Spatial Interpolation Techniques on Participatory Sensing Data

Abstract

Spatial distributions of data of natural phenomena can be estimated by using different spatial interpolation techniques. These techniques can be used for the purpose of developing urban noise pollution monitoring applications, so they can truly describe the actual urban noise pollution scenario of any region of interest to make effective and informed decisions. In this context, our aim is to use IoT-cloud based framework to generate dynamic (i.e., changes in terms of time and space) noise maps as a service with the help of spatial interpolation techniques. Noise map generation is an effective method for visualizing and assessing urban noise pollution. In this article, we have proposed three spatial interpolation techniques (GLIDW, I-GLIDW, GLIDW-OK) that work on participatory sensing-based noise pollution data collected using smartphones as IoT devices to generate dynamic noise maps. Proposed techniques can address diverse scenarios such as sparse datasets , high accuracy , better response time , and so on. Depending on the situation, we can choose an appropriate technique. We evaluate our proposed methods based on a real-world urban noise pollution dataset collected by participants over a period of two years in an urban area of the city Kolkata. The results are compared with inverse distance weighting (IDW) and Ordinary Kriging (OK) methods. The method GLIDW is proposed for a dense dataset. The results validate that in the case of a dense dataset GLIDW dominates over other methods. But, when the data sparsity level is medium, I-GLIDW performs well. However, if the dataset is very sparse, then GLIDW-OK dominates in terms of predictive accuracy. The results also show that Relative Improvement (RI) of I-GLIDW and GLIDW-OK is always positive compared to baseline methods IDW and OK.

Obtención de un modelo de predicción semi empírico mediante una herramienta de información geográfica para ambiente urbano

“…Finamente, al realizar un análisis de los modelos matemáticos obtenidos para la densidad de potencia [30] , como se observa en la Figura 6, se aprecia un efecto de caida de 20 dB apartir de las distancias superiores a 900 metros debido al factor σ L y que se ve mas pronunciado con los datos medidos, como lo demuestran las normas tecnicas de la estudiadas ITU-R como es el caso de la [27,31,32].…”

Georeferenced Analysis of Urban Nightlife and Noise Based on Mobile Phone Data

“…In ref. [34], Middya et al also use IoT technologies, combined with spatial interpolation techniques and real-world participatory sensing-based datasets collected by participants over a period of one year, to help monitor noise pollution.…”

Double Auction Based Incentive Mechanism for Participatory Sensing Systems

“…The main focus of this paper is to come up with an incentive mechanism for the sensing tasks that are related to different dynamics of a city (e.g. road condition monitoring [3,4], mobility pattern [5], air pollution monitoring [6,7], noise pollution monitoring [8,9], etc.). From this perspective, a city can be viewed as different functional regions such as educational areas, residential areas, business areas, areas with major road segments, etc.…”