Adaptive sensing using internet-of-things with constrained communications

Rahman, M. Sohel; Hong, Hua-Jun; Rahman, Amatur; Tsai, Pei-Hsuan; Afrin, Afia; Uddin, Yusuf Sarwar; Venkatasubramanian, Nalini; Hsu, Cheng-Hsin

doi:10.1145/3152881.3152887

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The second experience in calibration comes from lessons learned while deploying IoT-enabled environmental sensing in the SCALE project [13], [14]; here, we deployed inexpensive multi-sensor platforms (called SCALE boxes) at multiple locations worldwide including Irvine, CA; Montgomery County, MD [13], and Dhaka, Bangladesh [15], [16]. A SCALE node is a Raspberry-Pi-based multi-sensor box with middleware (SCALE client) that provides flexible interfaces for data collection for a wide range of sensor types (gas, light, air quality, 1 www.inria.fr/en/centre/paris/news/launch-of-soundcity-mobile-application temperature, seismic, camera, etc.).…”

Section: Motivation and Background Experiencementioning

confidence: 99%

Multi-Sensor Calibration Planning in IoT-Enabled Smart Spaces

Zhu

Sailhan

Uddin

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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Emerging applications in smart cities and communities require massive IoT deployments using sensors/actuators (things) that can enhance citizens' quality of life and public safety. However, budget constraints often lead to limited instrumentation and/or the use of low-cost sensors that are subject to drift and bias. This raises concerns of robustness and accuracy of the decisions made on uncertain data. To enable effective decisionmaking while fully exploiting the potential of low-cost sensors, we propose to send mobile units (e.g., trained personnel) equipped with high-quality (more expensive) and freshly-calibrated reference sensors so as to carry out calibration in the field. We design and implement an efficient cooperative approach to solve the calibration planning problem, which aims at minimizing the cost of the recurring calibration of multiple sensor types in the long-term operation. We propose a two-phase solution that consists of a sensor selection phase that minimizes the average cost of recurring calibration, and a path planning phase that minimizes the travel cost of multiple calibrators which have load constraints. We provide fast and effective heuristics for both phases. We further build a prototype that facilitates the mapping of the deployment field and provides navigation guidance to mobile calibrators. Extensive use-case-driven simulations show our proposed approach significantly reduces the average cost compared to naïve approaches: up to 30% in a moderate-sized indoor case, and higher in outdoor cases depending on the scale.

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Section: Motivation and Background Experiencementioning

confidence: 99%

Multi-Sensor Calibration Planning in IoT-Enabled Smart Spaces

Zhu

Sailhan

Uddin

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

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“…While most efforts were focused on the sensor device design, materials, and dimensions, the data-analytical characterization to enhance the performance of gas sensors has recently been attracting certain interest due to the development of IoT and Big Data. − Some researches tried to enhance the discrimination ability when detecting different kinds of gases with a single sensor. − The maximum resistance response is the commonly used parameter to characterize the measurements in this research. − Some other approaches such as the integral over a period and the width at half-height were proposed as well. , These methods are quite susceptible by the overlap issue in the discrimination of the chemical vapors and gases which are chemically and structurally similar. In general, the prediction accuracy from these conventional methods was much less than 90%.…”

Section: Introductionmentioning

confidence: 99%

Enhance the Discrimination Precision of Graphene Gas Sensors with a Hidden Markov Model

Nallon

Schnee

et al. 2018

Anal. Chem.

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Sensors are the key element to enable smart electronics and will play an important role in the emerging big data era. In this work, we reported an experimental study and a data-analytical characterization method to enhance the precision of discriminating chemically and structurally similar gases. Graphene sensors were fabricated by conventional photolithography and measured with feature analysis against different chemicals. A new hidden Markov model assisted with frequency spectral analysis, and the Gaussian mixture model (K-GMM-HMM) is developed to discriminate similar gases. The results indicated that the new method achieved a high prediction accuracy of 94%, 27% higher than the maximum value obtained by the conventional methods or other feature transient analysis methods. This study indicated that graphene gas sensors with the new K-GMM-HMM analysis are very attractive for chemical discrimination used in future smart electronics.

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Environmental Sensors

Sinha

2023

Encyclopedia of Sensors and Biosensors

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Adaptive sensing using internet-of-things with constrained communications

Cited by 4 publications

References 12 publications

Multi-Sensor Calibration Planning in IoT-Enabled Smart Spaces

Multi-Sensor Calibration Planning in IoT-Enabled Smart Spaces

Enhance the Discrimination Precision of Graphene Gas Sensors with a Hidden Markov Model

Environmental Sensors

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