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首页> 外文期刊>Industrial Electronics, IEEE Transactions on >A Loss Inference Algorithm for Wireless Sensor Networks to Improve Data Reliability of Digital Ecosystems
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A Loss Inference Algorithm for Wireless Sensor Networks to Improve Data Reliability of Digital Ecosystems

机译：用于提高数字生态系统数据可靠性的无线传感器网络损耗推断算法

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摘要

Digital ecosystems (DEs) are based on a large amount of distributed data, and these data are gathered from physical devices, particularly from wireless sensor networks (WSNs). Due to the inherent stringent bandwidth and energy constraints, energy-efficient mechanisms of performance measurement are the key to the proper operation of WSNs and thereby important for the data reliability of DEs. This paper presents a novel algorithm, i.e., Loss Inference based on Passive Measurement (LIPM), to infer WSN link loss performance. The LIPM algorithm passively monitors the application traffic between sensor nodes and the sink (base station), and then uses network tomography technology to infer the network internal performance. Furthermore, contour maps, the well-known representation of data, are first taken into account in WSN loss performance inference, which can help the LIPM algorithm identify lossy areas rapidly. Finally, the algorithm is validated through simulations and exhibits good performance and scalability.
机译：数字生态系统（DE）基于大量分布式数据，这些数据是从物理设备（尤其是从无线传感器网络（WSN））收集的。由于固有的严格带宽和能量限制，性能测量的节能机制是WSN正常运行的关键，因此对DE的数据可靠性至关重要。本文提出了一种新颖的算法，即基于被动测量（LIPM）的损耗推断来推断WSN链路损耗性能。 LIPM算法被动地监视传感器节点与接收器（基站）之间的应用程序流量，然后使用网络层析成像技术推断网络内部性能。此外，首先在WSN损失性能推断中考虑等高线图，即众所周知的数据表示形式，这可以帮助LIPM算法快速识别损失区域。最后，通过仿真验证了该算法的有效性和可扩展性。

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