Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform

Abstract

Environmental monitoring has gained significant importance in outdoor air quality measurement and assessment for fundamental survival as well as ambient assisted living. In real-time outdoor urban scale, instantaneous air quality index estimation, the electrochemical sensors warm-up time, cross-sensitivity computation-error, geo-location typography, instantaneous capacity or back up time; and energy efficiency are the six major challenges. These challenges lead to real-time gradient anomalies that effect the accuracy and pro-longed lags in air quality index mapping campaigns for state and environmental/meteorological agencies. In this work, a gradient-aware, multi-variable air quality sensing node is proposed with event-triggered sensing based on position, gas magnitudes, and cross-sensitivity interpolation. In this approach, temperature, humidity, pressure, geo-position, photovoltaic power, volatile organic compounds, particulate matter (2.5), ozone, Carbon mono-oxide, Nitrogen dioxide, and Sulphur dioxide are the principle variables. Results have shown that the proposed system optimized the real-time air quality monitoring for the chosen geo-spatial cluster (Qatar University).

State-of-the-Art Low-Cost Air Quality Sensors, Assemblies, Calibration and Evaluation for Respiration-Associated Diseases: A Systematic Review

“…Firstly, the lifeline cost, representing the long-term financial commitment, influences the sustainability of sensor deployment across different health conditions, such as asthma, COPD, ischemic heart disease, strokes, pneumonia, lung cancer, and sick building syndrome (SBS). The works with co-respiratory-cardiological association hold a key life-saving capacity [149][150][151][152]. Secondly, the manufacturing setup cost, involving the initial investment and scalability for mass production, significantly impacts the feasibility of widespread sensor deployment for health monitoring.…”

An IoT-based Bi-Cluster Forecasting using Automated ML-Model Optimization for COVID19

“…For the precise impact of CO2 and temperature for COVID19, a real-time multi-variable structured data time-series vector was needed to proceed with geospatial profiling of gradient awareness as per our past work [25,26]. Let us consider an EPA standard outdoor air quality index (O-AQI) real-time variables as temperature T in centigrade, pressure P in pascals, humidity H in %, volatile organic compounds VoC (ppm), particulate matter as PM (ppm), Ozone as O3 (ppm), Nitrogen Dioxide as NO2 (ppm), Carbon Monoxide as CO2 (ppm), and Sulphur Dioxide as SO2(ppm).…”

“…The proposed model and applied algorithm were tested and validated using our TRL7 autonomous AQI mapping system from past works [25,26]. A 1-1 correspondence electronics and instrumentation system were designed in a single package, i.e.…”

“…The existing O-AQNs, Urban AirQ, Smart Citizen Kit, Air SensUR 4.0, SeReNo V1, and AirQ Mesh needed improvement in AQI dependent principal component approach [25] in the scope of automated optimization of forecasting. The multi-time series-based forecasting required novel melioration in linear regression and tree-based time-series learning, regression, and forecasting tree that was an innovative step in the SeReNoV2 AQM systems.…”

Multi-Channel Networked MIMO-MPC-based SiL/HiL/MiL for CC/CV Sections’ Optimization in 6th Gen Hybrid/Islanded Inverters for Mobile Green Nano-Grids in FEW Nexus

“… The Selection of potential and stable sensors and controllers (with better outdoor life) for real-time measurement of variables and critical data processing procedures and statistical evaluations to compute real-time CFGRI [30,31].  Design of flexible/programable transformer-less smart converter with embedded IoT gateway system to achieve the required degree of autonomy by improved deep learning approaches for embedded IoT systems [32].…”