Intelligent based novel embedded system based IoT enabled air pollution monitoring system

Senthilkumar, Radha; Venkatakrishnan, P.; Balaji, N.

doi:10.1016/j.micpro.2020.103172

Cited by 69 publications

(22 citation statements)

References 15 publications

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“…A study presented in [35] proposed using fog computing to monitor the environmental parameters and response in real-time based on the decision-making process that can be done on fog nodes. As an extension of cloud computing, in [36,37], fog computing has been promoted due to its inherent property of bringing the intelligence to the proximity of the edge of the network, and the effects on the performance of the service execution. The proposed AQM system based on fog computing presented in [38] introduces a distributed fog computing layer to effectively process the air pollutants' data sent by the sensor layer.…”

Section: Related Workmentioning

confidence: 99%

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Popović

Radovanović

Vajs

et al. 2022

Sensors

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Because the number of air quality measurement stations governed by a public authority is limited, many methodologies have been developed in order to integrate low-cost sensors and to improve the spatial density of air quality measurements. However, at the large-scale level, the integration of a huge number of sensors brings many challenges. The volume, velocity and processing requirements regarding the management of the sensor life cycle and the operation of system services overcome the capabilities of the centralized cloud model. In this paper, we present the methodology and the architectural framework for building large-scale sensing infrastructure for air quality monitoring applicable in urban scenarios. The proposed tiered architectural solution based on the adopted fog computing model is capable of handling the processing requirements of a large-scale application, while at the same time sustaining real-time performance. Furthermore, the proposed methodology introduces the collection of methods for the management of edge-tier node operation through different phases of the node life cycle, including the methods for node commission, provision, fault detection and recovery. The related sensor-side processing is encapsulated in the form of microservices that reside on the different tiers of system architecture. The operation of system microservices and their collaboration was verified through the presented experimental case study.

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Section: Related Workmentioning

confidence: 99%

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Popović

Radovanović

Vajs

et al. 2022

Sensors

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show abstract

“…According to the Bayesian theory, P(θ|D) = P(D|θ)P(θ) P(D) (6) and the definition of the Kullback-Leibler (KL) divergence, Equation (5) can be transformed to:…”

Section: Bayesian Lstm As the Sub-predictormentioning

confidence: 99%

“…Measurements have been obtained and saved in many multi-sensor systems, such as mobile robots [ 1 ], unmanned aerial vehicles (UAVs) [ 2 , 3 ], smart agriculture [ 4 , 5 ], air quality monitoring systems [ 6 , 7 ], etc. It is very meaningful to analyze these data and understand and predict the information in the sensor system [ 8 ], for example the analysis and prediction of meteorological elements in precision agriculture or environmental management systems [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Jin

et al. 2021

Entropy

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Trend prediction based on sensor data in a multi-sensor system is an important topic. As the number of sensors increases, we can measure and store more and more data. However, the increase in data has not effectively improved prediction performance. This paper focuses on this problem and presents a distributed predictor that can overcome unrelated data and sensor noise: First, we define the causality entropy to calculate the measurement’s causality. Then, the series causality coefficient (SCC) is proposed to select the high causal measurement as the input data. To overcome the traditional deep learning network’s over-fitting to the sensor noise, the Bayesian method is used to obtain the weight distribution characteristics of the sub-predictor network. A multi-layer perceptron (MLP) is constructed as the fusion layer to fuse the results from different sub-predictors. The experiments were implemented to verify the effectiveness of the proposed method by meteorological data from Beijing. The results show that the proposed predictor can effectively model the multi-sensor system’s big measurement data to improve prediction performance.

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“…It does not need to be where the engine is located to monitor engine parameters but can be done in any area as long as the internet network covers the area. Monitoring devices with internet line communication are also used to monitor air pollution [92], [93]. Using the internet line requires an MCU node module device to transmit data from the microprocessor to the internet network [94].…”

Section: ) Communicationmentioning

confidence: 99%

Modern Monitoring Instrument to Support Fishing Vessel Operation and Maintenance: A Review

Abrori¹,

Sidhi²,

Prasetyo³

2021

International Journal on Advanced Science, Engineering and Information Technology

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Determining the maintenance strategy for fishing vessels propulsion engines can use engine operational monitoring data. This data is used to prepare the implementation time, spare parts, labor, and costs. Unfortunately, many fishing vessels do not have this data. Currently, electronic devices are available for monitoring machine operations. This device is cheap, easy to use, takes responsibility for various physical quantity parameters, displays it on LCD, stores it digitally, and transmits data wirelessly. This paper aims to review the application of engine operational monitoring tools to apply to fishing vessel propulsion engines. The material used is scientific articles consisting of journals and proceedings indexed by online databases. Several keywords are used to obtain scientific articles that are suitable for the study. The method used is a literature review that is systematically reviewed by applying inclusion and exclusion. The results obtained in this article are the parameters for monitoring propulsion engines, namely temperature, pressure, rotational speed, and hours the engine runs. The monitoring device requires sensors to detect physical parameters, a microcontroller to process data, an LCD to display monitoring results, and memory to store data to obtain this data. The device supports wireless communication via Bluetooth or WiFi radio signal. Fishing vessels are operating in offshore areas that are not covered by internet networks as IoT communication transmissions. Further research on how to transmit operational engine data from offshore to onshore without an internet signal so that data can be accessed using the internet network.

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Intelligent based novel embedded system based IoT enabled air pollution monitoring system

Cited by 69 publications

References 15 publications

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Building Low-Cost Sensing Infrastructure for Air Quality Monitoring in Urban Areas Based on Fog Computing

Distributed Deep Fusion Predictor for a Multi-Sensor System Based on Causality Entropy

Modern Monitoring Instrument to Support Fishing Vessel Operation and Maintenance: A Review

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