EDGE ANALYTICS AND COMPLEX EVENT PROCESSING FOR REAL TIME AIR POLLUTION MONITORING AND CONTROL

The advent of the Internet of Things (IoT) has led to the generation of tremendous amounts of data from various sources. Cloud based systems are effective in storage and application of machine learning algorithms on such datasets. However, in some cases it is important to enable real time processing for making immediate decisions. There are many applications which require instantaneous analysis of the generated data for remedies in event of an anomaly. Data associated with such use cases remains significant only for a short duration of time. Various electronic sensors, e.g. Temperature, Moisture, Air Quality, Pressure, Wind Velocity etc. present in a Wireless Sensor Network generate streams of values. It can be processed using pipelines which provide prompt and quick analysis for decision making. Stream Processing Systems can be helpful in such cases as they analyse data streams within a few milliseconds to a few seconds. In this paper, we discuss an event based processing of streaming data from air pollution sensors to create a real time anomaly detection system. To reduce the delays associated with the generation of alarms in our pipeline, Apache Foundation's Stream Processing Tools, Kafka and Flink were used for operations on our streams. To further accelerate the process, all the analysis is conducted on an embedded edge computing gateway device rather than sending data to the cloud for batch processing. The results are obtained in the form of a geographical map visualization using ELK stack. The map highlights the coordinates of the location with an unhealthy air quality index in real time.