Sampling and predicting geographic areas using participatory sensing

• Main Author: Wang, Wei
• Format: Others
• Language: English
• Published: Uppsala universitet, Institutionen för informationsteknologi 2015
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-272194

Participatory sensing is the concept that people contribute information they retrieved independently from the environment using sensors to build a whole body of knowledge. With the popularity of mobile devices, such as smart phones, which have multiple sensors and wireless interfaces, "participatory sensing" has become feasible in a large-scale. Spatial sampling is a technique using a limited number of geographical samples to achieve high credibility in measurement, and then predicting data values for unsampled areas. In this paper, participatory sensing is combined with spatial sampling and prediction, and evaluated under various scenarios. In this paper, an approach based on participatory sensing, sampling and predicting spatial data and evaluating participatory sensing involving prediction results is designed. A Java system prototype is implemented based on the design. Perlin noise and the ONE simulator are used to implement simulation for spatial sampling with participatory sensing. In the prediction, three different prediction algorithms are applied, Voronoi diagram, Delaunay triangulation with gradient and ordinary Kriging. Evaluation of participatory sensing and spatial sampling is measured by root-mean-square-error between true map and predicted map by pixels. The results of the experiments indicate that generally the Voronoi diagram has larger error value than Delaunay triangulation with gradient when only having a few samples. And ordinary Kriging produces the most accurate results but it has highest time complexity and requires a large number of samples to achieve high accuracy. In addition, more evenly distributed samples contribute to higher accuracy of prediction. Given a proper guide, participants in participatory sensing can improve the spatial sampling quality a lot.