Abstract
The collection of sensory data is crucial for cyber-physical systems. Employing mobile agents (MAs) to collect data from sensors offers a new dimension to reduce and balance their energy consumption but leads to large data collection latency due to MAs’ limited velocity. Most existing research effort focuses on the offline mobile data collection (MDC), where the MAs collect data from sensors based on preoptimized tours. However, the efficiency of these offline MDC solutions degrades when the data generation of sensors varies. In this paper, we investigate the on-demand MDC; that is, MAs collect data based on the real-time data collection requests from sensors. Specifically, we construct queuing models to describe the First-Come-First-Serve-based MDC with a single MA and multiple MAs, respectively, laying a theoretical foundation. We also use three examples to show how such analysis guides online MDC in practice.
Highlights
Collecting data from sensors is a core function of large cyberphysical systems such as wind farm and smart grid [1,2,3,4]
We formulate two queuing models to capture the ondemand mobility-assisted data collection (MDC) with the First-Come-First-Serve (FCFS) discipline (FCFS is a simple and natural choice to maintain request fairness and is preferred in certain node-centric scenarios.), on the cases where a single mobile agents (MAs) and multiple MAs are deployed for data collection, respectively, and corresponding analytical results on the data collection performance are Wireless Communications and Mobile Computing derived
Targeting on these scenarios with diverse data generations, we investigate the on-demand MDC, where the MAs collect data based on real-time demands from sensor nodes in this paper
Summary
Collecting data from sensors is a core function of large cyberphysical systems such as wind farm and smart grid [1,2,3,4]. We formulate two queuing models to capture the ondemand MDC with the First-Come-First-Serve (FCFS) discipline (FCFS is a simple and natural choice to maintain request fairness and is preferred in certain node-centric scenarios.), on the cases where a single MA and multiple MAs are deployed for data collection, respectively, and corresponding analytical results on the data collection performance are Wireless Communications and Mobile Computing derived. (i) Formulation of an M/G/1 queuing model to capture and analytically evaluate the on-demand MDC when a single MA is deployed for data collection (Section 4). (ii) An M/G/c queuing model for the case when multiple MAs are deployed, based on which the data collection performance is explored via approximation (Section 5).
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