Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.
This paper develops a novel and simple algorithm to perform joint carrier frequency offset and channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. Carrier frequency offset of upto several frequency subcarrier spacings has to be estimated and compensated in order to enhance the OFDM system performance. The time domain synchronization algorithms using the preamble structure provided for fixed broadband wireless OFDM uplink access (IEEE 802.16 LAN/MAN standards) can only measure a frequency offset with an ambiguity equal to an even number of subcarrier spacings, in case of the actual offset being more than a subcarrier spacing. Our proposed algorithm uses the received preamble to jointly estimate this remaining integer part of the frequency offset and the initial channel impulse response using a least squares criterion in an iterative manner. Computer simulations show that the proposed estimator is very robust at low signal to noise ratios (SNRs) and operate near the Cramer-Rao bound for the variance of the frequency offset estimate.
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