CN103024797B - Statistic-based wireless sensor network flow evaluation method - Google Patents
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Abstract
The invention discloses a statistic-based wireless sensor network flow evaluation method. The method comprises the following steps of: A, judging whether time intervals of data packets emitted from each node conform to exponential distribution through an Anderson-Darling test on the basis of window cutting; B, judging whether the sequences of the time intervals of the data packets emitted from the nodes are independent from one another through an autocorrelation method; and C, carrying out poisson distribution modeling on a wireless sensor network according to the judgment results of the step A and the step B when the time intervals of the data packets emitted from the nodes meet the exponential distribution and the time interval sequences of the data packets emitted from the nodes are independent from one another. The method can be used for evaluating the flow of the wireless network sensor on the basis of an actual statistical result and judging whether the flow meets a poisson process, and not only is closer to actual situations and high in efficiency, but also avoids a lot of sampling expenses.
Description
Technical field
The present invention relates to wireless sensor network field, particularly relate to a kind of wireless sensor network flow evaluation method of Corpus--based Method.
Background technology
Along with the fast development of the communication technology, sensor technology and embedding assembly technology and increasingly mature, there is communication, the microsensor node of sensing and computing capability starts to occur, and to be produced by being applied to gradually and among life.This wireless sensor network can perception in phase, various environment in acquisition and processing network's coverage area or monitoring target information, and be distributed to the user needing these information.Information world in logic and real physical world merge by wireless sensor network, profoundly change the interactive mode of man and nature, can be widely used in the numerous areas such as environmental monitoring, industrial or agricultural control, biologic medical, national defense and military.Usual wireless sensor network by battery-operated, and is often deployed in unfrequented place, battery is replaced and becomes abnormal trouble.In order to solve the contradiction between limited battery capacity and long-time deployment requirements, the mode of operation of cycling (Duty-cycling) has become the scheme of the extensive use of industry.Node in wireless sensor network mainly completes two class work: data acquisition and transfer of data.Data acquisition comprises periodic data acquisition, as the humiture in environment, and gas concentration lwevel; And the data acquisition of accident, as forest fire, vehicle detection.Transfer of data work is determined by the feature of wireless sensor network itself, and a large amount of sensor node forms wireless network in the mode of self-organizing (ad-hoc), and helps other nodes that data are beamed back base station in a multi-hop fashion.Large quantity research shows, in the wireless sensor network application of the periodicity image data such as environmental monitoring, and the Time dependent that energy expense is opened by wireless transceiver (radio) substantially.Therefore, the key point that the wireless transceiver opening time has become the prolonging wireless sensor network life-span how is reduced.
Cycling technology has become the node energy-saving scheme widely adopted.In cycling wireless sensor network, wireless transceiver is periodically opened, thus in the most of the time, wireless transceiver is all in closed condition, thus has saved a large amount of energy.Wireless transceiver is divided into two kinds of operating states: opening and idle condition.When wireless transceiver is in opening, sensor node sends or accepts packet.When being in idle condition, wireless transceiver is closed, thus has saved large energy.
Based on cycling technology, create broad medium cut-in method.These methods are mainly divided into two classes: synchronous cut-in method and asynchronous cut-in method.Synchronous cut-in method needs the wireless transceiver of sensor node synchronously to keep alert while in bed, but can cause a large amount of energy ezpenditure like this in synchronous maintenance.In asynchronous cut-in method, sensor node does not need the plan of keeping alert while in bed synchronously between them, but the problem brought like this is, when transmit leg is waken up, recipient may be in idle condition, will miss the packet sent.Can be received side in order to ensure packet to receive, low-power consumption monitoring technique (LPL) is widely used in asynchronous cycling technology.Specifically, transmit leg, before transmission packet, sending the preamble (preamble) that a length is enough to cover recipient's free time interval, guaranteeing that recipient at least can receive a packet when waking up like this.
The network traffics of the wireless sensor network of low-power consumption monitoring technique are adopted to help to a lot of wireless senser application band, especially in energy-conservation.In low-power consumption monitoring technique, most of energy ezpenditure is on preamble.There are some researches show, in the application of conventional environmental monitoring wireless senser, the length of mean preamble is 65 times of average actual transmissions data packet length, and this illustrates that most of energy ezpenditure is on preamble.If the pattern of known wireless sensor network data flow, recipient can predict the arrival time of next packet, and such recipient just can wake up before transmit leg starts to send, thus greatly reduced the length of preamble transmission.The process that the packet of wireless sensor network arrives can be modeled as Poisson process (Poisson Process).Poisson process requires the time interval obeys index distribution that packet arrives, but in low-power consumption monitoring technique, owing to periodically keeping alert while in bed, the time interval that packet is arrived cannot meet the characteristic of exponential distribution, thus cannot meet the characteristic of Poisson process.Specifically, the data flow of serving the wireless sensor network of the application that periodic data gathers is mainly by the impact of the duty ratio (dutycycle) in Low-power Technology and data collection cycle.
Summary of the invention
For above-mentioned technical problem, the object of the present invention is to provide a kind of wireless sensor network flow evaluation method of Corpus--based Method, it is based on actual count result, line sensor network traffics are assessed, judge whether it meets Poisson process, not only more fit reality, efficiency is high, and avoids the expense of a large amount of sampling.
For reaching this object, the present invention by the following technical solutions:
A wireless sensor network flow evaluation method for Corpus--based Method, it comprises the steps:
A, by judging the time interval whether obeys index distribution of the packet that each node sends based on Anderson-Da Lin (Andson-daring) test of window cutting;
Whether B, to be judged that by autocorrelation method each node sends packet time intervening sequence separate;
C, judged result according to steps A and step B, the time interval of the packet sent when each node meets exponential distribution, and each node send packet time intervening sequence separate time, Poisson distribution modeling is carried out to wireless sensor network.
Especially, the Anderson-Da Lin test based on window cutting in described steps A specifically comprises:
The variance of the channel strength RSS of each time point of each node calculate, and it can be used as network dynamic parameter K; The process of the transmission packet of each node is cut into K section; Significance level S1 is set, if S1*K section actual transmission packet time intervening sequence is tested by Anderson-Da Lin, then judges that this node sends the distribution of packet time intervening sequence index of coincidence; Arrange significance level S2, if S1*N node is tested by Anderson-Da Lin, then judge the time interval obeys index distribution of the packet that each node sends in wireless sensor network, wherein, N is the number of described wireless sensor network interior joint.
3, the wireless sensor network flow evaluation method of Corpus--based Method according to claim 2, it is characterized in that, described steps A specifically comprises:
A1, the node number N calculated in wireless sensor network; Each node is observed in T time section, to send the process of packet and record;
The variance of the channel strength RSS of A2, each time point of each node calculate, it can be used as network dynamic parameter K, and the process of the transmission packet of each node is cut into K section;
A3, calculate each node and actual send the exponential distribution that sequence of data packet has identical geometric average;
A4, reality sent to sequence that sequence of data packet and steps A 3 generate and carry out Anderson-Da Lin and test, significance level S1 is set, if S1*K section actual transmission packet time intervening sequence is tested by Anderson-Da Lin, then judge that this node sends the distribution of packet time intervening sequence index of coincidence;
A5, significance level S2 is set, if S1*N node test by Anderson-Da Lin, then the time interval obeys index distribution of the packet that each node sends in judgement wireless sensor network.
Especially, described step B specifically comprises:
The distance calculating the time interval sequence of each node transmission packet is the auto-correlation coefficient of 1, if the cutting window of 95% all have passed independence test, then the transmission packet time intervening sequence of predicate node is separate.
Especially, described step B specifically comprises:
B1, the node number N calculated in wireless sensor network; Each node is observed in T time section, to send the process of packet and record;
The variance of the channel strength RSS of B2, each time point of each node calculate, it can be used as network dynamic parameter K, and the process of the transmission packet of each node is cut into K section;
B3, every section sent the time interval sequence of packet ask distance be 1 auto-correlation coefficient;
B4, more every segment distance be the auto-correlation coefficient of 1 result and
obtain the cut length number R tested by independence, wherein, n is time interval sequence samples number;
If B5 R/N is greater than 95%, then judge that this node sends packet time intervening sequence separate.
The characteristic of Mining Cyclic data collection radio sensor network data bag of the present invention, based on actual count result, assesses line sensor network traffics, judges whether it meets Poisson process.The present invention judges according to data traffic actual in wireless sensor network, more more accurate than conventional method, reality of more fitting; Only need to sample to node, and deterministic process is the process of time complexity, time complexity is low; Only need the data random sampling in wireless sensor network, avoid the expense of a large amount of sampling.
Accompanying drawing explanation
The flow chart of the wireless sensor network flow evaluation method of the Corpus--based Method that Fig. 1 provides for the embodiment of the present invention.
Embodiment
For making the object, technical solutions and advantages of the present invention clearly, below in conjunction with drawings and Examples, the invention will be further described.
Please refer to shown in Fig. 1, the flow chart of the wireless sensor network flow evaluation method of the Corpus--based Method that Fig. 1 provides for the embodiment of the present invention.
The wireless sensor network flow evaluation method of Corpus--based Method in the present embodiment, it comprises the steps:
Step S101, by judging the time interval whether obeys index distribution of the packet that each node sends based on Anderson-Da Lin (Andson-daring) test of window cutting.
The detailed process of time interval of the packet that decision node sends whether obeys index distribution is as follows:
Step S1011, the node number N calculated in wireless sensor network; Each node is observed in T time section, to send the process of packet and record.
The variance of the channel strength RSS of step S1012, each time point of each node calculate, it can be used as network dynamic parameter K, and the process of the transmission packet of each node is cut into K section.
Owing to considering the dynamic of channel, the variance of the channel strength RSS of each time point of each node calculate, it can be used as network dynamic parameter K, when channel dynamics is larger, K is less, and dynamic is less, and K is larger.Specifically, K=ceiling (1/variance (RSS
i)), wherein RSS
iit is the time series of the channel strength RSS of node i.
Step S1013, calculate each node and actual send the exponential distribution that sequence of data packet has identical geometric average.
Step S1014, reality sent to sequence that sequence of data packet and step S1013 generate and carry out Anderson-Da Lin and test, significance level S1 is set, if S1*K section actual transmission packet time intervening sequence is tested by Anderson-Da Lin, then judge that this node sends the distribution of packet time intervening sequence index of coincidence;
Step S1015, significance level S2 is set, if S1*N node test by Anderson-Da Lin, then the time interval obeys index distribution of the packet that each node sends in judgement wireless sensor network.
Whether step S102, to be judged that by autocorrelation method each node sends packet time intervening sequence separate.
Judge that the detailed process that whether separate each node send packet time intervening sequence is as follows:
Step S1021, the node number N calculated in wireless sensor network; Each node is observed in T time section, to send the process of packet and record.
The variance of the channel strength RSS of step S1022, each time point of each node calculate, it can be used as network dynamic parameter K, and the process of the transmission packet of each node is cut into K section;
Step S1023, every section sent the time interval sequence of packet ask distance be 1 auto-correlation coefficient;
Step S1024, more every segment distance be the auto-correlation coefficient of 1 result and
obtain the cut length number R tested by independence, wherein, n is time interval sequence samples number;
If step S1025 R/N is greater than 95%, then judge that this node sends packet time intervening sequence separate.
Step S103, judged result according to step S101 and step S102, the time interval of the packet sent when each node meets exponential distribution, and each node send packet time intervening sequence separate time, Poisson distribution modeling is carried out to wireless sensor network, otherwise adopt alternate manner to carry out modeling to wireless sensor network, such as based on the statistical model (linear regression, SVMs etc.) of study.
The present invention has been successfully applied to GreenOrbs(green field thousand biography being positioned at Zhejiang A & F University) sensor network system.This sensor network system 500 nodes, this system can collecting temperature, humidity, light, etc. information, for angles of science monitoring forest environment provides important information.
Technical scheme of the present invention, based on actual count result, is assessed line sensor network traffics, and judge whether it meets Poisson process, reality of not only fitting, efficiency is high, and avoids the expense of a large amount of sampling.
Above are only preferred embodiment of the present invention and institute's application technology principle, be anyly familiar with those skilled in the art in the technical scope that the present invention discloses, the change that can expect easily or replacement, all should be encompassed in protection scope of the present invention.
Claims (4)
1. a wireless sensor network flow evaluation method for Corpus--based Method, is characterized in that, comprise the steps:
A, by judging the time interval whether obeys index distribution of the packet that each node sends based on Anderson-Da Lin (Andson-daring) test of window cutting; Wherein, the described Anderson-Da Lin test based on window cutting specifically comprises: the variance of the channel strength RSS of each time point of each node calculate, and it can be used as network dynamic parameter K; The process of the transmission packet of each node is cut into K section; Significance level S1 is set, if S1*K section actual transmission packet time intervening sequence is tested by Anderson-Da Lin, then judges that this node sends the distribution of packet time intervening sequence index of coincidence; Arrange significance level S2, if S1*N node is tested by Anderson-Da Lin, then judge the time interval obeys index distribution of the packet that each node sends in wireless sensor network, wherein, N is the number of described wireless sensor network interior joint;
Whether B, to be judged that by autocorrelation method each node sends packet time intervening sequence separate;
C, judged result according to steps A and step B, the time interval of the packet sent when each node meets exponential distribution, and each node send packet time intervening sequence separate time, Poisson distribution modeling is carried out to wireless sensor network.
2. the wireless sensor network flow evaluation method of Corpus--based Method according to claim 1, it is characterized in that, described steps A specifically comprises:
A1, the node number N calculated in wireless sensor network; Each node is observed in T time section, to send the process of packet and record;
The variance of the channel strength RSS of A2, each time point of each node calculate, it can be used as network dynamic parameter K, and the process of the transmission packet of each node is cut into K section;
A3, calculate each node and actual send the exponential distribution that sequence of data packet has identical geometric average;
A4, reality sent to sequence that sequence of data packet and steps A 3 generate and carry out Anderson-Da Lin and test, significance level S1 is set, if S1*K section actual transmission packet time intervening sequence is tested by Anderson-Da Lin, then judge that this node sends the distribution of packet time intervening sequence index of coincidence;
A5, significance level S2 is set, if S1*N node test by Anderson-Da Lin, then the time interval obeys index distribution of the packet that each node sends in judgement wireless sensor network.
3. the wireless sensor network flow evaluation method of Corpus--based Method according to claim 2, it is characterized in that, described step B specifically comprises:
The distance calculating the time interval sequence of each node transmission packet is the auto-correlation coefficient of 1, if the cutting window of 95% all have passed independence test, then the transmission packet time intervening sequence of predicate node is separate.
4. the wireless sensor network flow evaluation method of Corpus--based Method according to claim 3, it is characterized in that, described step B specifically comprises:
B1, the node number N calculated in wireless sensor network; Each node is observed in T time section, to send the process of packet and record;
The variance of the channel strength RSS of B2, each time point of each node calculate, it can be used as network dynamic parameter K, and the process of the transmission packet of each node is cut into K section;
B3, every section sent the time interval sequence of packet ask distance be 1 auto-correlation coefficient;
B4, more every segment distance be the auto-correlation coefficient of 1 result and
obtain the cut length number R tested by independence, wherein, n is time interval sequence samples number;
If B5 R/N is greater than 95%, then judge that this node sends packet time intervening sequence separate.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101267446A (en) * | 2007-12-29 | 2008-09-17 | 中国科学院计算技术研究所 | Time domain data amalgamation method for wireless sensor network |
| CN101511099A (en) * | 2009-04-01 | 2009-08-19 | 南京邮电大学 | Collection method for wireless sensor network data based on time series prediction model |
| CN102098731A (en) * | 2011-01-25 | 2011-06-15 | 无锡泛联物联网科技股份有限公司 | Hop-based flow adaptive dormancy scheduling method in wireless sensor network |
| EP2445270A1 (en) * | 2010-10-21 | 2012-04-25 | Fujitsu Limited | Wireless network apparatus, wireless network system and wireless network node controlling method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN101267446A (en) * | 2007-12-29 | 2008-09-17 | 中国科学院计算技术研究所 | Time domain data amalgamation method for wireless sensor network |
| CN101511099A (en) * | 2009-04-01 | 2009-08-19 | 南京邮电大学 | Collection method for wireless sensor network data based on time series prediction model |
| EP2445270A1 (en) * | 2010-10-21 | 2012-04-25 | Fujitsu Limited | Wireless network apparatus, wireless network system and wireless network node controlling method |
| CN102098731A (en) * | 2011-01-25 | 2011-06-15 | 无锡泛联物联网科技股份有限公司 | Hop-based flow adaptive dormancy scheduling method in wireless sensor network |
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