| Full text | |
| Author(s): |
Oliveira, Egberto
;
da Rocha, Atslands R.
;
Mattoso, Marta
;
Delicato, Flavia C.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | Journal of Grid Computing; v. 20, n. 3, p. 25-pg., 2022-09-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 15/24144-7 - Technologies and solutions for enabling the cloud of things paradigm |
| Grantee: | José Neuman de Souza |
| Support Opportunities: | Research Projects - Thematic Grants |