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IoT applications usually rely on cloud computing services to perform dataanalysis such as filtering, aggregation, classification, pattern detection, andprediction. When applied to specific domains, the IoT needs to deal with uniqueconstraints. Besides the hostile environment such as vibration andelectric-magnetic interference, resulting in malfunction, noise, and data loss,industrial plants often have Internet access restricted or unavailable, forcingus to design stand-alone fog and edge computing solutions. In this context, wepresent STEAM++, a lightweight and extensible framework for real-time datastream processing and decision-making in the network edge, targetinghardware-limited devices, besides proposing a micro-benchmark methodology forassessing embedded IoT applications. In real-case experiments in asemiconductor industry, we processed an entire data flow, from values sensing,processing and analyzing data, detecting relevant events, and finally,publishing results to a dashboard. On average, the application consumed lessthan 500kb RAM and 1.0% of CPU usage, processing up to 239 data packets persecond and reducing the output data size to 14% of the input raw data size whennotifying events.

international journal of computer science and information technology/international journal of computer science and information technology  (chennai. print)

STEAM++ An Extensible End-To-End Framework for Developing IoT Data Processing Applications in the Fog