Towards a Smart Data Transmission Strategy for IoT Monitoring Systems: Application to Air Quality Monitoring

Lounas, Razika; Salhi, Dhai Eddine; Mokrani, Hocine; Djerbi, Rachid; Bennai, Mohamed Tahar

doi:10.1109/ictaacs48474.2019.8988119

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…The proposed system consists of a web portal for data management as well as a mobile app for displaying data and historical analysis of the laboratory air quality. In Reference [22], a smart data transmission strategy has been discussed by applying it on the air quality data set. The optimal use of physical resources is central to IoT based systems, particularly in those settings where data is produced and ingested in voluminous amounts.…”

Section: Related Workmentioning

confidence: 99%

Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

et al. 2021

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Indoor air quality typically encompasses the ambient conditions inside buildings and public facilities that may affect both the mental and respiratory health of an individual. Until the COVID-19 outbreak, indoor air quality monitoring was not a focus area for public facilities such as shopping complexes, hospitals, banks, restaurants, educational institutes, and so forth. However, the rapid spread of this virus and its consequent detrimental impacts have brought indoor air quality into the spotlight. In contrast to outdoor air, indoor air is recycled constantly causing it to trap and build up pollutants, which may facilitate the transmission of virus. There are several monitoring solutions which are available commercially, a typical system monitors the air quality using gas and particle sensors. These sensor readings are compared against well known thresholds, subsequently generating alarms when thresholds are violated. However, these systems do not predict the quality of air for future instances, which holds paramount importance for taking timely preemptive actions, especially for COVID-19 actual and potential patients as well as people suffering from acute pulmonary disorders and other health problems. In this regard, we have proposed an indoor air quality monitoring and prediction solution based on the latest Internet of Things (IoT) sensors and machine learning capabilities, providing a platform to measure numerous indoor contaminants. For this purpose, an IoT node consisting of several sensors for 8 pollutants including NH3, CO, NO2, CH4, CO2, PM 2.5 along with the ambient temperature & air humidity is developed. For proof of concept and research purposes, the IoT node is deployed inside a research lab to acquire indoor air data. The proposed system has the capability of reporting the air conditions in real-time to a web portal and mobile app through GSM/WiFi technology and generates alerts after detecting anomalies in the air quality. In order to classify the indoor air quality, several machine learning algorithms have been applied to the recorded data, where the Neural Network (NN) model outperformed all others with an accuracy of 99.1%. For predicting the concentration of each air pollutant and thereafter predicting the overall quality of an indoor environment, Long and Short Term Memory (LSTM) model is applied. This model has shown promising results for predicting the air pollutants’ concentration as well as the overall air quality with an accuracy of 99.37%, precision of 99%, recall of 98%, and F1-score of 99%. The proposed solution offers several advantages including remote monitoring, ease of scalability, real-time status of ambient conditions, and portable hardware, and so forth.

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Section: Related Workmentioning

confidence: 99%

Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

et al. 2021

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“…It is important to note that some authors, as in [ 10 ], make a distinction between methods that rely on the different data encoding and transformation techniques to achieve compression and ones that adjust the sampling rate of the device dynamically, describing them as sampling optimization solutions rather than compression.…”

Section: Data Compressionmentioning

confidence: 99%

“…In the approach of [ 10 ], a multi-layer efficient data transmission strategy for IoT monitoring systems was proposed. The proposed solution was based primarily on two mechanisms.…”

Section: Related Workmentioning

confidence: 99%

“…These sensors monitor the physical properties associated with their deployment environments—twenty-four hours a day, seven days a week—making them a critical facilitator to increase the volume of the data generated, due to the collection performed in a wide variety of scenarios [ 9 , 10 ]. Note that these data streams can form a large volume of data very rapidly, and as a consequence, they generate the need for a vast amount of disk storage space [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Signoretti

Silva

Andrade

et al. 2021

Sensors

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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

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Rainfall variability over multiple cities of India: analysis and forecasting using deep learning models

Panda,

Nagar,

Mukherjee

et al. 2024

Earth Sci Inform

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Towards a Smart Data Transmission Strategy for IoT Monitoring Systems: Application to Air Quality Monitoring

Cited by 4 publications

References 25 publications

Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

Internet of Things (IoT) Based Indoor Air Quality Sensing and Predictive Analytic—A COVID-19 Perspective

An Evolving TinyML Compression Algorithm for IoT Environments Based on Data Eccentricity

Rainfall variability over multiple cities of India: analysis and forecasting using deep learning models

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