On Big Data Management in Internet of Things
Thesis
The Internet of Things (IoT) has generated a large amount of research interest across a wide variety of technical areas. These include the physical devices themselves, communications among them, and relationships between them. One of the effects of ubiquitous sensors networked together into large ecosystems has been an enormous flow of data supporting a wide variety of applications. In this work, we propose a new “IntelliFog-Cloud” approach to IoT Big Data Management by leveraging mined historical intelligence from a Big Data platform and combining it with real-time actionable events from IoT devices at the Fog layer to reduce action latency in IoT applications. This approach is demonstrated through an advertisement service simulation with VoltDB technology where advertisements are being served on mobile phones based on geo-location and highest bids, and displayed from user interests determined by data analytics of activities on the web. Results from the demonstration show very low latency overhead of processing large hundreds of thousands of transactions. This approach improves both action latency and accuracy of real-time decisions in IoT applications.