Internet of Things (IoT) is one of the main technological trends in the recent years. It allows machine-to-machine communication over the internet. Almost each device may transmit information from its sensors over the web to enable centralized insights derivation in an appropriate cloud architecture. In this paper we review analytical aspects of the sensory information processing. We emphasize the importance of multisensory approach, in which the joint distribution of all sensors values of a device is used to derive insights out of the stream of sensory data. We introduce a novel information theoretic multivariate change detection method based on k-nearest neighbor (kNN) estimation. The algorithm is designed and implemented to satisfy the requirements of IoT for fast online parallel multisensory information processing. We provide a numerical evidence of the validity of the proposed method on simulated and real world data.
|Title of host publication
|2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 18 May 2016
|41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: 20 Mar 2016 → 25 Mar 2016
|ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
|41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
|20/03/16 → 25/03/16
Bibliographical notePublisher Copyright:
© 2016 IEEE.
- Change detection
- Internet of Things