Content-Centric Event-Insensitive Big Data Reduction in Internet of Things

Abstract

As more knowledge discovery functions or sensing units for event detection are added to sensor devices in the Internet of Things (IoT), devices acquire big data that is bigger than they are able to deliver using their radios in a given time window. As a result, energy consumption for big data acquisition and transmission and real-time data processing are great challenges. In this paper, we introduce BigReduce, a low-cost IoT framework for event detection that reduces a big amount of data at the time of data acquisition and before the data transmission across the network. BigReduce works on the analysis of the frequency content of signals as they are acquired and efficiently adapts the frequency rate based on the sensitivity to a respective event, such as fire event. Instead of transmitting the entire set of acquired data, BigReduce transmits only the signals that have a high event-sensitivity. We provide a detailed algorithm for fire event sensitivity indication based on the frequency consents. Results achieved through a lab testbed show that BigReduce is able to reduce energy consumption by at least 78% and data volume by 82% in comparison to other frameworks.