CN106452919A - Fog node optimization method based on fussy theory - Google Patents
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Abstract
The invention particularly relates to a fog node optimization method based on a fussy theory. The fog node optimization method based on the fussy theory comprises the following steps of performing quantitative evaluation of fuzzy evaluation on various current network connections by a fog node, comprehensively considering various factors, optimally using a network bandwidth interacting with a cloud center, and providing personalized services, wherein the fog node is responsible for managing a network bandwidth of a node end, acquiring terminal sensor data and processing the terminal sensor data, and effectively optimizing the preservation of a cache and historical data; and the cloud center is responsible for collecting fog node data and issuing a cloud center instruction, and providing high-performance data communication services. The fog node optimization method based on the fussy theory provided by the invention effectively solves the quantization problem of the fuzzy uncertain factors, greatly improves the use efficiency of the network bandwidth, and meets the individual requirements of each connection terminal.
Description
Technical field
The present invention relates to Internet of Things, cloud computing and mist computing technique field, particularly to a kind of mist based on fuzzy theory
Node optimization method.
Background technology
In recent years, technology of Internet of things quickly grows, and application is throughout traffic, logistics, public safety, household, doctor
Multiple industries such as treatment.By all kinds of sensing equipments, the information of sensing equipment can be derived from Real-time Collection, to realize the knowledge to equipment
Not, monitoring, positioning, connection, tracking and management, allows equipment, network and interaction become more intelligent.
Cloud computing and virtual technology popularize so that the Real-time and Dynamic management of magnanimity article and intelligence in Internet of Things
Analysis becomes possibility.Meanwhile, the development with 5G technology and be more suitable for data transmission of internet of things NB-IoT standard formulation,
Mist calculates and gradually catches on, and " cloud computing+mist calculates " makes Internet of Things bring new probability.
Mist calculating platform is made up of substantial amounts of mist node, and these mist nodes have computing capability, also possess storage capacity, this
A little abilities more efficiently can utilize edge network bandwidth, has less network delay.Substantial amounts of Internet of Things sensing
The mobile device such as terminal unit and mobile phone, notebook, flat board is linked in the Internet by these mist nodes, and eventually by number
According to being pooled to cloud center.These panoramic equipment requirements to network service are had nothing in common with each other, in this case, how
Enough effectively utilize the network bandwidth, can effectively assess customer demand, and be carried according to the actual demand of internet-of-things terminal equipment
For personalized service, improve Consumer's Experience and become the problem of urgent need to resolve.
Based on the problems referred to above, the present invention proposes a kind of mist node optimization method based on fuzzy theory.
Content of the invention
The present invention is in order to make up the defect of prior art, there is provided a kind of simply efficient mist node based on fuzzy theory
Optimization method.
The present invention is achieved through the following technical solutions：
A kind of mist node optimization method based on fuzzy theory it is characterised in that：By mist node to each network connection current
Carry out fuzzy evaluation quantitatively evaluating, comprehensive considering various effects, optimize using the network bandwidth interacting with cloud center, and provide individual
Propertyization services；Wherein, mist node is responsible for the network bandwidth of node side, and acquisition terminal sensing data is simultaneously processed,
Effectively optimize the preservation of caching and historical data；The collection of mist node data and issuing of cloud center instruction are responsible in cloud center, and
High performance data communication service is provided.
Quantitatively evaluating and optimization that described mist node access networks network connects, comprise the following steps：
（1）Definition impact terminal connects the factor of network communication, and defines corresponding weight value；
（2）According to the weights of influence factor and judgement, computation model is arranged by mist node；
（3）Mist node, according to current network actual state and network connecting request situation, calculates each network connecting request current
Fuzzy evaluation value；
（4）Mist node is by step（4）Calculated fuzzy evaluation value is ranked up classifying, and selects preferably to ask, enters line number
According to communication；
（5）The status information that mist node is collected to communication is processed, and is saved in the data storage area of mist node；
（6）Mist node is learnt according to the historical information in data storage area, optimizes local mist node computation model；
（8）Repeat step（3）To step（6）, persistently improve computation model.
Described mist nodal cache optimizes data and downloads, and comprises the following steps：
（1）The factor of definition impact cache optimization, and define corresponding weight value；
（2）According to the weights of influence factor and judgement, computation model is arranged by mist node；
（3）Terminal request accesses the service at cloud center, and mist node inspection caches, if there is cache information and cache non-mistake
Phase, can directly return data to terminal；If there is no cache information or data cached out of date, mist node use meter
Calculate model to be calculated, judge whether to cache according to evaluation result；Enter row cache if necessary, then access cloud center and download
Data, and by data buffer storage in mist node, return data to terminal simultaneously；If not needing to cache, by downloading data
It is directly returned to terminal；
（4）The status information that mist node is collected to caching is processed, and is saved in the data storage area of mist node；
（5）Mist node is learnt according to the historical information in data storage area, optimizes local mist node computation model；
（6）Mist node, according to current network conditions, regularly updates data cached；
（7）Repeat step（3）To step（6）, persistently improve and optimize caching.
It is somebody's turn to do the mist node optimization method based on fuzzy theory, what hinge structure obtained has the beneficial effect that：
（1）Access terminal is gone through for the customization demand of network, the service condition of current network and mist meshed network situation
The various factors such as Records of the Historian record are considered, and using expert judgments and fuzzy theory, effectively solve these moulds
The quantification problem of paste uncertain factor, greatly improves network bandwidth service efficiency, and meets each connection terminal
Individual demand.
（2）Mist node has learning capacity, according to the status information collected, constantly optimizes computation model by study,
The analyzing and processing ability of sustained improvement mist node.
（3）Fully take into account the feature of Internet of Things application, using effective caching mechanism so that such as distribution subscription
Message can be buffered in mist node side, effectively save the network bandwidth by the data transfers such as message；And caching mechanism also can
Meet client to accelerate to access the personalized demand such as network.
Brief description
Accompanying drawing 1 is mist node of the present invention and cloud division center schematic diagram.
Accompanying drawing 2 is the quantitatively evaluating and Optimizing Flow schematic diagram that mist node access networks network of the present invention connects.
Accompanying drawing 3 is that mist nodal cache of the present invention optimizes data download schematic flow sheet.
Specific embodiment
In order that the technical problem to be solved, technical scheme and beneficial effect become more apparent, below tie
Close drawings and Examples, the present invention will be described in detail.It should be noted that specific embodiment described herein is only used
To explain the present invention, it is not intended to limit the present invention.
It is somebody's turn to do the mist node optimization method based on fuzzy theory, fuzzy commenting is carried out to each network connection current by mist node
Sentence quantitatively evaluating, comprehensive considering various effects, optimize using the network bandwidth interacting with cloud center, and provide personalized service；
Wherein, mist node is responsible for the network bandwidth of node side, and acquisition terminal sensing data is simultaneously processed, and effectively optimizes slow
Deposit and historical data preservation；The collection of mist node data and issuing of cloud center instruction are responsible in cloud center, and provide high-performance
Data communication service.
The terminal that described mist node connects includes Internet of Things sensing terminal and mobile terminal.
On the basis of above-mentioned cloud center and mist node, it is possible to achieve the data transfer between each side.Adopt in the present embodiment
With being relatively suitable for the MQTT agreement of Internet of Things sensing equipment data transfer.Meanwhile, it should be noted that except being assisted using MQTT
Outside view, construction according to the embodiment of the present invention can also apply on other agreements.
The quantitatively evaluating separately below mist node access networks network being connected and optimization, mist nodal cache optimizes data and downloaded
Journey illustrates.
Quantitatively evaluating and optimization that described mist node access networks network connects, comprise the following steps：
（1）Definition impact terminal connects the factor of network communication, and defines corresponding weight value；
The QoS information for example comprising in client's request, including the transmission maximum delay time of packet, sending mode（At most one
Secondary, at least one times or only once）Deng, the load state of current network, the historical record of network state, connect paying of user
Take the many factors such as situation；The historical record of network state sets weights as 15%；
（2）According to the weights of influence factor and judgement, computation model is arranged by mist node；Such as sending mode is at most once,
It is transparent transmission mode, do not send the application that this packet does not interfere with reality yet in this case；
（3）Mist node, according to current network actual state and network connecting request situation, calculates each network connecting request current
Fuzzy evaluation value；Here it is mainly current real data as input, quantified according to computation model；
（4）Mist node is by step（4）Calculated fuzzy evaluation value is ranked up classifying, and selects preferably to ask, enters line number
According to communication；For example using evaluation of estimate sequence front 20% as a group, it is processed, upload the data to described cloud center；
（5）The status information that mist node is collected to communication is processed, and is saved in the data storage area of mist node；
（6）Mist node is learnt according to the historical information in data storage area, optimizes local mist node computation model；
（8）Repeat step（3）To step（6）, persistently improve computation model.
Described mist nodal cache optimizes data and downloads, and comprises the following steps：
（1）The factor of definition impact cache optimization, and define corresponding weight value；The Consumer's Experience for example connecting user requires, currently
The load state of network, the historical record of cache information state, type of message（Under such as message broadcast, publish-subscribe model
News release, message subscribing）Deng many factors；The historical record of cache information state sets weights as 15%；
（2）According to the weights of influence factor and judgement, computation model is arranged by mist node；The common subject matter that for example equipment is subscribed to disappears
Breath, type of message is message subscribing, in this case, it is possible to use caching mechanism；
（3）Terminal request accesses the service at cloud center, and mist node inspection caches, if there is cache information and cache non-mistake
Phase, can directly return data to terminal；If there is no cache information or data cached out of date, mist node use meter
Calculate model to be calculated, judge whether to cache according to evaluation result；Enter row cache if necessary, then access cloud center and download
Data, and by data buffer storage in mist node, return data to terminal simultaneously；If not needing to cache, by downloading data
It is directly returned to terminal；
（4）The status information that mist node is collected to caching is processed, and is saved in the data storage area of mist node；
（5）Mist node is learnt according to the historical information in data storage area, optimizes local mist node computation model；
（6）Mist node, according to current network conditions, regularly updates data cached；For example, current network bandwidth load is less, permissible
The data buffer storage of the high priority in caching is updated；
（7）Repeat step（3）To step（6）, persistently improve and optimize caching.
It is somebody's turn to do the mist node optimization method based on fuzzy theory, what hinge structure obtained has the beneficial effect that：
（1）Access terminal is gone through for the customization demand of network, the service condition of current network and mist meshed network situation
The various factors such as Records of the Historian record are considered, and using expert judgments and fuzzy theory, effectively solve these moulds
The quantification problem of paste uncertain factor, greatly improves network bandwidth service efficiency, and meets each connection terminal
Individual demand.
（2）Mist node has learning capacity, according to the status information collected, constantly optimizes computation model by study,
The analyzing and processing ability of sustained improvement mist node.
（3）Fully take into account the feature of Internet of Things application, using effective caching mechanism so that such as distribution subscription
Message can be buffered in mist node side, effectively save the network bandwidth by the data transfers such as message；And caching mechanism also can
Meet client to accelerate to access the personalized demand such as network.
Claims (3)
1. a kind of mist node optimization method based on fuzzy theory it is characterised in that：By mist node to each network current even
Tap into row fuzzy evaluation quantitatively evaluating, comprehensive considering various effects, optimize using the network bandwidth interacting with cloud center, and provide
Personalized service；Wherein, mist node is responsible for the network bandwidth of node side, and acquisition terminal sensing data simultaneously is processed locating
Reason, effectively optimizes the preservation of caching and historical data；The collection of mist node data and issuing of cloud center instruction are responsible in cloud center,
And high performance data communication service is provided.
2. the mist node optimization method based on fuzzy theory according to claim 1 is it is characterised in that described mist node connects
Enter quantitatively evaluating and the optimization of network connection, comprise the following steps：
（1）Definition impact terminal connects the factor of network communication, and defines corresponding weight value；
（2）According to the weights of influence factor and judgement, computation model is arranged by mist node；
（3）Mist node, according to current network actual state and network connecting request situation, calculates each network connecting request current
Fuzzy evaluation value；
（4）Mist node is by step（4）Calculated fuzzy evaluation value is ranked up classifying, and selects preferably to ask, enters line number
According to communication；
（5）The status information that mist node is collected to communication is processed, and is saved in the data storage area of mist node；
（6）Mist node is learnt according to the historical information in data storage area, optimizes local mist node computation model；
（8）Repeat step（3）To step（6）, persistently improve computation model.
3. the mist node optimization method based on fuzzy theory according to claim 1 is it is characterised in that described mist node delays
Deposit optimization data to download, comprise the following steps：
（1）The factor of definition impact cache optimization, and define corresponding weight value；
（2）According to the weights of influence factor and judgement, computation model is arranged by mist node；
（3）Terminal request accesses the service at cloud center, and mist node inspection caches, if there is cache information and cache non-mistake
Phase, can directly return data to terminal；If there is no cache information or data cached out of date, mist node use meter
Calculate model to be calculated, judge whether to cache according to evaluation result；Enter row cache if necessary, then access cloud center and download
Data, and by data buffer storage in mist node, return data to terminal simultaneously；If not needing to cache, by downloading data
It is directly returned to terminal；
（4）The status information that mist node is collected to caching is processed, and is saved in the data storage area of mist node；
（5）Mist node is learnt according to the historical information in data storage area, optimizes local mist node computation model；
（6）Mist node, according to current network conditions, regularly updates data cached；
（7）Repeat step（3）To step（6）, persistently improve and optimize caching.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107071027A (en) * | 2017-04-19 | 2017-08-18 | 济南浪潮高新科技投资发展有限公司 | A kind of restructural mist node and the Internet of things system based on the mist node |
| CN107766889A (en) * | 2017-10-26 | 2018-03-06 | 济南浪潮高新科技投资发展有限公司 | A kind of the deep learning computing system and method for the fusion of high in the clouds edge calculations |
| CN107871164A (en) * | 2017-11-17 | 2018-04-03 | 济南浪潮高新科技投资发展有限公司 | A kind of mist computing environment personalization deep learning method |
| CN108259573A (en) * | 2017-12-26 | 2018-07-06 | 西安电子科技大学 | A kind of vehicle self-organizing network system for mixing SDN and mist and calculating |
| CN108881355A (en) * | 2017-05-16 | 2018-11-23 | 纬创资通股份有限公司 | Monitoring method based on Internet of things architecture, fog operation terminal and Internet of things system |
| WO2019015615A1 (en) * | 2017-07-19 | 2019-01-24 | 中兴通讯股份有限公司 | Fog node deployment method and fog network system |
| CN109587715A (en) * | 2018-12-13 | 2019-04-05 | 广州大学 | A kind of distributed buffer memory strategy based on multiple agent intensified learning |
| CN109660598A (en) * | 2018-11-17 | 2019-04-19 | 华中科技大学 | A kind of buffer replacing method and system of Internet of Things Temporal Data |
| CN109936472A (en) * | 2017-12-18 | 2019-06-25 | 上海无线通信研究中心 | The node computing capability calculated towards mist describes method, exchange method and its equipment |
| CN110602178A (en) * | 2019-08-26 | 2019-12-20 | 杭州电子科技大学 | Method for calculating and processing temperature sensor data based on edge compression |
| CN110650463A (en) * | 2019-09-26 | 2020-01-03 | 赣南师范大学 | Agricultural Internet of things node energy consumption optimization method based on fuzzy mathematics |
| CN111124298A (en) * | 2019-12-17 | 2020-05-08 | 河海大学 | Mist computing network content cache replacement method based on value function |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107071027A (en) * | 2017-04-19 | 2017-08-18 | 济南浪潮高新科技投资发展有限公司 | A kind of restructural mist node and the Internet of things system based on the mist node |
| CN108881355B (en) * | 2017-05-16 | 2021-02-09 | 纬创资通股份有限公司 | Monitoring method based on Internet of things architecture, fog operation terminal and Internet of things system |
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| CN107871164A (en) * | 2017-11-17 | 2018-04-03 | 济南浪潮高新科技投资发展有限公司 | A kind of mist computing environment personalization deep learning method |
| CN107871164B (en) * | 2017-11-17 | 2021-05-04 | 浪潮集团有限公司 | Fog computing environment personalized deep learning method |
| CN109936472A (en) * | 2017-12-18 | 2019-06-25 | 上海无线通信研究中心 | The node computing capability calculated towards mist describes method, exchange method and its equipment |
| CN108259573A (en) * | 2017-12-26 | 2018-07-06 | 西安电子科技大学 | A kind of vehicle self-organizing network system for mixing SDN and mist and calculating |
| CN109660598A (en) * | 2018-11-17 | 2019-04-19 | 华中科技大学 | A kind of buffer replacing method and system of Internet of Things Temporal Data |
| CN109587715A (en) * | 2018-12-13 | 2019-04-05 | 广州大学 | A kind of distributed buffer memory strategy based on multiple agent intensified learning |
| CN109587715B (en) * | 2018-12-13 | 2022-03-25 | 广州大学 | Distributed caching method based on multi-agent reinforcement learning |
| CN110602178B (en) * | 2019-08-26 | 2021-11-26 | 杭州电子科技大学 | Method for calculating and processing temperature sensor data based on edge compression |
| CN110602178A (en) * | 2019-08-26 | 2019-12-20 | 杭州电子科技大学 | Method for calculating and processing temperature sensor data based on edge compression |
| CN110650463B (en) * | 2019-09-26 | 2023-04-07 | 赣南师范大学 | Agricultural Internet of things node energy consumption optimization method based on fuzzy mathematics |
| CN110650463A (en) * | 2019-09-26 | 2020-01-03 | 赣南师范大学 | Agricultural Internet of things node energy consumption optimization method based on fuzzy mathematics |
| CN111124298A (en) * | 2019-12-17 | 2020-05-08 | 河海大学 | Mist computing network content cache replacement method based on value function |
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