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Adaptive Sensor Aggregation With Self-Monitoring: A Rust-Based Concurrent Data Pipeline
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Author(s): Ruiman Huang (The Hong Kong Polytechnic University, Hong Kong), Shuxin Jia (The Hong Kong Polytechnic University, Hong Kong), Zeyu Min (The Hong Kong Polytechnic University, Hong Kong), Haoyue Zhang (The Hong Kong Polytechnic University, Hong Kong)and Hewa Majeed Zangana (Duhok Polytechnic University, Iraq)
Copyright: 2026
Pages: 32
Source title:
Advanced Concurrency, Buffering, and Web-Sync in Sensor Platforms: Zero-Loss Streams
Source Author(s)/Editor(s): Aquil Mirza Mohammed (The Hong Kong Polytechnic University, Hong Kong)
DOI: 10.4018/979-8-2600-1101-0.ch005
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Abstract
This chapter implements a Rust-based sensor data aggregation platform, achieving zero data loss in the case of multiple sensors running at the same time. The system faces three main challenges: preventing overflow in each sensor's 128-element internal buffer, supporting real-time statistical aggregation, and ensuring storage integrity during concurrent writes. In order to solve these problems, we adopt a multi-layer design: the front end is per-sensor reader threads, and the reading frequency is dynamically adjusted in combination with adaptive polling; a bounded mpsc::sync_channel is used as the intermediate buffer to provide backpressure when downstream processing becomes slow. A multi-worker aggregation engine processes sensor data in time-sliced windows. Additionally, we also implemented advanced features including real-time visualization, adaptive thread pooling and automated benchmarking suite. The Benchmark results show that the system sustains zero data loss under stress and achieves high throughput in large-scale tests.
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