CN103260238A - Mobile node positioning method based on speed sampling zone in wireless sensor network - Google Patents
Mobile node positioning method based on speed sampling zone in wireless sensor network Download PDFInfo
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Abstract
The invention provides a mobile node positioning method in a wireless sensor network to construct a node motion model. The Newton interpolation method is used for roughly obtaining node motion tracks and assuming a maximum moving speed and a minimum moving speed aiming at node moving speed. With the assumed speed interval, a sampling zone is narrowed as much as possible, finally, node position filtration is carried out, positions where nodes can impossibly exist are eliminated, and therefore the possible range of the nodes and prediction workload are further reduced. Through node motion model construction, node motion prediction is carried out, the positions where the nodes can impossibly exist are eliminated, the positioning error rate is lowered, and therefore positioning precision of nodes to be positioned is improved.
Description
Technical field
The present invention relates to the wireless sensor network location algorithm, more particularly, is that a kind of being used for carried out the method that mobile node is located at wireless sensor network.
Background technology
In today of information technology develop rapidly, the Internet provides convenient, communication platform rapidly for ordinary populace, be very easy to interpersonal information interchange, the generation of wireless sensor network technology will thoroughly change the present situation that human sense of touch, vision, the sense of smell that only depends on self since ancient times comes perception information, improved human accuracy and sensitivity of obtaining information greatly, wireless sensor network (WSN) can make people obtain a large amount of full and accurate and reliable information whenever and wherever possible, thus real " ubiquitous calculating " theory that realizes.Because wireless sensor network is a recent studies on field of computer science and technology, has broad prospects in actual applications, therefore, its appearance has caused mondial extensive concern.
Wireless sensor network is proposing have challenging research work to the researcher as an emerging research field aspect basic theory and the application technology, and the related node locating technique of this paper is exactly one of them.In the real world applications of wireless sensor network, one of them major issue that people are concerned about is exactly the particular location that event takes place.Use for great majority, as large-scale animal tracking, enemy district information is scouted, the environmental monitoring in the abominable area of geological environment, specified place of accident etc., node locating is particularly important, knows perception data and does not know that the corresponding particular location perception of its perception does not have practical significance for those.Therefore, determine that by obtaining node location the position of event generation is one of sensor network most basic function, the sensor network effectiveness of application is had important effect.Though the GPS GPS (Global Position System) can provide more accurate geographical location information, but it only is applicable to unscreened outdoor location, and lower deployment cost costliness, energy consumption are big, therefore are not suitable for the wireless sensor network of low cost, low-power consumption, self-organizing, applied environment relative complex.
Wireless sensor network is an important component part of Internet of Things simultaneously, also is a research focus in current information field.To the research of node locating problem in the wireless sensor network, meet the accurate Position Research of node of production application aspect demand especially for the location, have certain research and use value.
In many application of wireless sensor network, need sensor node to be kept in motion.Such as: we will investigate the life habit of open-air certain animal, can install sensor node on one's body additional at it, utilize sensor node to record one day whereabouts of animal and life habit, and then these species are understood to a deeper level and studied.Therefore, the node that originally remains static, because the motion of animal can be in mobile state along with the motion of animal, simultaneously, the positional information of animal is not changing all the time, so the positional information of node also is to be among the variation constantly.When node is in mobile status, whole topology of networks can change frequently, this moment is if still adopt the location mechanism of static node location algorithm, then need constantly the more positional information of new node, also be constantly to communicate the switch coordinate information between ordinary node and the anchor node, this can cause the too much consumption of node energy undoubtedly, very easily cause node because too early the using up of energy, and the death of node appears, also can reduce respective capabilities and the locating accuracy of network simultaneously.So, how in the network of node motion, realize low cost, low-power consumption, the higher location of precision, become the emphasis that this chapter studies.
Wireless sensor network is that the anchor node by the ordinary node of unknown self-position and known self-position is constituted.Therefore, at the state of node, generally have the situation of following three kinds of motions in the mobile wireless sensor network environment: anchor node is in mobile status, and ordinary node remains static; Anchor node remains static, and ordinary node is in mobile status; Anchor node and ordinary node all are in mobile status.
The pure running fix algorithm of wireless sensor network has: comprise that all nodes of ordinary node and anchor node have all installed the movable positioning system of GPS device additional, individual nodes is used the movable positioning system of GPS and the movable positioning system that does not use GPS.Whole nodes all uses the movable positioning system of GPS device that the ZebraNet system is arranged, individual nodes uses the movable positioning system of GPS device that MCL (Monte Carlo Localization) algorithm (Nelson T Y is arranged, Boots B, Wulder M, et al. Predicting Forest Age Classes from High Spatial Resolution Remotely Sensed Imagery Using Voronoi Polygon Aggregation[J]. GeoInformatica, 2004,8:143-155.); Do not use the movable positioning system of GPS device have DL (directional localization) system (Qiu Yan, Zhao Chongchong. wireless sensor node Study of location [J]. computer science, 2008,35 (5): 47-50.).
Monte Carlo localization method (MCL) at first is applied in the robot positioning field.In 2004, and the Lingxuan Hu of Virginia university (Hu L, Evans D. Localization for Mobile sensor Networks[C]. Pro.of the 10
ThAnnual International Conference on Mobile Computing and Networking, 2004:45-47.) the sequence Monte Carlo positioning mode that waits the people will be applied to localization for Mobile Robot for the first time is incorporated in the node locating of mobile wireless sensor network.This algorithm utilizes the mobility of node to improve positioning accuracy, has reduced the location cost.
The MCL method utilizes the mobility of node to help the location, a new approaches (Capkun S is provided for the solution of mobile radio sensor network node orientation problem, Hamdi M. GPS-free positioning in mobile Ad Hoc networks [J]. Cluster Computing, 2002,5 (2): 1157-1167.), increasing Chinese scholars derives some improved plans on the basis of this algorithm.As: MCB (Monte Carlo Localization Boxed) method, solved the low problem of MCL method sampling efficiency, and then proposed a kind of based on MCL location technology (the Aline Baggio that finds range, Koen Langendoen. Monte Carlo localization for mobile wireless sensor networks [J] .Lecture Notes in Computer Science, 2006,4325 (11): 317-328.), ranging information is applied in the MCL algorithm, improves positioning accuracy.By introducing node motion noise, make dynamic running fix and static immobilization (the Y. Kwon of MCL location algorithm simultaneous adaptation node first, K.M echitov, S.Sundr esh, W.Kim. Resilient Localization for Sensor Networks in Outdoor Environments. Int Proceedings of the 25th IEEE International Conference on Distributed omputing Systems (ICDCS'05), 2005, pp.643-652.).A kind of antithesis Monte Carlo location algorithm and a kind of mixing Monte Carlo location algorithm (K.Yu. 3-D localization error analysis in wireless networks, IEEE Trans. Wireless Communications, vol.10, pp.3473-3481, Oct.2007.), overcome the sample number in the position fixing process seldom, the problem that unknown node just can not accurately be located.Dual-MCL, Mixture-MCL algorithm (Langendoen K, Baggio A. Monte Carlo localization for wireless sensor networks [A] .Proc.of the 2nd Int'l Conf on Mobile Ad-hoc and Sensor Networks (MSN 2006) [C]. Hong Kong:Springer Verlag Press, 2006:317-328.) improve in prediction and filtering stage by the methods such as sample range of restriction sample.These methods are that new approach has been opened up in the application of MCL on mobile WSN node locating.In traditional MCL algorithm, what at first need is the size of delimiting sample region according to the speed of node for the first time, if the speed ratio of node is bigger, its corresponding sample region also can increase thereupon so, and this just means the uncertain top that can increase node location.Secondly, just in advance up to the translational speed of node, and and do not know the direction that it moves, when gathering sample, just need sample in whole border circular areas so, the zone is more big, the inaccuracy of the sample that collects can be caused, therefore the node location accuracy of predicting can be had influence on.
Summary of the invention
At above-mentioned problem, the present invention proposes a kind of based on Monte Carle mobile node location algorithm maximum, the minimum speed sample region.
At first make up the motion model of node, utilize Newton interpolating method roughly to obtain the movement locus of node, namely obtain speed and direction that node moves, suppose the translational speed that it has a maximum at the speed of node motion, and the translational speed of a minimum.
The present invention includes following three aspect contents:
At first, to motion model and the motion pose status predication of node.Use traditional Monte Carlo algorithm, node can obtain the preceding several moment position coordinateses of self, after obtaining position coordinates, it is left in the historical record stack in the sensor node, then utilize the trend of the history information prediction node motion of stack the inside.
Secondly, carry out choosing of node location based on minimum maximal rate sample region.Utilize record stack and Newton interpolating method, obtain node movement locus, the direction of motion and speed, because the node motion has inertia, namely within the short time period, the motion of node has continuity, therefore, utilizes the value in a last moment to sample.Because the node movement velocity exists a maximum and a minimum value, therefore, utilizes minimum and maximum speed further to dwindle the zone of specimen sample.Specifically comprise:
The method of mobile node location in a kind of wireless sensor network, utilize Newton interpolating method to obtain the initial motion track of node, maximum translational speed according to node motion, and minimum translational speed is determined the position sampling district, acquisition node sample in the sample region of position filters the node sample position, excludes the position that node can not occur, obtain the sampling node sample set, according to formula:
To predicted value
Weights
Carry out standardization, according to formula:
Determine the position of node i, wherein, α is the accumulative total weights of particle.Wherein, in the movement velocity interval, gather sample and comprise that specifically mobile node is gathered the self-position coordinate in real time, successively coordinate figure is pressed in the stack, choose the pose state of the position coordinates prediction node current time of three newly stacked group nodes; According to formula:
Computing node movement velocity V recently constantly, more than one constantly the position be the origin of coordinates, with node constantly movement velocity V, maximal rate V recently
Max, minimum speed V
MinThree velocity amplitudes are radius, on node movement velocity direction, start point θ angle, obtain three fan-shaped, according to formula:
Make up node set L, with in the sample region not the node in node set filter out, according to formula:
Obtain the sampling node sample set.
The present invention utilizes the speed interval of setting, the sample collection zone is dwindled as much as possible, improve the accuracy of sample collection, in the filtration of further carrying out node location, exclude the position that node can not occur, thereby further reduce the possible scope of node and the workload when prediction, to reach the purpose that improves the node locating precision and reduce computing.
Description of drawings
Fig. 1 mobile node location algorithm of the present invention flow chart;
The method of sampling schematic diagram of Fig. 2 node.
Embodiment
Below in conjunction with accompanying drawing mobile node of wireless sensor network location algorithm of the present invention is described.
As shown in Figure 1, for localization method of the present invention, its process flow is as follows:
At first to motion model and the motion pose status predication of node.Detailed process is as follows:
Use traditional Monte Carlo algorithm, node can obtain the preceding several moment position coordinateses of self, after obtaining position coordinates, it is left in the historical record stack in the sensor node, then the trend of the history information prediction node motion by the stack the inside.Utilize the implementation method of the historical record prediction node movement tendency in the stack as follows:
(1) after node obtains first position coordinates, be pressed in the stack, learn that according to the character of stack stacked at first information can be pressed at the bottom of the stack.
(2) after mobile node produces new elements of a fix value, successively these coordinate figures are pressed in the stack, timer is noted the time that position coordinate value enters stack at every turn simultaneously.Obtain the node location coordinate of random time point, but in order better to predict the pose state of node current time, choose the position coordinates of three newly stacked in stack group nodes, because it is the coordinate figure that enters the latest in the stack, therefore it has stronger ageingly, can satisfy the requirement of predicting the pose state of current time according to last one constantly positional information.
As, the position coordinates number of choosing node is 3, represents with K=3, namely the unknown node position coordinates is to obtain after the location for the third time.Suppose that be t the positioning time that timer obtains this minor node, and read new three times stacked historical records in the unknown node record stack successively.Represent historical record with node location coordinate and time, that is: historical record 1 is expressed as: (x
1, y
1, t
1), historical record 2 is expressed as: (x
2, y
2, t
2), historical record 3 is expressed as: (x
3, y
3, t
3), t wherein
3T
2T
1, clearly, historical record 3 is up-to-date position coordinates records.
Suppose function G (t
i), K (t
i) be with time t
iBe the function of independent variable, so according to newton interpolation polynomial, can utilize the x in these 3 moment
i=G (t
i), y
i=K (t
i) prediction t position coordinates x constantly
tAnd y
tFor:
x
t=G(t)=N
x(t)+R
x(t) (1)
y
t=K(t)=N
y(t)+R
y(t) (2)
N in the formula
x(t), N
y(t) be newton interpolation polynomial, R
x(t) R
y(t) be the Newton interpolation remainder.
By equation group
The character of recycling derivative is respectively to equation: x
t=G (t)=N
x(t)+R
x(t) and y
t=K (t)=N
y(t)+R
y(t) differentiate, just can obtain unknown node in t corresponding speed on x axle and y direction of principal axis of the moment, note is made V respectively
xAnd V
y:
Obtaining node t constantly respectively after the corresponding speed on x axle and y direction of principal axis, just can obtain the mobile orientation angles of its correspondence, namely under coordinate system, can utilize azimuth angle theta to represent the direction that it moves, according to formula
Determine the azimuth.
Unknown node in t movement velocity constantly is:
Carry out choosing of node location based on minimum maximal rate sample region, detailed process is as follows:
Obtain node movement locus and travel direction and speed roughly by above-mentioned steps, therefore, can utilize the value in a moment to sample.
(1) because the node movement velocity exists a maximum and a minimum value, therefore, uses V
MinThe minimum speed of expression node motion is used V
MaxThe maximal rate of expression node motion.Limit the minimum and maximum speed of node further to dwindle the zone of specimen sample.More than one be the origin of coordinates constantly, obtained node constantly movement velocity V recently by formula (6), then with node movement velocity V, the maximal rate V in the moment recently
Max, minimum speed V
MinThree speed determining are radius, on node movement velocity direction, start point θ angle, obtain three fan-shaped, wherein, according to formula (5)
Determine that fan-shaped angle θ is:
(2) get three fan-shaped crossing zones, as the position sampling district of node, the figure on this similar bent limit, zone.In this Qu Biantu shape, put as predicted value the method for sampling as shown in Figure 2 for picked at random N.
(3) after the position of node is filtered, filter out the position that node can not exist.If this moment, satisfactory future position number enlarges the θ angle less than N, and repeating step (2) is selected sample area, chooses the position of future position and node again, equals N until the future position number.
Constantly carry out future position choose and the purpose of location filtering is in order to find sample point abundant and that meet the demands.According to minimum speed V
MinWith maximal rate V
Max, the node set that meets the demands can be expressed as follows:
In the formula
It is the coordinate of k time of i random point.
The predicted position that filter node can not exist.The New Observer value that node receives according to current time abandons the predicted position that can not exist.When filtering, carry out in two kinds of situation: be positioned to merit constantly as if last one, use last one constantly the estimated position and in conjunction with motion model reposition is filtered, otherwise directly in sample region, sample, need not to filter, improved the probability of acceptance of node, reduce the specimen sample number of times, and reduced more circulation comparison operation in the filtration.The predicted position that filter node can not exist, concrete grammar is as follows:
The New Observer value that node receives according to current time
Abandon the predicted position that can not exist.
Section access time [k, k+1], node Q receives the current location information that anchor node is broadcasted in its communication radius, and ordinary node is transmitted communication radius r internal information after receiving anchor node information.Suppose DS be k to k+1 in the time period, 1 jumps the set of anchor node, IS be the set of jumping anchor nodes through 2, node is filtered acquisition sampling node sample set according to formula (7).
Wherein,
Be the distance of i estimation point in section access time [k, k+1] to node s.
After specimen sample finishes, if the predicted value of asking set then means the failure of location for empty, directly carry out stochastical sampling in sample region, as the sample of location estimation.If the predicted value set is not empty, then according to formula:
To the New Observer value
Weights
Carry out standardization, it is feasible carrying out standardization
, α is the accumulative total weights of particle.
According to formula:
Determine the position of node i.
By said method of the present invention, in the node moving region, determine the position sampling district, obtain node location, improved the node locating precision.
Claims (3)
1. the method for mobile node location in the wireless sensor network, it is characterized in that: utilize Newton interpolating method to obtain the initial motion track of node, maximum translational speed according to node motion, and minimum translational speed is determined the position sampling district, acquisition node sample in the sample region of position filters the node sample position, excludes the position that node can not occur, obtain the sampling node sample set, according to formula:
To predicted value
Weights
Carry out standardization, according to formula:
Determine the position of node i, wherein, α is the accumulative total weights of particle.
2. method according to claim 1, it is characterized in that: in the movement velocity interval, gather sample and specifically comprise, mobile node is gathered the self-position coordinate in real time, successively coordinate figure is pressed in the stack, chooses the pose state of the position coordinates prediction node current time of three newly stacked group nodes; According to formula:
Computing node movement velocity V recently constantly, more than one constantly the position be the origin of coordinates, with node constantly movement velocity V, maximal rate V recently
Max, minimum speed V
MinThree velocity amplitudes are radius, on node movement velocity direction, start point θ angle, obtain three fan-shaped, wherein,
Get three fan-shaped crossing zones, as the position sampling district of node, V
xAnd V
yBe engraved in the speed on x axle and the y direction of principal axis when being respectively node t.
3. method according to claim 1 is characterized in that: according to formula:
Make up node set L, with in the sample region not the node in node set filter out, according to formula:
Obtain the sampling node sample set, wherein,
Be the coordinate of k time of i node,
Be the distance of i node-to-node s in the time period [k, k+1], r is communication radius, DS be k to the k+1 time period, 1 jumps the set of anchor node, IS be the set of jumping anchor nodes through 2, N is scheduled to sampling number.
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Application publication date: 20130821 |