CN103997748B - A kind of difference covering method based on mixed type sensor network - Google Patents
A kind of difference covering method based on mixed type sensor network Download PDFInfo
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Abstract
The invention provides a kind of difference covering method based on mixed type sensor network.The present invention is based on particle swarm optimization, the single-goal function that linear combination after the Efficient Coverage Rate of key area and general area is weighted is formed is as adaptation value function, adaptation value function is controlled by adjusting weight coefficient, so as to which guiding sensor mobile node is gathered to key area, ensure the covering quality of key area, in order to improve the operation efficiency of particle swarm optimization, fictitious force velocity component is added to guide mobile node to be moved to uncovered area, the gravitational field that the size and Orientation of fictitious force is applied to mobile node according to the monitoring point for not meeting effective monitoring threshold in deployment region is tried to achieve, the present invention is applied to the mixed type sensor network containing stationary nodes and mobile node.
Description
Technical Field
The invention relates to a difference coverage method based on a hybrid sensor network.
Background
The development of wireless communication technology, embedded technology and microprocessor technology has led to an emerging technology for information acquisition and event tracking monitoring. The sensor nodes can sense environmental information, simply calculate and send information through wireless communication devices and form a self-organizing multi-hop network. The system can remotely acquire environmental information of a deployment area and monitor and track a target event in real time, so that the system has great application value in the aspects of military battlefields, environmental monitoring and forecasting, agricultural science and technology, smart homes, urban traffic and the like.
Network deployment is a fundamental problem in WSN research, with a significant impact on network performance. The node deployment is applied to the initial stage of the WSN, is the basis of key technologies such as a routing protocol, energy effectiveness, data fusion and the like, and has important research significance. In some monitoring areas with severe environmental conditions, such as nuclear radiation and chemical leakage areas, it is not feasible to deploy sensor nodes manually. In practice, the sensor nodes are randomly deployed within the monitored area, primarily by airplane-tossing or other methods. For a sensor network, a reasonable and effective node deployment scheme can greatly reduce the network construction time, quickly cover a target area, prolong the service life of the network through coordination control and adapt to a changed topological structure.
In order to ensure the coverage quality of the network, a large number of sensor fixed nodes are generally deployed randomly in a target area. However, the deployment method can generate a large number of redundant nodes while improving the network service quality, which leads to communication conflict and energy waste and shortens the network life. And the sensor network environment adaptability based on the fixed nodes is poor, self-repairing can not be realized, and once a certain node has a problem, the whole network topology structure can be damaged. The mobile sensor network composed of the sensor mobile nodes can improve the coverage rate and optimize the network quality through the node movement, but the sensor mobile nodes are expensive, so that the large-scale application of the sensor mobile nodes is limited. The hybrid sensor network composed of the sensor fixed nodes and the sensor mobile nodes can repair the coverage holes generated by the sensor fixed nodes by adjusting the positions of the sensor mobile nodes, can balance the economy and the network service quality, and has great practical application value.
In practical application, most deployment tasks need to intensively cover partial areas in a monitoring area, for example, for offshore water quality monitoring, the monitoring is intensively needed in a place with more pollution sources at a sewage outlet or a ship port compared with some regions with rare occurrence. And the difference coverage is carried out according to the importance degree of the target area, so that the limited sensor node resources can be fully utilized, and the coverage quality and the event monitoring probability of the network are improved.
Disclosure of Invention
The invention aims to provide a difference coverage method based on a hybrid sensor network, which can randomly deploy m sensor fixed nodes and n sensor mobile nodes in a given deployment area A, adjust the positions of the sensor mobile nodes and maximize a key monitoring area A hot And general monitoring area A ordinary Effective coverage of.
In order to solve the above problems, the present invention provides a hybrid sensor network-based differential coverage method, which includes:
acquiring the position of a monitoring environment, and deploying m sensor fixed nodes and n sensor mobile nodes in the monitoring environment in an airplane throwing mode;
each sensor fixed node and each sensor mobile node initialize own information and position the position of the sensor fixed node and each sensor mobile node form a network and send the information of the ID number, the initial position and the sensor performance of the sensor fixed node and the sensor mobile node to a sink node through the network;
after receiving information such as self ID numbers, initial positions, sensor performances and the like sent by each sensor fixed node and each sensor mobile node in a flooding or directional routing mode and the like, the sink node acquires the position of the monitoring environment, and analyzes the whole deployment area A and the key monitoring area A according to the position of the monitoring environment hot General monitoring area A ordinary ＝A-A hot Effective monitoring threshold c of key monitoring area th_hot And a general monitoring area effective monitoring threshold c th_ordinary Determining the virtual force (potential field force) suffered by the sensor mobile node adjacent to the monitoring point which does not reach the coverage standard in the whole deployment area
F x And F y The acting forces in the directions of the x axis and the y axis respectively, s is the position of the moving node of the sensor,
the mathematical expression of the monitoring capability of the sensor mobile node can be obtained
p is a monitoring point in the deployment area A, A s Moving a node r-r for a sensor e To r + r e Set A of monitoring points within the perception range that do not meet the coverage criterion s ＝A s_hot ∪A s_ordinary Wherein
r e (0＜r e < r) is a measurement reliability parameter of the sensor mobile node,
wherein, the monitoring point which does not reach the coverage standard in the deployment area A generates a gravitational field to the adjacent sensor mobile nodeA is the deployment area, c p/s Joint measurement probability generated for sensor mobile node placement at monitoring point p at point s
Set S for sensor fixed nodes and sensor mobile nodes in deployment area A ov Indicating a certain sensor fixed node or sensor mobile node s i Has a global coordinate of (x) i ,y i ) The coordinate of the p monitoring point in the deployment area A is (x) p ,y p ) Monitoring points p and s i Is a distance ofIn practical application, the sensing models of the sensor fixed node and the sensor mobile node are in certain probability distribution due to the influence of factors such as environment, the sensor process and the like, and the monitoring probability is along with the distance d(s) i P) decreasing, the mathematical expression of the monitoring capability of the sensor fixed node or the sensor mobile node is as follows:
wherein r is the sensor sensing radius of the sensor fixed node or the sensor mobile node, r e (0＜r e < r) is a measurement reliability parameter, α, of a sensor fixed node or a sensor mobile node 1 ,α 2 ,β 1 ,β 2 Is a parameter related to the measurement probability of the sensor, which parameter is related to the measurement characteristic of the sensing node, lambda 1 And λ 2 For inputting a parameter lambda 1 ＝r e -r+d(s i ,p),λ 2 ＝r e +r-d(s i P) if the monitoring point p is at the sensor fixed node or the sensor mobile node s i Within the sensing range of (i.e. d(s) i ,p)≤(r+r e ) Then sensor fixed node or sensor mobile node s i The joint measurement probability of the monitoring point p in the deployment area A is the joint measurement probability of the monitoring point p adjacent sensor fixed node and the adjacent sensor mobile node in the deployment area A at the monitoring point p:
V p set of fixed nodes of adjacent sensors and mobile nodes of adjacent sensors for monitoring point p, c th For a valid measurement probability threshold, if c p (S ov )≥c th If the monitoring point p is effectively covered by the sensor fixed node and the sensor mobile node;
abstracting the position coordinates of N sensor mobile nodes into particles in a particle group method for a network comprising m sensor fixed nodes and N sensor mobile nodes, the particle search space dimension N =2N, the position vector X of the particle i i ＝(x i1 ,x i2 ,…,x in ,y i1 ,y i2 ,…,y in ) Wherein x is ij ,y ij Respectively represent the abscissa and ordinate of the jth sensor mobile node, 1<j&N, taking a single target function formed by linear combination of weighted effective coverage rates of the key area and the general area as an adaptive value function f (X) i (t))＝α×f hot (X i (t))+(1-α)×f ordinary (X i (t)), wherein f hot (X i (t)) and f ordinary (X i (t)) respectively representing effective coverage rates of the key monitoring area and the common monitoring area, wherein alpha is a weight coefficient determined according to the key monitoring area;
determining the flight velocity of the particle, the flight velocity of the particle being determined by:
vi j (t+1)＝w(t)×v ij (t)+c 1 r 1j (t)×(p ij (t)-x ij (t))+c 2 r 2j (t)×(p gj (t)-x ij (t))+c 3 r 3j (t)g ij (t) wherein c 1 For local optimization of the weight factors, c 2 For global optimization of the weighting factors, c 3 Is a potential field force acceleration factor, r 1j 、r 2j And r 3j Is [0,1]Independent random numbers in the range, subscript i corresponds to ith particle, subscript j corresponds to jth dimension of particle, w is an inertia factor of past value on present value, usually 0.9-0.4, and is gradually decreased in iteration process, gi j Then, the distance of the j-th dimension element in the position vector of the corresponding particle i under the action of the potential field force is represented by the following formula:
wherein the content of the first and second substances,for the potential field force in the x direction experienced by the jth mobile node in the ith particle,for the potential field force in the y direction experienced by the jth mobile node in the ith particle,
updating the position of a particle according to its flight velocity x ij (t+1)＝x ij (t)+v ij (t + 1), substituting the updated positions of the particles into the fitness function f (X) i (t)) in determining a locally optimal solution for the particleThen, the global optimum position P of the particle experience in the population is found g (t)＝max{f(P 1 (t)),f(P 2 (t)),…,f(P M (t)) }, repeatedly iterating the global optimal position according to the adaptive value function and the local optimal solution until reaching a preset maximum iteration time, and finally obtaining an optimal solution P g ；
The sink node will optimize the solution P g The data packet is packaged and broadcast out, and the sensor mobile node analyzes P after receiving the data packet g To obtain a target location of the sensor mobile node, to which the sensor mobile node moves.
Further, in the above method, in the step of forming a network by each sensor fixed node and each sensor mobile node, each sensor fixed node and each sensor mobile node form a network by flooding or by way of directional routing.
Further, in the above method, each of the sensor fixed node and the sensor mobile node has the capability of collecting information, calculating and processing data, sending or receiving messages and positioning, and the sensor mobile node also has the capability of moving.
Further, in the above method, the information on the performance of the sensor includes a sensor sensing radius r and sensor sensing model parameters.
Further, in the above method, the sensor is movedThe node analyzes the received data packet P g to obtain a target location of the sensor mobile node,
for the ith sensor mobile node, the target position is (x) i ,y i )＝(P g (i),P g (i+n))。
Compared with the prior art, the method takes a particle swarm method as a basis, a single objective function formed by linear combination of weighted effective coverage rates of a key area and a general area is taken as an adaptive value function, the adaptive value function is controlled by adjusting a weight coefficient, a virtual force velocity component is added to guide the mobile node to move to an uncovered area, and the magnitude and the direction of the virtual force are obtained according to a gravitational field applied to the mobile node by a monitoring point which does not meet an effective monitoring threshold value in a deployment area, so that the method has the following advantages:
(1) The covering effect is good: and a difference deployment strategy is adopted according to the requirements of the monitoring tasks, so that the coverage quality of the key monitoring area is ensured.
(2) The convergence rate is high: the potential field force velocity component is added into the particle velocity updating formula, so that the sensor mobile node can be effectively guided to move to an area which does not meet the coverage, and the convergence rate of the algorithm is increased.
(3) The set parameters are few: according to the invention, differential deployment of the monitoring environment can be realized only by additionally setting three parameters of the position of a key monitoring area, an effective coverage monitoring threshold value and a weight coefficient alpha of the key monitoring area on the basis of the traditional particle swarm optimization method.
(4) The application range is wide: the invention relates to a hybrid sensor network coverage method, which is suitable for networks with different ratios of fixed nodes and mobile nodes of sensors.
Drawings
FIG. 1 is a block diagram of an exemplary wireless sensor network architecture;
fig. 2 is a flowchart of a sink node calculating a target position of a sensor mobile node according to an embodiment of the present invention;
fig. 3 is a distribution diagram of initialized randomly deployed sensor fixed nodes and sensor mobile nodes according to an embodiment of the present invention;
fig. 4 is a node distribution diagram in a network after redeployment when a key monitoring area is in a central position of a deployment area according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating the movement of the sensor mobile node when the key monitoring area is at the center position according to an embodiment of the present invention;
FIG. 6 is a node distribution diagram in a network after re-deployment when a key monitoring area is at the lower left corner of a deployment area according to an embodiment of the present invention;
fig. 7 is a diagram of the location movement of the sensor mobile node when the key monitoring area is at the lower left corner of the deployment area according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1 to 2, the present invention provides a hybrid sensor network-based differential coverage method, which includes:
the method comprises the following steps that S1, the position of a monitoring environment is obtained, and a sensor fixed node and a sensor mobile node are deployed in the monitoring environment in an airplane throwing mode; specifically, in practical application, most deployment tasks need to carry out non-uniform coverage on a monitoring area, for example, for offshore water quality monitoring, compared with some regions with rare occurrence, a place with more pollution sources at a sewage discharge port or a ship port needs to be monitored in a key way, so that the technical problem to be solved by the invention is as follows: in a given deployment area A shown in FIG. 1, m sensor fixed nodes and n sensor mobile nodes are randomly deployed, the positions of the sensor mobile nodes are adjusted, and the important monitoring area A is maximized hot And a general monitoring area A ordinary In fig. 1, a fixed node and a mobile node form a cluster structure as a common node, and multi-hop transmission is performed between clustersTransmitting the information data to the aggregation node; (ii) a
S2, each sensor fixed node and each sensor mobile node initialize self information and position the position of the sensor fixed node and each sensor mobile node form a network by adopting flooding or directional routing and other modes and send the information of the ID number, the initial position, the sensor performance and the like of the sensor fixed node and the sensor mobile node to a Sink node through the network; each sensor fixed node and each sensor mobile node have the capabilities of acquiring information, calculating and processing data, sending or receiving messages and positioning, each sensor mobile node has certain mobility besides all functions of the sensor fixed node, and the sensor performance comprises a sensor sensing radius r and sensor sensing model parameters;
s3, after receiving information such as self ID numbers, initial positions, sensor performances and the like sent by each sensor fixed node and each sensor mobile node in a flooding or directional routing mode and the like, the sink node acquires the position of the monitoring environment, and analyzes the whole deployment area A and the key monitoring area A according to the position of the monitoring environment hot General monitoring area A ordinary ＝A-A hot Effective monitoring threshold c of key monitoring area th_hot And a general monitoring area effective monitoring threshold c th_ordinary Determining the virtual force (potential field force) suffered by the sensor mobile node adjacent to the monitoring point which does not reach the coverage standard in the whole deployment area
F x And F y The acting forces in the directions of the x axis and the y axis respectively, s is the position of the moving node of the sensor,
the mathematical expression of the monitoring capability of the sensor mobile node can be obtained
p is a monitoring point in the deployment area A, A s Moving a node r-r for a sensor e To r + r e Set A of monitoring points within the sensing range not meeting the coverage criterion s ＝A s_hot ∪A s_ordinary Wherein
r e (0＜r e < r) is a measurement reliability parameter of the sensor mobile node,
wherein, the monitoring point which does not reach the coverage standard in the deployment area A generates a gravitational field to the adjacent sensor mobile nodeA is the deployment area, cp/s is the joint measurement probability generated by the sensor mobile node being placed at point s at monitoring point p
Set S for sensor fixed nodes and sensor mobile nodes in deployment area A ov Indicating a certain sensor fixed node or sensor mobile node s i Has a global coordinate of (x) i ,y i ) The coordinate of the p monitoring point in the deployment area A is (x) p ,y p ) Monitoring points p and s i Is a distance ofIn practical application, the sensing models of the fixed nodes and the mobile nodes of the sensor are in certain probability distribution due to the influence of factors such as environment, the self process of the sensor and the like, and the monitoring probability is along with the distanceFrom d(s) i P) decrementing, the mathematical expression of the monitoring capability of the sensor fixed node or the sensor mobile node is:
wherein r is the sensor sensing radius of the sensor fixed node or the sensor mobile node, r e (0＜r e < r) is a measurement reliability parameter, α, of a sensor fixed node or a sensor mobile node 1 ,α 2 ,β 1 ,β 2 Is a parameter related to the measurement probability of the sensor, which parameter is related to the measurement characteristic of the sensing node, lambda 1 And λ 2 For inputting a parameter lambda 1 ＝r e -r+d(s i ,p),λ 2 ＝r e +r-d(s i P) if the monitoring point p is at the sensor fixed node or the sensor mobile node s i Within the sensing range of (1), i.e. d(s) i ,p)≤(r+r e ) Then sensor fixed node or sensor mobile node s i The joint measurement probability of the monitoring point p in the deployment area A is the joint measurement probability of the monitoring point p adjacent sensor fixed node and the adjacent sensor mobile node in the deployment area A at the monitoring point p:
V p set of fixed nodes of adjacent sensors and mobile nodes of adjacent sensors for monitoring point p, c th For a valid measurement probability threshold, if c p (S ov) ≥c th If so, the monitoring point p is effectively covered by the sensor fixed node and the sensor mobile node; specifically, to implement the differential coverage strategy, a region A of the important monitoring region is set hot General monitoring area range A ordinary ＝A-A hot The monitoring quality of the key monitoring area is ensured;
step (ii) ofS4, after the sink node knows the position information of all the sensor fixed nodes and the sensor mobile nodes, the sink node dynamically adjusts the positions of the sensor mobile nodes according to the covering task requirement: for a hybrid sensor network comprising m sensor fixed nodes and N sensor mobile nodes, abstracting the location coordinates of the N sensor mobile nodes into particles in a Particle Swarm Optimization (PSO) method, the particle search space dimension N =2N, the location vector X of the particle i i ＝(x i1 ,x i2 ,…,x in , yi1 , yi2 ,…, yin ) Wherein xi j ,yi j Respectively represent the abscissa and ordinate of the jth sensor mobile node, 1<j&N, taking a single target function formed by linear combination of weighted effective coverage rates of the key area and the general area as an adaptive value function f (X) i (t))＝α×f hot (X i (t))+(1-α)×f ordinary (X i (t)), wherein f hot (X i (t)) and f ordinary (X i (t)) respectively representing effective coverage rates of the key monitoring area and the common monitoring area, wherein alpha is a weight coefficient determined according to the key monitoring area; in the step, the adaptive value function can be controlled by adjusting the weight coefficient, so that the sensor mobile node is guided to gather to the key area, and the coverage quality of the key area is ensured;
step S5, determining the flight speed of the particles, wherein the flight speed of the particles is determined according to the following formula:
v ij (t+1)＝w(t)×v ij (t)+c 1 r 1j (t)×(p ij (t)-x ij (t))+c 2 r 2j (t)×(p gj (t)-x ij (t))+c 3 r 3j (t)g ij (t) wherein c 1 For local optimization of the weight factors, c 2 For global optimization of the weighting factors, c 3 Is the acceleration factor of the potential field force, r 1j 、r 2j And r 3j Is [0,1]Independent random numbers in the range, subscript i corresponds to ith particle, subscript j corresponds to jth dimension of particle, w is an inertia factor of past value on present value, usually 0.9-0.4, and gradually decreases in the iteration process, g ij Is the bit corresponding to particle iThe distance of the j-th dimension element in the position vector under the action of the potential field force is as follows:
wherein the content of the first and second substances,for the potential field force in the x direction experienced by the jth mobile node in the ith particle,for the potential field force in the y direction experienced by the jth mobile node in the ith particle,specifically, in the step, a velocity component of a virtual force is added into a velocity updating formula of a Particle Swarm Optimization (PSO) method to guide the sensor mobile node to move to an uncovered area, so that the moving efficiency is improved, and the magnitude and the direction of the virtual force are obtained according to a gravitational field applied to an adjacent sensor mobile node by a monitoring point which does not meet an effective monitoring threshold in a deployment area;
step S6, updating the position of the particle x according to the flight speed of the particle ij (t+1)＝x ij (t)+v ij (t + 1), substituting the updated position of the particle into the fitness function f (X) i (t)) evaluation to determine a locally optimal solution for the particleThen, the global optimum position P of the particle experience in the population is calculated g (t)＝max{f(P 1 (t)),f(P 2 (t)),…,f(P M (t)) }, repeatedly iterating the global optimal position according to the adaptive value function and the local optimal solution until a preset maximum iteration time is reached, and finally obtaining an optimal solution P g (ii) a Specifically, the principle of steps S3 to S6 can be seen in fig. 2;
s7, the sink node optimizesSolution of P g The data packet is packaged and broadcast out, and the sensor mobile node analyzes P after receiving the data packet g To obtain a target location of the sensor mobile node, to which the sensor mobile node moves. Specifically, for the ith sensor mobile node, the target position is (x) i ,y i )＝(P g (i),P g (i+n))。
The invention will be further elucidated with reference to the following description of the drawings. The specific implementation is as follows:
scene one: as shown in fig. 3 to 5, a user determines the position of a monitoring environment according to task requirements, and 60 sensor mobile nodes and 40 sensor fixed nodes are randomly distributed in a 200 × 200 area by adopting an airplane tossing mode, and a key monitoring area is a square area with a center position of 100 × 100. In fig. 3, sensor fixed nodes are denoted by ". A." and sensor mobile nodes are denoted by ". A. The sensor fixed node and the sensor mobile node respectively comprise a sensor module, a processor module, a wireless communication module and a positioning module, wherein the processor module is respectively connected with the sensor module, the wireless communication module and the positioning module, the sensor module has the function of acquiring information, the processor module has the function of calculating and processing data, the wireless communication module has the function of sending or receiving messages, and the positioning module has the function of positioning. The sensor mobile node also comprises a mobile module connected with the processor module, has a mobile function, and can move forward to a target position after receiving information sent by Sink, the sensing radiuses of the sensor fixed node and the sensor mobile node are the same, and the measurement reliability parameter r =14m e =7m, other parameters α 1 ＝1,α 2 ＝0,β 1 ＝1,β 2 ＝1.5。
After each sensor fixed node and each sensor mobile node are deployed, a positioning module is switched on to determine the position of the sensor fixed node and the sensor mobile node in the monitoring area. And then the wireless sending modules are opened by the sensor fixed node and the sensor mobile node, and the information such as the ID number, the position, the sensor performance and the like of the sensor fixed node and the sensor mobile node are encapsulated into a data packet by adopting a flooding or directional routing mode and then sent to the Sink node.
And after receiving the data packets of all the nodes in the network, the Sink node analyzes the node ID number, the position and the sensor characteristic information in the data packets. Then the Sink node analyzes the whole deployment area A and the key monitoring area A according to the information sent by the client hot Effective monitoring threshold c of key monitoring area th_hot And a general monitoring area effective monitoring threshold c th_ordinary . And then setting relevant parameters such as maximum iteration times, weight coefficients and the like. After the setting is finished, the information such as the position of the sensor mobile node, the sensor characteristics and the like is substituted into a formula to calculate the target position of the sensor mobile node. Repeating iteration until the termination condition is met, and finally obtaining the optimal solution P g . The parameter configuration is as follows:
solving the optimal solution P at the Sink node g And then converting the target position of the sensor moving node into the target position of the sensor moving node. For the ith sensor mobile node, the target position is (x) i ,y i )＝(P g (i),P g (i + n)). And the Sink node encapsulates the ID number and the target position of each sensor mobile node into a data packet, and then sends the data packet to each sensor mobile node in a broadcasting mode. After receiving the broadcasted data packet, the sensor mobile node performs internal packet processing to obtain a target position of the sensor mobile node, and determines the direction and the moving distance of the target position by matching with a positioning module of the sensor mobile node. During the process that the sensor mobile node moves to the target position, the sensor module can sense the obstacle and bypass the obstacle to reach the target position by the best path. FIG. 3 is an initialization node distribution diagram, and FIGS. 4 and 5 are a node distribution diagram and a node displacement diagram of a scene after optimization, respectively, wherein when a key monitoring area is at the central position of a deployment area, a network after redeploymentThe nodes in the network are distributed as shown in fig. 4, the position of the sensor mobile node moves as shown in fig. 5 when the key monitoring area is at the central position, the sensor mobile node moves from the position ". Multidot..
Scene two: the 60 sensor mobile nodes and the 40 sensor fixed nodes are randomly distributed in a 200 × 200 area, the important monitoring area is a square area with the position of the lower left corner being 100 × 100, and the weight parameter is alpha =0.4. The rest parameters are set in the same scene one. Fig. 3 is an initialization node distribution diagram, and fig. 6 and 7 are a node distribution diagram and a node displacement diagram of a scene two after optimization, respectively, where when a key monitoring region is at the lower left corner of a deployment region, nodes in a network after re-deployment are distributed as shown in fig. 6, when the key monitoring region is at the lower left corner of the deployment region, the position of a sensor mobile node moves as shown in fig. 7, and the sensor mobile node moves from the position ". Multidot." to the position "o". A 100 × 100 square area surrounded by a dashed line frame is a key monitoring area.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative components and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (4)
1. A differential coverage method based on a hybrid sensor network is characterized by comprising the following steps:
acquiring the position of a monitoring environment, and deploying m sensor fixed nodes and n sensor mobile nodes in the monitoring environment in an airplane throwing mode;
each sensor fixed node and each sensor mobile node initialize own information and position the position of the sensor fixed node and each sensor mobile node form a network and send the information of the ID number, the initial position and the sensor performance of the sensor fixed node and the sensor mobile node to a sink node through the network;
after receiving information such as self ID numbers, initial positions, sensor performances and the like sent by each sensor fixed node and each sensor mobile node in a flooding or directional routing mode and the like, the sink node acquires the position of the monitoring environment, and analyzes the whole deployment area A and the key monitoring area A according to the position of the monitoring environment hot General monitoring area A ordinary ＝A-A hot Effective monitoring threshold c of key monitoring area th_hot And a general monitoring area effective monitoring threshold c th_ordinary Determining the virtual force received by the sensor mobile nodes adjacent to the monitoring point which does not reach the coverage standard in the whole deployment area
F x And F y Acting forces in the directions of the x axis and the y axis respectively, and s is a sensor mobile nodeIn the position of (a) in the first,
the mathematical expression of the monitoring capability of the sensor mobile node can be obtained
p is a monitoring point in the deployment area A, A s Moving a node r-r for a sensor e To r + r e Set A of monitoring points within the perception range that do not meet the coverage criterion s ＝A s_hot ∪A s_ordinary Wherein
r e Is a measurement reliability parameter of the sensor mobile node, 0 < r e ＜r，
Wherein, the monitoring point which does not reach the coverage standard in the deployment area A generates a gravitational field to the adjacent sensor mobile nodeA is the deployment area, c p/s Joint measurement probability generated for sensor mobile node placement at monitoring point p at point s
Set S for sensor fixed nodes and sensor mobile nodes in deployment area A ov Indicating a certain sensor fixed node or sensor mobile node s i Has a global coordinate of (x) i ，y i ) The coordinate of the p monitoring point in the deployment area A is (x) p ，y p ) Monitoring points p and s i Is a distance ofIn practical application, the sensing models of the sensor fixed node and the sensor mobile node are in certain probability distribution due to the influence of factors such as environment, the sensor process and the like, and the monitoring probability is along with the distance d(s) i P) decreasing, the mathematical expression of the monitoring capability of the sensor fixed node or the sensor mobile node is as follows:
wherein r is the sensor sensing radius of the sensor fixed node or the sensor mobile node, r e (0＜r e < r) is a measurement reliability parameter, α, of a sensor fixed node or a sensor mobile node 1 ，α 2 ，β 1 ，β 2 Is a parameter related to the measurement probability of the sensor, which parameter is related to the measurement characteristic of the sensing node, λ 1 And λ 2 For inputting a parameter lambda 1 ＝r e -r+d(s i ，p)，λ 2 ＝r e +r-d(s i P) if the monitoring point p is at the sensor fixed node or the sensor mobile node s i Within the sensing range of (1), i.e. d(s) i ，p)≤(r+r e ) Then sensor fixed node or sensor mobile node s i The joint measurement probability of the monitoring point p in the deployment area A is the joint measurement probability of the monitoring point p adjacent sensor fixed node and the adjacent sensor mobile node in the deployment area A at the monitoring point p:
V p set of fixed nodes of adjacent sensors and mobile nodes of adjacent sensors for monitoring point p, c th For a valid measurement probability threshold, if c p (S ov )≥c th If the monitoring point p is fixed by the sensor, the node is transmittedSensor mobile node active coverage;
abstracting the position coordinates of N sensor mobile nodes into particles in a particle group method for a network comprising m sensor fixed nodes and N sensor mobile nodes, the particle search space dimension N =2N, the position vector X of the particle i i ＝(x i1 ，x i2 ，…，x in ，y i1 ，y i2 ，…，y in ) Wherein x is ij ，y ij Respectively representing the horizontal and vertical coordinates of the jth sensor mobile node, wherein j is more than 1 and less than n, and a single target function formed by linear combination of weighted effective coverage rates of the key area and the general area is used as an adaptive value function f (X) i (t))＝α×f hot (X i (t))+(1-α)×f ordinary (X i (t)), wherein f hot (X i (t)) and f ordinary (X i (t)) respectively representing effective coverage rates of the key monitoring area and the common monitoring area, wherein alpha is a weight coefficient determined according to the key monitoring area;
the flight velocity of the particles is determined by:
v ij (t+1)＝w(t)×v ij (t)+c 1 r 1j (t)×(p ij (t)-x ij (t))+c 2 r 2j (t)×(p gj (t)-x ij (t))+c 3 r 3j (t)g ij (t) wherein c 1 For local optimization of the weight factors, c 2 For global optimization of the weighting factors, c 3 Is a potential field force acceleration factor, r 1j 、r 2j And r 3j Is [0,1]The index i corresponds to the ith particle, the index j corresponds to the jth dimension of the particle, w is an inertia factor of the past value on the current value, the value is 0.9-0.4, the index i is gradually decreased in the iteration process, and g is the value of the past value ij Then, the distance of the j-th dimension element in the position vector of the corresponding particle i under the action of the potential field force is represented by the following formula:
wherein，For the potential field force in the x direction experienced by the jth mobile node in the ith particle,for the potential field force in the y-direction experienced by the jth mobile node in the ith particle,MaxStep is the maximum moving step length;
updating the position of the particle based on the flight velocity of the particle x ij (t+1)＝x ij (t)+v ij (t + 1), substituting the updated position of the particle into the fitness function f (X) i (t)) evaluation to determine a locally optimal solution for the particleThen, the global optimum position P of the particle experience in the population is calculated g (t)＝max{f(P 1 (t))，f(P 2 (t))，…，f(P M (t)) }, repeatedly iterating the global optimal position according to the adaptive value function and the local optimal solution until a preset maximum iteration time is reached, and finally obtaining an optimal solution P g ；
The sink node will optimize the solution P g The data packet is packaged and broadcast out, and the sensor mobile node analyzes P after receiving the data packet g To obtain a target location of the sensor mobile node, to which the sensor mobile node moves.
2. The hybrid sensor network-based differential coverage method as claimed in claim 1, wherein in the step of forming the network by each sensor fixed node and the sensor mobile node, each sensor fixed node and the sensor mobile node form the network by flooding or directional routing.
3. The hybrid sensor network-based differential coverage method of claim 1, wherein each of the sensor fixed nodes and the sensor mobile nodes has capabilities of collecting information, calculating and processing data, sending or receiving messages, and positioning, and the sensor mobile nodes also have mobility capabilities.
4. The hybrid sensor network-based differential coverage method of claim 1, wherein the information of sensor performance comprises a sensor sensing radius r and sensor sensing model parameters.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101383736A (en) * | 2008-10-15 | 2009-03-11 | 中国科学院上海微系统与信息技术研究所 | Optimizing method for wireless sensor network node laying oriented to area monitoring |
| CN101902752A (en) * | 2010-05-21 | 2010-12-01 | 南京邮电大学 | Method for controlling coverage of directional sensor network |
-
2014
- 2014-06-06 CN CN201410250892.0A patent/CN103997748B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN101902752A (en) * | 2010-05-21 | 2010-12-01 | 南京邮电大学 | Method for controlling coverage of directional sensor network |
Non-Patent Citations (1)
| Title |
|---|
| 基于混沌粒子群算法的无线传感器网络覆盖优化;刘维亭, 范洲远;《计算机应用》;20110228;全文 * |
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