AEDS-IoT: Adaptive clustering-based Event Detection Scheme for IoT data streams

Abstract

The emergence of the Internet of Things is expected to produce a huge amount of data streams for sensing the phenomenon from the real world. These data patterns i.e. data distributions keep on evolving and are subject to change based on the changes in the external environment. Valuable and useful information should be extracted from this data stream in real-time- to find out the events of interest. Pattern extraction from stored data and its analytics is convenient, though stream analytics has major challenges associated with it like identifying the event patterns from limited data in the stream, concept drift detection, outlier detection, and handling, limited storage, and processing capacity etc. Traditional methods, in general, work well with historical data yet have limitations while dealing with real-time data streams. Hence, the present work proposes adaptive clustering-based Event Detection Scheme for IoT (AEDS-IoT) stream data, which can infer events of interest from data distribution patterns present in streaming data. Initially, the scheme uses an adaptive window to collect the streaming data and processes it to detect the events. Further, the event patterns are detected by adaptive clustering. After identifying event patterns, the user assigns labels based on spatial–temporal conditions. The proposed AEDS methodology updates the patterns generated from upcoming stream data received. The performance of the proposed scheme is compared against the state of the scheme using the Arhus dataset. To corroborate the scheme with experimental evidence, the scheme was applied to the data collected from the Ganga river(the life line of North India) using smart water sensors. The performance and efficacy of AEDS-IoT were also tested on these data to detect upcoming event patterns from the data stream.