Algorithmic Strategies for Sensing-as-a-Service in the Internet-of-Things Era

Abstract

The objective of this thesis is to design efficient algorithms and architectures for enabling a Sensing as a Service paradigm in the recent era of Internet-of-things. With the widespread deployment of sensor architectures and sensor-enabled applications all around the globe, our planet today is witnessing an unprecedented instrumentation. The emerging paradigm of Sensing as a Service is replete with many open challenges, starting from systematic sensor deployment, regulated data collection, efficient data aggregation, scalable execution and proper participation. This dissertation aims to address some of these open challenges and attempts to carve a niche proposition by handling these problems from a purely algorithmic perspective. The objective is to examine each of the crucial pieces outlined above in the light of algorithmic design and come up with efficient mechanisms that are both practical and theoretically well-founded. The experiments are planned on real world data and hence, are expected to allow us to examine the efficacy of our proposals in a realistic setting.