wireless sensor networks to obtain information about an environment. One interesting modality is that of audio, as it is highly informative for numerous applications including speech recognition, urban scene classification, city monitoring, machine listening and classifying domestic activities. However, as they operate at prohibitively high energy consumption, commercialisation of battery-powered wireless acoustic sensor networks has been limited. To increase the network's lifetime, this paper explores the joint use of decision-level fusion and dynamic sensor activation. Hereby adopting a topology where processing – including feature extraction and classification – is performed on a dynamic set of sensor nodes that communicate classification outputs which are fused centrally. The main contribution of this paper is the comparison of decision-level fusion with different dynamic sensor activation strategies on the use case of automatically classifying domestic activities. Results indicate that using vector quantisation to encode the classification output, computed at each sensor node, can reduce the communication per classification output to 8 bit without loss of significant performance. As the cost for communication is reduced, local processing tends to dominate the overall energy budget. It is indicated that dynamic sensor activation, using a centralised approach, can reduce the average time a sensor node is active up to 20% by leveraging redundant information in the network. In terms of energy consumption, this resulted in an energy reduction of up to 80% as the cost for computation dominates the overall energy budget.
AU - Dekkers,G
AU - Rosas,F
AU - van,Waterschoot T
AU - Vanrumste,B
AU - Karsmakers,P
DO - 10.1016/j.inffus.2021.07.022
EP - 210
PY - 2022///
SN - 1566-2535
SP - 196
TI - Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities
T2 - Information Fusion
UR - http://dx.doi.org/10.1016/j.inffus.2021.07.022
UR - https://www.sciencedirect.com/science/article/pii/S1566253521001470?via%3Dihub
VL - 77
ER -
Data Science Institute
William Penney Laboratory
Imperial College London
South Kensington Campus
London SW7 2AZ
United Kingdom
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