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论文题目: |
Reliability and Temporality Optimization for Multiple Coexisting WirelessHART Networks in Industrial Environments |
第一作者: |
Jin X(金曦);Kong FX(孔繁鑫);Kong LH(孔令和);Liu, W;Zeng P(曾鹏) |
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发表刊物: |
IEEE Transactions on Industrial Electronics |
发表年度: |
2017 |
卷,期,页: |
64,8,6591-6602 |
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论文摘要: |
WirelessHART is a networking technology that is widely-used in industrial wireless sensor networks. Its reliability and real-time performance are essential to industrial production. Many works have studied these two aspects, primarily focusing on a single WirelessHART network. However,multiple WirelessHART networks usually coexist in a real industrial environment. Applying existing approaches to such coexisting networks would cause performance degradation due to communication interference among these networks. In this paper, we propose a holistic framework that optimizes both reliability and temporality for multiple coexisting networks. The framework consists of two levels. The upper level targets communication channel management, and the lower level addresses data flow scheduling. For the upper level, we propose a network isolation algorithm that improves the data transmission reliability through dynamically adjusting channel assignments to different WirelessHART networks. For the lower level, we propose data flow scheduling algorithms that guarantee the temporality of data flows within each isolated network. These algorithms minimize the number of channels reserved by each isolated network and further enhance the transmission reliability through alleviating channel resource contention. We conduct trace-driven simulations of the channel management algorithm, and the results demonstrate that our algorithm exhibits stable performance and reduces packet loss by 36%. For the scheduling algorithms, the simulations demonstrate that in contrast with existing algorithms, the greater the number of coexisting networks, the fewer resources our algorithms use. When eight networks coexist, our algorithms outperform existing ones by consuming up to 63% fewer channel resources. |
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