CN107071848A - A kind of target tracking algorism based on cluster structured reduction energy consumption - Google Patents
A kind of target tracking algorism based on cluster structured reduction energy consumption Download PDFInfo
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- CN107071848A CN107071848A CN201710425508.XA CN201710425508A CN107071848A CN 107071848 A CN107071848 A CN 107071848A CN 201710425508 A CN201710425508 A CN 201710425508A CN 107071848 A CN107071848 A CN 107071848A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
- H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/18—Communication route or path selection, e.g. power-based or shortest path routing based on predicted events
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
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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
- H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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- 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
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Abstract
The present invention provides a kind of target tracking algorism based on cluster structured reduction energy consumption.The target tracking algorism based on cluster structured reduction energy consumption, first, line activating is entered to sensor node using the hierarchical network architecture of cluster and then target is tracked, secondly, in the moving process of target, cluster head is reported to after nodal test in monitoring range to target, cluster head is estimated target current location information, its possible the next position is predicted and the node near predicted position is waken up, finally, the positional information of target is passed to base station by the upper layer network of cluster head composition in real time.Compared with correlation technique, the target tracking algorism based on cluster structured reduction energy consumption that the present invention is provided is ensured in object tracking process by a Soft Handover Algorithm, its energy expenditure is low, and time delay is low, the target following under the higher large-scale sensor network environment of suitable node density.
Description
Technical field
The present invention relates to target following technical field, more particularly to a kind of target following based on cluster structured reduction energy consumption
Algorithm.
Background technology
The application of sensor network is varied, including invader's tracking, disaster relief and battlefield surroundings monitoring etc., target
Tracking is the basis of these applications.For example, during being tracked to invader, when invader is monitored by some network node
Then, usual network node tracks target immediately, and the real time position of invader is sent into base station immediately, to help to use
Its clear and definite current location of family, so as to arrange one's own side personnel to be intercepted.
When network size is little, the report speed of target location, but in catenet, information transfer is possible
Delay can cause positional information accuracy to decline, and be reported in real time to base station as the field a great problem.
In recent years, various track algorithms are also established, wherein information-driven is utilized, using the moving process of target as base
Plinth, selects the maximized node of information utility and carries out target following, so as to ensure the energy efficiency of target following；There is utilization
The interstitial content of detection target is kept to maintain reduced levels, constantly as wide node is cut in the movement of target, plus
Enter close node etc., these algorithms mostly take into account demand of the sensor network to Energy Efficient, and as the starting point
The design of algorithm is carried out, but often be have ignored under large-scale sensor network environment, target position information is reported to base station
Real-time is generally difficult to ensure.
Therefore, it is necessary to which in summary demand, designs the efficient target following calculation towards large-scale sensor network
Method.
The content of the invention
The present invention is for the requirement for large-scale sensor network to movable object tracking real-time, using clustering architecture,
Propose a kind of target tracking algorism based on cluster structured reduction energy consumption.
It is contemplated that a target tracking algorism based on cluster structured reduction energy consumption is built, first, using point of cluster
Layer network structure enters line activating to sensor node and then tracks target, and the hierarchical network architecture of the cluster includes：Different nets
Network node division is simultaneously classified as multiple clusters, and each cluster is constituted by a cluster head and several member nodes, and each member node is only
A cluster can be belonged to；Cluster head is not fixed, and takes cycle rotation system, and each node is likely to become cluster head；Sensor network
Network is layered, and the backbone network on upper strata is constituted by cluster head, and the cluster head between adjacent cluster can be communicated with base station, on
Layer network keeps independent with member node；Secondly, in the moving process of target, nodal test in monitoring range to mesh
Cluster head is reported to after mark, cluster head is estimated target current location information, its possible the next position is predicted and called out
Node near predicted position of waking up, the position of target, the arrival of monitoring objective, prediction mode tool are predicted using formula (1)~(9)
Body is：
Zk=HkXk+vk (1)
Wherein, ZkThe numerical value of the target location detected at the k moment, HkRefer to calculation matrix, vkRepresent making an uproar in measurement
Sound, if it is 0 to meet numerical value, variance is RkGaussian Profile,
The predicted state of target is as follows：
Predict that variance matrix is as follows：
Kalman gain is：
Dbjective state is updated to：
Xk+1|k+1=Xk+1|k+Kk+1(Zk+1-HkXk+1|k) (5)
Variance matrix now is updated to：
Pk+1|k+1=(1-Kk+1Hk)Pk+1|k (6)
It is givenWith at the uniform velocity mobility model, observing matrix is：
Wherein (xk|k, yk|k) feeling the pulse with the finger-tip is marked on the position at k moment,It is shifting of the target on x directions and y directions
Dynamic speed, the position of target subsequent time is：
OrderRefer to the target present position of prediction, current cluster head selection is to predicted position week
The node on side enters line activating, and activation radius is rα, set CjIt is the cluster of work at present, node is included in work at present cluster, and
In the range of active region, it can just be waken up, so the set expression for the node that is activated is：
Finally, the positional information of target is passed to base station by the upper layer network of cluster head composition in real time.
Compared with correlation technique, the present invention provide based on it is cluster structured reduction energy consumption target tracking algorism target with
Track process ensures that its energy expenditure is low, and time delay is low by a Soft Handover Algorithm, the higher extensive biography of suitable node density
Target following under sensor network environment.
Brief description of the drawings
Fig. 1 is three kinds of algorithm position errors and node density graph of a relation；
Fig. 2 is three kinds of algorithm node energy consumptions and node density graph of a relation；
Fig. 3 is three kinds of algorithm node densities and algorithm delay graph of a relation.
Embodiment
The present invention described further below.
Embodiment
A kind of target tracking algorism based on cluster structured reduction energy consumption that the present invention is provided,
First, line activating is entered to sensor node using the hierarchical network architecture of cluster and then tracks target, the layering of the cluster
Network structure includes：Different network nodes is divided and multiple clusters are classified as, each cluster is by a cluster head and several members
Node is constituted, and each member node can only belong to a cluster；Cluster head is not fixed, and takes cycle rotation system, each node
It is likely to become cluster head；Sensor network is layered, the backbone network on upper strata is constituted by cluster head, the cluster between adjacent cluster
Head can be communicated with base station, and upper layer network keeps independent with member node.Cluster head need not be with base station direct communication, and can
It is relaying by other cluster heads, by jumping back to base station, effectively reduces communication energy consumption more；In addition, by jumping back to cluster head more
The data transfer delay that base station is produced is substantially less than transmits delay more by recalling to member node to the data caused by base station.
When setting up upper strata backbone network, set up in the neighbor table safeguarded to each cluster head, the neighbor table and store this cluster head
Hop count of each neighbours' cluster head in periphery to base station.In the neighbor table hop count of this cluster head itself of storage to base station be initialized to compared with
Big value.Such as 1000, hop count is then arranged to 0 by base station.Specially：BBC bags are transferred to network by base station using the form of broadcast
In different nodes, as some cluster head ciReceive neighbor node cjDuring the BBC bags that transmission comes, this cluster head ciNeighbours can be compared
The size of hop count and itself hop count.With hopiAnd hopjCluster head c is represented respectivelyiAnd cjTo the hop count of base station, if hopi＞ hopj+ 1,
Then cluster head ciUpdate itself hop count hopi=hopj+ 1, at the same time c in neighbor tablejHop count be modified as hopj, then according to
Neighbor table sends the new BBC bags containing itself hop count to whole neighbours' cluster heads；If hopi≤hopj+ 1, cluster head ciOnly more
C in new neighbor tablejHop count, without broadcasting new BBC bags to neighbours' cluster head.After all cluster heads complete this process, each
Cluster head has just got itself to the hop count information of base station and neighbours to the hop count information of base station, and using this information, cluster head can
To select shortest path quickly to transfer data to base station.
But this structure is possible to that cluster head can be made to carry out data information from identical path every time, causes this
Cluster head energy dissipation speed on path is significantly faster than the cluster head on other paths, or even causes load imbalance.To understand
Certainly this problem, extends network life as far as possible, and Path selection will not only consider hop count, and dump energy is also considered simultaneously.Cluster
The dump energy that head equally regularly should currently have itself is transferred to all neighbours' cluster heads, in data transfer procedure, entirely
Face considers the reference index of hop count with remaining energy input alternatively, calculates the two integrated value in all neighbor nodes optimal
It is used as next hop node.
Under normal circumstances, node can't have been at the state of work, typically can constantly be carried out in resting state week
Phase revives.In the moving process of target, cluster head is reported to after nodal test in monitoring range to target, cluster head is to mesh
Mark current location information is estimated, its possible the next position is predicted and the node near predicted position is waken up, adopted
The position of target, the arrival of monitoring objective are predicted with formula (1)~(9), prediction mode is specially：
Zk=HkXk+vk (1)
Wherein, ZkThe numerical value of the target location detected at the k moment, HkRefer to calculation matrix, vkRepresent making an uproar in measurement
Sound, if it is 0 to meet numerical value, variance is RkGaussian Profile,
The predicted state of target is as follows：
Predict that variance matrix is as follows：
Kalman gain is：
Dbjective state is updated to：
Xk+1|k+1=Xk+1|k+Kk+1(Zk+1-HkXk+1|k) (5)
Variance matrix now is updated to：
Pk+1|k+1=(1-Kk+1Hk)Pk+1|k (6)
It is givenWith at the uniform velocity mobility model, observing matrix is：
Wherein (xk|k, yk|k) feeling the pulse with the finger-tip is marked on the position at k moment,It is shifting of the target on x directions and y directions
Dynamic speed, the position of target subsequent time is：
OrderRefer to the target present position of prediction, current cluster head selection is to predicted position week
The node on side enters line activating, and activation radius is rα, set CjIt is the cluster of work at present, node is included in work at present cluster, and
In the range of active region, it can just be waken up, so the set expression for the node that is activated is：
Finally, the positional information of target is passed to base station by the upper layer network of cluster head composition in real time.
It is assumed that sensor network can it is abstract be 2 dimensional region model, and the deployment randomness of node meets poisson process
(density is λ), given area area is set to A, then the probability for having κ node in region can be calculated by following equation：
In formula, N (A) refers to the number of nodes included in respective regions.
Same sensor network, various communication, levels can be divided into according to the difference of node, these communication, levels with
Communication distance is corresponding.Rank is directly proportional to distance.If certain node is cluster head, job class is higher, it is clear that corresponding communication
Distance is then more remote.
Comparative example
The algorithm (ECSL algorithms) and DCTC algorithms and ADCT algorithms of present specification are contrasted, simulated environment according to
Following parameter is configured：The number of nodes of sensor is arranged to 200, region model of this little sensor node in 100m × 100m
Middle carry out random distribution is enclosed, the sensing range that all the sensors node has is set as that radius size is 10m border circular areas,
The distance of communication is 20m, and active distance is then 20m.In addition, the numerical value of control bag is 10 bytes, the numerical value of packet is 40 words
Section.Science division is carried out using Leach agreement foundation group cluster algorithms and to node, all clusters are by a cluster head and several
Member node is constituted.
It is three kinds of algorithm position errors and node density graph of a relation to refer to Fig. 1.The position error of three kinds of algorithms all with
The increase of node density and progressively reduce.This is the computational methods decision of center coordination, and node density increase have impact on target
Interstitial content in monitor area, number is more, and it is more accurate to position.ECSL algorithm relative error numerical value is larger, and this is due to profit
During ECSL algorithms, the influence degree of cluster is relatively low suffered by the number of nodes of activation, causes error larger.
It is three kinds of algorithm node energy consumptions and node density graph of a relation to refer to Fig. 2.The energy consumption of three kinds of algorithms is close with node
The increase of degree and increase.The energy expenditure of ECSL algorithms is minimum in three kinds of algorithms, and the energy expenditure highest of DCTC algorithms.It is former
Because being ECSL algorithms without establishment and dismissing cluster, ADCT algorithm energy consumptions are compared smaller；DCTC algorithms do not use clustering architecture, but
Using tree construction, safeguard that dynamic tree needs to consume more multi-energy.In addition, when increase trend is presented in node density, ECSL algorithms
The rate of climb of energy expenditure will be less than remaining two kinds of algorithm.
It is three kinds of algorithm node densities and algorithm delay graph of a relation to refer to Fig. 3.Delay of the node density to algorithm
It can be ignored.The delay of ECSL algorithms is smaller with respect to other two kinds of algorithms because data can be built by cluster head it is upper
Layer network is transmitted to base station in time.
Contrast and draw more than, these three algorithms follow an identical rule, be i.e. node density is bigger, then positioning is missed
Difference will be reduced, while energy expenditure can rise, and be delayed then will not generally be influenceed by node density.Three kinds of algorithms
In, ECSL algorithms will then be considerably better than remaining two class algorithm in terms of energy expenditure and delay performance.
Embodiments of the invention are the foregoing is only, are not intended to limit the scope of the invention, it is every to utilize this hair
Equivalent structure or equivalent flow conversion that bright specification and accompanying drawing content are made, or directly or indirectly it is used in other related skills
Art field, is included within the scope of the present invention.
Claims (4)
1. a kind of target tracking algorism based on cluster structured reduction energy consumption, it is characterised in that first, using the hierarchical network of cluster
Structure enters line activating to sensor node and then tracks target, and the hierarchical network architecture of the cluster includes：Different network nodes
Divide and be classified as multiple clusters, each cluster is constituted by a cluster head and several member nodes, and each member node can only belong to
One cluster；Cluster head is not fixed, and takes cycle rotation system, and each node is likely to become cluster head；Sensor network is carried out
Layering, the backbone network on upper strata is constituted by cluster head, and the cluster head between adjacent cluster can be communicated with base station, upper layer network
Keep independent with member node；Secondly, in the moving process of target, converged after nodal test in monitoring range to target
Cluster head is offered, cluster head is estimated target current location information, its possible the next position is predicted and prediction is waken up
Node near position, the position of target, the arrival of monitoring objective are predicted using formula (1)~(9), and prediction mode is specially：
Zk=HkXk+vk (1)
Wherein, ZkThe numerical value of the target location detected at the k moment, HkRefer to calculation matrix, vkThe noise in measurement is represented,
If it is 0 to meet numerical value, variance is RkGaussian Profile,
The predicted state of target is as follows：
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Predict that variance matrix is as follows：
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Kalman gain is：
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Dbjective state is updated to：
Xk+1|k+1=Xk+1|k+Kk+1(Zk+1-HkXk+1|k) (5)
Variance matrix now is updated to：
Pk+1|k+1=(1-Kk+1Hk)Pk+1|k (6)
It is givenWith at the uniform velocity mobility model, observing matrix is：
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Wherein (xk|k, yk|k) feeling the pulse with the finger-tip is marked on the position at k moment,It is mobile speed of the target on x directions and y directions
Degree, the position of target subsequent time is：
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In the range of active region, it can just be waken up, so the set expression for the node that is activated is：
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Finally, the positional information of target is passed to base station by the upper layer network of cluster head composition in real time.
2. a kind of target tracking algorism based on cluster structured reduction energy consumption according to claim 1, it is characterised in that build
During vertical upper strata backbone network, set up in the neighbor table safeguarded to each cluster head, the neighbor table that to store this cluster head periphery each adjacent
Cluster head is occupied to the hop count of base station.
3. a kind of target tracking algorism based on cluster structured reduction energy consumption according to claim 2, it is characterised in that should
Hop count of this cluster head itself of storage to base station is initialized to higher value in neighbor table.
4. a kind of target tracking algorism based on cluster structured reduction energy consumption according to claim 2, it is characterised in that same
One sensor network, various communication, levels can be divided into according to the difference of node, and the communication, levels are relative with communication distance
Should.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107579780A (en) * | 2017-08-28 | 2018-01-12 | 哈尔滨工业大学深圳研究生院 | The tracking of parameter adaptive polarization state and the equalization methods of Kalman are oriented to based on radius |
| CN108289287A (en) * | 2018-01-19 | 2018-07-17 | 深圳市鑫汇达机械设计有限公司 | Environmental parameter wireless monitoring system towards rail traffic |
| CN108650634A (en) * | 2018-05-18 | 2018-10-12 | 南京邮电大学 | A kind of wireless sensor network target tracking method based on trajectory predictions |
| CN109890002A (en) * | 2019-01-04 | 2019-06-14 | 中国电子科技集团公司电子科学研究院 | A kind of method and device of the prediction Risk Degree of Maneuvering Environment of knowledge-based inference |
| CN113015118A (en) * | 2019-12-20 | 2021-06-22 | 中国科学院沈阳计算技术研究所有限公司 | Low-power-consumption communication device and system for industrial Internet of things |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101534470A (en) * | 2009-04-10 | 2009-09-16 | 华南理工大学 | System and method for tracking moving target based on wireless sensor network |
| CN101610567A (en) * | 2009-07-10 | 2009-12-23 | 华南理工大学 | A kind of dynamic group scheduling method based on wireless sensor network |
| CN102123473A (en) * | 2011-01-06 | 2011-07-13 | 山东大学 | Dynamic clustering mechanism-based target tracking method for wireless sensor network |
| US20120221274A1 (en) * | 2011-02-25 | 2012-08-30 | Hidetoshi Furukawa | Target tracking device and target tracking method |
-
2017
- 2017-05-27 CN CN201710425508.XA patent/CN107071848B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101534470A (en) * | 2009-04-10 | 2009-09-16 | 华南理工大学 | System and method for tracking moving target based on wireless sensor network |
| CN101610567A (en) * | 2009-07-10 | 2009-12-23 | 华南理工大学 | A kind of dynamic group scheduling method based on wireless sensor network |
| CN102123473A (en) * | 2011-01-06 | 2011-07-13 | 山东大学 | Dynamic clustering mechanism-based target tracking method for wireless sensor network |
| US20120221274A1 (en) * | 2011-02-25 | 2012-08-30 | Hidetoshi Furukawa | Target tracking device and target tracking method |
Non-Patent Citations (1)
| Title |
|---|
| 王志波: "基于无线传感器网络的目标检测与跟踪研究", 《中国优秀博士论文全文数据库》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107579780A (en) * | 2017-08-28 | 2018-01-12 | 哈尔滨工业大学深圳研究生院 | The tracking of parameter adaptive polarization state and the equalization methods of Kalman are oriented to based on radius |
| CN107579780B (en) * | 2017-08-28 | 2020-01-17 | 哈尔滨工业大学深圳研究生院 | Parameter self-adaptive polarization state tracking and balancing method based on radius-oriented Kalman |
| CN108289287A (en) * | 2018-01-19 | 2018-07-17 | 深圳市鑫汇达机械设计有限公司 | Environmental parameter wireless monitoring system towards rail traffic |
| CN108650634A (en) * | 2018-05-18 | 2018-10-12 | 南京邮电大学 | A kind of wireless sensor network target tracking method based on trajectory predictions |
| CN108650634B (en) * | 2018-05-18 | 2020-07-28 | 南京邮电大学 | Wireless sensor network target tracking method based on track prediction |
| CN109890002A (en) * | 2019-01-04 | 2019-06-14 | 中国电子科技集团公司电子科学研究院 | A kind of method and device of the prediction Risk Degree of Maneuvering Environment of knowledge-based inference |
| CN109890002B (en) * | 2019-01-04 | 2022-08-16 | 中国电子科技集团公司电子科学研究院 | Method and device for predicting environmental risk based on knowledge reasoning |
| CN113015118A (en) * | 2019-12-20 | 2021-06-22 | 中国科学院沈阳计算技术研究所有限公司 | Low-power-consumption communication device and system for industrial Internet of things |
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