With recent technological advances in sensor nodes, IoT enabled applications have great potential in many domains. However, sensing data may be inaccurate due to not only faults or failures in the sensor and network but also the limited resources and transmission capability available in sensor nodes. In this paper, we first model streams of IoT data as a handful of sampled data in the transformed domain while assuming the information attained by those sampled data reveal different sparsity profiles between normal and abnormal. We then present a novel approach called AD2 (Anomaly Detection using Approximated Data) that applies a transformation on the original data, samples top k-dominant components, and detects data anomalies based on the disparity in k values. To demonstrate the effectiveness of AD2 , we use IoT datasets (temperature, humidity, and CO) collected from real-world wireless sensor nodes. Our experimental evaluation demonstrates that AD2 can approximate and successfully detect 64%-94% of anomalies using only 1.9% of the original data and minimize false positive rates, which would otherwise require the entire dataset to achieve the same level of accuracy.
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An Optical Wireless Temperature Sensor
This paper presents a wireless temperature sensor that uses a GaAs solar cell as a wireless transmitter of information. Transmission of information with a solar cell is possible by modulating the luminescent radiation emitted by the solar cell. This technique, dubbed Optical Frequency Identification or OFID, was recently reported in the literature and in this work is used to transmit temperature measurements wirelessly. The hardware design of an OFID temperature sensor tag and its corresponding reader is described. A prototype of the proposed sensor was built as a proof of concept. Experimental results demonstrate wireless data transmission at a distance of 1 m distance and at a bit rate of 1200 bps. The wireless temperature sensor has a maximum error of 0.39°C (after calibration) with respect to a high-precision temperature meter.
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- Award ID(s):
- 1809637
- NSF-PAR ID:
- 10189899
- Date Published:
- Journal Name:
- 2019 IEEE Sensors Conference
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SENSORS43011.2019.8956813
