A time-limited charging scheduling method for improving heterogeneous sensing quality in wireless sensor networks

Abstract

The present invention relates to a time-limited charging scheduling method for improving heterogeneous perception quality in wireless sensor networks, comprising the following steps: defining a set of rechargeable sensors and wireless chargers; defining the discretization of charging time for wireless charging; defining the energy consumption of wireless chargers; defining system time; defining a monitoring utility model for wireless charging; formalizing the wireless charging scheduling problem of joint time constraints and heterogeneous perception quality; converting the wireless charging scheduling problem of joint time constraints and heterogeneous perception quality into a generalized wireless charging scheduling problem of heterogeneous perception quality; and determining a scheduling scheme for wireless chargers. The present invention considers the time-limited charging scheduling problem of heterogeneous perception in wireless sensor networks, and schedules mobile chargers to charge sensors under dual constraints, maximizing the monitoring utility of sensors while improving compared to traditional comparison algorithms.

Description

Technical Field

[0001] The present invention relates to the technical field of charging scheduling for wireless rechargeable sensor networks, and in particular to a time-limited charging scheduling method for improving heterogeneous sensing quality in wireless sensor networks. Background Art

[0002] With the rapid development of Internet of Things technology, a wireless sensor network that integrates data collection, information processing, and communication functions has attracted attention for its low cost, low power consumption, flexible deployment, wide coverage, and high real-time performance, and has been widely used in many industries. However, most of the existing wireless sensor networks are battery-powered, so the life of wireless sensors is limited, so batteries need to be replaced regularly to ensure the normal operation of the network, but this also brings expensive maintenance costs. For this reason, wireless power transmission technology has been developed, which can provide continuous and reliable power supply for rechargeable sensors, ensure the normal and reliable operation of sensor nodes, and has been widely used. With the further development of wireless power transmission technology, wireless rechargeable sensor networks have emerged, solving and optimizing the shortcomings of wireless sensor networks.

[0003] Although research on wireless rechargeable sensor networks is very popular, there is very little research on wireless charging scheduling with joint time constraints and heterogeneous sensing quality. In addition, some practical factors have been ignored in the research process, such as the difference in monitoring effects between sensors due to their quality, or the limited time that the system can replenish energy before executing a task, or the inflexible charging scheduling of the full charging scheme, resulting in low charging coverage and low efficiency for sensors that are fully or not fully charged. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network, so as to solve one or more problems in the prior art.

[0005] To achieve the above object, the technical solution of the present invention is as follows:

[0006] A time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network comprises the following steps:

[0007] Define a rechargeable sensor set and a wireless charger;

[0008] Define the charging time discretization for wireless charging;

[0009] Define the energy consumption of wireless chargers;

[0010] Define the system time. The calculation formula for defining the system time is as follows:

[0011]

[0012] Where: T Charger Represents the total charging time of the mobile charger, t min Indicates the minimum charging time, s i,h Indicates sensor i The hth virtual sensor, the subscript h represents the mobile charger at sensor s i Minimum charging time t for h-stage replenishment min The energy of , where h is an integer ranging from 1 to 5;

[0013] Defining a monitoring utility model for wireless charging, wherein the defining the monitoring utility model for wireless charging comprises the following steps:

[0014] This includes defining the initial sub-region,

[0015] The final sub-region is obtained by combining the initial sub-region with the monitoring areas of all sensors.

[0016] Define the final sub-region unit area monitoring utility and total monitoring utility,

[0017] Defining Sensors i For the perceptual quality of the final sub-region,

[0018] Defining Sensors i The energy obtained,

[0019] Define the total monitoring utility of the set of virtual sensors to be charged L;

[0020] The wireless charging scheduling problem with combined time constraints and heterogeneous perceived quality is formalized. The wireless charging scheduling problem with combined time constraints and heterogeneous perceived quality includes the following contents:

[0021] The goal is to maximize the total monitoring utility.

[0022] The constraint ensures that the total energy consumption of the mobile charger does not exceed the capacity of the mobile charger.

[0023] The constraint ensures that the charging time of the mobile charger does not exceed the system time. The constraint is shown in the following formula:

[0024]

[0025] Where: s i,h Indicates sensor i The hth virtual sensor, L is the set of virtual sensors to be charged, t min represents the minimum charging time, T is the system cut-off time;

[0026] The wireless charging scheduling problem with joint time constraints and heterogeneous perceived quality is transformed into a generalized wireless charging scheduling problem with heterogeneous perceived quality. By multiplying both sides of the above constraint by p, it is transformed into an energy constraint as shown in the following formula:

[0027]

[0028] Where: s i,h Indicates sensor i The hth virtual sensor, L is the set of virtual sensors to be charged, t min represents the minimum charging time, T is the system cut-off time, p is the charging power of the mobile charger, and Tp is the energy constraint converted from the cut-off time;

[0029] Determine a scheduling scheme for the wireless charger, wherein determining the scheduling scheme for the wireless charger comprises the following steps:

[0030] Input parameters,

[0031] Initialize the minimum charging unit time t min and the virtual sensor set S',

[0032] Initialize the traversed sensor set S * and the set of virtual sensors to be charged L are both empty sets,

[0033] Determine the virtual sensor set S' and the traversed sensor set S * Is the difference set of is an empty set? If the difference set is not an empty set, proceed to the next step. If the difference set is an empty set, end.

[0034] Traversing the virtual sensor set S' and the traversed sensor set S * Each sensor s in the set of differences i,h ,

[0035] Find the virtual sensor s with the largest marginal utility ratio to marginal cost among all virtual sensors * ,

[0036] Determine whether the total energy consumption of the mobile charger is greater than the smaller value between the mobile charger power and the energy constraint converted from the system time; if not, the virtual sensor s * Add to the current set L, if it is greater than, proceed to the next step,

[0037] Virtual Sensors * Add the traversed sensor set S * And return to determine the virtual sensor set S' and the traversed sensor set S * Is the difference of the empty set?

[0038] If the virtual sensor set S' is consistent with the traversed sensor set S * If the difference set of is an empty set, the scheduling plan of the wireless charger is returned, that is, the set L and the closed trip starting and ending from the base station produced by the nearest neighbor algorithm.

[0039] Furthermore, the definition of the charging time discretization of wireless charging includes the following steps:

[0040] Define the minimum charging time t for a rechargeable sensor min ;

[0041] Define placement and partial charging strategies for each sensor.

[0042] Further, defining the energy consumption of the wireless charger includes the following steps:

[0043] Define the mobile energy consumption C of the mobile charger Travel (L);

[0044] Defining Sensors i Receive at o j Charging efficiency of mobile charging vehicle Pr(s i ,o j );

[0045] Define the charging energy consumption C of the mobile charger Charger (L);

[0046] Define the total energy consumption C of the mobile charger in a closed charging route Total (L).

[0047] Furthermore, the traversed virtual sensor set S' and the traversed sensor set s * Each sensor s in the set of differences i,h The following steps are involved:

[0048] Calculate the current set L and add virtual sensor s i,h The marginal utility after U(L∪{s i,h})-U(L);

[0049] Calculate the current set L and add virtual sensor s i,h The marginal cost after Total (L∪{s i,h})-C Total (L).

[0050] Compared with the prior art, the beneficial technical effects of the present invention are as follows:

[0051] (i) The present invention considers the problem of time-limited charging scheduling for heterogeneous sensing in wireless sensor networks, and schedules mobile chargers to charge sensors under dual constraints, maximizing the monitoring utility of sensors while improving the performance compared to traditional comparison algorithms.

[0052] (II) In the heterogeneous sensing time-limited charging scheduling algorithm in wireless sensor networks, when E c When ≤Tp, the time-limited charging scheduling problem of heterogeneous sensing in wireless sensor networks has When E c >Tp, the wireless charging scheduling problem with heterogeneous perceived quality after conversion has For any input, the algorithm can find a solution whose objective function value is at least the global optimal solution of the corresponding problem. times, giving a lower bound on the quality of the algorithm's solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The overall system flow chart of a time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network according to an embodiment of the present invention is shown.

[0054] Figure 2 An algorithm flow chart of determining a scheduling scheme for wireless chargers in a time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network according to an embodiment of the present invention is shown.

[0055] Figure 3 A schematic diagram showing the final sub-areas of the division of a time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network according to an embodiment of the present invention.

[0056] Figure 4 A schematic diagram of a scenario of a time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network according to an embodiment of the present invention is shown.

[0057] Figure 5 A schematic diagram of an embodiment of a time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network according to an embodiment of the present invention is shown.

[0058] Figure 6 A data comparison chart showing the total monitoring utility of a time-limited charging scheduling method for improving heterogeneous perception quality in a wireless sensor network according to an embodiment of the present invention and a traditional method for changing system time is shown. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention clearer, the following is a further detailed description of a time-limited charging scheduling method for improving heterogeneous perception quality in a wireless sensor network proposed by the present invention in combination with the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are in a very simplified form and use non-precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, please refer to the accompanying drawings. It should be noted that the structure, proportion, size, etc. illustrated in the drawings of this specification are only used to match the content disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the present invention, so they have no technical substantive significance. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effect that the present invention can produce and the purpose that can be achieved, should still fall within the scope of the technical content disclosed by the present invention.

[0060] Embodiment 1:

[0061] See also Figure 1 and Figure 4 ,

[0062] Step 1: Define the rechargeable sensor collection and wireless charger:

[0063] Let the set S = {s 1 ,s 2 ,...,s n} is a set of n sensors arranged in a two-dimensional area R, sensor s i The monitoring radius and battery capacity of ∈S are d and E respectively i Assume that for sensor s i ∈S initially has 0.3E i The initial charge of the mobile charger is E c , the moving speed is V, and the unit moving energy consumption is μ, p is the charging power of the mobile charger.

[0064] Step 2: Define the charging time discretization for wireless charging:

[0065] Step 2.1: Since the strategy of full charging or no charging is not flexible enough and loses the freedom of energy allocation, the present invention adopts a partial charging method and defines the minimum charging time t of the sensor. min As shown in the following formula 1:

[0066]

[0067] Wherein X is a constant, which is set to 5 in the present invention, and p is the charging power of the mobile charger.

[0068] Step 2.2: Define placement and partial charging strategy for each sensor:

[0069] By using virtual sensors to represent all placement strategies after the charging time is discretized, s i,h Indicates sensor i The hth charging method, for sensor s i ∈S can create K i virtual sensors and use the collection In which, each virtual sensor only represents a scheduling and partial charging strategy. For example, s i,h corresponds to only one strategy for deciding the mobile charger at sensor s i Minimum charging time t for h-stage replenishment min energy.

[0070] Step 3: Define the energy consumption of the wireless charger:

[0071] Step 3.1: Define the mobile energy consumption C of the mobile charger Travel (L): The mobile energy consumption of the mobile charger is defined as shown in the following formula 2:

[0072]

[0073] Where: represents a closed charging route starting and ending at the base station BS, O(L) represents the coordinate set of the stop positions in the charging route, d(o i ,o j ) indicates the charging position o i and j The path length between .

[0074] Step 3.2: Define sensors i Receive at o j Charging efficiency of mobile charging vehicle Pr(s i ,o j ), the charging efficiency calculation formula of the mobile charging vehicle is shown in the following formula 3:

[0075]

[0076] Where D is the charging range of the mobile charging vehicle.

[0077] Step 3.3: Define the charging energy consumption C of the mobile charger Charger (L) is shown in Formula 4 below:

[0078]

[0079] Where: L is the set of virtual sensors selected by the mobile charging vehicle, and p is the charging power of the mobile charging vehicle.

[0080] Step 3.4: Define the total energy consumption C of the mobile charger in the closed charging route Total (L) is shown in Formula 5 below:

[0081]

[0082] Where: μ is the unit mobile energy consumption, L(L) represents the closed charging route starting and ending at the base station BS, and p is the charging power of the mobile charging vehicle.

[0083] Step 4: Define system time:

[0084] Step 4.1: Define that the system must start working before time T, so before the system starts working, the mobile charging vehicle has time T to recharge the sensor. Since the moving time is much shorter than the charging time, this paper ignores the moving time. The charging time is defined as shown in the following formula 6:

[0085]

[0086] Where: T Charger Represents the total charging time of the mobile charger, t min Indicates the minimum charging time, s i,h Indicates sensor i The hth virtual sensor, the subscript h represents the mobile charger at sensor s i Minimum charging time t for h-stage replenishment min The energy of , where h is an integer ranging from 1 to 5.

[0087] Step 5: Define the monitoring utility model for wireless charging:

[0088] Step 5.1: Define initial sub-regions: Please continue to Figure 3 , define the set is the initial known m in the region 0 The initial sub-region of the block, each sub-region φ z ∈Γ, its monitoring utility per unit area is w z .

[0089] Step 5.2: Combine the initial sub-area with the monitoring area of ​​all sensors to obtain the final sub-area: Combine the initial sub-area with the monitoring area of ​​all sensors: a circular monitoring area with the sensor as the center and the detection radius d as the radius, and you can get m final sub-areas that cannot be further subdivided, expressed as The red area is the final sub-area.

[0090] Step 5.3: Define the monitoring utility per unit area and the total monitoring utility of the final sub-region: Each final sub-region The area is a l Since each final sub-region belongs to a certain initial sub-region, we use w l To represent the final sub-region The monitoring utility per unit area, the final sub-area The total monitoring utility is a l w l And use the symbol u l express.

[0091] Step 5.4: Define Sensors i For the perception quality of the final sub-area: Since the random events in different initial sub-areas are different, and sensors with different power have different perception effects due to various factors, such as quality, whether they are good at perceiving random events in this area, etc., define sensor s i ∈S for the final sub-region The perceptual quality of is shown in Equation 7:

[0092]

[0093] Step 5.5: Define Sensors i The energy obtained is shown in Equation 8:

[0094]

[0095] Step 5.6: Define the total monitoring utility of the virtual sensor set L to be charged as shown in Equation 9:

[0096]

[0097] Where: Indicates monitoring the final sub-area The sensor set, u l The final sub-region The total monitoring utility.

[0098] Step 6: Formalize the wireless charging scheduling problem with joint time constraints and heterogeneous perceived quality:

[0099] Step 6.1: Maximize the total monitoring utility objective, as shown in Equation 10:

[0100] maxU(L)(10)

[0101] Step 6.2: Constraints ensure that the power of the mobile charger is not exceeded, as shown in Equation 11 below:

[0102]

[0103] Step 6.3: Constraints ensure that the charging time of the mobile charger does not exceed the system time, as shown in Equation 12 below:

[0104]

[0105] Step 7: The wireless charging scheduling problem with joint time constraints and heterogeneous perceived quality is transformed into a generalized wireless charging scheduling problem with heterogeneous perceived quality. By multiplying both sides of the inequality in equation 12 by p, it is transformed into an energy constraint as shown in equation 13:

[0106]

[0107] Furthermore, combined with the above formula 13, the wireless charging scheduling problem with joint time constraints and heterogeneous perceived quality can be transformed into a generalized wireless charging scheduling problem with heterogeneous perceived quality, which is formalized as shown in the above formula 10 and the following formula 14:

[0108] maxU(L)(10)

[0109]

[0110] Furthermore, satisfying the constraint 14 must satisfy the constraints 11 and 12. Therefore, the solution to the generalized wireless charging scheduling problem with heterogeneous perceived quality must be a feasible solution to the wireless charging scheduling problem with combined time constraints and heterogeneous perceived quality. Where T is the system deadline, p is the charging power of the mobile charger, and Tp is the energy constraint converted from the deadline. However, when E c >Tp, this transformation is not completely equivalent to the original problem, but when E c When ≤Tp, the overall approximation ratio of the original problem can be obtained.

[0111] Step 8: Determine the scheduling plan for wireless chargers.

[0112] Step 8.1: Input parameters: sensor set S, sensor s i ∈S's charge E i , base station BS, power of mobile charger E c and power p, three constants μ, α, β determined by the hardware parameters of the mobile charger, system time T, and the final sub-area set Φ to generate the corresponding virtual sensor set S'.

[0113] Step 8.2: Initialize the minimum charging unit time t min And virtual sensor set S': Initialize the minimum charging unit time to Initialize the virtual sensor set S' = {s 1,1 ,s 1,2 ,...,s 1,K1 ,...,sn,Kn}.

[0114] Step 8.3: Initialize the traversed sensor set S * and the set of virtual sensors to be charged L are both empty sets:

[0115] Step 8.4: Determine whether the virtual sensor set S' is consistent with the traversed sensor set S * The difference of Is it an empty set? If the difference set is not an empty set, proceed to the next step. If the difference set is an empty set, end.

[0116] Step 8.5: Traverse the virtual sensor set S' and the traversed sensor set S * The set of differences of is S'\S * Each sensor in i,h .

[0117] Step 8.5.1: Calculate the current set L and add virtual sensor s i,h The marginal utility after U(L∪{s i,h})-U(L).

[0118] Step 8.5.2: Use the nearest neighbor algorithm to calculate the current set L and add the virtual sensor s i,h The marginal cost after Total (L∪{s i,h})-C Total (L).

[0119] Step 8.6: Find the virtual sensor with the largest marginal utility ratio and upper marginal cost among all virtual sensors and set it as s * ,

[0120] Step 8.7: Determine whether the total energy consumption of the mobile charger is greater than the smaller value between the mobile charger power and the energy constraint converted from the system time; if not, set the virtual sensor s * Add to the current set L, if greater than, proceed to the next step

[0121] Step 8.7.1: Add virtual sensors * Add to the set L, that is, L = L∪{s *}.

[0122] Step 8.8: Add Virtual Sensors * Add the traversed sensor set S * S * =S * ∪{s *}, and returns the determination of the virtual sensor set S' and the traversed sensor set S* Is the difference of the empty set? This step is step 8.4

[0123] Step 8.9: If the virtual sensor set S' is consistent with the traversed sensor set S * If the difference set is an empty set, the scheduling scheme of the wireless charger is returned, that is, the set L and the closed trip starting and ending from the base station BS generated by the nearest neighbor algorithm

[0124] End the process.

[0125] In addition, the present invention provides the following three solutions and the effects of the comparative test of the present invention:

[0126] Greedy Algorithm: In each iteration, all sensor nodes that have not been visited are traversed, and the mobile charger selects the sensor node with the largest total monitoring utility. The iteration terminates when all sensors are visited or the energy capacity and system time are exceeded.

[0127] Nearest neighbor algorithm: In each iteration, all sensor nodes that have not been visited are traversed, and the mobile charger selects the nearest sensor node. The iteration terminates when all sensors have been visited or the energy capacity and system time are exceeded.

[0128] EDF algorithm: We first sort the sensor nodes in ascending order of their power. In each iteration, the sensor node with the minimum battery power is selected and assigned to the mobile charger, and the sum of the mobile charger's moving distance is minimized. The iteration terminates when all sensors are visited or the energy capacity and system time are exceeded.

[0129] Parameter setting: The wireless chargers and sensors are randomly distributed in a 100m*100m square two-dimensional plane. The default parameters are set as follows: the number of sensors n=100, the energy requirement E i ∈[10,15]KJ. Battery capacity E of wireless charger c =125KJ, energy consumption per unit distance μ = 50J / m, sensor power p = 15W, moving speed V = 5m / s, minimum charging time t min = 110s, fixed parameters α = 90, β = 10. The final sub-region weight w l = [5, 20]. System time T = 7000s. Next, we will change the values ​​of key parameters to explore their impact on the algorithm. Each measurement value is the average of more than 100 random topologies.

[0130] For further information, see Figure 6 , Figure 6The influence of system time on the total monitoring utility of the transformed generalized heterogeneous quality-aware wireless charging scheduling algorithms is shown. The total monitoring utility of all algorithms increases with the increase of system time. This is because the mobile charger can charge more sensors in a more ample system time. However, Figure 6 The end of the broken line rises slightly because for the overly generous system time, the power of the mobile charger is the constraint that limits the increase in monitoring utility. On average, the total monitoring utility of the converted generalized heterogeneous quality-aware wireless charging scheduling algorithm is improved by 8.33%, 269.75% and 158.62% respectively compared with the other three algorithms.

[0131] Embodiment 2:

[0132] Refer to step 1 above: Set the sensor device set to: S = {s 1 ,s 2 ,s 3 ,s 4}, the wireless charger and sensor devices are distributed on a 9m*9m two-dimensional plane, see Figure 5 , the coordinates of the blue circle of the sensor device are (2,4), (2,6), (4.5,6) and (7,4) respectively. The maximum charging distance of the wireless charger is 3m. Assume E c =30KJ, μ = 50J / m, p = 15W, V = 5m / s. The two-dimensional plane is divided into two initial sub-areas. The final sub-area corresponding to each sensor is as follows: Figure 5 As shown in the red numbers, the following table 1 shows the relevant parameters of the sensor equipment (the actual result is rounded to one decimal place, and this rule is adopted in the following embodiments).

[0133] Table 1 Parameters of mobile rechargeable sensor equipment

[0134]

[0135]

[0136] Referring to step 2 above, set X = 5, then the minimum charging time of the sensor is:

[0137]

[0138] Furthermore, the virtual sensor set is set to S' = {s 1,1 ,s 1,2 ,s 1,3 ,s 1,4 ,s 1,5 ,s 2,1 ,s 2,2 ,s 2,3 ,s2,4 ,s 2,5 ,s 3,1 ,s 3,2 ,s 3,3 ,s 3,4 ,s 3,5 ,s 4,1 ,s 4,2 ,s 4,3 ,s 4,4 ,s 4,5} to represent all placement strategies after the charging time is discretized. Each virtual sensor only represents one placement and partial charging strategy. For example, s 1,1 corresponds to only one strategy for deciding the mobile charger at sensor s 1 Replenishes energy for 1*200s.

[0139] Referring to step 3 above, set α = 90, β = 10, then the sensor node s i Receive at o j The charging efficiency of the mobile charging vehicle is:

[0140]

[0141] Referring to step 5 above, the relevant parameters of the final sub-area are shown in Table 2, and the relevant parameters of the sensor's perception effect on the final sub-area are shown in Table 4:

[0142] Table 3 Monitoring utility of the final sub-area

[0143]

[0144]

[0145] Table 4 Sensor perception effect on the final sub-area

[0146] <![CDATA[s 1 ]]> <![CDATA[s 2 ]]> <![CDATA[s 3 ]]> <![CDATA[s 4 ]]> Left area 0.9 0.5 0.7 0 Right area 0 0 0.6 0.8

[0147] Referring to the above step 7: Set T = 1200s, and transform it into a generalized wireless charging scheduling problem with heterogeneous perceived quality, which is formalized as follows:

[0148] maxU(L)

[0149]

[0150] Refer to step 8 above:

[0151] Refer to step 8.1 above: Input parameters: sensor set S = {s 1 ,s 2 ,s 3 ,s 4} and mobile charger, as well as other data.

[0152] Refer to step 8.2 above: Initialize the minimum charging unit time to t min = 200s. Initialize the virtual sensor set S'.

[0153] Refer to step 8.3 above: Initialize the traversed sensor set S * and the set of virtual sensors to be charged L are both empty sets:

[0154] Refer to the above step 8.4: Determine whether the virtual sensor set S' is consistent with the traversed sensor set S * The difference of Is it an empty set? If the difference set is not an empty set, proceed to the next step. If the difference set is an empty set, end.

[0155] Refer to the above step 8.5: traverse the virtual sensor set S' and the traversed sensor set S * The set of differences of is S'\S * Each sensor in i,h .

[0156] Refer to step 8.5.1 above: Calculate the current set L and add virtual sensor s i,h The marginal utility after U(L∪{s i,h})-U(L). For the virtual sensor set S', take s 1,1 For example, at this time L∪{s 1,1}={s 1,1}. Marginal utility U(L∪{s 1,1})-U(L)=34.51-0=34.51.

[0157] Refer to step 8.5.2 above: Use the nearest neighbor algorithm to calculate the current set L and add virtual sensor s i,h The marginal cost after Total (L∪{s 1,1})-C Total (L). For the virtual sensor set S', s 1,1 For example, at this time L∪{s 1,1}={s 1,1}. Marginal cost C Total (L∪{s 1,1})-C Total (L) = 3.25-0 = 3.25KJ.

[0158] Refer to step 8.6 above: Find the virtual sensor with the largest marginal utility ratio and marginal cost among all virtual sensors and set it as s * ,

[0159] Furthermore, in the first round, the virtual sensor set S' is traversed to select the candidate charging location s with the largest marginal cost in terms of marginal utility ratio. 2,3 For example, L∪{s 2,3}={s 2,3}, at this time there is a sensor s within the charging range 1 ,s 2 ,s 3 , covering the final sub-region is And the monitoring utilities of 30.92, 28.82, 16.79, 2.38, 9.54, 13.06, 6.95, 6.45, and 1.46 were obtained in the final covered sub-areas. Therefore, the marginal utility U(L∪{s 2,3})-U(L)=116.37-0=116.37.

[0160] Furthermore, the candidate charging location s with the largest marginal cost is selected based on the marginal utility ratio 2,3 For example, L∪{s 2,3}={s 2,3}. C Total (L∪{s 2,3})-C Total (L) = 9.29-0 = 9.29KJ.

[0161] Furthermore, the candidate charging location s with the largest marginal cost is selected based on the marginal utility ratio 2,3 For example. At this time, is the maximum value.

[0162] Refer to step 8.7 above to determine whether the total energy consumption of the mobile charger is greater than the smaller value between the mobile charger power and the energy constraint converted from the system time; if not, the virtual sensor s * Add it to the current set L. If it is greater than, proceed to the next step.

[0163] Refer to step 8.7.1 above: Set the virtual sensor s * Add to the set L, that is, L = L∪{s *}.

[0164] Furthermore, at this time C Total (L∪{s 2,3})=9.29KJ≤18KJ, the virtual sensor s 2,3 Add to the set set L, then L = {s 2,3}.

[0165] Refer to step 8.8 above: Add virtual sensor s * Add the traversed sensor set S * S* =S * ∪{s *}, and returns the determination of the virtual sensor set S' and the traversed sensor set S * The step of determining whether the difference set of is an empty set is step 8.4.

[0166] Furthermore, the virtual sensor s 2,3 Add to collection collection S * At this time, S * ={s 2,3}

[0167] Refer to the above step 8.4: Determine whether the virtual sensor set S' is consistent with the traversed sensor set S * The difference of Is it an empty set? If the difference set is not an empty set, proceed to the next step. If the difference set is an empty set, end.

[0168] Refer to the above step 8.5: traverse the virtual sensor set S' and the traversed sensor set S * The set of differences of is S'\S * Each sensor in i,h .

[0169] Refer to step 8.5.1 above: Calculate the current set L and add virtual sensor s i,h The marginal utility after U(L∪{s i,h})-U(L).

[0170] Refer to step 8.5.2 above: Use the nearest neighbor algorithm to calculate the current set L and add virtual sensor s i,h The marginal cost after Total (L∪{s i,h})-C Total (L).

[0171] Refer to step 8.6 above: Find the virtual sensor with the largest marginal utility ratio and marginal cost among all virtual sensors and set it as s * ,

[0172] Refer to step 8.6.1 above: In the second round, traverse the virtual sensor set S' and select the candidate charging location s with the largest marginal cost in terms of marginal utility ratio 1,1 For example, L∪{s 1,1}={s 2,3 ,s 1,1}, at this time there is a sensor s within the charging range 1 ,s 2 , covering the final sub-region is And the monitoring utilities of 45.77, 39.3, 20.68, 3.09, 9.6, and 14.72 were obtained in the final covered sub-areas. Therefore, the marginal utility U(L∪{s 1,1})-U(L)=148.01-116.37=31.64.

[0173] Refer to step 8.6.2 above: select the candidate charging location s with the largest marginal cost based on the marginal utility ratio 1,1 For example, L∪{s 1,1}={s 2,3 ,s 1,1}. C Total (L∪{s 1,1})-C Total (L) = 12.37-9.29 = 3.08KJ.

[0174] Refer to step 8.6.3 above: select the candidate charging location s with the largest marginal cost based on the marginal utility ratio 2,3 For example. At this time, is the maximum value.

[0175] Continue to repeat the above steps.

[0176] Refer to step 8.9 above: If the virtual sensor set S' is consistent with the traversed sensor set S * If the difference of is an empty set, the scheduling scheme of the wireless charger is returned, that is, the set L and the closed trip L(L) starting and ending at the base station BS generated by the nearest neighbor algorithm. The final result is L = {s 2,3 ,s 1,1 ,s 3,1}, L(L)={BS,s 3,1 ,s 2,3 ,s 1,1 ,BS}, the total cost is 15.43KJ and the total utility is 164.34.

[0177] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0178] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A time-limited charging scheduling method for improving heterogeneous sensing quality in wireless sensor networks, characterized in that The following steps are involved: Define a rechargeable sensor set and a wireless charger; Define the charging time discretization for wireless charging; Define the energy consumption of wireless chargers; Define the system time. The calculation formula for defining the system time is as follows: Where: T Charger Represents the total charging time of the mobile charger, t min Indicates the minimum charging time, s i,h Indicates sensor i The hth virtual sensor, the subscript h represents the mobile charger at sensor s i Minimum charging time t for h-stage replenishment min The energy of , where h is an integer ranging from 1 to 5; Defining a monitoring utility model for wireless charging, wherein the defining the monitoring utility model for wireless charging comprises the following steps: Define the initial sub-region, The final sub-region is obtained by combining the initial sub-region with the monitoring areas of all sensors. Define the final sub-region unit area monitoring utility and total monitoring utility, Defining Sensors i For the perceptual quality of the final sub-region, Defining Sensors i The energy obtained, Define the total monitoring utility of the set of virtual sensors to be charged L; The wireless charging scheduling problem with combined time constraints and heterogeneous perceived quality is formalized. The wireless charging scheduling problem with combined time constraints and heterogeneous perceived quality includes the following contents: The goal is to maximize the total monitoring utility. The constraint ensures that the total energy consumption of the mobile charger does not exceed the capacity of the mobile charger. The constraint ensures that the charging time of the mobile charger does not exceed the system time. The constraint is shown in the following formula: Where: T is the system cut-off time; The wireless charging scheduling problem with joint time constraints and heterogeneous perceived quality is transformed into a generalized wireless charging scheduling problem with heterogeneous perceived quality. By multiplying both sides of the above constraint by p, it is transformed into an energy constraint as shown in the following formula: Wherein: p is the charging power of the mobile charger, Tp is the energy constraint converted from the cut-off time; determining the scheduling scheme of the wireless charger, the determining the scheduling scheme of the wireless charger comprises the following steps: Input parameters, Initialize the minimum charging unit time t min and the virtual sensor set S', Initialize the traversed sensor set S * and the set of virtual sensors to be charged L are both empty sets, Determine the virtual sensor set S' and the traversed sensor set S * Is the difference set of is an empty set? If the difference set is not an empty set, proceed to the next step. If the difference set is an empty set, end. Traversing the virtual sensor set S' and the traversed sensor set S * Each sensor s in the set of differences i,h , Find the virtual sensor s with the largest marginal utility ratio to marginal cost among all virtual sensors * , Determine whether the total energy consumption of the mobile charger is greater than the smaller value between the mobile charger power and the energy constraint converted from the system time; if not, the virtual sensor s * Add to the current set L, if it is greater than, proceed to the next step, Virtual Sensors * Add the traversed sensor set S * And return to determine the virtual sensor set S' and the traversed sensor set S * Is the difference of the empty set? If the virtual sensor set S' is consistent with the traversed sensor set S * If the difference set of is an empty set, the scheduling plan of the wireless charger is returned, that is, the set L and the closed trip starting and ending from the base station produced by the nearest neighbor algorithm.

2. A time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network as claimed in claim 1, characterized in that: Defining the charging time discretization of wireless charging includes the following steps: Define the minimum charging time t for a rechargeable sensor min ; Define placement and partial charging strategies for each sensor.

3. The time-limited charging scheduling method for improving heterogeneous sensing quality in a wireless sensor network according to claim 1, characterized in that: Defining the energy consumption of the wireless charger includes the following steps: defining the mobile energy consumption C of the mobile charger Travel (L); Defining Sensors i Receive at o j Charging efficiency of mobile charging vehicle Pr(s i ,o j ); Define the charging energy consumption C of the mobile charger Charger (L); Define the total energy consumption C of the mobile charger in a closed charging route Total (L).

4. The method for time-limited charging scheduling for improving heterogeneous sensing quality in a wireless sensor network according to claim 1, characterized in that: The traversed virtual sensor set S' and the traversed sensor set S * Each sensor s in the set of differences i,h The following steps are involved: Calculate the current set L and add virtual sensor s i,h The marginal utility after U(L∪{s i,h })-U(L); Calculate the current set L and add virtual sensor s i,h The marginal cost after Total (L∪{s i,h })-C Total (L).

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