Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-net
Advantages and disadvantages of low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of Things（IoT） directly affect the endurance and network life of nodes. In view of problem of high energy consumption in hardware and software partitioning of intelligen...
| Main Authors: | , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Industry and Mine Automation
2018-09-01
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| Series: | Gong-kuang zidonghua |
| Subjects: | |
| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018020045 |
| _version_ | 1797869027897376768 |
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| author | LIU Xiaoxia LI Fang |
| author_facet | LIU Xiaoxia LI Fang |
| author_sort | LIU Xiaoxia |
| collection | DOAJ |
| description | Advantages and disadvantages of low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of Things（IoT） directly affect the endurance and network life of nodes. In view of problem of high energy consumption in hardware and software partitioning of intelligent sensor nodes of IoT, a low power consumption hardware and software partitioning model based on π-net was proposed. Firstly, the intelligent sensor nodes of IoT was defined with constraints, and the constrained model of the intelligent sensor nodes was obtained. Then, the hardware and software partitioning model of intelligent sensing nodes based on π-net was established by using the π-net theory, and the low power consumption hardware and software partitioning based on IP core power consumption of hardware and software and the overall power consumption constraints of the system were realized, and the model was analyzed for evolution. The analysis and simulation results show that the model has certain advantages and practicability in terms of fitness, execution time division and minimum system partition energy consumption compared with models based on tabu search algorithm and genetic algorithm, which can reduce the energy consumption of intelligent sensing nodes of IoT and improve their endurance. |
| first_indexed | 2024-04-10T00:05:06Z |
| format | Article |
| id | doaj.art-fb23970ca45042899bb908cbb26f4dd1 |
| institution | Directory Open Access Journal |
| issn | 1671-251X |
| language | zho |
| last_indexed | 2024-04-10T00:05:06Z |
| publishDate | 2018-09-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj.art-fb23970ca45042899bb908cbb26f4dd12023-03-17T01:19:10ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2018-09-01449596610.13272/j.issn.1671-251x.2018020045Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-netLIU Xiaoxia0LI Fang1Department of Information Engineering, Sichuan Water Conservancy Vocational College, Chongzhou 611231， ChinaCollege of Computer Science, Chongqing University, Chongqing 400044, ChinaAdvantages and disadvantages of low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of Things（IoT） directly affect the endurance and network life of nodes. In view of problem of high energy consumption in hardware and software partitioning of intelligent sensor nodes of IoT, a low power consumption hardware and software partitioning model based on π-net was proposed. Firstly, the intelligent sensor nodes of IoT was defined with constraints, and the constrained model of the intelligent sensor nodes was obtained. Then, the hardware and software partitioning model of intelligent sensing nodes based on π-net was established by using the π-net theory, and the low power consumption hardware and software partitioning based on IP core power consumption of hardware and software and the overall power consumption constraints of the system were realized, and the model was analyzed for evolution. The analysis and simulation results show that the model has certain advantages and practicability in terms of fitness, execution time division and minimum system partition energy consumption compared with models based on tabu search algorithm and genetic algorithm, which can reduce the energy consumption of intelligent sensing nodes of IoT and improve their endurance.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018020045internet of thingsintelligent sensing nodeπ-netlow power consumption hardware and software partitioningendurance |
| spellingShingle | LIU Xiaoxia LI Fang Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-net Gong-kuang zidonghua internet of things intelligent sensing node π-net low power consumption hardware and software partitioning endurance |
| title | Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-net |
| title_full | Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-net |
| title_fullStr | Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-net |
| title_full_unstemmed | Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-net |
| title_short | Modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of Internet of things based on π-net |
| title_sort | modeling on low power consumption hardware and software partitioning for intelligent sensing nodes of internet of things based on π net |
| topic | internet of things intelligent sensing node π-net low power consumption hardware and software partitioning endurance |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2018020045 |
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