# Percepta: High Performance Stream Processing at the Edge

arXiv: https://arxiv.org/abs/2510.05149 · HTML: https://mlsanity.com/paper/2510.05149/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 2 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Management](https://mlsanity.com/paper/2404.02361/) (2024)
- [Challenges in designing edge-based middlewares for the Internet of Things: A survey](https://mlsanity.com/paper/1912.06567/) (2019)
- [AgileDART: An Agile and Scalable Edge Stream Processing Engine](https://mlsanity.com/paper/2407.14953/) (2024)
- [Control of Renewable Energy Communities using AI and Real-World Data](https://mlsanity.com/paper/2505.17321/) (2025)

## Abstract (arXiv)

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.