CN109618358A - A wireless sensor network node energy optimization method based on adaptive control - Google Patents
A wireless sensor network node energy optimization method based on adaptive control Download PDFInfo
- Publication number
- CN109618358A CN109618358A CN201910087036.0A CN201910087036A CN109618358A CN 109618358 A CN109618358 A CN 109618358A CN 201910087036 A CN201910087036 A CN 201910087036A CN 109618358 A CN109618358 A CN 109618358A
- Authority
- CN
- China
- Prior art keywords
- energy
- time
- power
- node
- night
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 25
- 238000005457 optimization Methods 0.000 title claims abstract description 11
- 230000003044 adaptive effect Effects 0.000 title claims description 13
- 230000008859 change Effects 0.000 claims abstract description 5
- 239000013589 supplement Substances 0.000 claims description 10
- 230000005059 dormancy Effects 0.000 claims description 7
- 230000007958 sleep Effects 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- ONUFESLQCSAYKA-UHFFFAOYSA-N iprodione Chemical compound O=C1N(C(=O)NC(C)C)CC(=O)N1C1=CC(Cl)=CC(Cl)=C1 ONUFESLQCSAYKA-UHFFFAOYSA-N 0.000 claims description 3
- 230000008447 perception Effects 0.000 claims description 3
- 230000036578 sleeping time Effects 0.000 claims description 3
- 230000008901 benefit Effects 0.000 abstract description 2
- 230000004622 sleep time Effects 0.000 abstract 1
- 238000004891 communication Methods 0.000 description 5
- 238000009826 distribution Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 5
- 238000004146 energy storage Methods 0.000 description 4
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000005286 illumination Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 239000003990 capacitor Substances 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000004083 survival effect Effects 0.000 description 2
- 238000009825 accumulation Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000001934 delay Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000004907 flux Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000006266 hibernation Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000004321 preservation Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000032258 transport Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
本发明涉及一种基于自适应控制的无线传感器网络节点能量优化方法，该方法通过自我感知电池板剩余能量随时间变化的序列，对输出功率进行自我选择改变，包括以下步骤：S1：利用传感器节点参数获取传感器节点在最大可允许时延的情况下的休眠时间和工作时间相互交叉的平均功耗和传感器所有传感元件同时全力工作的功耗；S2：收集下一个自然日的天气预测数据和自我功率的消耗参数；S3：根据数据中的当前时刻的剩余能量进行不同时间里的功率运行判断；S4：将不同时间的最终功率运行判断结果组合成最终能量优化运行结果。与现有技术相比，本发明具有集体优化，自适应控制，控制时间短等优点。
The invention relates to a wireless sensor network node energy optimization method based on self-adaptive control. The method self-selects and changes the output power by self-perceiving the sequence of the change of the remaining energy of the battery panel with time. The method includes the following steps: S1: using a sensor node Parameter acquisition: The average power consumption of the sensor node's sleep time and working time intersecting with each other under the condition of the maximum allowable delay and the power consumption of all sensing elements of the sensor working at full capacity at the same time; S2: Collect the weather forecast data for the next natural day and Self-power consumption parameters; S3: Judging power operation at different times according to the remaining energy at the current moment in the data; S4: Combining the final power operation judgment results at different times into the final energy optimization operation result. Compared with the prior art, the present invention has the advantages of collective optimization, self-adaptive control, and short control time.
Description
Technical field
The present invention relates to wireless sensor network technology fields, more particularly, to a kind of wireless biography based on self adaptive control
Sensor network node energy optimization method.
Background technique
Wireless sensor network (WSN) is a kind of emerging network technology, it is by having perception, calculating and communication capacity
Sensor node cooperates network consisting in a manner of ad hoc network, passes through the letter of perceptive object in acquisition network's coverage area
Breath with the communication of multi-hop will be collected, treated, and information is transmitted to base station, and can finally be provided to terminal user.?
In power industry, with deepening continuously for smart grid construction, the links such as electric system power generation, transmission of electricity, power transformation, distribution, electricity consumption
To the monitoring of equipment running status, more stringent requirements are proposed, and the power plant monitoring requirements for being then based on wireless sensor network are answered
It transports and gives birth to.Therefore propose that a kind of energy strategy of the self adaptive control on WSN node based on greed selection optimizes with Logistics networks
More long survival, so as to can be carried out self energy-optimised for sensor node.
In wireless sensor network, since node energy supplement is insufficient or energy supplement is unpredictable, EnergyPolicy
The factor of overriding concern when optimization has become sensor network design.It is limited that power detection sensor network node is solved at present
The effective way of life problems is using chargeable wireless sensor network technology.In chargeable electric power monitoring sensor network
In network, if the energy of node acquisition is enough or energy can periodically, continuity be supplemented, node can
Not limited completely by for electric flux.However, in many occasions of solar energy, powered by wind energy, due to weather, node
The supplement of energy has unpredictability and unreliability.
The Main way studied at present is the optimization in the optimization and whole network node communication path for filling energy strategy, or
Person is mainly for trolley path planning problem (being sensor node charging with moving trolley).
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind based on self-adaptive controlled
The wireless sensor network node energy optimizing method of system.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of wireless sensor network node energy optimizing method based on self adaptive control, this method are perceived by self
The sequence that solar panel dump energy changes over time carries out self selective change to output power, comprising the following steps:
S1: dormancy time of sensor node in the case where maximum permissible time delay is obtained using sensor node parameters
The power consumption to work with all strength simultaneously with working time cross one another average power consumption and all sensing elements of sensor；
S2: the weather forecasting data of next consecutive days and the consumption parameter of self power are collected；
S3: the Power operation in different time is carried out according to the dump energy at the current time in data and is judged；
S4: the final Power operation judging result of different time is combined into final energy optimization operation result.
Further, the step S3 include it is following step by step:
S301: the interval judgement that makes a decision is carried out for moment at night dump energy；
S302: night Power operation scheme is determined according to different judging results.
Further, the step S301, describes formula are as follows:
En> Tn*Pmax+Eemonth；
Tn*Pmax+Eemonth> En> Tn*Pmin+Eemonth；
Tn*Pmin+Eemonth> En> Tn*Pmin；
Tn*Pmin> En
In formula, EnFor moment at night dump energy, EemonthEnergy, T are ensured to be monthly minimumnFor All Through The Night duration,
PmaxFor the power consumption that all sensing elements of sensor work simultaneously, P with all strengthminIt is sensor node in the maximum feelings for allowing time delay
The cross one another average power consumption of dormancy time and working time under condition.
Further, the step 302, specifically includes:
Work as En> Tn*Pmax+EemonthWhen, keep node to export P always at nightmax；
Work as Tn*Pmax+Eemonth> En> Tn*Pmin+EemonthWhen, keep node in night dynamic operation；
Work as Tn*Pmin+Eemonth> En> Tn*PminWhen, keep node to export P always at nightmin；
Work as Tn*Pmin> EnWhen, carry out artificial charging supplement energy.
Further, the step S4 further includes by night node output scheme result and node on daytime output scheme result
It combines, node on the daytime output scheme describes formula are as follows:
In formula, EE is photovoltaic energy.
Further, the monthly minimum guarantee energy, describes formula are as follows:
Eemonth=Pmin*Tmonthmax
In formula, TmonthmaxFor monthly night duration maximum value.
Further, the calculation constraint condition of the step S1 are as follows:
In formula, PZigBeeIt is about beam power, DZigBeeTo constrain propagation delay time, TC、TDWhen respectively node is powered and discharges
Between, TSFor the sleeping time of transceiver, TTThe time mutually converted between state, T are sended and received for transceiverWFor transceiver
The time mutually converted between sleep and idle state, PSFor the power that node is lost in a sleep state, PRXFor reception state
The mean power of lower loss, PTXFor the mean power being lost under emission state, L is retrieval length, T1When working for maximum power
Between, T2For the minimum power working time.
Further, the modifying factor for correcting daytime and the output of night node is additionally provided in the step S4,
Formula is described are as follows:
In formula, AnFor modifying factor.
Compared with prior art, the invention has the following advantages that
(1) control is easy, the sequence that the present invention is changed over time by self perception solar panel dump energy, to output work
Rate carries out self selective change, comprising the following steps: S1: obtaining sensor node using sensor node parameters can permit in maximum
Perhaps the dormancy time in the case where time delay and working time cross one another average power consumption and all sensing elements of sensor be simultaneously
The power consumption of work with all strength；S2: the weather forecasting data of next consecutive days and the consumption parameter of self power are collected；S3: according to
The dump energy at the current time in data carries out the Power operation judgement in different time；S4: by the final function of different time
Rate operation judging result is combined into final energy optimization operation result, and control is easy, is able to achieve node and preferably survives and more
High service efficiency.
(2) scheme is simple, and accuracy rate is high, and material object of the present invention includes: data acquisition module；Data processing module communicates mould
Block, energy storage module, energy collection module and energy management module, mainly for energy management module, 1, felt according to self
Oneself remaining energy is answered to determine the working method of next moment point --- turn between period suspend mode and full working condition
It changes and realizes power output as much as possible, node power is more accurately adjusted in the case where real-time, be divided into two cover dies at night daytime
Formula selects and was changed out force data with 30 minutes for a node, it is possible to reduce and photovoltaic is contributed unstable influence,
For optimizing the average output power of wireless sensor node.2, since " daytime " and " night " and actual day and night have one
Determine difference, therefore carry out a modified operators in ending moment (illumination terminates) daily, carries out a correction for energy storage.3, it devises
One least energy residue principle, that is, energy needed for preservation one guarded is similar to animal hibernation, to guarantee node more
It works long hours, accuracy rate is higher.
Detailed description of the invention
Fig. 1 is the device collocation figure of sensor node；
Fig. 2 is intra-node energy and management module interaction mode schematic diagram；
Fig. 3 is flow chart of the present invention；
Fig. 4 is the power supply energy curve graph that the present invention is compared with other methods.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiment is a part of the embodiments of the present invention, rather than whole embodiments.Based on this hair
Embodiment in bright, those of ordinary skill in the art's every other reality obtained without making creative work
Example is applied, all should belong to the scope of protection of the invention.
Embodiment
It arranges in pairs or groups and schemes for the device of sensor node in the present embodiment as shown in Figure 1 comprising data acquisition module；At data
Module, communication module, energy storage module, energy collection module and energy management module are managed, wherein data acquisition module includes
Sensor module and A D converter, data processing module specifically includes micro process and memory, and communication module includes channel radio
Letter.
It is illustrated in figure 2 internal energy and management module interaction mode schematic diagram in the present embodiment, from the external sun
The super capacitor energy-storage in wireless sensor network can first be made and deliver power to energy distribution module, energy predicting device is according to energy
The real-time parameter of amount distribution module control and back regulation super capacitor is made a response.
It is illustrated in figure 3 the specific method flow diagram of the corresponding embodiment of the present invention, comprising the following steps:
Step 1) determines existing power type.By dormancy mechanism, two or more working efficiencies, Zhi Houzai are obtained
Step 2 is required to be allocated according to such power in 3.Assuming that its there are two types of operating power and by certain model into
Row simple analysis calculates, and is P respectivelymaxAnd Pmin。PmaxFor the power consumption that all sensing elements of sensor work simultaneously, P with all strengthminFor
Dormancy time and working time cross one another average power consumption of the sensor node in the case where maximum permissible time delay.Pass through
Constraint output power of each of which the available node in different delay is calculated, when another constraint condition is transmission
Prolong, it is as follows to specifically describe formula:
In formula, PZigBeeIt is about beam power, DZigBeeTo constrain propagation delay time, TC、TDWhen respectively node is powered and discharges
Between, TSFor the sleeping time of transceiver, TTThe time mutually converted between state, T are sended and received for transceiverWFor transceiver
The time mutually converted between sleep and idle state, PSFor the power that node is lost in a sleep state, PRXFor reception state
The mean power of lower loss, PTXFor the mean power being lost under emission state, L is retrieval length, T1When working for maximum power
Between, T2For the minimum power working time.
The minimum power under suitable case propagation delays is solved using sensor node parameters.Solution can obtain maximum delay situation
Under: PminFor 130mW, PmaxFor 650mW.
Step 2) collects data.Date (year, month, day) comprising measuring and calculating since no light, next to there is illumination
Interlude is up to T according to (1) All Through The NightnTime and En> Tn*Pmax+Eemonth, then node is kept to export always at night
Pmax。
(2) if All Through The Night is up to TnTime and Tn*Pmax+Eemonth> En> Tn*Pmin+Eemonth, then according to following calculation
The high and low Power operation of method progress night hours:
Max T1 n
In formula, T1 nFor the maximum high-power operation time in n-th day evening,For the low-power operation time in n-th day evening.
Simultaneously because the realization of algorithm depends on capacity of self-regulation, what is drawn oneself up with enabling sensor node " greed " makes
With the high-power operation time --- the high-power operation time is first carried out, then starts the low-power operation time.Time i.e. at the beginning
It is worked using maximum power.Decision condition are as follows:
E > EPrevioust-(T-Δtall)*Pmin
E=EPrevioust-Δtall*Pmax
If E≤Eprevioust-(T-Δtall)*Pmin, then P is used alwaysminIt works, thereby realizes at night
More time high-power operation, i.e. transinformation is relatively more, and wherein E is
Above-mentioned two situations are outstanding situation and only carry out real-time control to it on daytime.
(3) if All Through The Night is up to TnTime and Tn*Pmin+Eemonth> En> Tn*Pmin, then P is keptminWork, but this
The case where being exactly dangerous tendency, needs timely to be corrected at the time of daytime.
(4) if Tn*Pmin> En, then since the remaining tangible deficiency of energy must use human intervention, charging is intelligently submitted to mend
It helps.
Step 3), for the first two situation in step 1:
It is controlled with the following conditions, selects its node operating power, by this treating method to ensure that energy is increasing
In the case where it is as much as possible used, and the use that energy is fewer in the case where increasing insufficient situation, this is also greedy algorithm
A kind of embodiment.In order to realize the reversion of situation, under light conditions, sensor node be must continue to using Pmin, until residue
ENERGY En> Eemonth, its purpose is to make the energy content of battery possess most solar month of 30 days guarantee electricity once again, later further according to photovoltaic energy
Amount carries out self selective power consumption compared with operating power, in conclusion the operating power scheme on daytime is described as follows:
In formula, EE is photovoltaic energy.
Photovoltaic energy amendment；
This correction time be step 2 terminate, i.e., by night at the time of, carry out the supplement of a modified operators, reduce
Energy error.Due to it is considered herein that " daytime " and " night " and actual day and night have certain difference, i.e. EE < PminPortion
The time divided is interpreted as the night of no illumination supplement, so the energy error certain with physical presence, however due to its mistake
Poor very little, approximation are ignored, therefore fixing tentatively does not influence daily power distribution.However when the supplement accumulation of a large amount of small-power photovoltaics also can be right
Gross energy generates certain deviation, so the present invention is by daily EE > PminTime energy by modifying factor into
The daily improvement of row, has thus ensured the error small as far as possible of energy, the description formula of modifying factor are as follows:
In formula, AnFor modifying factor.
The energy of sensor node can excessively using and cause it that can not spend longest night, so be provided with the moon it is excellent
Change --- of that month minimum guarantee energy describes formula are as follows:
Eemonth=Pmin*Tmonthmax
In formula, TmonthmaxFor monthly night duration maximum value.
According to long-term observation test discovery, annual each moon longest no light time, approximation substantially was (due to the recurrence of the sun
Property it is basic annual constant), as long as therefore thinking that the sensor residual energy can satisfy the every month longest unglazed time is safety
Range.So having set of that month minimum guarantee energy, minimum peace is determined according to the time T at the prior year same moon at longest night
Full energy datum is to ensure to spend most difficult that day every month.
The present invention realizes the distribution of energy in the case where energy supplement amount is unknown in advance for maximum system performance:
It will consider energy-optimised strategy in real time, since the short time will not excessively be mutated photovoltaic energy supplement under normal circumstances, so
It is considered that the photovoltaic energy EE output power of a upper timing node is similar with the output power of next timing node.In reality
The size between energy output and photovoltaic energy supply is obtained under the requirement of when property, when photovoltaic energy supply is completely defeated greater than maximum
When out, sensor output power reaches peak value；When photovoltaic energy is less than maximum output, for the sustainable use of sensor,
Using minimum output power, it thus ensure that the survival of node and system performance are more excellent to a certain extent, as shown in figure 4, phase
Than with normal operation algorithm, the present invention designed by scheme corresponding energy control has been carried out according to the characteristic of different time,
With good control effect.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can readily occur in various equivalent modifications or replace
It changes, these modifications or substitutions should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with right
It is required that protection scope subject to.
Claims (8)
1. a kind of wireless sensor network node energy optimizing method based on self adaptive control, which is characterized in that this method is logical
The sequence that self perception solar panel dump energy changes over time is crossed, self selective change is carried out to output power, including following
Step:
S1: dormancy time and work of the sensor node in the case where maximum permissible time delay are obtained using sensor node parameters
Make the power consumption that time cross one another average power consumption and all sensing elements of sensor work with all strength simultaneously；
S2: the weather forecasting data of next consecutive days and the consumption parameter of self power are collected；
S3: the Power operation in different time is carried out according to the dump energy at the current time in data and is judged；
S4: the final Power operation judging result of different time is combined into final energy optimization operation result.
2. a kind of wireless sensor network node energy optimizing method based on self adaptive control according to claim 1,
It is characterized in that, the step S3 include it is following step by step:
S301: the interval judgement that makes a decision is carried out for moment at night dump energy；
S302: night Power operation scheme is determined according to different judging results.
3. a kind of wireless sensor network node energy optimizing method based on self adaptive control according to claim 2,
It is characterized in that, the step S301, describes formula are as follows:
En> Tn*Pmax+Eemonth；
Tn*Pmax+Eemonth> En> Tn*Pmin+Eemonth；
Tn*Pmin+Eemonth> En> Tn*Pmin；
Tn*Pmin> En
In formula, EnFor moment at night dump energy, EemonthEnergy, T are ensured to be monthly minimumnFor All Through The Night duration, PmaxTo pass
The power consumption that all sensing elements of sensor work with all strength simultaneously, PminFor sensor node in the case where maximum permissible time delay
Dormancy time and working time cross one another average power consumption.
4. a kind of wireless sensor network node energy optimizing method based on self adaptive control according to claim 2,
It is characterized in that, the step 302, specifically includes:
Work as En> Tn*Pmax+EemonthWhen, keep node to export P always at nightmax；
Work as Tn*Pmax+Eemonth> En> Tn*Pmin+EemonthWhen, keep node in night dynamic operation；
Work as Tn*Pmin+Eemonth> En> Tn*PminWhen, keep node to export P always at nightmin；
Work as Tn*Pmin> EnWhen, carry out artificial charging supplement energy.
5. a kind of wireless sensor network node energy optimizing method based on self adaptive control according to claim 1,
The step S4 further include by night node output scheme result with daytime node output scheme result combine, the daytime
Point output scheme, describes formula are as follows:
In formula, EE is photovoltaic energy.
6. a kind of wireless sensor network node energy optimizing method based on self adaptive control according to claim 3,
The monthly minimum guarantee energy, describes formula are as follows:
Eemonth=Pmin*Tmonthmax
In formula, TmonthmaxFor monthly night duration maximum value.
7. a kind of wireless sensor network node energy optimizing method based on self adaptive control according to claim 1,
The calculation constraint condition of the step S1 are as follows:
In formula, PZigBeeIt is about beam power, DZigBeeTo constrain propagation delay time, TC、TDRespectively node energization and discharge time, TS
For the sleeping time of transceiver, TTThe time mutually converted between state, T are sended and received for transceiverWFor transceiver sleep with
The time mutually converted between idle state, PSFor the power that node is lost in a sleep state, PRXTo be lost under reception state
Mean power, PTXFor the mean power being lost under emission state, L is retrieval length, T1For maximum power working time, T2For
The minimum power working time.
8. a kind of wireless sensor network node energy optimizing method based on self adaptive control according to claim 5,
It is additionally provided with the modifying factor for correcting daytime and the output of night node in the step S4, describes formula are as follows:
In formula, AnFor modifying factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910087036.0A CN109618358B (en) | 2019-01-29 | 2019-01-29 | A wireless sensor network node energy optimization method based on adaptive control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910087036.0A CN109618358B (en) | 2019-01-29 | 2019-01-29 | A wireless sensor network node energy optimization method based on adaptive control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109618358A true CN109618358A (en) | 2019-04-12 |
| CN109618358B CN109618358B (en) | 2021-10-08 |
Family
ID=66021367
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910087036.0A Active CN109618358B (en) | 2019-01-29 | 2019-01-29 | A wireless sensor network node energy optimization method based on adaptive control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109618358B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110213472A1 (en) * | 2007-01-26 | 2011-09-01 | Autani Corporation | Upgradeable Automation Devices, Systems, Architectures, and Methods |
| CN102595550A (en) * | 2012-02-16 | 2012-07-18 | 河海大学常州校区 | Self-adaptive wireless sensor network routing method based on cross-layer optimization |
| CN102695249A (en) * | 2011-03-23 | 2012-09-26 | 北京天地互连信息技术有限公司 | Energy saving method for IPv6 wireless sensor network |
| CN203135479U (en) * | 2013-01-18 | 2013-08-14 | 西北农林科技大学 | Self-powered environmental monitoring equipment |
| CN106452513A (en) * | 2016-10-09 | 2017-02-22 | 华侨大学 | Mobile energy supplementation method in delay-constrained wireless sensor network |
| CN108601035A (en) * | 2018-04-26 | 2018-09-28 | 南京邮电大学 | WMSNs node scheduling strategies based on solar energy collecting model |
| CN108738118A (en) * | 2018-05-22 | 2018-11-02 | 广东石油化工学院 | Power exchange method in wireless chargeable sensor network |
-
2019
- 2019-01-29 CN CN201910087036.0A patent/CN109618358B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110213472A1 (en) * | 2007-01-26 | 2011-09-01 | Autani Corporation | Upgradeable Automation Devices, Systems, Architectures, and Methods |
| CN102695249A (en) * | 2011-03-23 | 2012-09-26 | 北京天地互连信息技术有限公司 | Energy saving method for IPv6 wireless sensor network |
| CN102595550A (en) * | 2012-02-16 | 2012-07-18 | 河海大学常州校区 | Self-adaptive wireless sensor network routing method based on cross-layer optimization |
| CN203135479U (en) * | 2013-01-18 | 2013-08-14 | 西北农林科技大学 | Self-powered environmental monitoring equipment |
| CN106452513A (en) * | 2016-10-09 | 2017-02-22 | 华侨大学 | Mobile energy supplementation method in delay-constrained wireless sensor network |
| CN108601035A (en) * | 2018-04-26 | 2018-09-28 | 南京邮电大学 | WMSNs node scheduling strategies based on solar energy collecting model |
| CN108738118A (en) * | 2018-05-22 | 2018-11-02 | 广东石油化工学院 | Power exchange method in wireless chargeable sensor network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109618358B (en) | 2021-10-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107521365B (en) | A charging and discharging scheduling method for electric vehicles based on user economic benefit optimization | |
| CN105680474B (en) | Control method for restraining rapid power change of photovoltaic power station through energy storage | |
| CN113839423B (en) | Control management method, device, equipment and storage medium | |
| CN107332286A (en) | A kind of cogeneration of heat and power containing heat accumulation and wind-powered electricity generation coordinated scheduling method | |
| CN104822162B (en) | Green base station shunt method and device in a kind of energy mix network | |
| Mannepalli et al. | Allocation of optimal energy from storage systems using solar energy | |
| CN108599138A (en) | Meter and the probabilistic energy storage system capacity configuration method of micro-capacitance sensor distributed energy | |
| CN107221965A (en) | A kind of plan computational methods a few days ago based on Distributed Design | |
| CN109904865B (en) | Intelligent peak-valley load balance management and control main system of high-voltage distribution network | |
| Yuan et al. | Bess aided renewable energy supply using deep reinforcement learning for 5g and beyond | |
| CN112736950B (en) | Public energy storage power station configuration method and system for micro-grid group | |
| CN114825388A (en) | New energy comprehensive consumption scheduling method based on source network load-storage cooperation | |
| CN101232195A (en) | Industrial wireless sensor network system based on solar energy and its sleep wake-up method | |
| CN116722561B (en) | Reactive power compensation system | |
| CN105391093A (en) | Optimal scheduling method for grid-connected operation of small hydropower station group | |
| CN114336775A (en) | Power distribution network scheduling control system and method including distributed energy access | |
| Shen et al. | EFCon: Energy flow control for sustainable wireless sensor networks | |
| CN106524277B (en) | Regional energy supply system of heat supply in winter of multipotency source form | |
| CN114662751B (en) | Garden multifunctional short-term load forecasting and optimizing method based on LSTM | |
| CN108449009A (en) | Photovoltaic power generation intelligent energy storage system | |
| CN108601035A (en) | WMSNs node scheduling strategies based on solar energy collecting model | |
| CN117639106B (en) | Regulation and control method for photovoltaic hydropower energy storage cluster micro-grid | |
| CN117412367B (en) | Energy collecting wireless sensor energy management method | |
| CN104967123B (en) | Under power distribution network, intermittent energy source is dissolved monitoring device and its monitoring method | |
| CN104469910A (en) | A method for reducing energy consumption of base station |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |