{"ID":2861385,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.05149","arxiv_id":"2510.05149","title":"Percepta: High Performance Stream Processing at the Edge","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.","short_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:...","url_abs":"https://arxiv.org/abs/2510.05149","url_pdf":"https://arxiv.org/pdf/2510.05149v1","authors":"[\"Clarisse Sousa\",\"Tiago Fonseca\",\"Luis Lino Ferreira\",\"Ricardo Venâncio\",\"Ricardo Severino\"]","published":"2025-10-02T08:57:45Z","proceeding":"cs.DC","tasks":"[\"cs.DC\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}