Percepta: High Performance Stream Processing at the Edge
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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.
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.
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4 of 14 references.
References · 4
- 2024EnergAIze: Multi Agent Deep Deterministic Policy Gradient for Vehicle to Grid Energy Managementcited by 3
- 2019Challenges in designing edge-based middlewares for the Internet of Things: A surveycited by 1
- 2024AgileDART: An Agile and Scalable Edge Stream Processing Enginecited by 1
- 2025Control of Renewable Energy Communities using AI and Real-World Datacited by 1
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- 2025Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning
- 2024The Fusion of Deep Reinforcement Learning and Edge Computing for Real-time Monitoring and Control Optimization in IoT Environments
- 2024Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks
- 2025Cognitive Edge Computing: A Comprehensive Survey on Optimizing Large Models and AI Agents for Pervasive Deployment
- 2025Multi-Rate Task-Oriented Communication for Multi-Edge Cooperative Inference
- 2026MAcPNN: Mutual Assisted Learning on Data Streams with Temporal Dependence
- 2024To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
- 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks