Latency and Energy-Awareness in Data Stream Processing for Edge Based IoT Systems

• Published in: Journal of grid computing Vol. 20; no. 3
• Main Authors: Oliveira, Egberto, Rocha, Atslands R da, Mattoso, Marta, Delicato, Flavia C
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01-09-2022; Springer Nature B.V
• Subjects: Accuracy; Adaptive sampling; Computer Science; Data processing; Data transmission; Edge computing; Electronic devices; Energy; Energy consumption; Internet of Things; Management of Computing and Information Systems; Prediction models; Processor Architectures; Sensors; User Interfaces and Human Computer Interaction; Workloads; Internet of things; Adaptive sampling; Data stream processing; Active node selection; IoT; Edge computing
• ISSN: 1570-7873; 1572-9184
• DOI: 10.1007/s10723-022-09611-4

LE-STREAM is a framework for IoT data stream processing. Data processing in IoT is challenging due to its dynamic and heterogeneous nature, and the massive amount of generated data. Sensor data suffers from uncertainty and inconsistency issues, that can affect its accuracy. Several IoT applications are time sensitive, requiring fast data processing. Finally, as IoT devices are often battery powered, processing tasks must be performed in an energy-efficient way. Therefore, challenges in IoT data stream processing span three dimensions: accuracy, latency and energy; and LE-STREAM jointly addresses them. It leverages edge computing to bring the data processing closer to the data sources, thus minimizing latency. Adaptive sampling combined with data prediction model reduce the energy consumption of devices without compromising data accuracy. An active node selection schema improves the workload distribution among devices, also tackling the energy dimension by promoting a graceful degradation of device resources.