IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Adaptive Sensor Aggregation With Self-Monitoring: A Rust-Based Concurrent Data Pipeline

Adaptive Sensor Aggregation With Self-Monitoring: A Rust-Based Concurrent Data Pipeline
View Sample PDF
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

Purchase

View Adaptive Sensor Aggregation With Self-Monitoring: A Rust-Based Concurrent Data Pipeline on the publisher's website for pricing and purchasing information.

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.

Related Content

Licheng Huang, Bochen Xue, Yiming Chen, Peihang Wu, Yuezhong Wang, Aquil Mirza Mohammed. © 2026. 34 pages.
Hong Rui Zhou, Min Hao Ling, Tong Yao Li, Xiang Li, Yi Ran Wu, Cong Wu. © 2026. 34 pages.
Chenyu Liu, Yaxin Luo, Jingyan Zeng, Liyuan Fan, Mingyuan Tang, Cong Wu. © 2026. 28 pages.
Haochen Shi, Xuan Luo, Junhao Huang, Yixiong Feng, Zihan Meng, Aquil Mirza Mohammed. © 2026. 34 pages.
Ruiman Huang, Shuxin Jia, Zeyu Min, Haoyue Zhang, Hewa Majeed Zangana. © 2026. 32 pages.
Shu Kei Ling, Pak Sun Wong, Kwan Ho Yuen, Mohammad Al Khaldy. © 2026. 40 pages.
Enlong Dong, Huakun Huang, Huakai Huang, Ruize Liu, Hengxian Li. © 2026. 34 pages.
Body Bottom