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.

翻译：实时数据的兴起与物联网（IoT）设备的激增凸显了以云为中心解决方案的局限性，尤其是在延迟、带宽和隐私方面。这些挑战推动了边缘计算的发展。伴随物联网出现的一系列其他问题包括：多源数据速率协调、协议转换、数据丢失处理以及与人工智能（AI）模型的集成。本文提出Percepta，一种专为支持边缘AI工作负载而设计的轻量级数据流处理（DSP）系统，特别关注强化学习（RL）等任务。该系统引入了奖励函数计算、模型再训练数据存储以及实时数据准备等专用功能，以支持连续决策。其他功能包括数据归一化、跨异构协议与采样率的协调，以及对缺失或不完整数据的鲁棒处理，使其能很好地应对基于边缘的AI部署所面临的挑战。