An Optimization Method for Wireless Sensor Network Coverage Based on a Composite Discretization Strategy

Abstract

The present invention discloses a method for optimizing the coverage of a wireless sensor network with a composite discretization strategy, including: converting coordinates into binary string form in combination with the range of the detection area; initializing the coordinates of each wireless sensor, and iteratively updating the coordinates of each wireless sensor in combination with the coverage rate: in each iteration, calculating the coverage rate according to the coordinates of the wireless sensor; determining whether the coverage rate meets the set requirements; if so, updating the coordinates of each wireless sensor using a V-shaped conversion function that changes with time; if not, updating the coordinates of each wireless sensor using an S-shaped conversion function that changes with time; when the set number of iterations is reached, outputting the updated coordinates of each wireless sensor. The above method can make the coordinate data more conform to the data characteristics, and make the optimization scheme achieve better performance, so as to optimize the coverage range of the wireless sensor network.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensor network coverage optimization, and particularly to a method for optimizing the coverage of a wireless sensor network with a composite discretization strategy. Background Art

[0002] A wireless sensor network (WSN) consists of numerous micro sensor nodes, which are deployed in a detection area and build a multi-hop, self-organizing wireless network through wireless communication. Common application fields of WSN include smart home, smart agriculture, forest fire prevention, and environmental detection, etc. Limited by cost and the sensor sensing range, how to deploy a limited number of sensors to a specified detection area to maximize the sensing coverage range of the sensors in this detection area has become a challenging problem.

[0003] To solve the coverage problem of WSN, intelligent computing technology can be adopted. The idea of transforming the WSN coverage problem into an intelligent optimization problem is to digitalize the geographical data of the detection area, then take the horizontal and vertical coordinates of each sensor node as two-dimensional variables for optimization, and finally, for each solution, calculate the coverage rate by calculating how many points with integer coordinates in the detection area are covered by the sensors. Currently, there are various methods for solving this problem. Intelligent optimization algorithms are used to solve the node coverage problem of WSN because of their high search accuracy, wide search range, and strong adaptability to complex problems. Due to the low accuracy requirement and the difficulty of completely continuously digitalizing the geographical map of the monitoring area, usually only the height of the points with integer coordinates in the monitoring area is taken, which leads to the fact that intelligent optimization algorithms often use the rounding method to obtain the integer point coordinates of the sensors. However, this simple rounding method is too rough on the one hand, and on the other hand, it cannot match the characteristics of the data itself, resulting in a significant reduction in the accuracy of the optimization result or even falling into a local optimum and being unable to be resolved, thus leading to poor optimization effects for the node coverage optimization of WSN based on intelligent computing. In view of the above problems, the method of discretization can be considered to improve the intelligent algorithm to optimize the coverage problem. However, traditional discretization improvement methods usually only use one conversion function for discretization, and there is no solution for adaptively selecting different conversion functions according to data characteristics.

[0004] In view of this, the present invention is specifically proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for optimizing the coverage of a wireless sensor network with a composite discretization strategy, which is an intelligent optimization method for the coverage of a wireless sensor network based on adaptively selecting a composite discretization improvement strategy. Compared with the basic intelligent optimization algorithm, better optimization effects are achieved in some WSN coverage problems.

[0006] The purpose of the present invention is achieved through the following technical solutions:

[0007] A method for optimizing the coverage of a wireless sensor network with a composite discretization strategy, comprising:

[0008] Combining the range of the detection area, convert the coordinates into binary string form;

[0009] Initialize the coordinates of each wireless sensor. For each solution, iteratively update the coordinates of each wireless sensor in combination with the coverage rate: for each solution, at each iteration, calculate the coverage rate according to the coordinates of the wireless sensor; determine whether the coverage rate meets the set requirements; if so, update the coordinates of each wireless sensor using a V-shaped conversion function that changes over time; if not, update the coordinates of each wireless sensor using an S-shaped conversion function that changes over time; when the set number of iterations is reached, select the solution with the highest coverage rate as the optimal solution.

[0010] It can be seen from the technical solution provided by the present invention above that by introducing an improved strategy based on adaptive selection of composite discretization, the coordinate data may be more in line with the data characteristics, and the optimization solution can achieve better performance, so as to optimize the coverage range of the wireless sensor network. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0012] Figure 1 It is a flowchart of a method for optimizing the coverage of a wireless sensor network with a composite discretization strategy provided by an embodiment of the present invention;

[0013] Figure 2 It is a detailed flowchart of a method for optimizing the coverage of a wireless sensor network with a composite discretization strategy provided by an embodiment of the present invention;

[0014] Figure 3 It is a schematic diagram of a V-shaped conversion function that changes over time provided by an embodiment of the present invention;

[0015] Figure 4 It is a schematic diagram of a unimodal terrain provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the protection scope of the present invention.

[0017] First, the following explanations are given for the terms that may be used in this article:

[0018] Descriptions with semantic meanings such as "including", "comprising", "containing", "having", or other similar ones should be interpreted as non-exclusive inclusion. For example: including a certain technical feature element (such as raw materials, components, ingredients, carriers, dosage forms, materials, dimensions, parts, components, mechanisms, devices, steps, processes, methods, reaction conditions, processing conditions, parameters, algorithms, signals, data, products, or articles, etc.) should be interpreted as not only including the clearly listed certain technical feature element, but also including other technical feature elements well-known in the art that are not clearly listed.

[0019] The term "consisting of..." means excluding any technical feature element that is not clearly listed. If this term is used in a claim, this term will make the claim a closed type, making it not contain technical feature elements other than the clearly listed ones, except for the related conventional impurities. If this term only appears in a certain clause of a claim, then it only limits the elements clearly listed in that clause, and the elements recorded in other clauses are not excluded from the overall claim.

[0020] The following gives a detailed description of a method for optimizing the coverage of a wireless sensor network with a composite discretization strategy provided by the present invention. The content not described in detail in the embodiments of the present invention belongs to the prior art well-known to those of ordinary skill in the art. In the embodiments of the present invention, those not specified in specific conditions are carried out according to the conventional conditions in the art or the conditions recommended by the manufacturer. The reagents or instruments used in the embodiments of the present invention that are not indicated by the manufacturer are all conventional products that can be obtained through commercial purchase.

[0021] As Figure 1 shown, a method for optimizing the coverage of a wireless sensor network with a composite discretization strategy mainly includes the following steps:

[0022] Step 1: Combine the range of the detection area and convert the coordinates into the form of a binary string.

[0023] In the embodiments of the present invention, the original continuous search space is discretized, so that the real number coordinates become a binary string, thereby preventing the appearance of decimals and making the coordinates change between integers.

[0024] Step 2: Initialize the coordinates of each wireless sensor. For each solution, iteratively update the coordinates of each wireless sensor in combination with the coverage rate, and finally obtain the optimal solution.

[0025] In the embodiments of the present invention, for each solution, at each iteration, calculate the coverage rate according to the coordinates of the wireless sensors; determine whether the coverage rate meets the set requirements; if so, update the coordinates of each wireless sensor using a V-shaped conversion function that changes with time; if not, update the coordinates of each wireless sensor using an S-shaped conversion function that changes with time; after reaching the set number of iterations, select the solution with the highest coverage rate as the optimal solution.

[0026] In the embodiments of the present invention, it is possible to determine whether the coverage rate meets the set requirements by setting a coverage rate threshold. If it exceeds the coverage rate threshold, it is considered that the coverage rate is high and meets the set requirements; otherwise, it is considered that the coverage rate is low and does not meet the set requirements.

[0027] In the embodiments of the present invention, different optimization methods are used for different solutions. Specifically, for solutions with a high coverage rate, a V-shaped conversion function that changes with time is used for discretization, while for solutions with a low coverage rate, an S-shaped conversion function that changes with time is used for discretization, so that the original continuous search space is converted into a conversion between 0 and 1.

[0028] Use the improved intelligent optimization algorithm to search for the optimal solution (that is, the solution of updating the coordinates using the two conversion functions provided later). The coverage rate is calculated by converting the binary string into the corresponding integer coordinates in the middle, and the optimal solution is obtained through continuous iteration.

[0029] In order to more clearly show the technical solutions provided by the present invention and the technical effects produced, the following Figure 2 uses specific embodiments to describe in detail the method provided by the embodiments of the present invention.

[0030] 1. Coordinate binaryization.

[0031] In the embodiments of the present invention, it is necessary to binaryize the coordinates. Specifically, the number of bits of the binary string needs to be determined in combination with the range of the detection area, and the value of the number of bits can make the maximum value of the binary string greater than or equal to the maximum range of the detection area.

[0032] Each solution represents the coordinate positions of all wireless sensors. The coordinates of each wireless sensor are expressed as:

[0033] X = {x1, y1, x2, y2, x3, y3, …, x num , y num ,}

[0034] Among them, num is the number of wireless sensors, and X is the set formed by the coordinates of all wireless sensors; x and y respectively represent the abscissa and ordinate of the wireless sensors, and the subscript numbers are the numbers of the wireless sensors.

[0035] Both x and y are in the form of binary strings and are expressed as:

[0036] x = {b1, b2, b3, …, b n}

[0037] y = {b1′, b2′, b3′, …, b n ′}

[0038] b, b′ ∈ {0, 1}

[0039] Among them, b and b′ refer to each item (i.e., each binary digit) of the binary string, and n is the number of bits of the binary string.

[0040] II. Calculate the coverage rate according to the coordinate positions of the wireless sensors.

[0041] Calculating the coverage rate requires traversing each point in the detection area and comparing the distance with each sensor to determine whether it is covered.

[0042] The distance calculation formula is as follows:

[0043]

[0044] Among them, and respectively represent the abscissa and ordinate in integer form of the wireless sensor, and respectively represent the abscissa and ordinate in integer form of a point in the detection area, represents the height at represents the height at; distance represents the distance between and If distance is less than the sensing radius of the wireless sensor, it means that

[0045] The integer coordinates used in the above calculations can be obtained by converting the corresponding binary strings, and the conversion methods involved can be implemented with reference to conventional techniques, which will not be elaborated in this invention.

[0046] Finally, the coverage rate can be calculated according to the coverage situation of each point in the detection area.

[0047] In the embodiments of the present invention, if it is the first iteration, the horizontal and vertical coordinates of each wireless sensor are initialized coordinates; if it is not the first iteration, the horizontal and vertical coordinates of each wireless sensor are the horizontal and vertical coordinates updated in the previous iteration.

[0048] Specifically: For some special cases, it may be necessary to ensure that there are no obstacles between the sensor and the monitoring point. In this case, it is necessary to determine whether there are obstacles in the connection line between the monitoring point and the sensor.

[0049] III. Use corresponding optimization schemes according to the coverage rate.

[0050] In the embodiments of the present invention, multiple solutions are set at the same time, and the method of using the V-shaped conversion function and the S-shaped conversion function that change with time is used to discretize. As an example, in addition to using the threshold to define the quality of the coverage rate, the present invention can also take the better half of the solutions with better coverage rate to run the V-shaped conversion function, and the worse half to run the S-shaped conversion function, that is, calculate the coverage rate of each solution, and then sort them in descending order. The first half of the sorted solutions are considered to belong to the solutions with better coverage rate, and the remaining solutions are considered to belong to the solutions with better coverage rate. 1. The V-shaped conversion function that changes with time.

[0051] In the embodiments of the present invention, the V-shaped conversion function is a V-shaped function that changes with the number of iterations, as Figure 3 shown. The V-shaped conversion function that changes with time is expressed as:

[0052]

[0053] where V(a) is the V-shaped conversion function that changes with time, a ∈ (b, b′), and (b, b′) corresponds to each binary digit in the horizontal coordinate x and the vertical coordinate y in the form of the binary string of the wireless sensor; erf is the error function, t is the current number of iterations, T is the maximum number of iterations, and δ is a constant representing the stretching degree of the later curve. As an example, δ = 5.0 can be set.

[0054] In the embodiments of the present invention, using the V-shaped conversion function that changes with time to update the coordinates of each wireless sensor is expressed as:

[0055]

[0056] where the symbol ← is an assignment symbol, indicating that the result on the right side is assigned to the left side part, and rand is a random number from 0 to 1.

[0057] 2. The S-shaped conversion function that changes with time.

[0058] Similarly, the S-shaped conversion function is also an S-shaped function that changes with the number of iterations, expressed as:

[0059]

[0060] Among them, S(a) is an S-shaped conversion function that changes with time, a ∈ (b, b′), and (b, b′) corresponds to each binary digit in the abscissa x and ordinate y representing the binary string form of the wireless sensor; tanh is the hyperbolic tangent function, t is the current iteration number, T is the maximum iteration number, and δ is a constant representing the stretching degree of the later curve.

[0061] In the embodiment of the present invention, updating the coordinates of each wireless sensor using an S-shaped conversion function that changes with time is expressed as:

[0062]

[0063] Among them, the symbol ← is an assignment symbol, indicating that the result on the right side is assigned to the left side, and rand is a random number from 0 to 1.

[0064] Iterate continuously in this way. Each iteration will record the solution with the highest coverage rate. After reaching the maximum iteration number, the solution with the highest coverage rate is used as the optimal solution.

[0065] The above solution provided by the present invention optimizes the wireless sensor network coverage problem by using binary instead of real numbers. Its improvement strategy uses an adaptive selection of a V-shaped conversion function that changes with time and an S-shaped conversion function that changes with time to discretize the corresponding solutions. Finally, the intelligent optimization algorithm using the improved strategy of adaptive selection composite discretization has better optimization performance than the basic intelligent optimization algorithm in some wireless sensor network coverage problems.

[0066] To illustrate the superiority of the above solution of the present invention, the particle swarm optimization algorithm is taken as an example for the above improvement below, and the particle swarm optimization before and after the improvement is respectively applied to Figure 4 the unimodal topographic map shown for comparison.

[0067] The idea of the particle swarm optimization algorithm stems from the study of the foraging behavior of bird flocks, and has the advantages of fast convergence speed, few parameters, and simple and easy implementation of the algorithm. As Figure 1 shown in the flowchart, the particle swarm optimization algorithm is improved without changing the update formula and parameters of the algorithm to obtain the improved particle swarm optimization algorithm.

[0068] Figure 3 The unimodal topographic map shown is a topographic map in the range of 100 by 100 generated by calculating the height of each grid point using the two-dimensional Gaussian distribution formula. In this application, the obstruction of the terrain to the sensor induction will be considered, that is, if there is an obstacle on the straight line between the sensor and the induction coordinate, it will be determined that the induction coordinate is not covered by the sensor.

[0069] In this application, 50 sensors are used with a sensing radius of 10. The particle swarm optimization algorithm improved based on the adaptive selection composite discretization improvement strategy (BVSPSO) and the basic particle swarm optimization algorithm (PSO) each perform 20 iterations per round on a unimodal topographic map, run for 20 rounds, and the mean and variance of the final coverage rate are taken for comparison. The comparison results are shown in Table 1.

[0070] Table 1: Comparison Results of BVSPSO and PSO

[0071] mean (average value) std (variance) BVSPSO 0.66189 0.0115202750332666 PSO 0.647265 0.0109783171464674

[0072] The results show that the particle swarm optimization algorithm improved based on the adaptive selection composite discretization improvement strategy has a certain degree of improvement in the running performance on the unimodal topographic map compared to the basic particle swarm optimization algorithm. According to the idea of the adaptive selection composite discretization improvement strategy, the conversion function used can also be modified, and different combinations may further improve the results.

[0073] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiments can be implemented by software or by means of software plus a necessary general hardware platform. Based on this understanding, the technical solutions of the above embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0074] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A method for optimizing the coverage of a wireless sensor network with a composite discretization strategy, characterized in that Including: Combining the range of the detection area, converting coordinates into binary string form; Initializing the coordinates of each wireless sensor, for each solution, iteratively updating the coordinates of each wireless sensor in combination with the coverage rate: for each solution, at each iteration, calculating the coverage rate according to the coordinates of the wireless sensor; determining whether the coverage rate meets the set requirements; if so, updating the coordinates of each wireless sensor using a V-shaped conversion function that changes with time; if not, updating the coordinates of each wireless sensor using an S-shaped conversion function that changes with time; when the set number of iterations is reached, selecting the solution with the highest coverage rate as the optimal solution; Among them, the V-shaped conversion function that changes with time is expressed as: Among them, V(a) is the V-shaped conversion function that changes with time, a ∈ (b, b′), (b, b′) corresponds to each binary digit in the abscissa x and ordinate y representing the binary string form of the wireless sensor; erf is the error function, t is the current iteration number, T is the maximum iteration number, and δ is a constant representing the stretching degree of the later curve; The S-shaped conversion function that changes with time is expressed as: Among them, S(a) is the S-shaped conversion function that changes with time, and tanh is the hyperbolic tangent function.

2. The wireless sensor network coverage optimization method with a composite discretization strategy according to claim 1, characterized in that The combining the range of the detection area and converting the spatial coordinates into binary string form includes: Determining the number of bits of the binary string in combination with the range of the detection area, and the value of the number of bits can make the maximum value of the binary string greater than or equal to the maximum range of the detection area.

3. The wireless sensor network coverage optimization method with a composite discretization strategy according to claim 1, characterized in that, The coordinates of each wireless sensor are expressed as: X = {x1, y1, x2, y2, x3, y3, …, x num , y num} Among them, num is the number of wireless sensors, and X is the set formed by the coordinates of all wireless sensors; x and y respectively represent the abscissa and ordinate of the wireless sensor, and the subscript number is the number of the wireless sensor; Both x and y are in binary string form, expressed as: x = {b1, b2, b3, …, b n} y = {b1′, b2′, b3′, …, b n ′} b, b′ ∈ {0, 1} Among them, b and b′ refer to each item of the binary string, and n is the number of bits of the binary string.

4. A method for optimizing the coverage of a wireless sensor network with a composite discretization strategy according to claim 1, characterized in that The calculating the coverage rate according to the coordinates of the wireless sensor includes: Traversing each point in the detection area and calculating the distance from the coordinates of each wireless sensor, expressed as: Among them, x and y respectively represent the abscissa and ordinate of the wireless sensor, x′ and y′ respectively represent the abscissa and ordinate of a point in the detection area, high(x, y) represents the height at (x, y), and high(x′, y′) represents the height at (x′, y′); distance represents the distance between (x′, y′) and (x, y), if distance is less than the sensing radius of the wireless sensor, it means that the point at (x′, y′) is covered by the wireless sensor; Calculating the coverage rate according to the coverage situation of each point in the detection area.

5. The wireless sensor network coverage optimization method using a composite discretization strategy according to claim 1, characterized in that Updating the coordinates of each wireless sensor using a V-shaped conversion function that changes with time is expressed as: Among them, the symbol ← is the assignment symbol, indicating that the result on the right is assigned to the left part, and rand is a random number from 0 to 1.

6. The wireless sensor network coverage optimization method using a composite discretization strategy according to claim 1, characterized in that, Updating the coordinates of each wireless sensor using an S-shaped conversion function that changes with time is expressed as: Among them, the symbol ← is the assignment symbol, indicating that the result on the right is assigned to the left part, and rand is a random number from 0 to 1.

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