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학술대회 A Method for Co-existing Heterogeneous IoT Environments based on Compressive Sensing
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저자
이형극, 조성균, 이남경, 이현우
발행일
201602
출처
International Conference on Advanced Communication Technology (ICACT) 2016, pp.206-209
DOI
https://dx.doi.org/10.1109/ICACT.2016.7423330
협약과제
15MC1200, WoT기반 스마트홈 서비스 오픈생태계 구축을 위한 웹 컨넥티비티 디바이스 솔루션 개발, 박준희
초록
Compressive Sensing (CS) is a stable and robust technique that allows for the sub-sampling of data at a given data rate: 'compressive sampling' or 'compressive sensing' at rates smaller than the Nyquist sampling rate. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). CS-based signal and information acquisition/compression paradigm combines the nonlinear reconstruction algorithm and random sampling on a sparse basis that provides a promising approach to compress signal and data in information systems. In this paper, we investigates how CS can provide new insights into coexisting heterogeneous IoT environments. First, we briefly introduce the CS theory with respect to the sampling through providing a compressed sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the hub nodes measure, transmit, and store the sampled data into the fusion center. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Therefore, compression should be performed locally at each Access Point (AP) and reconstruction is executed jointly to consider dependencies in the acquired data by the final fusion center.
키워드
Compressive Sensing (CS), Fusion Center, Internet of Things (IoT), Wireless Sensor Networks (WSNs)
KSP 제안 키워드
Access point, Compressed sampling, Compressive sensing, Energy Efficiency, Fusion center, In-network compression, Information systems(IS), Internet of thing(IoT), IoT environment, Low computation, Nyquist sampling rate