Computing Techniques for Classifying Community Sensor Network Data

Abstract

Community sensing has emerged as an attractive research topic in the past decade. It involves public participation through an interactive sensor network via privately owned sensors embedded on devices, such as cellular phones. It enables the community to collect data, process and distribute resulted information that is useful in a variety of monitoring tasks. Important application areas of community sensing include infrastructural, health and environmental sensing. Tremendous increase in the number of smartphone users has been witnessed in the recent years. Smartphones owned by people are equipped with many low-cost sensors like tri-axial accelerometer, microphone, and GPS. Moreover, these phones have processing and communication capabilities and large storage. With such features, it is viable to use smartphones in different monitoring activities. In addition, to alleviate the necessity of costly traditional monitoring equipment, the novelty of such a paradigm is community sensing. It empowers the user community to deploy sensing applications at wider scale and collect data from heterogeneous sensors owned by a large number of people. This is a paradigm shift from the standard engineering practice where trained experts use reliable high precision equipment for monitoring. While the new paradigm promises to deliver a large amount of information with very little cost, reliability of the data obtained from heterogeneous sources must be critically examined. Sensor-enabled smartphones have been potentially used in different monitoring activities reliably and with high precision. Thereby, smartphones have the potential in precise motion sensing. Over the last few years, community sensing have been developed in domains ranging from information sharing to collaborative monitoring. Smartphone is identified as a powerful and widely used community sensing platforms in a number of such applications. This thesis mainly focuses on the development of a community sensing system through smartphones.