CN106454732A - Method for improving energy high efficiency in passive locating system based on wireless sensor network - Google Patents
Method for improving energy high efficiency in passive locating system based on wireless sensor network Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
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- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- H—ELECTRICITY
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- H—ELECTRICITY
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Abstract
The invention discloses a method for improving energy high efficiency in a passive locating system based on a wireless sensor network. The method comprises a data collection phase, an RRS change measure calculation phase and a locating and tracking phase. The data collection phase comprises an offline phase and an online phase, in the offline phase, RSS readings of all wireless links are collected when there is no person in a monitoring area, and in the online phase, real-time RSS readings are collected when there are people in the monitoring area; in the RRS change measure calculation phase, the RSS readings collected in two different phases are compared to generate a RRS change measure value; in the locating and tracking phase, a locating system running on a PC terminal runs a wireless scanning imaging algorithm through the collected wireless link RSS change measure value to locate the positions of people; and the area where people may occur at the next moment is predicted by Kalman filtering. According to the method disclosed by the invention, since a newly designed 1-bit RSS change measure value is used, and the redundant wireless link measurement is identified and reduced, the method can be applied to security and protection applications, such as intruder detection, movement detection, etc.
Description
Technical field
The present invention relates to wireless sensor network field, it is in particular to a kind of nothing based on wireless sensor network
The method improving energy efficient in source location system.
Background technology
Positioning is the typical case's application in wireless sensor network.Emerging passive location is the special positioning side of a class
Method, can be used for position monitor and does not carry the people of wireless device (as invader will not Portable device cooperation alignment system realize determining
Position).Passive location system operation principle is to occur in using people to affect wireless channel near link, by monitoring wireless channel
Change system can deduce that people is located at wireless link information nearby.Because sensor is generally battery-powered, based on wireless
Consider in the passive location system of sensor network that energy efficient problem is necessary.The design of existing passive positioning field stresses
The impact to wireless link for the presence of the different metrics quantization people of design to improve the precision of passive location, and rare design is special
How research realizes energy efficient.And sensor only is used as RSS measurement in the wireless sensor network in existing system
Collect, have ignored the computing capability of sensor.How in application network, the computing capability of sensor realizes the passive of energy efficient
Positioning is challenging.Additionally, the target position of positioning has certain locality it means that to there are some in network wireless
The remotely located target location of link is actual not to contribute to positioning target, when sensor network every wheel scan monitored area, such as
It is also challenging that what identification reduces the measurement of redundant wireless link.
Content of the invention
It is an object of the invention to provide it is a kind of high based on improving energy in the passive location system of wireless sensor network
The method of effect property, by the measurement of redundant wireless link is reduced using newly-designed 1 bit RSS change degree value and identification,
On the premise of not reducing positioning precision, improve the energy efficient of passive location.
For reaching above-mentioned purpose, the present invention propose a kind of based on improving energy in the passive location system of wireless sensor network
The method of high efficiency, including data collection phase, RSS measure of variation calculation stages and locating and tracking stage, wherein；
Data collection phase includes off-line phase and on-line stage, in off-line phase, collect monitored area nobody when
The RSS reading of all wireless links, in on-line stage, collects real-time RSS reading in monitored area for the someone；
RSS measure of variation calculation stages, compare the RSS reading that two different phases are collected, and generate a RSS measure of variation
Value；
In the locating and tracking stage, the alignment system operating in PC end is passed through to collect the wireless link RSS change degree value of returning
Run the position that radio scan imaging algorithm orients people；And be likely to occur in the next one moment using Kalman prediction people
Region.
The invention has the beneficial effects as follows：The method the calculating of RSS change degree value is localized in sensor node real
Existing.The KL- divergence being distributed by thresholding RSS obtains the RSS measure of variation of 1 bit, and decreasing sensor needs the number of transmission
According to amount, shorten sensor network and scan the time needed for monitored area.Under same rate of scanning, reduce meaning sweep time
Taste sensor network and more can be realized the scanning to monitored area in energy-conservation ground.Additionally, method uses Kalman filtering to people
Moving area be predicted, thus reducing the measurement of the sensor in unrelated link.Reception sensor on unrelated link can
To realize energy-conservation in a dormant state.The present invention can be used for the security protections such as invasive noise, motion detection application.
Brief description
Accompanying drawing is not intended to drawn to scale.In the accompanying drawings, identical or approximately uniform group of each illustrating in each figure
One-tenth partly can be indicated by the same numeral.For clarity, in each figure, not each ingredient is all labeled.
Now, by by example and the embodiment of various aspects of the invention is described in reference to the drawings, wherein：
Fig. 1 be the embodiment of the present invention the passive location system based on wireless sensor network in improve energy efficient
The flow chart of method.
Fig. 2 is the superframe structure used in Fig. 1 embodiment.
Specific embodiment
In order to know more about the technology contents of the present invention, especially exemplified by specific embodiment and coordinate institute's accompanying drawings to be described as follows.
As shown in figure 1, it is high to improve energy in the passive location system based on wireless sensor network of the embodiment of the present invention
The method of effect property includes 3 stages：Data collection phase, RSS measure of variation calculation stages, locating and tracking stage.
Stage 1：Data collection phase
Wireless senser is deployed in around monitored area and sensor node deployment is on liftoff 1 meter of high spider.Pass
Sensor node is operated in 2.4GHz frequency range and follows IEEE802.15.4 standard.IEEE802.15.4 standard defines 2.4G frequency range to be had
16 channels (No. 11 to No. 26 channels).For avoiding WiFi signal to disturb, the present invention selects No. 26 channel (mid frequencyes
2.48GHz) it is used for radio communication.Sensor node sends energy with 0dBm on No. 26 channels and sends according to node serial number order
Broadcast packet.The sensor being nearby connected on PC serves as base-station node, monitors the data transfer in all-network, and the number receiving
According to bag, PC is passed to by USB.PC positions target by running positioning and track algorithm.We by the stage below successively
Introduce the content generation of sensor side broadcast packet and the positioning at PC end and follow the trail of realization.In data collection phase, according to prison
Survey whether region has monitoring objective to occur, data collection is divided into offline background data to collect and online real-time data capture.Below
Introduce the working condition in data collection phase for the sensor node.
The description of the agreement of sensor network generally can be described with superframe structure.Superframe structure features in network
In the working condition in each moment, Fig. 2 shows superframe structure used in the present invention to sensor node.Superframe is mainly by three
It is grouped into：Base-station node beacon sends, sensor node working condition, sensor node dormancy state.In beacon transmission process
In, the sensor in network receives the broadcast packet that base-station node sends, and includes the link information participating in scanning.Worked in node
Cheng Zhong, working time of node is divided into n time slot (n is nodes number).Assume that node is numbered as 1..n, then
Node i sends broadcast packet in i-th time slot, includes the RSS change degree value of link.Meanwhile, other nodes receive section
The broadcast packet that point i sends, records oneself RSS value of link calculate the RSS measure of variation of this link and node i between
Value.Also in i-th time slot, base station monitoring wireless broadcast packet, the link RSS measure of variation that record broadcast packet includes
Value.In node dormancy part, node is scheduled and closes Anneta module to save energy.Repeat the superframe structure in Fig. 2, sensor
Node actual cycle in network is in the state of keeping alert while in bed (duty cycling).When superframe interior joint dormancy time is 0, pass
Node in sense net is just constantly in working condition, the sequentially broadcast packet in the way of token ring of the node in system.When in network
When all nodes sequentially broadcast a wheel, the PC connecting base-station node can receive the RSS change degree of all wireless links in network
Value.
Stage 2：RSS measure of variation calculation stages
By the broadcast of sensor network interior joint, the receiving node of all links in network is often taken turns and can be obtained this link
RSS reading.We introduce how sensor node obtains approximate RSS distribution from many wheel RSS readings first.Work as monitored area
During middle nobody, RSS distribution referred to as nobody RSS obtaining from RSS reading is distributed, i.e. offline background RSS distribution.Work as system
The RSS distribution obtaining during on-line operation is referred to as real-time online RSS distribution.By comparing two RSS distributions, system can deduce that people is
The no wireless link that causes is decayed.For a particular link in wireless sense network, the receiving node of link receives and sends
The node packet that periodically (T express time cycle) sends.While receiving bag, receiving node can obtain packet
RSS value (riRepresent the RSS value arriving in iT reception node measurement).According to the data book of TelosB node, riValue
It is the finite integer value that -90dBm arrives 0dBm.Then in moment t, give the time window that a length is s, we can be by statistics
RSS value (r in time windowt-s+1,…,rt) frequency of occurrence obtain t wireless link RSS distribution H (with the column asking distribution
Figure is similar).The representation in components of i-th of H is worth the frequency that the RSS for-(90+i) dBm occurs in time window.Notice someone
Both contained background RSS information in real-time online RSS distribution during appearance to also contains foreground information (people is near link to chain
Path channels produce impact).
In order to obtain the impact to link for the people it would be desirable to be distributed " deducting " offline background RSS with real-time online RSS
Distribution.RSS distribution " difference " that the distribution of background RSS is occurred with someone mathematically can be quantified with KL- divergence.KL- dissipates
Degree is generally used to describe the distance of two stochastic variable distributions in theory of probability.It is assumed that HdFor real-time online RSS distribution, HbBe from
Line background RSS is distributed, and KL- divergence can be calculated by following equation：WhereinBecome based on the RSS that the KL- divergence present invention devises new 1
Change metric KLDB (binaryzation KL- divergence) and by the calculating distributed implementation of KLDB in wireless sensor network.And other
Work is compared, and this design decreases the load of packet transmission so that the calculating of data collection and RSS measure of variation more saves
Can.For example, the RSS change degree value KLDB of j-th strip link can be obtained by KL- divergence thresholding.KL- divergence value when link
YjDuring≤threshold, the KLDB value Y ' of linkj=0；In the case of other, Y 'j=1.Parameter threshold can be according to deployment
Experimental data in environment determines.
Sensor node can calculate all wireless with this node as receiving node according to the computational methods of above-mentioned KLDB value
The KLDB value of link, and generate broadcast packet content during lower whorl broadcast according to the KLDB value of these links.For example, by 16 nodes
The wireless sensor network of composition, broadcast packet form is nx_int8_t nodeid and nx_uint16_t KLDB.Here,
Nodeid represents the node number of receiving node, and whether the link that the i-th bit of KLDB represents between sending node i and receiving node has
Substantially decay.Additionally, the calculating of KL- divergence needs to calculate log function.Because sensor node computing capability is limited, in node
It is very time-consuming for calculating log on platform.We are not intended to calculate the time of KLDB value impact data collection.We employ and play table
Method it would be possible to the log value used is calculated in advance in PC, each node is played table and is stored log value as KLDB value afterwards
Calculate.Stage 3：The locating and tracking stage
Radio scan is imaged the technology that (RTI) technology is a kind of imaging of position by people in wireless sensor network.People's
The transmission of wireless signal can be affected in the presence of monitor area.In order to position and follow the trail of the people occurring in monitor area, this
Bright oval link model portrays the relation between the change degree value of wireless reception of signals and the position of people.Given wireless link
RSS change degree value and oval link model, we can be positioned on the position of the people of monitor area.
We dispose a jump sensor network of a n node in monitor area.For n node, in network at most
There is the oriented wireless link of M=n (n-1) bar.Due to people monitor area appearance can change sensor wireless signal reception,
We can obtain the channel variance situation that M RSS change degree value features M wireless links.We make YiRepresent i-th chain
The RSS change degree value on road, the RSS change degree value of therefore all links can be with a vectorial Y=[Y1,Y2,…,YM] come
Represent.In order to represent the possible position of target, monitored area is discretized as N number of pixel.Make XiRepresentative is in ith pixel
Signal attenuation value on point, then vectorial X=[X1,X2,…,XN] can be used to represent people on each pixel of whole monitored area
Signal attenuation vector.Position due to N number of pixel during discretization monitored area can be known a priori by, and we can be from X vector
The signal attenuation value of each pixel deduces the position of target.Existing research work shows attenuation vector X and RSS measure of variation
Relation between vectorial Y can be represented with following linear relationship：Y=WX+noise, wherein, noise represents in RSS reading
Noise, W represents link overlay model.Actually one size of W is the weight matrix of M × N.Because people is in specific pixel point
Position can produce different decay to different wireless links, so needing to introduce a weight matrix to portray this difference.
Wherein matrix element WijThe weighing factor value that representative causes to the signal reception of i-th link in the position of j-th pixel.Power
Element W in weight matrix WijCan be to be turned to by form：
WhereinFor the distance of j-th pixel and the sending node of i-th link,For j-th pixel and i-th
The distance of the receiving node of bar link.D is the distance between sending node and receiving node of i-th link.D+ λ is oval chain
The size of the semi-major axis of road model, can affect the area size of wireless link for featuring people.From element WijDefinition understand
Why link model is called model of ellipse for we.In oval link model, people is with receiving node to the influence area of link
The ellipse being two focuses with sending node.People receives to link signal inside elliptic region and has an impact, outside right in ellipse
Link signal receives not to be affected.
Known RSS change degree value Y and oval link model W, the task of positioning is changed into solution linear equation and obtains X's
Value.To the resolution of radio image, generally for obtaining higher resolution, the dimension of X is greater than no the length relation of X
The number of wired link, i.e. the dimension of Y.The linear equation needing to solve of this meaning is ill-conditioning problem.Optimization field solves this kind of
A kind of method of equation is introduced into new constraint.The Tikhonov regularization that for example we adopt.The place of Tikhonov regularization
Reason method is that original linear equation is changed into an optimization problem, as follows：
argminX||WX-Y||2+δ||X||2,
Wherein δ is a weight parameter, and the constraint that Tikhonov regularization introduces is element all very littles of X.Weight parameter δ
Control the balance between the error of WX-Y and two norms of X.The solution of this optimization problem can be expressed as：
WhereinU and V is by W matrix singular value decomposition (W=UDVT) obtain, di
It is i-th diagonal element of diagonal matrix D.Notice in actual location system,Can precalculate out offline
Calculate X for later.When RSS measure of variation vector Y has been calculated by the RSS value of sensor, X vector can pass through matrix
Vector is taken advantage of to calculate in real time, time complexity is O (MN).Element X in XiRepresentative occurs in ith pixel point position
Probability size.The position L of pixel iiWe can calculate before network design.So pass through to observe in element in X relatively
Big value, we just can speculate the position that people is located.We employ average weighted method to calculate the position of people
Lperson.We first sort element in X in descending order.Represent it will be assumed that the element of X has been descending for convenience, then people
Position can be obtained by the position of T pixel before weighted average, and formalization representation is：
The false code that algorithm 1 is listed describes above-mentioned calculating process.
Algorithm 1 passive localization algorithm
Node in Sensor Network in the present invention is given out a contract for a project according to the order circulation of node serial number.When a sensor broadcast packet
When, other nodes records receive the RSS value of bag, coordinate the RSS value calculating KL- divergence above recording and thresholding obtains two-value
Change KL- divergence.After the binaryzation KL- divergence of all links is collected in base station, the present invention (is set to when can deduce RSS measurement
The position of moment t) people.Therefore, to time t, we have a series of track L of peoplei=(xi,yi), i=1 ... t.Under hypothesis
The position that one moment people occurs meets linear Gauss model, and we can adopt Kalman filtering to the track of people.Kalman filters
Ripple can formalization representation be：
P(Lt+1|Lt)=N (FLt,ΣL)(Lt+1)
P(L′t|Lt)=N (HLt,ΣL′)(L′t)
Wherein, F and ΣLRepresent the covariance matrix of noise in linear transition model and metastasis model respectively.H and ΣL′Point
Not Biao Shi measurement error in observation model and observation model covariance matrix.According to above-mentioned formula, we can deduce
People's position distribution in next moment t+1.Confidence probability is set to the Probability Area meeting Two dimension normal distribution, can deduce
The region that people is occurred with confidence probability in the next moment.Region is likely to occur according to next moment people, some unrelated radio chains
RSS value on road can be without surveying record.Here the next moment people of unrelated expression is likely to occur pixel and chain in region
The model of ellipse on road is not occured simultaneously.Position estimating due to unrelated link pair people does not help, and what we can be safe ignores this
A little links.In the present invention, before lower whorl RSS measurement, base station can first broadcast one in the beacon times in superframe structure
Packet indicates which link can participate in the measurement of next round RSS value.The receiving node of unrelated link is in next round DATA REASONING
Resting state can be entered thus realizing energy-conservation.Because each node can upload two-value KL- of the link being associated with oneself
Divergence, because node is not engaged in measuring the RSS value of all links, each node may upload some insignificant data.
But because base station knows that epicycle needs the link of measurement, corresponding for unrelated link two-value KL- divergence can be put by base station in positioning
For 0.By reducing the link measurement on unrelated link, the present invention effectively reduces the energy consumption of Passive Location and Tracking.Above-mentioned algorithm
Process is listed in algorithm 2.
The relevant link algorithm based on Kalman filtering for the algorithm 2
Although the present invention is disclosed above with preferred embodiment, so it is not limited to the present invention.The affiliated skill of the present invention
Has usually intellectual, without departing from the spirit and scope of the present invention, when can be used for a variety of modifications and variations in art field.Cause
This, protection scope of the present invention ought be defined depending on those as defined in claim.
Claims (6)
1. a kind of based in the passive location system of wireless sensor network improve energy efficient method it is characterised in that
Including data collection phase, RSS measure of variation calculation stages and locating and tracking stage, wherein：
Data collection phase includes off-line phase and on-line stage, in off-line phase, collect monitored area nobody when all
The RSS reading of wireless link, in on-line stage, collects real-time RSS reading in monitored area for the someone；
RSS measure of variation calculation stages, compare the RSS reading that two different phases are collected, and generate a RSS change degree value；
In the locating and tracking stage, the alignment system operating in PC end passes through the wireless link RSS change degree value operation that collection is returned
Radio scan imaging algorithm orients the position of people；And using the Kalman prediction people next one area that is likely to occur of moment
Domain.
2. the side based on raising energy efficient in the passive location system of wireless sensor network according to claim 1
Method it is characterised in that in data collection phase, sensor network each node broadcasted successively containing RSS using token ring agreement
The wireless data packet of change degree value, in network other sensors serve as receiving node surveying record offline and online in the case of
RSS reading builds RSS statistics Nogata.
3. the side based on raising energy efficient in the passive location system of wireless sensor network according to claim 1
Method is it is characterised in that in RSS measure of variation calculation stages, in each wireless links in sensor network, receiving node passes through meter
Calculate offline background RSS count Nogata with online in real time RSS count the KL- divergence of Nogata and set appropriate threshold and obtain 1 bit
RSS change degree value.
4. the side based on raising energy efficient in the passive location system of wireless sensor network according to claim 1
Method it is characterised in that in RSS measure of variation calculation stages, by the broadcast of sensor network interior joint, all chains in network
The receiving node on road often takes turns the RSS reading that can obtain this link；As nobody in monitored area, obtain from RSS reading
The referred to as unmanned RSS distribution of RSS distribution, i.e. offline background RSS distribution；The RSS distribution obtaining when system on-line operation is referred to as
Real-time online RSS is distributed；By comparing two RSS distributions, system can deduce whether people causes wireless link decay.For no
For a particular link in line Sensor Network, the receiving node of link receives sending node periodicity, the T express time cycle sends
Packet；While receiving bag, receiving node can obtain the RSS value of packet, riRepresent and survey in iT reception node
The RSS value measured；According to the data book of TelosB node, riValue be -90dBm arrive 0dBm finite integer value；Then exist
Moment t, gives the time window that a length is s, and we can be by RSS value (r in timing statisticses windowt-s+1,…,rt) occur
The frequency obtains the RSS distribution H of t wireless link, and i-th of H of representation in components is worth for-(90+i) dBm in time window
The frequency that RSS occurs；Both contain background RSS information in real-time online RSS distribution when someone occurs and also contains prospect letter
Breath is that people produces impact to link channel near link.
5. the side based on raising energy efficient in the passive location system of wireless sensor network according to claim 1
Method is it is characterised in that in order to obtain the impact to link for the people, divided with offline background RSS of real-time online RSS distribution " deducting "
Cloth；RSS distribution " difference " that the distribution of background RSS is occurred with someone is mathematically quantified with KL- divergence；KL- divergence exists
It is generally used in theory of probability describe the distance of two stochastic variable distributions；It is assumed that HdFor real-time online RSS distribution, HbBe from
Line background RSS is distributed, and KL- divergence can be calculated by following equation：WhereinDevise the RSS measure of variation of new 1 based on KL- divergence
Value KLDB be binaryzation KL- divergence and by the calculating distributed implementation of KLDB in wireless sensor network；Sensor node meeting
Computational methods according to above-mentioned KLDB value calculate the KLDB value of all wireless links with this node as receiving node, and according to this
The KLDB value of a little links generates broadcast packet content during lower whorl broadcast.
6. the side based on raising energy efficient in the passive location system of wireless sensor network according to claim 1
Method is it is characterised in that passive location method：
Input：(1) the RSS measure of variation signal S that wireless sensor network scanning one wheel monitored area generates1,S2,…,Sn, its
In, n is the node number in network, SiRepresent the KLDB value of all wireless links with i-th node as receiving node；
(2) deployment sensing station and monitored area, regularization weight coefficient δ；
(3) using number T of weighted pixel point when positioning.
Output：The position L of peopleperson；
Algorithm：
(1) initialize weight matrix W：
Monitored area stress and strain model is some pixels.Based on sensing station and pixel position, according to model of ellipse life
Become weight matrix W；
(2) solve the decay X of each pixel position：
Take turns whenever wireless sensor network scans monitored area one, a wheel RSS measure of variation signal S can be collected in base station1,S2,…,
Sn, according to S1,S2,…,SnSystem obtains the vectorial Y ' that all wireless link KLDB values are constituted；
According to the linear relationship of decay X and Y ', solving-optimizing problem obtains the decay X of each pixel position；
(3) position the position of people：
Decay size according to pixel position is ranked up to each component of X；
Front T pixel position obtains the position of people according to decay size weighted average
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