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Next-generation stream processing systems for community scale IoT applications must handle complex nonfunctional needs, e.g. scalability of input, reliability/timeliness of communication and privacy/security of captured data. In many IoT settings, efficiently batching complex workflows remains challenging in resource-constrained environments. High data rates, combined with real-time processing needs for applications, have pointed to the need for efficient edge stream processing techniques. In this work, we focus on designing scalable edge stream processing workflows in real-world IoT deployments where performance and privacy are key concerns. Initial efforts have revealed that privacy policy execution/enforcement at the edge for intensive workloads is prohibitively expensive. Thus, we leverage intelligent batching techniques to enhance the performance and throughput of streaming in IoT smart spaces. We introduce BatchIT, a processing middleware based on a smart batching strategy that optimizes the trade-off between batching delay and the end-to-end delay requirements of IoT applications. Through experiments with a deployed system we demonstrate that BatchIT outperforms several approaches, including micro-batching and EdgeWise, while reducing computation overhead.
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adaPARL: Adaptive Privacy-Aware Reinforcement Learning for Sequential Decision Making Human-in-the-Loop Systems
Reinforcement learning (RL) presents numerous benefits compared to rule-based approaches in various applications. Privacy concerns have grown with the widespread use of RL trained with privacy- sensitive data in IoT devices, especially for human-in-the-loop systems. On the one hand, RL methods enhance the user experience by trying to adapt to the highly dynamic nature of humans. On the other hand, trained policies can leak the user’s private information. Recent attention has been drawn to designing privacy-aware RL algorithms while maintaining an acceptable system utility. A central challenge in designing privacy-aware RL, especially for human-in-the-loop systems, is that humans have intrinsic variability, and their preferences and behavior evolve. The effect of one privacy leak mitigation can differ for the same human or across different humans over time. Hence, we can not design one fixed model for privacy-aware RL that fits all. To that end, we propose adaPARL, an adaptive approach for privacy-aware RL, especially for human-in-the-loop IoT systems. adaPARL provides a personalized privacy-utility trade-off depend- ing on human behavior and preference. We validate the proposed adaPARL on two IoT applications, namely (i) Human-in-the-Loop Smart Home and (ii) Human-in-the-Loop Virtual Reality (VR) Smart Classroom. Results obtained on these two applications validate the generality of adaPARL and its ability to provide a personalized privacy-utility trade-off. On average, adaPARL improves the utility by 57% while reducing the privacy leak by 23% on average.
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- Award ID(s):
- 2105084
- PAR ID:
- 10417999
- Date Published:
- Journal Name:
- IoTDI '23: Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation
- Page Range / eLocation ID:
- 262 to 274
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3576842.3582325
