Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks
A continuous-time exhaustive-limited (K = 2) two-level polling control system is proposed to address the needs of increasing network scale, service volume and network performance prediction in the Internet of Things (IoT) and the Long Short-Term Memory (LSTM) network and an attention mechanism is us...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-11-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023893?viewType=HTML |
| _version_ | 1797403680867090432 |
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| author | Zhijun Yang Wenjie Huang Hongwei Ding Zheng Guan Zongshan Wang |
| author_facet | Zhijun Yang Wenjie Huang Hongwei Ding Zheng Guan Zongshan Wang |
| author_sort | Zhijun Yang |
| collection | DOAJ |
| description | A continuous-time exhaustive-limited (K = 2) two-level polling control system is proposed to address the needs of increasing network scale, service volume and network performance prediction in the Internet of Things (IoT) and the Long Short-Term Memory (LSTM) network and an attention mechanism is used for its predictive analysis. First, the central site uses the exhaustive service policy and the common site uses the Limited K = 2 service policy to establish a continuous-time exhaustive-limited (K = 2) two-level polling control system. Second, the exact expressions for the average queue length, average delay and cycle period are derived using probability generating functions and Markov chains and the MATLAB simulation experiment. Finally, the LSTM neural network and an attention mechanism model is constructed for prediction. The experimental results show that the theoretical and simulated values basically match, verifying the rationality of the theoretical analysis. Not only does it differentiate priorities to ensure that the central site receives a quality service and to ensure fairness to the common site, but it also improves performance by 7.3 and 12.2%, respectively, compared with the one-level exhaustive service and the one-level limited K = 2 service; compared with the two-level gated- exhaustive service model, the central site length and delay of this model are smaller than the length and delay of the gated- exhaustive service, indicating a higher priority for this model. Compared with the exhaustive-limited K = 1 two-level model, it increases the number of information packets sent at once and has better latency performance, providing a stable and reliable guarantee for wireless network services with high latency requirements. Following on from this, a fast evaluation method is proposed: Neural network prediction, which can accurately predict system performance as the system size increases and simplify calculations. |
| first_indexed | 2024-03-09T02:41:55Z |
| format | Article |
| id | doaj.art-019d53ad69c34e7287e8e82684ebce9b |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-09T02:41:55Z |
| publishDate | 2023-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-019d53ad69c34e7287e8e82684ebce9b2023-12-06T01:14:20ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-11-012011201552018710.3934/mbe.2023893Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networksZhijun Yang 0Wenjie Huang1Hongwei Ding2Zheng Guan3Zongshan Wang41. Educational Instruments and Facilities Service Center, Educational Department of Yunnan Province, Kunming 650223, China 2. School of Information Science and Technology, Yunnan University, Kunming 650500, China 3. Key Laboratory of Education Informatization for Nationalities of Ministry of Education, Yunnan Normal University, Kunming 650500, China2. School of Information Science and Technology, Yunnan University, Kunming 650500, China2. School of Information Science and Technology, Yunnan University, Kunming 650500, China2. School of Information Science and Technology, Yunnan University, Kunming 650500, China2. School of Information Science and Technology, Yunnan University, Kunming 650500, ChinaA continuous-time exhaustive-limited (K = 2) two-level polling control system is proposed to address the needs of increasing network scale, service volume and network performance prediction in the Internet of Things (IoT) and the Long Short-Term Memory (LSTM) network and an attention mechanism is used for its predictive analysis. First, the central site uses the exhaustive service policy and the common site uses the Limited K = 2 service policy to establish a continuous-time exhaustive-limited (K = 2) two-level polling control system. Second, the exact expressions for the average queue length, average delay and cycle period are derived using probability generating functions and Markov chains and the MATLAB simulation experiment. Finally, the LSTM neural network and an attention mechanism model is constructed for prediction. The experimental results show that the theoretical and simulated values basically match, verifying the rationality of the theoretical analysis. Not only does it differentiate priorities to ensure that the central site receives a quality service and to ensure fairness to the common site, but it also improves performance by 7.3 and 12.2%, respectively, compared with the one-level exhaustive service and the one-level limited K = 2 service; compared with the two-level gated- exhaustive service model, the central site length and delay of this model are smaller than the length and delay of the gated- exhaustive service, indicating a higher priority for this model. Compared with the exhaustive-limited K = 1 two-level model, it increases the number of information packets sent at once and has better latency performance, providing a stable and reliable guarantee for wireless network services with high latency requirements. Following on from this, a fast evaluation method is proposed: Neural network prediction, which can accurately predict system performance as the system size increases and simplify calculations.https://www.aimspress.com/article/doi/10.3934/mbe.2023893?viewType=HTMLwireless sensor networksexhaustive-limited (k = 2) polling systemaverage lengthaverage delaylstm neural networks and attentionperformance predictionfast evaluation |
| spellingShingle | Zhijun Yang Wenjie Huang Hongwei Ding Zheng Guan Zongshan Wang Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks Mathematical Biosciences and Engineering wireless sensor networks exhaustive-limited (k = 2) polling system average length average delay lstm neural networks and attention performance prediction fast evaluation |
| title | Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks |
| title_full | Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks |
| title_fullStr | Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks |
| title_full_unstemmed | Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks |
| title_short | Performance analysis of a two-level polling control system based on LSTM and attention mechanism for wireless sensor networks |
| title_sort | performance analysis of a two level polling control system based on lstm and attention mechanism for wireless sensor networks |
| topic | wireless sensor networks exhaustive-limited (k = 2) polling system average length average delay lstm neural networks and attention performance prediction fast evaluation |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023893?viewType=HTML |
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