The design of an IoT-GIS platform for performing automated analytical tasks. (March 2019) - Cao, Hung; Wachowicz, Monica
Abstract: Society has a very ambitious vision of building smart interconnected cities through the Internet of Things (IoT). Billions of data streams will be generated by devices using different networking infrastructures of smart cities, enabling the automation of how the data that are being collected can be analysed for. However, significant scientific and technological challenges need to be overcome before IoT-GIS platforms can be widely used. This paper is a first step towards designing an IoT-GIS platform for performing automated analytical tasks that are able to retrieve, integrate and contextualize data streams with the purpose of adding value to the provision of transit services. Three automated tasks are used to describe our platform: (1) data ingestion for retrieving data streams; (2) data cleaning for handling missing and redundant data streams; and (3) data contextualization for representing the mobility context of transit driving behaviour. The Codiac Transit System of the Greater Moncton area, NB, Canada was used for building a mobility context and evaluating the cloud architecture that was used to implement our IoT-GIS platform. From the experimental results, the need for cloud computing for achieving scalability and high performance of our IoT-GIS platform is validated. Suggestions for the operational management of routes to improve service quality are proposed based on the analytical outcomes. Highlights: This article proposes an IoT-GIS platform for supporting an automated analytical process using a mobility context. The aim of this study is to propose a new approach to support the smart transit application for the small urban areas. There are three main automated tasks: (1) data ingestion for retrieving data streams; (2) data cleaning for handling missing and redundant data streams; and (3) data contextualization for representing human mobility . A cloud architecture is used to implement our IoT-GIS platform and evaluate our new automated analytical process. The outcomes of this study is promising for the operational management to improve service quality.