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Coordinating Cross-Process Sensor Aggregation in Rust With IPC and Shared Memory
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Author(s): Haochen Shi (The Hong Kong Polytechnic University, Hong Kong), Xuan Luo (The Hong Kong Polytechnic University, Hong Kong), Junhao Huang (The Hong Kong Polytechnic University, Hong Kong), Yixiong Feng (The Hong Kong Polytechnic University, China), Zihan Meng (The Hong Kong Polytechnic University, Hong Kong)and Aquil Mirza Mohammed (The Hong Kong Polytechnic University, Hong Kong)
Copyright: 2026
Pages: 34
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.ch004
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Abstract
This chapter, per the authors, presents a distributed sensor data aggregation platform in Rust demonstrating operating systems concepts including inter-process communication and concurrent data processing. The system employs a process-based architecture with ten child processes: five sensor readers connected via anonymous pipes, four aggregation workers sharing a named FIFO pipe, and one web server. A collector process groups sensor readings into fixed-duration time windows forwarded through POSIX shared memory with atomic counters. The aggregation engine applies Welford's online variance algorithm to compute streaming statistics; minimum, maximum, mean, and standard deviation; without a second data pass. Anomaly detection uses a configurable z-score threshold per sensor type. Five benchmark trials confirm zero data loss across 698 readings, a throughput of 71.0 requests per second, a p99 latency of 14.08 milliseconds, and peak buffer utilisation of 0.01%, validating the platform's reliability guarantees under the intended workload.
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