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An Integrated Framework for Real-Time Sensor Data Aggregation and Adaptive Buffer Management in Concurrent Systems
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Author(s): Licheng Huang (Hong Kong Polytechnic University, Hong Kong), Bochen Xue (Hong Kong Polytechnic University, Hong Kong), Yiming Chen (Hong Kong Polytechnic University, Hong Kong), Peihang Wu (Hong Kong Polytechnic University, Hong Kong), Yuezhong Wang (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.ch001
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Abstract
This chapter presents an integrated framework for real-time sensor data aggregation and adaptive buffer management in concurrent systems. The architecture comprises a two-level buffer inspired by two-stage caching principles: a internal queue inside each sensor and a central buffer manager that receives data via unnamed pipes. The buffer manager employs dynamic capacity expansion to prevent overflow. The aggregation engine uses multiple worker threads that process readings concurrently; per-sensor windows are stored in a hash map protected by an RwLock. It supports three anomaly detection methods with explainable outputs. A data storage module provides thread-safe file persistence with automatic rotation, and a multi-threaded web server delivers a live dashboard, file statistics, and an API. Evaluation demonstrates zero data loss at 80k entries/s. The framework offers a blueprint for embedding cognitive capabilities into sensor-based research infrastructures.
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