A method and system for optimizing charging utility in a sensor network with limited space and budget

Abstract

The present invention relates to a method and system for optimizing charging utility in a sensor network based on space and budget constraints, and belongs to the technical field of wireless rechargeable sensor networks. The method includes: constructing a charging service model for a sensor network, wherein the charging service model includes at least a static charging station and a movable wireless rechargeable sensor; determining the total charging utility and total charging service cost constraints of the sensor, and determining a charging utility optimization model based on the total charging utility and the total charging service cost constraints; solving the charging utility optimization model to obtain a charging scheduling strategy for the sensor network. The method constructs and optimizes a charging strategy based on the limitation of the number of charging locations and the limitation of the charging cost budget, and can improve the charging utility of the sensor network.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless rechargeable sensor networks, and in particular to a method and system for optimizing charging utility in a sensor network with limited space and budget. Background Art

[0002] Wireless Sensor Networks (WSNs) are widely used in many fields such as electric power industry, intelligent transportation, and intelligent agricultural production, providing convenience for the information development of many industries. However, in traditional wireless sensor networks, sensor nodes can usually only carry batteries with limited capacity. Long-term maintenance of sensor networks requires regular replacement of sensor nodes or replacement of batteries for sensor nodes, which not only brings high sensor network maintenance costs but also reduces the quality of service (QoS) of sensor networks. In recent years, wireless power transfer technology (WireleSS Power Transfer, WPT) has developed rapidly. With the support of this technology, wireless rechargeable sensor networks (WRSNs) have been realized.

[0003] At present, smart devices and related applications that use wireless charging technology have been widely used in commercial applications, but commercial applications related to wireless rechargeable sensor networks are mostly limited to device sales, and there are few applications of paid wireless charging scheduling services for sensor networks as commercial services. As wireless rechargeable sensor network technology is continuously applied to actual production environments, the issues arising from paid wireless charging scheduling services as a commercial service are worthy of in-depth study. Summary of the invention

[0004] The main purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a method and system for optimizing charging efficiency in a sensor network with limited space and budget. According to the limitation on the number of charging locations and the limitation on the charging cost budget, a charging strategy is constructed and optimized to improve the charging efficiency of the sensor network.

[0005] According to one aspect of the present invention, the present invention provides a method for optimizing charging utility in a sensor network based on space and budget constraints, the method comprising the following steps:

[0006] S1: constructing a charging service model of a sensor network, wherein the charging service model at least includes a static charging station and a movable wireless rechargeable sensor;

[0007] S2: Determine the total charging utility and total charging service cost constraints of the sensor, and determine a charging utility optimization model based on the total charging utility and the total charging service cost constraints;

[0008] S3: Solve the charging utility optimization model to obtain a charging scheduling strategy for the sensor network.

[0009] Preferably, determining the total charging utility of the sensor comprises:

[0010] For any subset of sensors Total charging utility F t (U′) is the electrical utility F e (U′) and coverage utility F c The weighted sum of (U′):

[0011] F t (U′)=αF e (U′)+βF c (U′)

[0012] Among them, F e (U′)=∑ u∈U′ ω(u)·(E exp (u)-E r (u)), ω(u) represents the weight of sensor u, represents the set of interest points monitored by sensor u, E exp (u) and E r (u) represents the expected power and current remaining power of u respectively, and α and β are two non-negative coefficients.

[0013] Preferably, the constraint condition for determining the total charging service fee of the sensor includes:

[0014] The total charging service fee is:

[0015]

[0016] in, For charging stations j The corresponding charging service time, It is the charging service fee required for the unit charging service time of the charging station.

[0017] Preferably, determining the charging utility optimization model includes:

[0018]

[0019]

[0020] in, represents the sensor assignment scheduling strategy, B is the total budgeted charging service cost, Indicates that it is assigned to charging station s j The sensor set at the i-th charging position, L j For power stations j The number of charging locations available.

[0021] Preferably, solving the charging utility optimization model to obtain a charging scheduling strategy for the sensor network includes:

[0022] The charging utility optimization model is solved using an improved greedy algorithm to generate a charging scheduling strategy for the sensor network, specifically:

[0023] An initial set is set for each charging station and the charging position of each charging station, and the initial set is empty; under the condition that the current total charging cost does not exceed the given budget and there are still unassigned sensors, the sensor with the largest ratio of the current marginal value to the generated cost is greedily selected from the remaining unassigned charging sensors, and it is assigned to the charging position of the corresponding charging station; the above selection process is repeated to complete the sensor allocation, and the optimal allocation strategy of the sensor and the corresponding total charging utility value are obtained.

[0024] Preferably, the method further comprises:

[0025] Design an appropriate waiting time for each sensor to meet the following two conditions: (1) seamless exclusivity condition: each charging position is continuously and seamlessly occupied by several sensors assigned to the position in time, and at any time, the charging position is exclusively occupied by at most one sensor; (2) concurrency condition: several different charging positions of the same charging station start to provide charging services at the same time. Given a sensor assignment scheduling strategy, determine a feasible sensor waiting time scheduling strategy in the following way: first, iteratively determine the waiting time of each sensor node at each charging position to meet the seamless exclusivity condition, and then adjust and update the waiting time of sensor nodes at different charging positions to meet the concurrency condition.

[0026] According to another aspect of the present invention, the present invention further provides a charging utility optimization system in a sensor network based on space and budget constraints, the system comprising:

[0027] A construction module, used to construct a charging service model of a sensor network, wherein the charging service model at least includes a static charging station and a movable wireless rechargeable sensor;

[0028] A determination module, configured to determine the total charging utility and the total charging service cost constraint conditions of the sensor, and determine a charging utility optimization model based on the total charging utility and the total charging service cost constraint conditions;

[0029] The solution module is used to solve the charging utility optimization model to obtain a charging scheduling strategy for the sensor network.

[0030] Preferably, the determining module determines the total charging utility of the sensor including:

[0031] For any subset of sensors Total charging utility F t (U′) is the electrical utility F e (U′) and coverage utility F c The weighted sum of (U′):

[0032] F t (U′)=αF e (U′)+βF c (U′)

[0033] Among them, F e (U′)=∑ u∈U′ ω(u)·(E exp (u)-E r (u)), ω(u) represents the weight of sensor u, represents the set of interest points monitored by sensor u, E exp (u) and E r (u) represents the expected power and current remaining power of u respectively, and α and β are two non-negative coefficients.

[0034] Preferably, the determination module determines the total charging service fee constraint of the sensor including:

[0035] The total charging service fee is:

[0036]

[0037] in, For charging stations j The corresponding charging service time, It is the charging service fee required for the unit charging service time of the charging station.

[0038] Preferably, the determining module determines the charging utility optimization model including:

[0039]

[0040]

[0041] in, represents the sensor assignment scheduling strategy, B is the total budgeted charging service cost, Indicates that it is assigned to charging station s j The sensor set at the i-th charging position, L j For power stations j The number of charging locations available.

[0042] Beneficial effects: The present invention adopts a combination of movable sensors and static charging stations. Through the cooperation between sensor devices, an efficient charging scheduling scheme is proposed while satisfying charging location constraints and other constraints, thereby improving the charging utility of the sensor network.

[0043] The features and advantages of the present invention will become clear through reference to the following drawings and detailed description of specific embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a flow chart of a charging utility optimization method in a sensor network based on space and budget constraints;

[0045] Figure 2 This is an example diagram of a network model;

[0046] Figure 3 It is a schematic diagram of the charging utility optimization system in a sensor network with space and budget constraints. DETAILED DESCRIPTION

[0047] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] See also Figure 1 and Figure 2 This embodiment provides a method for optimizing charging utility in a sensor network based on space and budget constraints, the method comprising the following steps:

[0050] S1: constructing a charging service model of a sensor network, wherein the charging service model at least includes a static charging station and a movable wireless rechargeable sensor;

[0051] S2: Determine the total charging utility and total charging service cost constraints of the sensor, and determine a charging utility optimization model based on the total charging utility and the total charging service cost constraints;

[0052] S3: Solve the charging utility optimization model to obtain a charging scheduling strategy for the sensor network.

[0053] This embodiment considers a charging service model initiated by a device for a wireless rechargeable sensor network. This model adopts a mobile collaborative charging strategy initiated by the device, and all charging stations are operated by a charging service provider (CSP). In this model, the user of the sensor network requires the base station to periodically obtain the remaining power of each sensor device, and then formulates a charging scheduling strategy initiated by the device according to a specific charging optimization goal, and then broadcasts the scheduling strategy to all sensor devices. The sensor device moves to the corresponding charging station for charging according to the received broadcast information. At the same time, the user will send a charging service request to the charging service provider to notify each charging station to provide charging services for the arrived sensor devices. After charging is completed, the sensor device returns to the original deployment location to continue to perform the monitoring task, and the user will eventually pay the corresponding charging service fee to the charging service provider based on the charging time.

[0054] The method of this embodiment constructs and optimizes the charging strategy according to the limitation of the number of charging locations and the limitation of the charging cost budget, so as to improve the charging efficiency of the sensor network.

[0055] Preferably, determining the total charging utility of the sensor comprises:

[0056] For any subset of sensors Total charging utility F t (U′) is the electrical utility F e (U′) and coverage utility F c The weighted sum of (U′):

[0057] F t (U′)=αF e (U′)+βF c (U′)

[0058] Among them, F e (U′)=∑ u∈U′ ω(u)·(E exp (u)-E r (u)), ω(u) represents the weight of sensor u, represents the set of interest points monitored by sensor u, E exp (u) and E r (u) represents the expected power and current remaining power of u respectively, and α and β are two non-negative coefficients.

[0059] Specifically, for the problem of maximizing charging utility in space- and budget-constrained sensor networks, a mobile cooperative charging service model initiated by devices with a number of charging locations is constructed; the specific steps are as follows:

[0060] Assume that the network contains n mobile sensor devices, and the sensor devices form a set U = {u1, ..., u n Sensor devices are deployed in a two-dimensional monitoring area to monitor k points of interest (POIs), which form a set The sensor network also includes m charging stations deployed at fixed locations, all of which are operated by a CSP. The set of charging stations is represented by S = {s1, ..., s m Each charging station is equipped with an omnidirectional charger. In order to improve charging efficiency, each sensor device needs to be moved from its original deployment location to the designated charging location of the charging station for charging. After charging is completed, the sensor returns to its original deployment location to continue monitoring the point of interest. After the entire charging process is completed, the user needs to pay the CSP the corresponding charging service fee based on the charging time.

[0061] Assuming that the charging service fee is proportional to the charging service time, the charging service fee required per unit charging service time of any charging station is Based on the characteristics of omnidirectional wireless charging technology, any charging station j Several sensor nodes arriving at a designated charging location can be charged simultaneously with the same fixed charging power, which means that multiple sensor nodes arriving at the charging station in the same time period j The sensor devices can share the charging service fee. It is assumed that each charging station has a limited number of charging positions, and each charging position can accommodate at most one sensor at any time. It is also assumed that the charging received power of sensor devices staying at different charging positions of the same charging station is approximately equal.

[0062] Use P r The charging received power at any charging position in any charging station is represented by L j Indicates charging stations j The number of charging locations available, Indicates charging stations j The i-th charging position of j}. In addition, use T m (u i ,s j ) and E m (u i ,s j ) represent sensor devices u i One-way movement to s j The moving time and mobile power consumed are assumed to be symmetrical, that is, u i The time and u of moving from the original deployment location to the charging stationi The time to return from the charging station to the original deployment location is the same, and the mobile power consumption is also the same. Each sensor is responsible for monitoring a subset of points of interest in the environment. Indicates sensor u i The set of monitored points of interest. In addition, ω(u i ) represents sensor u i The weight represents the importance of the sensor in the network. The higher the weight value, the more important the sensor is.

[0063] Assume that for any sensor u i , if u i Go to charging stations j Charging, i The total charging schedule time will include the sensor waiting time delay (u i ), round trip time 2T m (u i ,s j ) and charging time T c (u i ,s j ), the charging time is calculated as:

[0064] T c (u i ,s j )=(E exp (u i )-E r (u i )+2E m (u i ,s j )) / P r

[0065] Among them de / ay(u i ) is the sensor u i Waiting time at the initial deployment location, E exp (u i ) and E r (u i ) represent u i The expected power and current remaining power of the sensor are as follows: The expected power refers to the expected power of the sensor after charging is completed, which will not exceed the maximum capacity of the sensor battery. Figure 2 shown.

[0066] For any subset of sensors Define the total charging utility F t (U′) is the electrical utility F e (U′) and coverage utility F cThe weighted sum of (U′), α and β represent two non-negative system coefficients, which can be adjusted accordingly according to the target demand. The Space and Budget constrained Charging Utility Maximization (SBCUM) problem can be defined as follows: Given a sensor network consisting of k points of interest, n sensors and m charging stations, any charging station s j (j∈{1,…,m}) has L j How to give a charging scheduling strategy for the sensor equipment to maximize the total charging utility under the condition that the total charging service cost does not exceed the given budget B.

[0067] Preferably, the constraint condition for determining the total charging service fee of the sensor includes:

[0068] The total charging service fee is:

[0069]

[0070] in, For charging stations j The corresponding charging service time, It is the charging service fee required for the unit charging service time of the charging station.

[0071] Specifically, let M = {1, ..., m}, L j = {1, ..., L j},use Indicates that it is assigned to charging station s j The sensor set of the i-th charging position. represents a charging scheduling strategy, where represents the sensor assignment scheduling strategy, represents the waiting time scheduling strategy of the sensor. For convenience, we define Obviously, if is a feasible strategy, then it must satisfy the exclusivity condition, that is, it must satisfy that each charging position can accommodate at most one sensor at any time. Charging Stations j The corresponding charging service time is calculated as follows:

[0072]

[0073] where f e (u,s j )=delay(u)+T m(u,s j )+T c (u,s j ), f s (u,s j )=delay(u)+T m (u,s j ). Therefore the total charging service fee is:

[0074] Preferably, determining the charging utility optimization model includes:

[0075]

[0076]

[0077] in, represents the sensor assignment scheduling strategy, B is the total budgeted charging service cost, Indicates that it is assigned to charging station s j The sensor set at the i-th charging position, L j For power stations j The number of charging locations available.

[0078] Specifically, the SBCUM problem is formalized. In the definition of the SBCUM problem, the first constraint indicates that the total charging service cost does not exceed the given budget B, and the second and third constraints indicate that each sensor node can only be assigned to one charging position in a charging station at most. The main goal of the SBCUM problem is to find the optimal allocation strategy.

[0079] Preferably, solving the charging utility optimization model to obtain a charging scheduling strategy for the sensor network includes:

[0080] The charging utility optimization model is solved using an improved greedy algorithm to generate a charging scheduling strategy for the sensor network, specifically:

[0081] An initial set is set for each charging station and the charging position of each charging station, and the initial set is empty; under the condition that the current total charging cost does not exceed the given budget and there are still unassigned sensors, the sensor with the largest ratio of the current marginal value to the generated cost is greedily selected from the remaining unassigned charging sensors, and it is assigned to the charging position of the corresponding charging station; the above selection process is repeated to complete the sensor allocation, and the optimal allocation strategy of the sensor and the corresponding total charging utility value are obtained.

[0082] Specifically, a polynomial time algorithm (Greedy-based Charging Utility Optimization, GCUO) is designed using a greedy strategy to generate a charging scheduling strategy for sensor networks; the specific steps are as follows:

[0083] Define the algorithm input as follows: U, m,L j (j∈{1,…,m}), F t (·), B, and T c (u, s)(u∈U, s∈S), defines the optimal allocation schedule for the final output It is the fee you need to pay for charging service per unit time at any charging station. represents the set of all sensors assigned to the tth charging position of the kth charging station. Indicates that in the current allocation schedule Under this condition, the sensor node u is assigned to the charging station s j The charging service fee generated by the i-th charging location.

[0084]

[0085] in

[0086]

[0087] Set Save the sensors that have been assigned to the charging station and set the collection The sensors stored for each charging position of each charging station, u∈U, j∈M, i∈L j .

[0088] set up when and The greedy loop selects the sensor that maximizes the ratio of the current marginal value to the cost, that is, satisfies The sensors are assigned to the charging positions of the corresponding charging stations and the collection is updated. U′ and

[0089] After looping through the above steps, we get This is the final allocation scheduling strategy.

[0090] In this embodiment, the greedy loop steps are as follows: and If it still satisfies, the following greedy selection and set update steps are performed in sequence: (1) (2) (3)U′=U′u{u *};(4)

[0091] Preferably, the method further comprises:

[0092] Design an appropriate waiting time for each sensor to meet the following two conditions: (1) seamless exclusivity condition: each charging position is continuously and seamlessly occupied by several sensors assigned to the position in time, and at any time, the charging position is exclusively occupied by at most one sensor; (2) concurrency condition: several different charging positions of the same charging station start to provide charging services at the same time. Given a sensor assignment scheduling strategy, determine a feasible sensor waiting time scheduling strategy in the following way: first, iteratively determine the waiting time of each sensor node at each charging position to meet the seamless exclusivity condition, and then adjust and update the waiting time of sensor nodes at different charging positions to meet the concurrency condition.

[0093] Specifically, in order to optimize the charging service efficiency, it is necessary to design an appropriate waiting time for each sensor to meet the following two conditions:

[0094] (1) Seamless exclusivity condition: Each charging position is continuously and seamlessly occupied by a number of sensors assigned to the position in time, and at any time a charging position is exclusively occupied by at most one sensor;

[0095] (2) Concurrency condition: Several different charging locations at the same charging station start providing charging services at the same time, that is, Where j∈M.

[0096] Given a sensor assignment scheduling strategy There must be a feasible sensor waiting time scheduling strategy In order to obtain the optimal total charging service cost in

[0097] For a given This can be obtained in polynomial time by a simple method as follows Firstly, the waiting time of each sensor node at each charging position is iteratively determined to satisfy the seamless exclusivity condition, and then the waiting time of updating sensor nodes at different charging positions is adjusted to satisfy the concurrency condition.

[0098] This embodiment adopts a combination of movable sensors and static charging stations. Through the cooperation between sensor devices, an efficient charging scheduling scheme is proposed while satisfying charging location constraints and other constraints, thereby improving the charging utility of the sensor network.

[0099] In order to evaluate the performance of this method (GCUO algorithm) in different environments and scenarios, this embodiment adjusts different parameters to conduct simulation experiments. Set the number of charging stations m = 10, the number of charging positions in each charging station L1 = ... = L m =rand(10, 20), charging cost budget B=18000, where rand(x, y) represents a random value between x and y. Assume that all sensors, points of interest, and charging stations in the network are randomly deployed in a 1000m×1000m plane area. In this experiment, the number of sensors is set to 100 by default, the number of points of interest is set to 300, and the battery capacity of each sensor is 1.5V×2A×3600sec=10800J. For each sensor u i , suppose u i The remaining power E r (u i ) = rand(2000J, 3799J), u i Expected power E exp (u i )=rand(8500J,10299J), sensor weight ω(u i ) = rand(1,5). Assume that the sensor moving energy consumption rate is 10 J / m. Assume that the charging power of the charging station is P r =5W, The coefficients in the objective function are set to α = 1 and β = 100. All experimental results are averaged from 50 calculations.

[0100] In order to verify the performance of the GCUO algorithm, this method is compared with the following three algorithms: 1. Marginal Value First and Closest Charger First (MVFCCF): The MVFCCF algorithm iteratively selects the sensor that can maximize the marginal value of the current objective function from the sensor set, assigns it to the charging station closest to the sensor, selects the location with the shortest cumulative charging time for the sensor from the charging position of the nearest charging station, and determines whether the allocation of the sensor to the location will exceed the budget constraint. If it does not exceed the constraint, the sensor is confirmed to be assigned to the charging position, otherwise the sensor selection is cancelled and the cycle continues. The cycle continues until all sensors are assigned or the charging cost budget reaches the upper limit. 2. Multiple Charging Stations Random Selection (MCSRS): The MCSRS algorithm iteratively randomly selects a sensor from the sensor set and assigns it to a random charging position of a random charging station. Before each assignment confirmation, it is necessary to determine whether the cost budget constraint is met. The cycle continues until all sensors are assigned or the charging cost budget reaches the upper limit. 3. Smallest Cost First and Closest Charger First (SCFCCF): The SCFCCF algorithm iteratively selects the sensor that can make the current charging cost the lowest from the sensor set and assigns it to the charging station closest to the sensor. Before confirming the allocation, it is necessary to determine whether the cost budget constraint is met. The cycle continues until all sensors are assigned or the charging cost budget reaches the upper limit.

[0101] Tables 1 to 4 below show the impact of the four system inputs, namely, the number of charging stations, the number of sensors, the number of points of interest, and the charging service fee budget, on the total charging utility in the case of multiple charging stations. As shown in Table 1, with the increase in the number of charging stations, the total charging utility generated by executing the GCUO algorithm does not have a significant change trend, while the charging utility generated by executing the MVFCCF algorithm, the MCSRS algorithm, and the SCFCCF algorithm decreases by 19.2%, 28.9%, and 21%, respectively.

[0102] Table 1 The impact of the number of charging stations on charging utility

[0103]

[0104]

[0105] As shown in Table II, the change in the number of POIs has no significant effect on the total charging utility.

[0106] Table 2 The impact of the number of points of interest on charging utility

[0107]

[0108] As shown in Table III, as the number of sensors increases from 10 to 100, the total charging utility generated by executing the GCU0 algorithm increases by 7.6 times.

[0109] Table 3 Effect of number of sensors on charging efficiency

[0110]

[0111] As shown in Table IV, as the charging service fee budget B increases from 2000 to 20000, the total charging utility generated by executing the GCUO algorithm increases by 8.6 times.

[0112] Table 4 Impact of charging cost budget on charging utility

[0113]

[0114] The experimental results in Tables 1 to 4 show that the GCUO algorithm is significantly better than other comparison algorithms in terms of performance. The total charging utility generated by the GCUO algorithm is 13.6% higher than that generated by the MVFCCF algorithm, 130% higher than that generated by the MCSRS algorithm, and 37.8% higher than that generated by the SCFCCF algorithm.

[0115] Example 2

[0116] Figure 3 This is a schematic diagram of the charging utility optimization system in a sensor network with limited space and budget. Figure 3 As shown, this embodiment provides a charging utility optimization system in a sensor network based on space and budget constraints, and the system includes:

[0117] A construction module 301 is used to construct a charging service model of a sensor network, wherein the charging service model at least includes a static charging station and a movable wireless rechargeable sensor;

[0118] A determination module 302, configured to determine the total charging utility and the total charging service cost constraint conditions of the sensor, and determine a charging utility optimization model based on the total charging utility and the total charging service cost constraint conditions;

[0119] The solution module 303 is used to solve the charging utility optimization model to obtain a charging scheduling strategy for the sensor network.

[0120] Preferably, the determining module 302 determines the total charging utility of the sensor including:

[0121] For any subset of sensors Total charging utility F t (U′) is the electrical utility F e (U′) and coverage utility F c The weighted sum of (U′):

[0122] F t (U′)=αF e (U′)+βF c (U′)

[0123] Among them, F e (U′)=∑ u∈U′ ω(u)·(E exp (u)-E r (u)), ω(u) represents the weight of sensor u, represents the set of interest points monitored by sensor u, E exp (u) and E r (u) represents the expected power and current remaining power of u respectively, and α and β are two non-negative coefficients.

[0124] Preferably, the determination module 302 determines the total charging service fee constraint of the sensor including:

[0125] The total charging service fee is:

[0126]

[0127] in, For charging stations j The corresponding charging service time, It is the charging service fee required for the unit charging service time of the charging station.

[0128] Preferably, the determining module 302 determines the charging utility optimization model including:

[0129]

[0130]

[0131] in, represents the sensor assignment scheduling strategy, B is the total budgeted charging service cost, Indicates that it is assigned to charging station s j The sensor set at the i-th charging position, L j For power stations j The number of charging locations available.

[0132] The specific implementation process of the functions realized by each module in this embodiment 2 is the same as the implementation process in embodiment 1, and will not be repeated here.

[0133] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for optimizing charging utility in a sensor network with limited space and budget, characterized in that: The method comprises the following steps: S1: constructing a charging service model of a sensor network, wherein the charging service model at least includes a static charging station and a movable wireless rechargeable sensor; S2: Determine the total charging utility and total charging service cost constraints of the sensor, and determine a charging utility optimization model based on the total charging utility and the total charging service cost constraints; S3: Solving the charging utility optimization model to obtain a charging scheduling strategy for the sensor network; determining the total charging utility of the sensor includes: For any subset of sensors Total charging efficiency F t (U′) is the electrical utility F e The weighted sum of (U′) and coverage utility F(U′): F t (U)=αF e (U)+βF c (U') Among them, F e (U′)=Σ u∈U′ ω(u)·(E exp (u)-E r (u)), ω(u) represents the weight of sensor u, represents the set of interest points monitored by sensor u, E exp (u) and E r (u) represents the expected power and current remaining power of u respectively, α and β are two non-negative coefficients; the constraint conditions for determining the total charging service cost of the sensor include: The total charging service fee is: in, For charging stations j The corresponding charging service time, The charging service fee required for the unit charging service time of the charging station; the charging utility optimization model includes: in, represents the sensor assignment scheduling strategy, B is the total budgeted charging service cost, Indicates that it is assigned to charging station s j The sensor set at the i-th charging position, L j For power stations j The number of charging locations available.

2. The method according to claim 1, characterized in that Solving the charging utility optimization model to obtain a charging scheduling strategy for the sensor network includes: The charging utility optimization model is solved using an improved greedy algorithm to generate a charging scheduling strategy for the sensor network, specifically: An initial set is set for each charging station and the charging position of each charging station, and the initial set is empty; under the condition that the current total charging cost does not exceed the given budget and there are still unassigned sensors, the sensor with the largest ratio of the current marginal value to the generated cost is greedily selected from the remaining unassigned charging sensors, and it is assigned to the charging position of the corresponding charging station; the above selection process is repeated to complete the sensor allocation, and the optimal allocation strategy of the sensor and the corresponding total charging utility value are obtained.

3. The method according to any one of claims 1 to 2, characterized in that: The method further comprises: Design an appropriate waiting time for each sensor to meet the following two conditions: (1) Seamless exclusivity condition: each charging position is occupied by several sensors assigned to the position continuously and seamlessly in time, and at any time the charging position is occupied by at most one sensor; (2) Concurrency condition: several different charging positions of the same charging station start to provide charging services at the same time. Given a sensor assignment scheduling strategy, determine a feasible sensor waiting time scheduling strategy in the following way: first, iteratively determine the waiting time of each sensor node at each charging position to meet the seamless exclusivity condition, and then adjust and update the waiting time of sensor nodes at different charging positions to meet the concurrency condition.

4. A charging utility optimization system in a sensor network with limited space and budget, characterized in that: The system comprises: A construction module, used to construct a charging service model of a sensor network, wherein the charging service model at least includes a static charging station and a movable wireless rechargeable sensor; A determination module, configured to determine the total charging utility and the total charging service cost constraint conditions of the sensor, and determine a charging utility optimization model based on the total charging utility and the total charging service cost constraint conditions; The solving module is used to solve the charging utility optimization model to obtain the charging scheduling policy of the sensor network; the determining module determines the total charging utility of the sensor including: For any subset of sensors Total charging efficiency F t (U′) is the electrical utility F e (U′) and coverage utility F c The weighted sum of (U′): F t (U)=αF e (U′)+βF c (U') Among them, F e (U′)=∑ u∈U′ ω(u)·(E exp (u)-E r (u)), ω(u) represents the weight of sensor u, represents the set of interest points monitored by sensor u, E exp (u) and E r (u) represents the expected power and current remaining power of u respectively, α and β are two non-negative coefficients; the determination module determines the total charging service cost constraint of the sensor including: The total charging service fee is: in, For charging stations j The corresponding charging service time, The charging service fee required for the unit charging service time of the charging station; the determination module determines the charging utility optimization model including: in, represents the sensor assignment scheduling strategy, B is the total budgeted charging service cost, Indicates that it is assigned to charging station s j The sensor set at the i-th charging position, L j For power stations j The number of charging locations available.

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