Gridding edge terminal wireless sensor network covering and positioning method and system

Abstract

The invention discloses a gridding edge terminal wireless sensor network covering and positioning method and system, and the method comprises the steps: building a wireless sensor coordinate system model of a monitoring region based on an actual operation environment of a wireless sensor; configuring the position information of each wireless sensor node as a layout scheme, and constructing a wireless sensor coverage evaluation model to evaluate the network coverage of each wireless sensor layout; optimizing the positions of the wireless sensor nodes based on an evaluation result to obtain an optimal node layout scheme; and based on the optimal node layout scheme, calculating the distance of the unknown node relative to the known reference point, optimizing the estimation of the relative distance between the wireless sensor nodes, and accurately positioning the unknown node. According to the invention, self-organizing network coverage and node positioning can be realized, the dependence on a central node is reduced, and the robustness and expandability of the system are enhanced; through cooperative work of multiple agents, a more accurate node position is found in an uncertain or dynamically changing environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless sensor network positioning, and particularly relates to a meshed edge terminal wireless sensor network coverage and positioning method and system. BACKGROUND

[0002] Swarm intelligence is derived from the study of the collective behavior of social insects represented by ants, bees and the like. It was first used in the description of the cellular robot system. Its control is distributed, and there is no central control. The swarm has self-organization. Therefore, it can better adapt to the working state under the current network environment, and has strong robustness, that is, it will not affect the solution of the whole problem by the failure of one or several individuals. Each individual in the swarm can change the environment, which is an indirect communication between individuals. This way is called inspired work. Since swarm intelligence can transmit and cooperate information through non-direct communication, as the number of individuals increases, the increase of communication overhead is small, so it has good scalability. The ability of each individual in the swarm or the behavior rule followed is very simple, so the implementation of swarm intelligence is relatively easy, and it has the characteristics of simplicity.

[0003] There are some challenges and shortcomings in the coverage and positioning method of wireless sensor network; as the network scale expands, the computational complexity and communication overhead of the positioning algorithm will also increase accordingly; the existing technology often uses non-ranging-based positioning methods (such as centroid positioning, DV-Hop, etc.), which have low precision; the nodes in the wireless sensor network are usually powered by batteries and have limited energy. The positioning algorithm and continuous communication may increase the energy consumption of the nodes and shorten the network life to maintain network coverage. SUMMARY

[0004] In view of the above existing problems, the present application is proposed. Therefore, the present application provides a meshed edge terminal wireless sensor network coverage and positioning method and system to solve the problems of high energy consumption, low positioning accuracy and high complexity of the existing wireless sensor network coverage and positioning.

[0005] To solve the above technical problems, the present application provides the following technical solutions:

[0006] In a first aspect, the embodiment of the present application provides a meshed edge terminal wireless sensor network coverage and positioning method, comprising: establishing a wireless sensor coordinate system model of a monitoring area based on the actual running environment of the wireless sensor;

[0007] The position information of each wireless sensor node is configured as a layout scheme, and a wireless sensor coverage range evaluation model is constructed to evaluate the network coverage range of each wireless sensor layout;

[0008] optimizing the position of the wireless sensor node based on the evaluation result to obtain an optimal node layout scheme;

[0009] based on the optimal node layout scheme, calculating the distance of the unknown node relative to the known reference point, optimizing the relative distance estimation between each wireless sensor node, and accurately positioning the unknown node.

[0010] As a preferred scheme of the mesh edge terminal wireless sensor network coverage and positioning method, the wireless sensor coverage evaluation model takes the maximization of the wireless sensor coverage as the objective function, and takes the position boundary constraint, the energy consumption constraint, and the node number constraint as the constraint condition, calculates the fitness value of each wireless sensor, and evaluates the network coverage of each wireless sensor layout.

[0011] As a preferred scheme of the mesh edge terminal wireless sensor network coverage and positioning method, the calculation of the fitness value of each wireless sensor includes:

[0012] taking any wireless sensor position as the center, calculating the coverage range of any sensor based on the target radius;

[0013] based on the intersection of the coverage range of the sensor and the monitoring area, obtaining the actual effective coverage area of the sensor; taking the union of the effective coverage area of each sensor to obtain the total area covered by all sensors; and dividing the total coverage area by the total area of the monitoring area to obtain the wireless sensor coverage.

[0014] The beneficial effect of the preferred technical scheme is that by calculating the fitness value of each sensor node layout scheme in the wireless sensor network, the coverage effect of the wireless sensor network is quantified, and based on this, different node layout schemes are evaluated to find the most effective layout to maximize the coverage efficiency, which helps to improve the overall performance of the network and prolong the network life in the case of limited resources

[0015] As a preferred scheme of the mesh edge terminal wireless sensor network coverage and positioning method, the optimization of the position of the wireless sensor node based on the evaluation result to obtain an optimal node layout scheme includes: calculating the selection probability of each wireless sensor layout scheme, randomly selecting a wireless sensor layout scheme according to the selection probability, and if the fitness value of the new position is better than the fitness value of the original position, updating the coordinate information and the fitness value of the sensor node.

[0016] As a preferred scheme of the mesh edge terminal wireless sensor network coverage and positioning method, it further includes: checking the update times of each wireless sensor layout scheme;

[0017] If any layout scheme reaches the preset maximum number of updates, the layout scheme is replaced by a randomly generated new layout scheme, the position of the wireless sensor node is updated, and the fitness value is recalculated until the maximum number of iterations is reached.

[0018] As a preferred scheme of the grid-based edge terminal wireless sensor network coverage and positioning method, wherein: the distance of the unknown node relative to the known reference point is calculated, the relative distance estimation between each wireless sensor node is optimized, and the accurate positioning of the unknown node includes:

[0019] For the position of any unknown point P of the wireless sensor, the unknown point P has n measurable distance reference points, the coordinates of each reference point and the distance from the unknown point P to the reference point are known, and the position relationship equation is established, which is represented as:

[0020]

[0021] Where (x, y, z) is the coordinates of the unknown point P, (x i ,y i ,z i ) and d i are the coordinates of the i-th reference point and the distance from the unknown point P to the reference point, respectively.

[0022] The beneficial effects of the preferred technical scheme are that the position of the unknown node in the wireless sensor network is calculated based on the distance measurement data of multiple reference points, which is suitable for various scenarios, especially in the case where the GPS signal is not strong or unavailable, providing an effective alternative for node positioning.

[0023] As a preferred scheme of the grid-based edge terminal wireless sensor network coverage and positioning method, wherein: the wireless sensor coordinate system model of the monitoring area includes: the wireless sensor coordinate system model is a two-dimensional coordinate system model; the coordinate range of the wireless sensor coordinate system model is the length and width of the monitoring area.

[0024] In a second aspect, the present application provides a grid-based edge terminal wireless sensor network coverage and positioning system, comprising:

[0025] The coordinate establishing module is configured to establish a wireless sensor coordinate system model of the monitoring area based on the actual operating environment of the wireless sensor;

[0026] The evaluation module is configured to configure the position information of each wireless sensor node as a layout scheme, and construct a wireless sensor coverage area evaluation model to evaluate the network coverage area of each wireless sensor layout.

[0027] a coverage optimization module, configured to optimize the wireless sensor node positions based on the evaluation results, and obtain an optimal node layout scheme;

[0028] a positioning module, configured to calculate distances of unknown nodes relative to known reference points based on the optimal node layout scheme, optimize relative distance estimations between each wireless sensor node, and accurately position the unknown nodes.

[0029] In a third aspect, the present application provides an electronic device, comprising:

[0030] a memory and a processor;

[0031] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, so as to implement the steps of the grid-based edge terminal wireless sensor network coverage and positioning method.

[0032] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer executable instructions, and the computer executable instructions, when executed by a processor, implement the steps of the grid-based edge terminal wireless sensor network coverage and positioning method.

[0033] Compared with the prior art, the present application has the following beneficial effects: the present application can realize self-organizing network coverage and node positioning without a central control, reduces the dependence on a central node, and enhances the robustness and scalability of the system; the present application considers the energy limitation of nodes in a wireless sensor network, can effectively prolong the network lifetime while reducing communication overhead and computational complexity; the present application finds more accurate node positions in an uncertain or dynamic environment through the cooperative work of multiple agents, can adapt to different network topologies and environmental conditions, and can provide relatively accurate position information even in the case where GPS is unavailable or limited. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor. Among them:

[0035] Figure 1 A method flowchart of a grid-based edge terminal wireless sensor network coverage and positioning method according to an embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the protection scope of the present application.

[0037] Embodiment 1, reference Figure 1 For an embodiment of the present application, the embodiment provides a meshed edge terminal wireless sensor network coverage and positioning method, comprising:

[0038] S100: based on the actual operation environment of the wireless sensor, a wireless sensor coordinate system model of the monitoring area is established;

[0039] S200: the position information of each wireless sensor node is configured as a layout scheme, and a wireless sensor coverage range evaluation model is constructed to evaluate the network coverage range of each wireless sensor layout;

[0040] S300: based on the evaluation result, the position of the wireless sensor node is optimized to obtain an optimal node layout scheme;

[0041] S400: based on the optimal node layout scheme, the distance of the unknown node relative to the known reference point is calculated, the relative distance estimation between each wireless sensor node is optimized, and the unknown node is accurately positioned.

[0042] It should be noted that as the network size expands, the computational complexity and communication overhead of the positioning algorithm will also increase accordingly, which is a big challenge for resource-limited sensor nodes; the prior art often uses non-ranging-based positioning methods such as centroid positioning and DV-Hop, which have low accuracy; the nodes in the wireless sensor network are usually powered by batteries and have limited energy, and the positioning algorithm and continuous communication may increase the energy consumption of the nodes and shorten the network life. The meshed edge terminal wireless sensor network coverage and positioning method provided by the present application can realize self-organizing network coverage and node positioning without central control, reduces the dependence on the central node, and enhances the robustness and scalability of the system. The present application takes into account the energy limitation of the nodes in the WSN, which can effectively prolong the network life while reducing the communication overhead and computational complexity. The present application finds more accurate node positions in uncertain or dynamic environments through the cooperative work of multiple agents, can adapt to different network topologies and environmental conditions, and can provide relatively accurate position information even in the case of unavailable or limited GPS.

[0043] In the embodiment of the present application, the wireless sensor coordinate system model of the monitoring area established in step S100 comprises: the wireless sensor coordinate system model is a two-dimensional coordinate system model; and the coordinate range of the wireless sensor coordinate system model is the length and width of the monitoring area.

[0044] Specifically, the two-dimensional coordinate system model of the monitoring area is represented as:

[0045] A={(x,y)|0≤x≤L,0≤y≤W}

[0046] wherein L and W are the length and width of the monitoring area respectively.

[0047] In the embodiment of the present application, the wireless sensor coverage evaluation model in step S200 takes the maximization of the wireless sensor coverage as the objective function, takes the position boundary constraint, the energy consumption constraint and the node number constraint as the constraint conditions, calculates the fitness value of each wireless sensor, and evaluates the network coverage of each wireless sensor layout.

[0048] Specifically, the fitness function is designed as the maximization of the wireless sensor coverage by means of reverse calibration, and the mathematical model is as follows:

[0049] maximize f(x)

[0050] subject to g(x)≤0

[0051] h(x)=0

[0052] wherein x is an n-dimensional variable vector, f(x) is the objective function to be maximized, and g(x) and h(x) are inequality constraint conditions and equality constraint conditions respectively.

[0053] Further, the inequality constraint condition g(x)≤0 comprises: the position boundary constraint and the energy consumption constraint.

[0054] Specifically, the position boundary constraint is: 0≤xi≤L, 0≤yi≤W, i=1, 2, …, n.

[0055] Specifically, the energy consumption constraint is: Ei≤Emax, i=1, 2, …, n.

[0056] The equality constraint condition h(x)=0 comprises: the node number constraint.

[0057] Specifically, the node number constraint is: |X|=n.

[0058] In the embodiment of the present application, the calculation of the fitness value of each wireless sensor in step S300 comprises:

[0059] The coverage range of any sensor is calculated based on a target radius with any wireless sensor position as the center;

[0060] The actual effective coverage area of the sensor is obtained based on the intersection of the coverage range of any sensor and the monitoring area; the total area covered by all sensors is obtained by taking the union of the effective coverage area of each sensor; and the coverage rate of the wireless sensor is obtained by dividing the total coverage area by the total area of the monitoring area.

[0061] Specifically, the fitness value of each wireless sensor is calculated by substituting each wireless sensor into the target function f(x) in the following manner:

[0062] The coverage area of sensor i is represented as: C i = {(x, y) | (x-x θ ) 2 +(y-y i ) 2 ≤R 2}

[0063] The effective coverage area is represented as: C i ′=C i ∩A

[0064] The total coverage area is S_covered=Area(∪ i＝1 n C i ′), the total area of the monitoring area is S_total=L×W, and the coverage rate is CoverageRate=S_covered / S_total.

[0065] The target function f(X)=CoverageRate(X)

[0066] The fitness value F(X)=CoverageRate(X)

[0067] In the embodiment of the application, the step S300 of optimizing the position of the wireless sensor node based on the evaluation result to obtain the optimal node layout scheme includes: calculating the selection probability of each wireless sensor layout scheme, randomly selecting a wireless sensor layout scheme according to the selection probability, updating the coordinate information and the fitness value of the sensor node if the fitness value of the new position is better than the fitness value of the original position.

[0068] In the embodiment of the application, the step S300 further includes: checking the number of updates of each wireless sensor layout scheme.

[0069] If any layout scheme reaches the preset maximum number of updates, the layout scheme is replaced by a newly generated layout scheme, the position of the wireless sensor node is updated, and the fitness value thereof is recalculated until the maximum number of iterations is reached.

[0070] In a preferred embodiment, the present application realizes the meshing edge terminal wireless sensor network coverage and positioning through swarm intelligence algorithm;

[0071] In an optional embodiment, by defining the actual environment of the wireless sensor, a two-dimensional coordinate system model of the monitoring area is established, and the population size and iteration number of the swarm intelligence algorithm are set according to the node number, initial position and monitoring area of the wireless sensor;

[0072] The two-dimensional coordinate position information of the wireless sensor node is encoded into the individual solution vector in the artificial bee colony algorithm, wherein each individual solution contains the coordinate information of all movable sensor nodes, and the encoding mode is:

[0073] X=[(x1,y1),(x2,y2),...,(x n ,yn)]

[0074] Wherein, (x i ,y i ) represents the coordinate position of the i-th sensor node, and n is the total number of optimizable nodes. The coverage of each wireless sensor is evaluated by the fitness function;

[0075] The artificial bee colony algorithm is adopted, the excellent wireless sensor individuals are selected for reproduction, the state of each wireless sensor individual is constantly updated, a better wireless sensor node layout is searched, and random disturbance is introduced to avoid local optimum;

[0076] The artificial bee colony algorithm and the multiple measurement method are combined, the relative distance estimation between each wireless sensor node is optimized, the accurate positioning of the unknown node is realized, the positioning algorithm runs to the predetermined iteration number, the best solution is selected for network deployment or node position adjustment, and the coverage and positioning effect is verified through simulation or field test.

[0077] In an optional embodiment, the iteration optimization of the artificial bee colony algorithm includes steps A1 to A4:

[0078] Step A1 employed bee stage: let the wireless sensor individual be i, i = 1, 2,..., n;

[0079] Calculate the updated vector of the wireless sensor individual

[0080] phi=a\cdot rand([-1,1],[1,nVar])

[0081] rand generates an nVar-dimensional random vector with elements ranging between [-1, 1];

[0082] Select another wireless sensor individual k, k is not equal to i, and update the position according to the formula:

[0083] [NewPosition i =PopPosition i +phi\cdot(PopPosition i -PopPosition k )]

[0084] Calculate the fitness value of the new position, if the fitness value is better than the original position, then update PopPosition i and PopCost i , wherein PopPosition i represents the position vector of the ith individual, storing the coordinate information of the sensor node; PopCost i represents the fitness value of the ith individual;

[0085] Step A2 follows the bee phase: calculate the selection probability of each wireless sensor individual, randomly select a wireless sensor individual i according to the selection probability, and update the position according to the steps of step A1, if the updated position is not better than the original position, then select the update;

[0086] Step A3 scout bee phase: check the update times of each wireless sensor individual, if the maximum update times are reached, randomly generate a new position and calculate its fitness value, directly replace the wireless sensor with poor original position; wherein the maximum update times can be set to 20.

[0087] Step A4: after each iteration optimization, update the global optimal solution, and repeat steps A1-A3 until the maximum iteration times are reached.

[0088] In the embodiment of the application, the distance of the unknown node relative to the known reference point is calculated in step S400, and the relative distance estimation between each wireless sensor node is optimized, and the accurate positioning of the unknown node includes:

[0089] For the position of any unknown point P of a wireless sensor, the unknown point P has n measurable distance reference points, the coordinates of each reference point and the distance from the unknown point P to the reference point are known, and a position relationship equation is established, which is represented as:

[0090]

[0091] Wherein, (x,y,z) is the coordinate of the unknown point P, (x i ,y i ,z i ) and d irespectively, are the coordinates of the i-th reference point and its distance to the unknown point P.

[0092] It should be noted that in the multilateration method, the case of three-dimensional space and only three reference points can be converted into the intersection problem of three spherical equations, which can be solved by geometric or algebraic methods, but when there are more than three reference points, an iterative optimization algorithm is usually used to solve the nonlinear equation set.

[0093] In the artificial bee colony algorithm, when selecting the wireless sensor individual to follow the bee, the selection probability (p i ) is usually calculated according to the fitness value: i = \frac{f i}{\sum_{j=1}^{nPop}f j}];where f i is the fitness value of the wireless sensor individual i (the inverse or proportional of the objective function value, depending on whether the optimization goal is maximization or minimization).

[0094] In the calculation of the fitness function, the calculation efficiency of the fitness function is ensured, especially in large-scale problems, the fitness function needs to be adjusted according to the characteristics of the problem to ensure that the algorithm can correctly guide the search process. By simulating the behavior of biological populations searching for food or optimal paths, swarm intelligence algorithms can effectively distribute sensor nodes, ensuring good coverage of the entire monitoring area and reducing blind spots, especially in complex or dynamic environments such as mines, underwater, and other special scenarios.

[0095] Therefore, the grid-based edge terminal wireless sensor network coverage and positioning method based on swarm intelligence algorithm can realize self-organizing network coverage and node positioning without the need for central control, reducing the dependence on the central node and enhancing the robustness and scalability of the system. The present application takes into account the energy limitations of nodes in WSN, which can effectively extend the network lifetime while reducing communication overhead and computational complexity. The present application finds more accurate node positions in uncertain or dynamic environments through the collaborative work of multiple agents. They can adapt to different network topologies and environmental conditions, and can provide relatively accurate location information even when GPS is not available or limited.

[0096] Embodiment 2 is an embodiment of the present application, which is different from the first embodiment in that a grid-based edge terminal wireless sensor network coverage and positioning system is provided, comprising:

[0097] The coordinate establishment module is used to establish a wireless sensor coordinate system model of the monitoring area based on the actual operating environment of the wireless sensor.

[0098] an evaluation module, configured to configure position information of each wireless sensor node as a layout scheme, and to construct a wireless sensor coverage area evaluation model to evaluate network coverage area of each wireless sensor layout;

[0099] a coverage optimization module, configured to optimize wireless sensor node positions based on the evaluation result to obtain an optimal node layout scheme;

[0100] a positioning module, configured to calculate distance of an unknown node relative to a known reference point based on the optimal node layout scheme, to optimize relative distance estimation between each wireless sensor node, and to accurately position the unknown node.

[0101] Specifically, each module of the grid-based edge terminal wireless sensor network coverage and positioning system in the embodiment is implemented to perform steps of the grid-based edge terminal wireless sensor network coverage and positioning method in Embodiment 1, for example:

[0102] In an implementation, a grid-based edge terminal wireless sensor network coverage and positioning system can perform the following steps:

[0103] The wireless sensor coordinate system model is a two-dimensional coordinate system model, and the coordinate range of the wireless sensor coordinate system model is the length and width of the monitoring area.

[0104] The wireless sensor coverage range evaluation model takes maximizing wireless sensor coverage degree as an objective function, and takes position boundary constraint, energy consumption constraint, and node number constraint as constraint conditions, to calculate fitness value of each wireless sensor, and to evaluate network coverage range of each wireless sensor layout.

[0105] The coverage range of any sensor is calculated based on a target radius with any wireless sensor position as a center.

[0106] The actual effective coverage area of the sensor is obtained based on the intersection of the coverage range of any sensor and the monitoring area, the total area of all sensors commonly covered is obtained by taking the union of the effective coverage area of each sensor, and the wireless sensor coverage degree is obtained by dividing the total coverage area by the total area of the monitoring area.

[0107] The selection probability of each wireless sensor layout scheme is calculated, a wireless sensor layout scheme is randomly selected according to the selection probability, and if the fitness value of the new position is better than the fitness value of the original position, the coordinate information and the fitness value of the sensor node are updated.

[0108] The updating times of each wireless sensor layout scheme are checked.

[0109] If any layout scheme reaches the preset maximum number of updates, the layout scheme is replaced with a randomly generated new layout scheme, the wireless sensor node positions are updated, and the fitness values are recalculated until the maximum number of iterations is reached.

[0110] For the position of any unknown point P of a wireless sensor, suppose that the unknown point P has n measurable distance reference points, the coordinates of each reference point and the distance from the unknown point P to the reference point are known, and a position relationship equation is established, denoted as:

[0111]

[0112] where (x, y, z) is the coordinate of the unknown point P, (x i ,y i ,z i ) and d i are the coordinates of the i-th reference point and the distance from the unknown point P to the i-th reference point, respectively.

[0113] Embodiment 3 provides an electronic device suitable for the case of the grid-based edge terminal wireless sensor network coverage and positioning method, comprising:

[0114] a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to realize the grid-based edge terminal wireless sensor network coverage and positioning method proposed in the above embodiments.

[0115] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to realize the grid-based edge terminal wireless sensor network coverage and positioning method proposed in the above embodiments.

[0116] The storage medium proposed in the embodiment belongs to the same inventive concept as the grid-based edge terminal wireless sensor network coverage and positioning method proposed in the above embodiments, and the technical details not described in detail in the embodiment can be referred to the above embodiments, and the embodiment has the same beneficial effects as the above embodiments.

[0117] Those skilled in the art can clearly understand that the embodiments of the present application can be provided as a method, a system or a computer program product by the description of the above embodiments. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. In addition, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage and the like) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and direct script languages such as JavaScript.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0119] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0121] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications to the embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0122] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the spirit and scope of the present application. Thus, it is intended that the present application cover the modifications and changes as long as they come within the scope of the appended claims and their equivalents.

[0123] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for meshed edge terminal wireless sensor network coverage and localization, characterized in that, The method comprises the following steps: Based on the actual operating environment of the wireless sensor, a wireless sensor coordinate system model of the monitoring area is established; The position information of each wireless sensor node is configured as a layout scheme, and a wireless sensor coverage evaluation model is constructed to evaluate the network coverage of each wireless sensor layout; Based on the evaluation result, the position of the wireless sensor node is optimized to obtain an optimal node layout scheme; Based on the optimal node layout scheme, the distance of the unknown node relative to the known reference point is calculated, the relative distance estimation between each wireless sensor node is optimized, and the unknown node is accurately positioned.

2. The meshed FTEWSN coverage and localization method of claim 1, wherein: The wireless sensor coverage evaluation model takes maximizing the wireless sensor coverage as the objective function, takes the position boundary constraint, the energy consumption constraint and the node number constraint as the constraint condition, calculates the fitness value of each wireless sensor, and evaluates the network coverage of each wireless sensor layout.

3. The meshed FTEWSN coverage and localization method of claim 2, wherein, The calculation of the fitness value of each wireless sensor comprises: Taking any wireless sensor position as the center, the coverage range of any sensor is calculated based on the target radius; Based on the intersection of the coverage range of the sensor and the monitoring area, the actual effective coverage area of the sensor is obtained; the union of the effective coverage area of each sensor is taken to obtain the total area covered by all sensors; and the total coverage area is divided by the total area of the monitoring area to obtain the wireless sensor coverage.

4. The meshed FTEWSN coverage and localization method of claim 3, wherein, Based on the evaluation result, the position of the wireless sensor node is optimized to obtain an optimal node layout scheme, which comprises: calculating the selection probability of each wireless sensor layout scheme, randomly selecting a wireless sensor layout scheme according to the selection probability, and updating the coordinate information and the fitness value of the sensor node if the fitness value of the new position is better than that of the original position.

5. The meshed FTEWS network coverage and localization method of claim 4, wherein, Further comprising: Checking the number of updates of each wireless sensor layout scheme; If any layout scheme reaches the preset maximum number of updates, the layout scheme is replaced by a newly generated layout scheme, the position of the wireless sensor node is updated, and the fitness value is recalculated until the maximum number of iterations is reached.

6. The meshed FTEWS network coverage and localization method of claim 5, wherein, Calculating the distance of the unknown node relative to the known reference point, optimizing the relative distance estimation between each wireless sensor node, and accurately positioning the unknown node comprise: For the position of the unknown point P of any wireless sensor, suppose that the unknown point P has n measurable distance reference points, the coordinates of each reference point and the distance from the unknown point P to the reference point are known, and a position relationship equation is established, which is expressed as: where (x, y, z) is the coordinate of unknown point P, (x i ,y i ,z i ) and d i are the coordinate of the ith reference point and its distance to unknown point P, respectively.

7. The meshed FTEWS network coverage and localization method of claim 6, wherein, The wireless sensor coordinate system model of the monitoring area comprises: the wireless sensor coordinate system model is a two-dimensional coordinate system model; and the coordinate range of the wireless sensor coordinate system model is the length and width of the monitoring area.

8. A meshed edge terminal wireless sensor network coverage and localization system, applied to the method of any one of claims 1-7, characterized in that, The method comprises the following steps: A coordinate establishing module is configured to establish a wireless sensor coordinate system model of a monitoring area based on the actual operating environment of the wireless sensor; An evaluation module is configured to configure the position information of each wireless sensor node as a layout scheme, and construct a wireless sensor coverage evaluation model to evaluate the network coverage of each wireless sensor layout; A coverage optimization module is configured to optimize the position of the wireless sensor node based on the evaluation result to obtain an optimal node layout scheme; A positioning module is configured to calculate the distance of the unknown nodes relative to the known reference points based on the optimal node layout scheme, optimize the relative distance estimation between each wireless sensor node, and accurately locate the unknown nodes. 9.An electronic device, comprising: a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, which, when executed by the processor, implement the steps of the method for meshing edge terminal wireless sensor network coverage and positioning according to any one of claims 1 to 7. 10.A computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the method for meshing edge terminal wireless sensor network coverage and positioning according to any one of claims 1 to 7.

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