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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.05149 |
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| _version_ | 1866914078775050240 |
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| author | Sousa, Clarisse Fonseca, Tiago Ferreira, Luis Lino Venâncio, Ricardo Severino, Ricardo |
| author_facet | Sousa, Clarisse Fonseca, Tiago Ferreira, Luis Lino Venâncio, Ricardo Severino, Ricardo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Percepta: High Performance Stream Processing at the Edge Sousa, Clarisse Fonseca, Tiago Ferreira, Luis Lino Venâncio, Ricardo Severino, Ricardo Distributed, Parallel, and Cluster Computing Artificial Intelligence 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. |
| title | Percepta: High Performance Stream Processing at the Edge |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2510.05149 |