Mobile Global Sensor Network for enhanced sensor management

Abstract

Internet of Things introduces many applications and services where sensor data need to be easily accessible. There are a number of open source middleware platforms to collect and publish sensor data. Some of those platforms support data annotation which provides the context of the data and make them meaningful. However, current architectures of those platforms facilitate the annotation of the sensor data to happen in centralized servers. This process requires the sensor data to travel to the cloud-based server in order to be annotated. There are some research works that focus on mobile devices e.g., smart phones, tablets, mobile routers etc. to be able to run an instance of such platforms so that data acquisition becomes easier in a distributed manner. But in these approaches also, data annotation, if supported, happens only after pushing them to the server. Whereas, the ability to annotate sensor data close to the source would improve the ability to understand the data and could play important role in data filtering and provenance. This thesis focuses on annotating the sensor data in the mobile devices. It proposes Mobile Global Sensor Network (MGSN) that can collect data from sensors, annotate them, make them available for other applications and push them to the server for further use. It also can combine on-board sensor data with external sensor data and capture user behavior by annotating the on-board data. Thus MGSN opens up opportunities for context aware applications and can help end-users practice sustainable behavior. It can manage sensor life time by suggesting power saving mode based on the annotated data. Also, data can be filtered in the mobile devices to reduce uplink traffic.