Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm

Chaudhri, Shiv Nath; Rajput, N. S.; Alsamhi, Saeed Hamood; Shvetsov, Alexey V.; Almalki, Faris A.

doi:10.3390/s22083039

Cited by 20 publications

(13 citation statements)

References 34 publications

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“…This paper uses spatial upscaling approach, which can upscale samples having a limited number of features, e.g., (2×2) obtained as the steady-state response from a gas sensor array to a (6×6), as in our case. The upscaled samples are used for training and testing purposes for pattern analysis using CNNs, followed upscaling uses the recursion as depicted in Figure 6 [30]. A CNN has been then trained and tested to verify the effectiveness of the scaled-up dataset at the sample level.…”

Section: Resulsmentioning

confidence: 99%

Spatial Upscaling-Based Algorithm for Detection and Estimation of Hazardous Gases

et al. 2023

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Recently, society/industry is getting smarter and sustainable through artificial intelligencebased solutions. However, this rapid advancement is also polluting our air ambience. Hence real-time detection and estimation of hazardous gases/odors in the air ambiance has become a critical need. In this paper, a convolutional neural network (CNN) based multi-element gas sensor arrays signature response analysis approach has been presented to achieve higher accuracy in detection and estimation of hazardous gases. Accordingly, the real-time gas sensor array responses are spatially upscaled and processed on the edge, using lightweight CNNs. For the verification of our hypothesis, we have used a four-element metaloxide semi-conductor (MOS)-based thick-film gas sensor array, fabricated by our group, by using SnO2, ZnO, MoO, CdS materials for detection and estimation of four target hazardous gases, viz., acetone, carbon-tetrachloride, ethyl-methyl-ketone, and xylene. The four-element (2×2) raw sensor responses are first upscaled to 6×6 responses and a lightweight CNN is trained on 42 samples of 6×6 input vectors. The trained system is then tested using 16 unknown (not used during training) test samples of the considered gases/odors. All the 16 test samples are detected correctly. The Mean Squared Error (MSEs) of detection has been 1.42×10 -14 while the estimation accuracy of 2.43×10 -3 were achieved for the considered gases. Our designed system is generic in design and can be extended to other gases/odors of interest.

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Section: Resulsmentioning

confidence: 99%

Spatial Upscaling-Based Algorithm for Detection and Estimation of Hazardous Gases

et al. 2023

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“…These array of sensor consumes a large amount of power. (Chaudhri et al, 2022) developed a novel algorithmapproach by using zeropadded virtual sensors and spatial augmentation and Convolutional Neural Network (CNN) algorithm reduced the power consumption of a sensor array by 50 percent. The sensorarray was consuming 10 Watts of power when unoptimized and after optimization it consumed 5 Watts of power.…”

Section: : Algorithms For Discriminationmentioning

confidence: 99%

“…They found the best results for SnO2, ZnO-based sensors array. By using the approach of (Chaudhri et al, 2022) there can be a reduction in cost, hardware, and power consumption for the smart sensors.…”

Section: : Algorithms For Discriminationmentioning

confidence: 99%

Review on Discrimination of Hazardous Gases by Smart Sensing Technology

Bandewad

Datta

Gawali

et al. 2023

AIA

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Real-time detection of hazardous gases in the ambient and indoors has become the prime motive for curbing the problem of air pollution. Keeping the concentration of hazardous gases in control is the main task before human society so as to keep environmental balance. Smart sensors are attracting researchers to focus on fabricating devices that could detect and predict the presence of gas in real-time, give accurate results about the gas concentration, detection of a target gas from the mixture of gases. This smart gas sensor system can have applications in the field of military, space, underwater, indoor, outdoors, factories, vehicles, and wearable smart devices. This study reviews recent advances in smart sensor technology with respect to the material structure, sensing technique, and discrimination algorithm. Focus is given on reducing the power consumption and area of a sensor circuitry with the help of different techniques.

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“…The SNR predicted result demonstrates a vital parameter that seen as a wireless link quality indicator. The SNR value is ranging between upper and lower bounds, where above values are viewed as wasted transmitter power, while lower ones are considered undesirable [35][36][37][38].…”

Section: Communication Predictionsmentioning

confidence: 99%

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Almalki¹,

Mutawi²,

Turkistani³

et al. 2022

Adv. sci. technol. eng. syst. j.

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Many consequences can be resulted in mismanagement of crowd, which might get people injured or even lose their lives. Thus, crowd management helps in controlling overcrowded areas during events, and allowing authorities to monitor, manage and reduce incidents. Space science and technology have made huge leap in crowd management, let alone when this technology integrated with Artificial intelligent. Hence, space-based systems like CubeSats are seen as the best approach to help monitoring and providing various reasons. For instance, wide coverage footprint, collect specific area data simultaneously, assist in aerial photography, as well as providing wide range of wireless communications services that can integrate appropriately with AI and wearable devices. This work aims to design a CubeSat vehicle to manage crowds during short-term, large-scale events. The proposed system is, also, coupled with AI framework along with the camera and wearable devices to monitor the crowds' performance continuously by relying on two aspects: Firstly, aerial imaging (e.g., photos and videos). Secondly, using wearable devices that can be worn to monitor vital signs of crowds. Moreover, the proposed system can independently relieve congestion, as well as notify the ground controller of all problems to take further actions. The obtained results confirm that the proposed innovative solution is for crowd management with accuracy reached 95%, and MSE equal to 0.049.

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Zero-Padding and Spatial Augmentation-Based Gas Sensor Node Optimization Approach in Resource-Constrained 6G-IoT Paradigm

Cited by 20 publications

References 34 publications

Spatial Upscaling-Based Algorithm for Detection and Estimation of Hazardous Gases

Spatial Upscaling-Based Algorithm for Detection and Estimation of Hazardous Gases

Review on Discrimination of Hazardous Gases by Smart Sensing Technology

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

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