Processing and visualizing traffic pollution data in Hanoi City from a wireless sensor network

Abstract

Hanoi city is currently dealing with rapidly increasing air pollution that result from variety of sources. The main cause of pollution is exhaust gas from traffic system with a very large number of private vehicles. In order to help the city's environment authorities monitor the level of air pollution, a wireless sensor network is currently under development to collect traffic pollution data measured by a number of gas sensors. This paper focuses on how to process pollution data and visualize level of pollution relying on available datasets collected from sensor network. The volume of data collected from each area of the city can be very large and dynamic due to the number of mobile sensors deployed in the same area at the same time and their measurement frequency. First, we present a method for processing raw data using calibration and data clustering techniques. Second, we describe how measurement datasets are visually represented on the city's online map on the basis of mathematical interpolation method that corresponding to characteristics of environmental data. And then we also use computer graphic technique to improve the visualization quality. Finally, this paper show the result of those methods with sample data collected from an urban district of Hanoi City on a website by which we do not only provide to viewer the actual level of pollution by position but also by time.

Sensors on Internet of Things Systems for the Sustainable Development of Smart Cities: A Systematic Literature Review

“…Similarly, Wen et al [42] deployed IoT systems at crossroads and main roads in Taipei to monitor carbon monoxide levels in urban areas. Hoang et al [43] used an IoT system to monitor air pollution levels caused by transportation in Hanoi. They visualized the data using calibration and data clustering techniques, mathematical interpolation methods, and computer graphics.…”

“…For the calibration of chemical sensors, Arfire, Marjovi, andMartinoli [50] proposed a model-based sliding window single-hop rendezvous calibration algorithm to estimate the baseline and gain characteristics of chemical sensors, taking into account temperature dependencies and temporal drift of the sensors. Not specified [45,46,49,51,53,54,56] Carbon monoxide (CO) CO-B4, Alphasense, Essex, UK [45] MiCS-4514 [51] MiCS-5525 metal oxide semiconductor [41,42] TGS 2442 [43] A3CO, City Technology Ltd., Portsmouth, UK [50] Not specified [41][42][43]46,50,[52][53][54]56] SO 2 SO 2 -B4, Alphasense, Essex, UK [45] Not specified [46,[52][53][54]56] O 3 MQ-131 [51] OX-B431, Alphasense, Essex, UK [45] Not specified [46,[51][52][53]56] TVOC Not specified [55] VOC Not specified [54] NO 2 MiCS-4514 [51] NO 2 -B43F, Alphasense, Essex, UK [45] Not specified [46,51,53,54,…”

Using Mobile Elements as Dynamic Bridges in Sparse Wireless Sensor Networks

“…Recently, a path planning algorithm has been proposed that allows mobile nodes to autonomously navigate through the field for improving the area coverage [27]. As alternative, our method uses pre-defined trajectories for moving MEs in order to cover the desired area [29,32]. From the viewpoint of traffic-generated pollution, only main streets are of interest for the coverage.…”

“…Volume E3, No7 (11) a number of MEs for testing the proposed protocol using a ZigBee mesh network. Our network is based on a Meshlium base station and eight Waspmotes [32]. The sensor nodes move on eight trajectories based on the roads with most traffic pollution in order to cover an urban district of Hanoi City with an area of about 14.6 km2 as shown in the Fig.…”

Data-driven sensors clustering and filtering for communication efficient field reconstruction

“…Sensors' main task is to monitor a field and continuously acquire spatio-temporal samples that contain information about the physical phenomena of interest. Such phenomena are induced by sources, such as thermal or chemical sources, causing temperature variations in a field [35], air pollution, or light intensity variations [11]. One popular architecture when using sensing units is the fusion-center (FC) based sensor network, where the sensing units transmit their measurements to the FC.…”