Charging and coating decision-making method and device for optimizing service life of wireless sensor network

Abstract

The invention provides a charging and coating decision-making method and device for optimizing the service life of a wireless sensor network, and relates to the technical field of wireless sensor networks, and the method comprises the steps: taking the maximization of the total activation time of all activation coverage as a first target, taking the minimization of the sum of the coating cost as a second target, and taking the maximum activation time as a second target; constructing a target function according to a preset balance factor; based on constraint conditions of operation of a sensor in a wireless sensor network, a target function is solved through a heuristic method fusing dynamic planning and a simulated annealing algorithm so as to determine target activation time and a coating distribution scheme, and related optimal network total activation time and coating total cost. And the charging strategy of the sensor and the application of the protective coating are adjusted. The application decision of the protective coating is introduced when the activation scheduling and charging strategy of the sensor is considered, so that the fire risk caused by overheating of the battery can be reduced, the service life of the network is remarkably prolonged, and the safety of the network is kept under the limited budget.

Description

Technical Field

[0001] This application relates to the technical field of wireless sensor networks, and in particular, to a charging and coating decision method and device for optimizing the lifespan of a wireless sensor network. Background Art

[0002] With the rapid development of Internet of Things technology, wireless sensor networks (WSNs) have become an important infrastructure in the fields of information science and intelligent transportation systems. Traditional WSN optimization research mainly focuses on the effective deployment and energy management of sensors, aiming to improve energy utilization efficiency and network coverage performance.

[0003] However, these methods often neglect two equally crucial dimensions: security and cost-effectiveness. Especially in scenarios where battery life is limited and potential environmental risks exist, how to extend the network lifespan while ensuring security is an urgent problem to be solved. Summary of the Invention

[0004] This application aims to solve at least one of the technical problems in the related art to some extent.

[0005] To this end, the first objective of this application is to propose a charging and coating decision method for optimizing the lifespan of a wireless sensor network.

[0006] The second objective of this application is to propose a charging and coating decision device for optimizing the lifespan of a wireless sensor network.

[0007] The third objective of this application is to propose an electronic device.

[0008] The fourth objective of this application is to propose a computer-readable storage medium.

[0009] The fifth objective of this application is to propose a computer program product.

[0010] To achieve the above objectives, the first aspect embodiment of this application proposes a charging and coating decision method for optimizing the lifespan of a wireless sensor network, including:

[0011] Determine the activation time and battery charging strategy of each sensor in the sensor network, and determine the individual cost of applying a protective coating to each battery;

[0012] Taking maximizing the total activation time of all active coverage as the first objective and minimizing the total coating cost as the second objective, construct an objective function according to a preset balance factor;

[0013] Based on the constraints for the operation of sensors in a wireless sensor network, a heuristic method that combines dynamic programming and simulated annealing algorithms is used to solve the objective function to determine the target activation time and coating allocation scheme, as well as the associated optimal total network activation time and total coating cost;

[0014] According to the target activation time and coating allocation scheme, adjust the charging strategy of the sensors and the application of the protective coating.

[0015] Optionally, the objective function is:

[0016]

[0017] where c j is the coverage set of the j-th sensor, c i is the coverage set of the i-th sensor, w j is the time when the coverage c j is activated, z i is the coating decision, indicating whether the battery i is wrapped with a protective coating. If it is wrapped with a protective coating, then z i is 1, otherwise it is 0; is the individual cost of applying the protective coating to the battery i, λ is the balance factor, and I is the index combination of all batteries i.

[0018] Optionally, the constraints include:

[0019] Activation coverage constraint:

[0020]

[0021] In the formula, C is the set of all possible coverages, a ij is the coverage decision, indicating whether the j-th sensor s i belongs to the coverage c j . If it belongs, then a ij is 1, otherwise it is 0; y i is the amount of electricity transmitted to the sensor s i , r i is the amount of electricity of the sensor s i before the charging device intervenes, and I is the index combination of all batteries i;

[0022] Among them, the activation coverage constraint is used for

[0023] Charging device capacity constraint:

[0024]

[0025] Among them, R a is the capacity of the charging device;

[0026] Battery capacity constraint:

[0027]

[0028] Non - negative activation time constraint:

[0029]

[0030] Non - negative charge constraint:

[0031]

[0032] Coating decision binary constraint:

[0033]

[0034] Fire risk probability constraint:

[0035]

[0036] Wherein, is the fire occurrence probability of sensor s i , which depends on the power y i , ambient temperature T and coating decision z j ; A is the scaling factor of fire risk, used to convert the influence of power and temperature on fire risk into probability; B is the influence coefficient of power on fire risk, reflecting the non - linear influence of power on fire risk; C is the influence coefficient of ambient temperature on fire risk, reflecting the non - linear influence of ambient temperature on fire risk; D is the influence coefficient of protective coating on fire risk, reflecting the role of protective coating in reducing fire risk; θ is the maximum acceptable probability threshold of fire occurrence;

[0037] Safety performance constraint:

[0038]

[0039] Wherein, f i,j is the safety performance function, reflecting the given power y i , activation time w j , ambient temperature T and coating decision z j when the sensor s i and coverage c jThe safe state; E is the proportionality factor of the safety performance, indicating the degree of adjustment of the safety performance decline according to the power and temperature of the sensor; F is the influence coefficient of the power on the safety performance, reflecting the non-linear influence of the power on the safety performance; G is the influence coefficient of the ambient temperature on the safety performance, reflecting the non-linear influence of the ambient temperature on the safety performance; H is the influence coefficient of the monitoring time on the safety performance, reflecting the positive contribution of the monitoring time to the safety performance; I is the influence coefficient of the protective coating on the safety performance, reflecting the role of the protective coating in improving the safety performance; Saft_threshold is the safe operation threshold.

[0040] Optionally, based on the constraints of the sensor operating in the wireless sensor network, a heuristic method that combines dynamic programming and simulated annealing algorithms is used to solve the objective function to determine the target activation time and coating allocation scheme, as well as the relevant optimal total network activation time and coating total cost, including:

[0041] Set the initial temperature T0 of the simulated annealing algorithm;

[0042] Randomly generate an initial solution S0, and calculate the current energy level E according to the initial solution S0 current ;

[0043] Use the dynamic programming method to optimize the activation time w j , with the goal of maximizing the total activation time covered by all activations;

[0044] According to the coating cost c coat and the budget B budget Adjust the coating decision z i , with the goal of minimizing the sum of the coating costs;

[0045] When the temperature T of the annealing algorithm is higher than the temperature lower limit epsilon, perform simulated annealing iteration and output the current best solution;

[0046] Use local search to optimize the current best solution, and output the final solution including w j , y i , and z i The final solution is used as the target activation time and coating allocation scheme, and the relevant optimal total network activation time and coating total cost are determined.

[0047] Optionally, the randomly generating the initial solution S0 includes:

[0048] Randomly determine the charge amount y of each sensor i and the coating decision z i .

[0049] Optionally, when the temperature T of the annealing algorithm is higher than the temperature lower limit epsiloin, perform simulated annealing iteration and output the current best solution, including:

[0050] When T > epsilon is satisfied, generate a new solution S by perturbing y i and z i ; new ;

[0051] Based on S new calculate the new energy level E new ;

[0052] If E nrw is better than E current , or the simulated annealing acceptance probability condition is satisfied, then accept S new and update E current . The expression of the simulated annealing acceptance probability condition is:

[0053] exp(-(E current - E new ) / T) > rand()

[0054] where rand() is a preset acceptance probability threshold;

[0055] Update the temperature T by multiplying the current temperature T by the cooling coefficient α to lower the temperature;

[0056] Repeat the above iteration process until the temperature of the annealing algorithm is not higher than the temperature lower limit, and output the current best solution.

[0057] To achieve the above object, an embodiment of the second aspect of the present application proposes a charging and coating decision device for optimizing the lifetime of a wireless sensor network, including:

[0058] A determination module for determining the activation time and battery charging strategy of each sensor in the sensor network, and determining the individual cost of applying a protective coating to each battery;

[0059] An objective function construction module for constructing an objective function according to a preset balance factor with maximizing the total activation time of all active coverage as the first objective and minimizing the total coating cost as the second objective;

[0060] A solving module for solving the objective function by a heuristic method that combines dynamic programming and simulated annealing algorithm based on the constraints of the sensor running in the wireless sensor network to determine the optimal total network activation time and total coating cost;

[0061] A decision module for adjusting the charging strategy and the application of the protective coating of the sensor according to the target activation time and coating allocation scheme.

[0062] To achieve the above object, an embodiment of the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0063] The memory stores computer-executable instructions;

[0064] The processor executes the computer-executable instructions stored in the memory to implement the method described in any one of the first aspect.

[0065] To achieve the above object, an embodiment of the fourth aspect of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method described in any one of the first aspect.

[0066] To achieve the above object, an embodiment of the fifth aspect of the present application provides a computer program product, and when the computer program is executed by a processor, it implements the method described in any one of the first aspect.

[0067] The charging and coating decision-making method, device, electronic device, and storage medium for optimizing the lifespan of a wireless sensor network provided by the present application propose a comprehensive optimization framework by considering battery safety and the cost-effectiveness of protective coatings during the optimization process of the network lifespan, achieving effective management of the sensor network lifespan and cost control, avoiding network interruptions caused by battery depletion or sensor failure, improving the reliability and service life of the network, and realizing the dual optimization of safety performance and economic cost; by using a heuristic method that combines dynamic programming and simulated annealing algorithms to solve the objective function, dynamic programming is used for precise activation time allocation, and simulated annealing is used to explore the global optimal solution for applying protective coatings under a given budget. The combination of the two improves the robustness and solution quality of the algorithm, realizes finding the optimal solution under complex constraints, avoids the problem that traditional methods may fall into local optima, and improves the accuracy and efficiency of the solution.

[0068] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0070] Figure 1 is a schematic flowchart of a charging and coating decision-making method for optimizing the lifespan of a wireless sensor network provided by an embodiment of the present application;

[0071] Figure 2Schematic diagram of the structure of a charging and coating decision-making device for optimizing the lifespan of a wireless sensor network provided by an embodiment of the present application. Detailed implementation manners

[0072] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, but should not be construed as a limitation to the present application.

[0073] In earthquake-prone areas, a wireless sensor network composed of high-precision sensors spreads like a large net along key geological fault lines. These sensors are like fine nerve endings, responsible for real-time monitoring of geological movements and capturing the slightest vibrations that may indicate an earthquake. In this scenario, the wireless sensor network is not only a monitoring system but also a life-saving alarm system.

[0074] To ensure the stable operation of this sensitive and critical network, each sensor is equipped with a small battery, and these batteries are regularly charged through a specially designed charging protocol. In this network, there is one or more mobile charging stations that patrol along a preset path to replenish energy for the sensors. The mobile charging stations may be drones or ground robots that approach the sensors within a safe time window and wirelessly transmit electrical energy to ensure that each node always remains online and fully functional.

[0075] To enhance the security of the system, considering the potential fire risk caused by overheating of the battery, the battery of each sensor is covered with a layer of high-tech protective coating. This coating has the dual functions of flame retardancy and heat dissipation, and can significantly reduce the risk of fire caused by the battery in abnormal situations. During daily monitoring and charging, the operation software of the sensor will calculate the energy demand and the probability of fire risk of each sensor in real time, and adjust the charging strategy and the application of the safety coating accordingly. In this Mesh networking scenario, each sensor is a key point, and the stability of the entire network depends on the continuous operation of individual nodes and the collective cooperation.

[0076] Traditional research on optimizing wireless sensor networks mainly focuses on the effective deployment and energy management of sensors, aiming to improve energy utilization efficiency and network coverage performance. However, these methods often ignore two equally crucial dimensions: security and cost-effectiveness. Especially in scenarios where battery life is limited and potential environmental risks exist, how to extend the network lifespan while ensuring safety is an urgent problem to be solved.

[0077] To address this problem, the embodiments of the present application provide a charging and coating decision-making method for optimizing the lifespan of a wireless sensor network. Figure 1Schematic flowchart of a charging and coating decision method for optimizing the lifespan of a wireless sensor network provided by an embodiment of this application. As Figure 1 shown, this method includes the following steps:

[0078] Step 101: Determine the activation time and battery charging strategy of each sensor in the sensor network, and determine the individual cost of applying a protective coating to each battery.

[0079] In the wireless sensor network proposed in the embodiment of this application, each sensor is equipped with a small battery, and these batteries are regularly charged through a specially designed charging protocol. In this network, there is one or more mobile charging stations that patrol along a preset path to replenish energy for the sensors. The mobile charging stations may be drones or ground robots that approach the sensors within a safe time window and wirelessly transmit electrical energy to ensure that each node always remains online and fully functional. To enhance the safety of the system, considering the fire risk that may be caused by overheating of the battery, the battery of each sensor is covered with a layer of high-tech protective coating. This coating has the dual functions of flame retardancy and heat dissipation, and can significantly reduce the risk of fire caused by the battery under abnormal conditions.

[0080] To effectively manage the lifespan of the sensor network and control costs in the future, in this embodiment, it is necessary to first determine the activation time and battery charging strategy of each sensor in the sensor network, and determine the individual cost of wrapping with the protective coating.

[0081] It can be understood that in actual operation, this information can usually be obtained through various channels. For example, cooperate with sensor manufacturers to obtain the technical parameters of the equipment, negotiate with material suppliers to obtain the specific prices of coating materials, and use existing research literature and technical reports to support the decision-making process. In addition, the research results of the academic community and industry standards can also be used to assist in formulating corresponding strategies. The embodiment of this application does not make specific limitations on the acquisition channels.

[0082] Step 102: Construct an objective function according to a preset balance factor, with maximizing the total activation time of all activated coverage as the first objective and minimizing the total coating cost as the second objective.

[0083] In the embodiment of this application, the objective function should reflect two main objectives: one is to maximize the activation time w i of all activated coverage c j , and the other is to reduce the coating cost. The objective function is expressed as:

[0084] where c j is the coverage set of the j-th sensor, c i is the coverage set of the i-th sensor, w j is the coverage cj Activation time, z i For coating decision, it indicates whether battery i is wrapped with a protective coating. If it is wrapped with a protective coating, then z i is 1, otherwise it is 0; is the single cost of applying a protective coating to battery i. λ is a balancing factor that provides a way to trade off between maximizing network lifetime and coating cost, allowing its value to be adjusted according to specific applications to reflect different priorities. I is the index combination of all batteries i.

[0085] It can be understood that the value of the balancing factor λ needs to be set according to the actual scenario, and the present application does not make specific limitations on its value.

[0086] For the above objective function, the first objective is to maximize the total activation time of all activation covers, which is directly related to maximizing the network lifetime. The second item is the sum of coating costs. Since it is a cost, it is desired to be as small as possible. By subtracting this item from the total objective value, the increase in cost can be controlled while increasing the network lifetime.

[0087] Step 103: Based on the constraint conditions for the operation of sensors in a wireless sensor network, solve the objective function by a heuristic method that combines dynamic programming and simulated annealing algorithms to determine the target activation time and coating allocation scheme, as well as the relevant optimal total network activation time and total coating cost.

[0088] In the embodiments of the present application, the constraint conditions are as follows:

[0089] 1. Activation coverage constraint condition:

[0090]

[0091] In the formula, C is the set of all possible covers, a ij is the coverage decision, indicating whether the j-th sensor s i belongs to the coverage c j , if it belongs, then a ij is 1, otherwise it is 0; y i is the power transmitted to the sensor s i , r i is the power of the sensor s i before the charging device intervenes. I is the index combination of all batteries i.

[0092] Among them, this constraint ensures that for each sensor s i , the total activation time it obtains from all activation covers, minus its power consumption y i , will not exceed its remaining power r iThis constraint can prevent the battery from over-discharging and ensure that the sensor operates normally within the remaining battery power.

[0093] 2. Capacity constraint of the charging device:

[0094]

[0095] Among them, R a is the capacity of the charging device.

[0096] Among them, this constraint ensures that the total charging amount of all sensors does not exceed the maximum output capacity R a of the charging device. Thus, it can ensure that the charging device works within its capacity and avoid overload.

[0097] 3. Battery capacity constraint:

[0098]

[0099] Among them, this constraint ensures that the charging amount y i of each sensor s i does not exceed the part of its battery capacity minus the initial remaining power r i . Thus, it can prevent the battery from overcharging.

[0100] 4. Non-negative activation time constraint:

[0101]

[0102] Among them, this constraint ensures that the activation time w j of all coverage sets c j is non-negative, which is a logical requirement because time cannot be negative.

[0103] 5. Non-negative charging amount constraint:

[0104]

[0105] Among them, this constraint ensures that the charging amount y i of all sensors s i is non-negative, which is also based on physical and logical necessity.

[0106] 6. Binary constraint for coating decision:

[0107]

[0108] Among them, this constraint ensures that the coating decision z i of each sensor s i can only be 0 or 1, that is, it means that the sensor does not apply or applies a protective coating, ensuring the clarity and enforceability of the decision.

[0109] 7. Fire risk probability constraint:

[0110]

[0111] In the formula, is the fire occurrence probability of sensor s i , which depends on the power y i , environmental temperature T, and coating decision z j ; A is the scaling factor of fire risk, used to convert the influence of power and temperature on fire risk into probability; B is the influence coefficient of power on fire risk, reflecting the non - linear influence of power on fire risk; C is the influence coefficient of environmental temperature on fire risk, reflecting the non - linear influence of environmental temperature on fire risk; D is the influence coefficient of the protective coating on fire risk, reflecting the role of the protective coating in reducing fire risk; θ is the maximum acceptable probability threshold of fire occurrence.

[0112] Among them, this constraint ensures that the calculated fire occurrence probability of the sensor under given power, environmental temperature, and coating status is less than the maximum acceptable probability threshold of fire occurrence. exp(B·y i +C·T) reflects the non - linear influence of power and temperature on fire risk. When the power is higher or the temperature is higher, the possibility of risk increase is greater. (1 - D·z j ) takes into account the risk reduction effect of the protective coating. If the sensor is coated (zi = 1), the fire risk is reduced. The entire formula provides a way to comprehensively evaluate the fire occurrence probability of the sensor under specific conditions, which involves power, environmental temperature, and protective coating status. This risk assessment is crucial for ensuring the safe operation of wireless sensor networks, especially in environments with fire risks, such as industrial settings or natural disaster monitoring sites. In the entire optimization model, this risk assessment function is directly associated with constraints such as power management and coating cost to ensure that while maximizing network lifetime or other performance metrics, safety risks are not ignored.

[0113] It should be noted that A, B, C, and D are coefficients determined by experience, reflecting the influence of different parameters on fire risk and safety performance. Their values need to be set according to the actual scenario, and specific values are not limited in the embodiments of this application.

[0114] 8. Safety performance constraint:

[0115]

[0116] In the formula, f i,j is the safety performance function, reflecting the given power y i , activation time w j , environmental temperature T, and coating decision z jTime sensor s i And coverage c j Of the safety state; E is the proportionality factor of safety performance, indicating the degree of safety performance degradation adjusted according to the power and temperature of the sensor; F is the influence coefficient of power on safety performance, reflecting the non-linear influence of power on safety performance; G is the influence coefficient of ambient temperature on safety performance, reflecting the non-linear influence of ambient temperature on safety performance; H is the influence coefficient of monitoring time on safety performance, reflecting the positive contribution of monitoring time to safety performance; I is the influence coefficient of the protective coating on safety performance, reflecting the role of the protective coating in improving safety performance; Saft_threshold is the safety operation threshold.

[0117] Among them, this constraint is used to calculate the safety performance score of the sensor under specific working conditions. E·[1-exp(F·y i +G·T)] represents the influence of power and temperature on safety performance. When the power is high or the temperature is high, the safety risk increases and the safety performance decreases. H·w j Represents the direct influence of monitoring time on safety performance. Here, it is based on the fact that a longer monitoring time allows for more frequent or more detailed monitoring, thus improving safety. I·z j This takes into account the improvement of safety performance due to the presence of the protective coating. If z j =1, that is, the sensor s i Has a protective coating, then this will directly improve the safety performance score of the sensor. Saft_threshold is a specified safety operation threshold, and f should exceed this threshold to ensure the security of network operation. This threshold is set based on the overall network security requirements or specific safety standards of individual sensors. If the value of f is lower than Saft_threshold, it means that the network or sensor is not operating safely under the current conditions, and additional measures need to be taken to improve its safety performance, such as increasing the charge, adjusting the monitoring time, or applying a protective coating.

[0118] It can be understood that after clarifying the objective function and constraints, further, it is necessary to solve it to determine the optimal total network activation time and coating total cost.

[0119] In the embodiments of the present application, based on the framework of the heuristic method, combined with dynamic programming and simulated annealing algorithms, the objective function is solved under the constraints to handle the maximization of the total network activation time and the minimization of the coating total cost.

[0120] The specific solution steps are as follows:

[0121] Step 1, set the initial temperature T0 of the simulated annealing algorithm.

[0122] It should be noted that the initial temperature T0 of the simulated annealing algorithm should be high enough to ensure a relatively large probability of accepting most solutions in the initial stage.

[0123] As a possible implementation, the initial temperature T0 of the simulated annealing algorithm can be set based on the problem scale or empirical rules.

[0124] Step 2: Randomly generate an initial solution S0, and calculate the current energy level E according to the initial solution S0 current ;

[0125] In the embodiment of the present application, a random initial activation time and coating allocation scheme are selected as the initial solution S0, that is, when determining the initial solution S0, the charging amount y of each sensor is randomly determined i and the coating decision z i , and calculate the current energy level E corresponding to the initial solution S0 current , where the energy level can be understood as a value opposite to the target, that is, the lower the energy level, the better.

[0126] Step 3: Use the dynamic programming method to optimize the activation time w j , with the goal of maximizing the total activation time covered by all activations. In this process, the state of the system changes over time, and the allocation of the activation time needs to be decided according to the current state. The specific optimization process involves solving sub-problems and gradually constructing the optimal solution.

[0127] In the embodiment of the present application, the system state is set as a quadruple:

[0128] Φ t =(C t ,y t ,T t ,S t )

[0129] The recurrence formula of the value function is:

[0130] V t (Φ t )=max[τ t+1 +γV t+1 (Φ t+1 )]

[0131] where γ∈(0,1] is the time decay factor, and τ t ∈N + is the cumulative effective activation duration.

[0132] Thus, find the optimal allocation strategy, by decomposing the problem into smaller sub-problems and gradually constructing the solution, so as to find the optimal solution, so that under the given charging amount, environmental temperature and sensor network state, the time w when the coverage c j is activatedj Maximize.

[0133] Step 4. Adjust the coating decision z coat according to the coating cost c budget and the budget B i , with the goal of minimizing the total coating cost.

[0134] In the embodiments of the present application, the coating decision z is adjusted based on the individual cost of the protective coating and the affordable budget i to minimize the total coating cost.

[0135] Step 5. When the temperature T of the annealing algorithm is higher than the temperature lower limit epsilon, perform simulated annealing iteration and output the current best solution.

[0136] Specifically, the simulated annealing iteration includes the following steps:

[0137] Step 51. When T > epsilon is satisfied, generate a new solution S by perturbing y i and z i ; new

[0138] Step 52. Calculate the new energy level E based on S new ; new

[0139] Step 53. If E new is better than E current , or the simulated annealing acceptance probability condition is satisfied, then accept S new and update E current . The expression of the simulated annealing acceptance probability condition is:

[0140] exp(-(E current - E new ) / T) > rand()

[0141] where rand() is a preset acceptance probability threshold;

[0142] Step 54. Update the temperature T by multiplying the current temperature T by the cooling coefficient α to reduce the temperature;

[0143] Step 55. Repeat the above iterative process, i.e., Step 51 - Step 54, until the temperature of the annealing algorithm is not higher than the temperature lower limit, and output the current best solution.

[0144] The simulated annealing process is represented by the simulated annealing acceptance probability formula, and the expression is:

[0145] AcceptanceProbability(E current , Enew , T) = exp(-(E current - E new ) / T)

[0146] This formula calculates the acceptance probability of transferring from the current solution E current to the new solution E new at temperature T. If E new is better than E current , then the acceptance probability is close to 1, and the new solution is likely to be accepted. If E new is worse than E current , but the difference is not particularly large, then there is still a possibility of accepting the new solution at a higher temperature, which helps the algorithm to jump out of the local optimal solution.

[0147] Step 6: Use local search to optimize the current best solution, and output the final solution containing w j , y i , and z i . Take the final solution as the target activation time and coating allocation scheme, and determine the relevant optimal total network activation time and coating total cost.

[0148] In the embodiments of the present application, local search is used to further optimize the current best solution. During the local search process, neighboring solutions are tried and the solutions that improve the objective function value are selected.

[0149] Finally, the final solution containing w j , y i , and z i is obtained, as well as the relevant optimal total network activation time and coating total cost.

[0150] Step 104: Adjust the charging strategy of the sensor and the application of the protective coating according to the target activation time and coating allocation scheme.

[0151] Finally, adjust the charging strategy of the sensor and the application of the protective coating according to the target activation time and coating allocation scheme obtained from the final solution.

[0152] In summary, this application proposes an innovative charging and coating decision-making method for optimizing the lifespan of wireless sensor networks. This method aims to maximize the operating lifespan of wireless sensor networks while considering limited charging resources and security requirements. This application presents a composite optimization model that not only includes the activation scheduling of sensors and battery charging decisions but also incorporates the application of protective coatings to reduce potential fire risks. Through a heuristic method that combines dynamic programming and simulated annealing algorithms, it is used to handle the multi-objective problem of maximizing activation time and minimizing coating costs. Experimental results show that compared with traditional charging strategies, the method proposed in this application can significantly improve the network lifespan and maintain the network security under a limited budget. This research not only advances the theoretical research of wireless sensor networks but also provides a new perspective for practical applications. Especially in traffic planning and the design of intelligent transportation systems, the role of wireless sensor networks as a key technology becomes more prominent. Moreover, with the help of the heuristic optimization method proposed in this application, network administrators can intelligently manage resources on the premise of ensuring security, enabling the network to maintain the longest monitoring time and providing critical data support in the event of a disaster.

[0153] To implement the above embodiments, this application also proposes a charging and coating decision-making device for optimizing the lifespan of wireless sensor networks. Figure 2 The structural schematic diagram of a charging and coating decision-making device 10 for optimizing the lifespan of wireless sensor networks provided by an embodiment of this application is as follows. Figure 2 As shown, this device includes:

[0154] A determination module 100, configured to determine the activation time and battery charging strategy of each sensor in the sensor network, and determine the individual cost of applying protective coatings to each battery;

[0155] An objective function construction module 200, configured to construct an objective function according to a preset balance factor, with maximizing the total activation time covered by all activations as the first objective and minimizing the total coating cost as the second objective;

[0156] A solution module 300, configured to solve the objective function through a heuristic method that combines dynamic programming and simulated annealing algorithms based on the constraint conditions for sensors to operate in the wireless sensor network, so as to determine the optimal total network activation time and total coating cost;

[0157] A decision module 400, configured to adjust the charging strategy of the sensors and the application of protective coatings according to the target activation time and coating allocation scheme.

[0158] To implement the above embodiments, the present application also provides an electronic device, including: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0159] To implement the above embodiments, the present application also provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method provided in the foregoing embodiments when executed by a processor.

[0160] To implement the above embodiments, the present application also provides a computer program product including a computer program, which implements the method provided in the foregoing embodiments when executed by a processor.

[0161] The collection, storage, use, processing, transmission, provision, and disclosure of the user's personal information involved in the present application and other processing are all in compliance with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0162] It should be noted that personal information from users should be collected for legal and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after obtaining the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization including authorizing relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others with access to the personal information data comply with their privacy policies and procedures.

[0163] The present application anticipates providing embodiments that allow users to selectively block the use or access of personal information data. That is, the present disclosure anticipates providing hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, the risk can be minimized by restricting data collection and deleting the data. In addition, when applicable, personal identifiers are removed from such personal information to protect the privacy of the user.

[0164] In the descriptions of the foregoing embodiments, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0165] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0166] Any process or method description in a flowchart or described in other ways herein may be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0167] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing if necessary, and then stored in a computer memory.

[0168] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

[0169] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0170] In addition, each functional unit in various embodiments of the present application may be integrated into a processing module, may exist separately physically for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0171] The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present application.

Claims

1. A charging and coating decision method for optimizing the lifespan of a wireless sensor network, characterized in that, It includes the following steps: Determine the activation time and battery charging strategy of each sensor in the sensor network, and determine the individual cost of applying the protective coating to each battery; Taking maximizing the total activation time of all activation covers as the first objective and minimizing the total coating cost as the second objective, construct an objective function according to a preset balance factor; Based on the constraint conditions for the sensors to operate in the wireless sensor network, solve the objective function by a heuristic method that combines dynamic programming and simulated annealing algorithms to determine the target activation time and coating allocation scheme, as well as the relevant optimal total network activation time and total coating cost; Adjust the charging strategy of the sensors and the application of the protective coating according to the target activation time and coating allocation scheme; 2. The method according to claim 1, characterized in that, The objective function is: Among them, c j is the coverage set of the j-th sensor, and c i is the coverage set of the i-th sensor, w j is the time when the coverage c j is activated, z i is the coating decision, indicating whether the battery i is wrapped with a protective coating. If it is wrapped with a protective coating, then z i is 1, otherwise it is 0; is the single cost of applying the protective coating to the battery i, λ is the balance factor, and I is the index combination of all batteries i.

3. The method according to claim 1, characterized in that, The constraint conditions include: Activation coverage constraint condition: where C is the set of all possible coverage, a ij is the coverage decision, indicating whether the j-th sensor s i belongs to the coverage c j , if it belongs, then a ij is 1, otherwise 0; y i is the power transmitted to the sensor s i , r i is the power of the sensor s i before the charging device intervenes, and I is the index combination of all batteries i; Among them, the activation coverage constraint condition is used for Charging device capacity constraint condition: Among them, R a is the capacity of the charging device; Battery capacity constraint condition: Non - negative activation time constraint condition: Non - negative charge constraint: Coating decision binary constraint condition: Fire risk probability constraint condition: In the formula, is the probability of fire occurrence of sensor s i , which depends on the power y i , ambient temperature T, and coating decision z j ; A is the scaling factor of fire risk, used to convert the influence of power and temperature on fire risk into probability; B is the influence coefficient of power on fire risk, reflecting the non-linear influence of power on fire risk; C is the influence coefficient of ambient temperature on fire risk, reflecting the non-linear influence of ambient temperature on fire risk; D is the influence coefficient of the protective coating on fire risk, reflecting the role of the protective coating in reducing fire risk; θ is the maximum acceptable probability threshold of fire occurrence; Safety performance constraint condition: where f i,j is the safety performance function, reflecting the safety state of the sensor s i and the coverage c j at a given power level y j , activation time w i , ambient temperature T, and coating decision z j ; E is the proportionality factor of the safety performance, indicating the degree of adjustment of the safety performance degradation according to the power level and temperature of the sensor; F is the influence coefficient of the power level on the safety performance, reflecting the non-linear influence of the power level on the safety performance; G is the influence coefficient of the ambient temperature on the safety performance, reflecting the non-linear influence of the ambient temperature on the safety performance; H is the influence coefficient of the monitoring time on the safety performance, reflecting the positive contribution of the monitoring time to the safety performance; I is the influence coefficient of the protective coating on the safety performance, reflecting the role of the protective coating in improving the safety performance; Saft_threshold is the safety operation threshold.

4. The method according to claim 1, characterized in that, The method of solving the objective function by a heuristic method that combines dynamic programming and simulated annealing algorithms based on the constraint conditions for the sensors to operate in the wireless sensor network to determine the target activation time and coating allocation scheme, as well as the relevant optimal total network activation time and total coating cost, includes: Set the initial temperature T0 of the simulated annealing algorithm; Randomly generate an initial solution S0, and calculate the current energy level E according to the initial solution S0 current ; Use the dynamic programming method to optimize the activation time w j , with the goal of maximizing the total activation time covered by all activations; Adjust the coating decision z coat according to the coating cost c budget and the budget B i with the goal of minimizing the total coating cost; When the temperature T of the annealing algorithm is higher than the temperature lower limit epsilon, perform simulated annealing iteration and output the current best solution; Optimize the current best solution using local search, and output the final solution containing w j , y i , and z i . Use the final solution as the target activation time and coating assignment scheme, and determine the relevant optimal total network activation time and coating total cost.

5. The method according to claim 4, wherein The randomly generating the initial solution S0 includes: Randomly determine the charging amount y of each sensor i and the coating decision z i .

6. The method according to claim 4, wherein The step of when the temperature T of the annealing algorithm is higher than the temperature lower limit epsilon, performing simulated annealing iteration and outputting the current best solution includes: When T > epsilon is satisfied, a new solution S is generated by perturbing y i and z i ; new ; Based on S new Calculate the new energy level E new ; If E new is better than E current , or meets the simulated annealing acceptance probability condition, then accept S new and update E current . The expression for the simulated annealing acceptance probability condition is: exp(-(E current -E new ) / T) > rand() Where rand() is a preset acceptance probability threshold; Update the temperature T by multiplying the current temperature T by the cooling coefficient α to reduce the temperature; Repeat the above iteration process until the temperature of the annealing algorithm is not higher than the temperature lower limit, and output the current best solution.

7. A charging and coating decision device for optimizing the lifespan of a wireless sensor network, characterized in that, It includes: A determination module, used to determine the activation time and battery charging strategy of each sensor in the sensor network, and determine the individual cost of applying the protective coating to each battery; An objective function construction module, used to take maximizing the total activation time of all activation covers as the first objective and minimizing the total coating cost as the second objective, and construct an objective function according to a preset balance factor; A solution module, used to solve the objective function by a heuristic method that combines dynamic programming and simulated annealing algorithms based on the constraint conditions for the sensors to operate in the wireless sensor network to determine the optimal total network activation time and total coating cost; A decision module, used to adjust the charging strategy of the sensors and the application of the protective coating according to the target activation time and coating allocation scheme.

8. An electronic device, characterized in that, It includes: A processor and a memory communicatively connected to the processor; The memory stores computer - executable instructions; The processor executes the computer - executable instructions stored in the memory to implement the method according to any one of claims 1 - 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement the method according to any one of claims 1-6.

10. A computer program product, characterized in that, It includes a computer program, and when the computer program is executed by a processor, it implements the method according to any one of claims 1-6.

Images