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arXiv · 2510.05149

Percepta: High Performance Stream Processing at the Edge

Clarisse Sousa·Tiago Fonseca·Luis Lino Ferreira·Ricardo Venâncio·Ricardo Severino
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Abstract

The rise of real-time data and the proliferation of Internet of Things (IoT) devices have highlighted the limitations of cloud-centric solutions, particularly regarding latency, bandwidth, and privacy. These challenges have driven the growth of Edge Computing. Associated with IoT appears a set of other problems, like: data rate harmonization between multiple sources, protocol conversion, handling the loss of data and the integration with Artificial Intelligence (AI) models. This paper presents Percepta, a lightweight Data Stream Processing (DSP) system tailored to support AI workloads at the edge, with a particular focus on such as Reinforcement Learning (RL). It introduces specialized features such as reward function computation, data storage for model retraining, and real-time data preparation to support continuous decision-making. Additional functionalities include data normalization, harmonization across heterogeneous protocols and sampling rates, and robust handling of missing or incomplete data, making it well suited for the challenges of edge-based AI deployment.

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Clarisse Sousa, Tiago Fonseca, Luis Lino Ferreira, Ricardo Venâncio, Ricardo Severino. 2025-10-02. Percepta: High Performance Stream Processing at the Edge. https://arxiv.org/abs/2510.05149

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