The collection of sensory data is crucial for cyber-physical systems. Employing mobile agents (MAs) to collect data from sensors\noffers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs�\nlimited velocity.Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from\nsensors based on preoptimized tours. However, the efficiency of these offlineMDC solutions degrades when the data generation of\nsensors varies. In this paper, we investigate the on-demand MDC; that is, MAs collect data based on the real-time data collection\nrequests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve-based MDC with a single\nMA and multiple MAs, respectively, laying a theoretical foundation.We also use three examples to show how such analysis guides\nonline MDC in practice.
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