Two-layer dynamic information update method and product for energy harvesting networks

Abstract

The present invention provides a two-layer dynamic information update method and product for energy harvesting networks, which relates to the field of edge computing technology. The technical solution provided by the embodiment of the present invention can realize the internal and external two-layer dynamic transmission and power adjustment mechanism of sensor group scheduling and intra-group sensor scheduling based on KKT (Karush-Kuhn-Tucker) conditions, and achieves a significant improvement in system performance while ensuring the quality of updated data. The strategy has the following advantages: (1) Accurate sensor selection mechanism: Establish a multi-dimensional evaluation model, comprehensively consider indicators such as sensor energy status, channel quality, and data importance, and select the optimal sensor combination through KKT condition optimization to ensure maximum system update efficiency; (2) Efficient power adjustment strategy: Dynamically adjust the transmission power to adapt to real-time changes in channel conditions and energy supply to achieve optimal allocation of energy resources and avoid ineffective energy consumption; (3) Significant performance improvement: Effectively reduce the number of invalid updates, significantly improve update efficiency, and maximize the overall system performance while ensuring data quality and AoI requirements.

Description

Technical Field

[0001] The present invention relates to the field of edge computing technology, and in particular to a two-layer dynamic information update method and product for energy harvesting networks. Background Art

[0002] With the rapid development of 5G communications, the Internet of Things (IoT), and edge computing technologies, real-time application systems have gained widespread adoption in areas such as industrial automation, intelligent transportation, and smart cities. These systems rely on data continuously generated by large-scale sensor networks for intelligent analysis and decision-making. However, the timeliness and quality of data directly impact the accuracy of analytical results. Outdated or low-quality data can lead to biased decisions, impacting overall system performance. Therefore, ensuring the timeliness and quality of data collection is critical to improving the accuracy of real-time decision-making.

[0003] To accurately characterize the timeliness of information in real-time applications, academia has proposed a new metric, the Age of Information (AoI). AoI is defined as the time elapsed between data generation and receipt at the destination. Unlike traditional network performance metrics, AoI not only measures data transmission latency but also takes into account data generation frequency, more accurately describing the dynamic changes in data generation intervals. This metric is particularly important in highly dynamic real-time applications because it directly reflects the freshness of the information the system relies on, which in turn influences the accuracy of system decisions.

[0004] Mobile edge computing effectively reduces data transmission latency and improves data timeliness and quality by deploying computing resources at edge nodes close to data sources. However, in real-world applications, wireless sensors have limited bandwidth resources and are often constrained by battery life. Frequent battery replacement increases maintenance costs and can cause system outages. While some source sensors use energy harvesting technologies to collect energy from the environment to maintain device operation, the limited energy and bandwidth resources still pose significant challenges to ensuring data timeliness and quality. Summary of the Invention

[0005] The present invention provides a dual-layer dynamic information update method and product for an energy harvesting network to at least partially solve the above problems.

[0006] A first aspect of the present invention provides a two-layer dynamic information update method for an energy harvesting network, which is applied to an edge node. The edge node is used to schedule K sensor groups to upload information, where the K sensor groups include N sensors. The method includes:

[0007] From 1 to K, traverse K sensor groups according to the following steps:

[0008] For the kth sensor group traversed, traverse the nth sensor belonging to the kth sensor group and calculate the power of the nth sensor belonging to the kth sensor group ;

[0009] The power of the nth sensor belonging to the kth sensor group When the power constraint condition is met, the nth sensor belonging to the kth sensor group is used as the primary sensor, and the kth sensor group is determined as the primary sensor group;

[0010] When multiple preliminary sensor groups are determined, the scheduled sensor groups, the scheduled sensors within the scheduled sensor groups, and the transmission powers of the scheduled sensors are determined with the goal of minimizing the transmission energy consumption of the scheduled sensors and the information age of the information uploaded by the scheduled sensor groups;

[0011] The scheduled sensor is scheduled to upload information at the transmission power.

[0012] Optionally, calculate the power of the nth sensor belonging to the kth sensor group ,include:

[0013] According to the cumulative throughput debt of the k-th sensor group in time slot t , the channel gain during the transmission of the nth sensor and the length of time slot t T d , calculate the power of the nth sensor belonging to the kth sensor group .

[0014] Optionally, when calculating the power of the nth sensor belonging to the kth sensor group After that, it also includes:

[0015] Verify the power of the nth sensor belonging to the kth sensor group Whether the transmission power constraint of the sensor is met, where the transmission power constraint indicates that the transmission power of the scheduled sensor is not greater than the maximum power of the scheduled sensor and is not greater than the power that can be provided by the actual energy of the scheduled sensor;

[0016] Under the condition that the transmission power constraint of the sensor is met, the nth sensor belonging to the kth sensor group is selected as the primary sensor;

[0017] When the kth sensor group is the scheduled sensor group, the power of the nth sensor of the kth sensor group is The transmission power of the scheduled sensors is determined.

[0018] Optionally, the method further includes:

[0019] If the transmission power constraint of the sensor is not met, the power of the nth sensor in the kth sensor group is reduced. Adjust to the power boundary value of the sensor, the power boundary value ;in, represents the remaining energy of the nth sensor in the previous time slot t-1, represents the energy collected by the nth sensor in time slot t, represents the basic energy consumption of the nth sensor;

[0020] When the adjusted power is greater than zero, the nth sensor belonging to the kth sensor group is used as the primary sensor;

[0021] When the kth sensor group is the scheduled sensor group, the power boundary value of the nth sensor of the kth sensor group is Determine the transmission power of the scheduled sensor;

[0022] In a case where the adjusted power is equal to zero, it is determined that the sensor does not meet the transmission power constraint of the sensor.

[0023] Optionally, determining the scheduled sensor group with the goal of minimizing the transmission energy consumption of the scheduled sensors and the information age of information uploaded by the scheduled sensor group includes:

[0024] According to the primary selection of sensor groups in the time slot Energy consumption of uploading information , the cumulative throughput debt of the primary sensor group in time slot t , the total amount of data uploaded by all the preliminary sensors in the preliminary sensor group within time slot t , Information age importance weight parameter of the primary sensor group , the information age of the information transmitted by the primary sensor group in time slot t , calculate the target value of the primary sensor group , which is defined as ;

[0025] Target values ​​for multiple pre-selected sensor groups Sort in ascending order, select the first Smaller The sensor group corresponding to the value is the scheduled sensor group;

[0026] in, represents the energy consumption of the nth sensor belonging to the kth sensor group uploading information at time sequence t , Belongs to the sensor group A collection of sensors; ; Indicates sensor The transmission rate; , Indicates whether the nth sensor of the kth sensor group is a primary selected sensor; represents a non-negative control parameter used to balance the target and throughput debt.

[0027] Optionally, the method further includes:

[0028] Before the next round of scheduling, update the following parameter values:

[0029] The remaining energy of each sensor in time slot t ; , Indicates the maximum storage energy of the sensor;

[0030] Each sensor group has a time slot Energy consumption of uploading information ;

[0031] The information age of the information transmitted by each sensor group in time slot t ;

[0032] The cumulative throughput debt of each sensor group in time slot t .

[0033] A second aspect of the present invention provides a two-layer dynamic information update device for an energy harvesting network, which is applied to an edge node. The edge node is used to schedule K sensor groups to upload information, where the K sensor groups include N sensors. The device includes:

[0034] The traversal module is used to traverse K sensor groups from 1 to K according to the following steps:

[0035] For the kth sensor group traversed, traverse the nth sensor belonging to the kth sensor group and calculate the power of the nth sensor belonging to the kth sensor group ;

[0036] The power of the nth sensor belonging to the kth sensor group When the power constraint condition is met, the nth sensor belonging to the kth sensor group is used as the primary sensor, and the kth sensor group is determined as the primary sensor group;

[0037] an optimization module for determining, when multiple preliminary sensor groups are determined, the scheduled sensor groups, the scheduled sensors within the scheduled sensor groups, and the transmission power of the scheduled sensors with the goal of minimizing the transmission energy consumption of the scheduled sensors and the information age of information uploaded by the scheduled sensor groups;

[0038] The scheduling module is used to schedule the scheduled sensor to upload information at the transmission power.

[0039] The third aspect of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when executed by the processor, the two-layer dynamic information update method for energy harvesting networks as described in the first aspect of the present invention is implemented.

[0040] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the two-layer dynamic information update method for an energy harvesting network as described in the first aspect of the present invention.

[0041] A fifth aspect of the present invention provides a computer program product, comprising a computer program / instruction, which is used by a processor to implement the steps in the double-layer dynamic information update method for energy harvesting networks as described in the first aspect of the present invention.

[0042] The technical solution provided by the embodiment of the present invention can realize the internal and external double-layer dynamic transmission and power adjustment mechanism of sensor group scheduling and intra-group sensor scheduling based on KKT (Karush-Kuhn-Tucker) conditions, and achieve a significant improvement in system performance while ensuring the quality of updated data. This strategy has the following advantages: (1) Accurate sensor selection mechanism: Establish a multi-dimensional evaluation model, comprehensively consider indicators such as sensor energy status, channel quality, and data importance, and select the optimal sensor combination through KKT condition optimization to ensure maximum system update efficiency; (2) Efficient power adjustment strategy: Dynamically adjust the transmission power to adapt to real-time changes in channel conditions and energy supply to achieve optimal allocation of energy resources and avoid ineffective energy consumption; (3) Significant performance improvement: Effectively reduce the number of invalid updates, significantly improve update efficiency, and maximize the overall system performance while ensuring data quality and AoI requirements.

[0043] Through innovative algorithm design and system architecture, the present invention achieves the following comprehensive performance advantages: (1) Strong adaptability: It can dynamically adapt to fluctuations in energy supply and changes in channel conditions; (2) High resource utilization: It can achieve optimal allocation of resources such as energy and bandwidth; (3) Good scalability: It is suitable for real-time systems of different scales and application scenarios; (4) Low implementation cost: It does not require additional hardware support and can be implemented through software upgrades.

[0044] These advantages give the present invention broad application prospects and significant practical value in real-time application scenarios such as industrial Internet of Things and smart cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the description of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 It is a flowchart of the steps of the double-layer dynamic information update method for energy harvesting network provided by the present invention;

[0047] Figure 2 It is a structural diagram of a two-layer dynamic information update system for energy harvesting networks provided by the present invention;

[0048] Figure 3 This is a structural block diagram of a double-layer dynamic information updating device for energy harvesting networks provided by the present invention. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0050] In edge computing, AoI-based data collection focuses on reducing the system's AoI, thereby improving the accuracy of real-time decision-making. Existing research categorizes these approaches into two categories based on energy constraints: energy-sufficient systems (e.g., stable power supplies) and energy-constrained systems (e.g., batteries or energy harvesting devices). The former emphasizes efficient resource allocation to optimize the AoI, while the latter balances energy efficiency with minimizing the AoI to meet real-time requirements. For status updates of energy-unconstrained devices, both centralized and distributed scheduling strategies can be applied. Centralized approaches leverage complete system status information, prioritizing the transmission of data with high or critical AoI, thereby minimizing the overall AoI of the system.

[0051] Current research on information updates for AoI optimization primarily uses centralized or distributed strategies to improve overall AoI performance, often assuming sufficient energy resources. However, these approaches often overlook the changing data volume requirements in real-time applications. Critical sensors or areas require frequent updates or higher data volumes to maintain analytical accuracy, while non-critical areas can tolerate fewer updates. Current techniques such as random scheduling, maximum weight, or drift-plus-penalty can balance throughput and AoI with low complexity, but fail to address the need for high-quality and timely data in real-time monitoring, especially in energy harvesting systems.

[0052] In the energy-constrained data update space, relevant technologies primarily employ energy-aware update strategies and sleep-wake-up mechanisms. These methods dynamically adjust transmission timing, transmission frequency, and data priority to achieve the dual goals of efficient energy utilization and minimized Age of Information (AoI).

[0053] The core mechanism of the energy-aware scheduling strategy is that the system triggers the data transmission process only when the information age reaches a preset threshold and the device has sufficient energy. This strategy optimizes the utilization efficiency of energy and transmission resources through a dual-condition judgment mechanism. Specifically, the system needs to monitor two key parameters in real time: (1) whether the current information age exceeds the preset threshold; (2) whether the available energy of the device meets the transmission requirements. However, this strategy has significant technical limitations: its basic assumption is that the energy collected per time slot is a fixed unit or can be accurately predicted, which is significantly different from the actual application scenario. In actual deployment, due to the dynamic changes in the relative position between the energy source and the receiving source, as well as the uncontrollability of environmental factors (such as weather conditions), the energy that can be collected in each time slot exhibits significant random characteristics. This randomness of energy collection severely limits the applicability of scheduling methods based on the fixed energy assumption in actual data update scenarios.

[0054] To reduce system energy consumption, researchers have proposed a transmission scheduling strategy based on a sleep-wake mechanism. This strategy uses an adaptive sleep mechanism. The device dynamically chooses to enter a sleep state to save energy based on the time sensitivity and importance of the data, and wakes up the device when a critical data transmission demand is detected. Although this mechanism can significantly reduce energy consumption when the device is idle, it has the following technical defects: (1) State transition energy consumption: Frequent switching between the sleep state and the working state of the device will generate additional energy consumption. This conversion energy consumption increases significantly with the increase in the state switching frequency; (2) Wake-up delay: It takes a certain delay to recover from the sleep state to the working state. This delay will cause data transmission lag and affect the real-time performance of the system; (3) Reduced responsiveness: The device in the sleep state cannot respond to sudden data transmission demands in time and may miss the opportunity to transmit critical data.

[0055] It can be seen that the current research on transmission scheduling for AoI optimization has the following limitations:

[0056] (1) Energy instability at the source: The energy supply of sensor devices is highly volatile, especially for sensors that rely on energy harvesting technology, whose energy harvesting is significantly affected by changes in environmental conditions. In addition, the dynamic nature of wireless networks during transmission increases the uncertainty of energy consumption.

[0057] (2) In energy-constrained sensor networks, factors such as channel fluctuations, network topology changes, and external interference cause the bandwidth resources between the source and destination to have large time-varying characteristics.

[0058] (3) Under the condition of limited energy and bandwidth, achieving a balance between data throughput, energy consumption, and AoI is a complex multi-objective optimization problem. Increasing data transmission power can increase throughput, but it will also significantly increase energy consumption, which may cause the sensor to lack sufficient energy in subsequent transmissions, thereby increasing the system AoI.

[0059] The above three limitations make it difficult to guarantee the quality and AoI performance of the information required for real-time energy harvesting applications. Based on this, the present invention specifically proposes a high-efficiency information update method for energy harvesting networks, which can significantly reduce the information age and energy required for updating information in real-time application systems while ensuring the quality of data required for updates, thereby ensuring the safe and efficient operation of real-time application systems. Specifically, Figure 1 , which shows a flowchart of a two-layer dynamic information update method for an energy harvesting network provided by an embodiment of the present invention, the method includes the following steps:

[0060] S101, from 1 to K, traverse K sensor groups according to the following steps:

[0061] For the kth sensor group traversed, traverse the nth sensor belonging to the kth sensor group and calculate the power of the nth sensor belonging to the kth sensor group ;

[0062] The power of the nth sensor belonging to the kth sensor group When the power constraint condition is met, the nth sensor belonging to the kth sensor group is used as the preliminary selected sensor, and the kth sensor group is determined as the preliminary selected sensor group.

[0063] S102, when multiple preliminary sensor groups are determined, determine the scheduled sensor groups, the scheduled sensors within the scheduled sensor groups, and the transmission power of the scheduled sensors with the goal of minimizing the transmission energy consumption of the scheduled sensors and the information age of the information uploaded by the scheduled sensor groups.

[0064] S103: Schedule the scheduled sensor to upload information at the transmission power.

[0065] The dual-layer dynamic information update method for energy harvesting network provided by the embodiment of the present invention is applied to edge nodes, wherein the edge nodes are used to schedule K sensor groups to upload information, and the K sensor groups include N sensors. Specifically, the dual-layer dynamic information update system for energy harvesting network in the embodiment of the present invention is as follows: Figure 2 As shown, edge nodes are used to analyze and determine sensor group scheduling decisions (scheduled sensor groups), sensor scheduling decisions (scheduled sensors), and power adjustment for scheduled sensors. Each observation area includes one or more sensors, and the multiple sensors in an observation area are considered a sensor group. When a sensor group is scheduled, the scheduled sensors within the group update information using the transmission power determined by the analysis.

[0066] This embodiment of the present invention defines relevant parameters for the information transmission scheduling process of a two-layer dynamic information update for an energy harvesting network, including observation area (sensor group), sensor group scheduling decision (scheduled sensor group), sensor scheduling decision (scheduled sensor), energy harvesting, AoI (Age of Information) status, energy status, and throughput status. Based on the data quality, AoI, channel status, and energy status of each sensor device required by the information update system, sensors are appropriately selected for data upload to minimize the AoI and energy consumption of the updated data at the destination during the update process, further ensuring the long-term reliable operation of the system. By setting different weight parameters, the overall AoI performance of the system can be improved.

[0067] In an embodiment of the present invention, the problem is first abstractly modeled based on the information update process, and a network model, an energy model, a minimum throughput requirement model for each group, and an AoI model are constructed based on the transmission process of the sensor device. The entire optimization problem model can be constructed based on the above models.

[0068] Specifically, the sensors and different observation area sets are defined as and In this embodiment of the present invention, all sensors deployed in the same observation area are regarded as a sensor group. To be deployed to the observation environment area (i.e. sensor group) If the sensor set is: The edge node allocates a sub-channel to each group. Due to the limitation of bandwidth resources, it is not allowed to exceed Each group transmits its update packets simultaneously without collision (e.g., via orthogonal channels). Indicates that the edge node is in the time slot Scheduling sensor groups Transmitting data to the space station, This means that the edge node does not schedule the sensor group Based on this, the following group scheduling constraints exist:

[0069] (1)

[0070] In the embodiment of the present invention, in step S102, the process of determining the scheduled sensor groups needs to satisfy the group scheduling constraint condition in formula (1), requiring that the number of the determined scheduled sensor groups is not greater than the number of sensor groups allowed to simultaneously transmit update data.

[0071] In the embodiment of the present invention, the sensors used for environmental perception use solar energy as their energy source. Therefore, the energy change process of each sensor is as follows:

[0072] (2)

[0073] in, represents the remaining energy of the nth sensor at time slot t, represents the remaining energy of the nth sensor in the previous time slot t-1, represents the energy collected by the nth sensor in time slot t, represents the basic energy consumption of the nth sensor, represents the energy consumption of the nth sensor uploading information at time t; Indicates the maximum storage energy of the sensor.

[0074] In order to maximize the success probability of data transmission, , the edge node will select a sensor with sufficient battery energy to transmit updated data, such as:

[0075] (3)

[0076] in, ; T d represents the length of time slot t, represents the transmission power of the nth sensor.

[0077] In each time slot , in addition to determining whether to schedule sensor group k and whether to schedule sensor n of sensor group (i.e. and ), the controller on the edge node also needs to transmit power to the scheduled sensors The power cannot exceed its maximum value, such as:

[0078] (4)

[0079] By combining inequalities (3) and (4), we can obtain The range is:

[0080] (5)

[0081] In the embodiment of the present invention, the above formula (5) is the power constraint condition of the sensor, which means that the transmission power of the scheduled sensor is not greater than the power that the actual energy of the sensor can support, nor is it greater than the maximum power of the sensor.

[0082] The size of the transmission power directly affects the amount of data transmitted. Adjustment sensor The power is , then the energy consumption of the sensor in the corresponding time slot is Based on this, each sensor group has a The energy consumption can be derived as:

[0083] (6)

[0084] According to the above formula, the average transmission energy consumption of all sensors in the system can be expressed as:

[0085] (7)

[0086] When the sensor In the time slot When selected for data transmission, its signal-to-noise ratio at this moment can be expressed as:

[0087] (8)

[0088] in 、 and Denote Gaussian white noise power spectral density, link bandwidth and channel gain respectively. Based on Shannon’s theorem, the sensor The transmission rate (or throughput) is:

[0089] (9)

[0090] It means in the time slot sensor The amount of data uploaded to the edge node, therefore, the time slot group can be obtained The total amount of data uploaded by all selected sensors is:

[0091] (10)

[0092] For each group , its long-term throughput needs to meet the following minimum requirements:

[0093] (11)

[0094] in, Is a strictly positive real number, indicating the real-time application of the sensor group Minimum throughput requirements.

[0095] make Time slot At the beginning, the edge node receives The evolution process of the AoI of the observation information of a region is:

[0096] (12)

[0097] Based on the evolution of AoI in a single region, we can further obtain the AoI of all edge nodes. The observation area is in continuous time slots The AoI expression for uploaded update data is:

[0098] (13)

[0099] in, It means the The predefined weight parameter of the AoI importance of each observation area.

[0100] Set by each time slot The energy consumption of the transmission node and the AoI of the observation area information are minimized by selecting the scheduled sensor groups, the scheduled sensors within the scheduled sensor groups, and adjusting the power of the selected sensors. The goal is to minimize the average total transmission energy of all sensors and the AoI of the observation area on the edge node side by jointly optimizing the scheduled sensor groups, scheduled sensors, and sensor transmission power adjustment in each time slot, while meeting the bandwidth, the throughput requirements of each sensor group, and the energy collection constraints of each sensor. The mathematical model can be expressed as:

[0101] (14)

[0102] in A weight parameter that balances AoI and energy consumption. It aims to design strategies for scheduling sensor groups, selecting scheduled sensors, and adjusting power to achieve the optimal trade-off between energy consumption, AoI, and throughput.

[0103] Problem P1 is still difficult to solve, so the present invention proposes to transform it based on Lyapunov optimization technology. First, in each time slot For each sensor group The following virtual queue is defined :

[0104] (15)

[0105] Virtual Queue Indicates sensor group exist The accumulated throughput debt in time slots is initialized to .Introduction The purpose is to ensure that inequality (11) satisfies the minimum throughput constraint, but it does not necessarily require that the throughput of each time slot is strictly greater than the threshold. Based on the above analysis of problem P1, we can first define the quadratic Lyapunov function as , and the one-step conditional Lyapunov drift function is defined as:

[0106] (16)

[0107] in Indicates time slot The network state at the beginning, where the use To ensure that the throughput is positive. According to the random network optimization technology, The smaller, The stability of the virtual queue is easier to ensure. Under the premise of ensuring the stability of the virtual queue, it is also necessary to consider the optimal sensor group scheduling, sensor selection and power adjustment strategy to minimize AoI and sensor energy consumption. In order to solve this problem, the embodiment of the present invention introduces the Lyapunov drift plus penalty function ,in represents a non-negative control parameter used to balance the optimization objective and the throughput debt queue length. By defining and bringing the objective function into the penalty term, we can deduce the upper bound of drift plus penalty as:

[0108] (17)

[0109] in . It can be found Depends solely on And with group scheduling decision , sensor selection decision and power regulation decisions Therefore, in each time slot, only the second term on the right side of inequality (17) needs to be optimized, and problem P1 can be transformed into:

[0110] (18)

[0111] In problem P2, due to the decision of the scheduled sensor group , Selection of dispatched sensors and adjustment of the selected sensor power There is a mutual coupling relationship between them, which is difficult to solve directly. Therefore, the embodiment of the present invention uses the idea of ​​drift plus penalty algorithm to set the sensor group scheduling decision in each time slot sequence as After the sensor group scheduling decision is given, based on the decision variables and The relationship between hour, ;otherwise Therefore, problem P2 is further simplified to the power regulation subproblem:

[0112] (19)

[0113] After solving sub-problem P3 by designing a better algorithm, we can obtain the problem P2 at any time. The optimal target value , which is defined as Then the target value Sort in ascending order, select the first Smaller The sensor groups corresponding to the values ​​are scheduled to ensure that there is no conflict when scheduling at the same time. Based on the above analysis, the group scheduling strategy set can be obtained as Next, we analyze the concavity and convexity of problem P3 to solve this subproblem.

[0114] By substituting equations (6), (8), (9) and (10) into P3, we define is the objective function of problem P3, which can be expressed as

[0115] (20)

[0116] According to convex optimization theory, the point that satisfies the KKT condition is the optimal solution to the optimization problem. Based on this, the embodiment of the present invention uses the Lagrangian relaxation method to find the point value that satisfies the KKT condition, thereby achieving the purpose of solving the problem. First, define the Lagrangian function as:

[0117] (twenty one)

[0118] in, and is the Lagrange multiplier, and is the upper bound of inequality (5), defined as The KKT condition for problem P3 can be expressed as:

[0119] ①Stability:

[0120] ②Original feasibility:

[0121] ③ Dual feasibility:

[0122] ④Supplement relaxation:

[0123]

[0124] Among them, ④ is the inequality constraint from problem P3. In order to evaluate the stationarity, it is necessary to calculate the Lagrangian function about According to the partial derivative, we can get:

[0125] (twenty two)

[0126] From equation (22), we can derive two possible situations: 1) and , then , indicating that the optimal value of power regulation is within the feasible region; 2) or , then or , indicating that the optimal value of power regulation is on the boundary. Therefore, by setting the two Lagrange multipliers to 0, and , preliminarily deduced the feasible region Then, by checking the derived feasibility, and find the boundary Finally, for all set up and , rearrange equation (22) and solve the equation to obtain The power regulation formula is:

[0127] (twenty three)

[0128] Based on formula (23) , it is also necessary to verify whether it satisfies If If it falls outside the feasible region, it needs to be adjusted to the nearest boundary value:

[0129] (twenty four)

[0130] Therefore, the embodiment of the present invention proposes to calculate the power of the nth sensor belonging to the kth sensor group: The steps include:

[0131] According to the cumulative throughput debt of the k-th sensor group in time slot t , the channel gain during the transmission of the nth sensor and the length of time slot t T d , calculate the power of the nth sensor belonging to the kth sensor group Specifically, the power of the nth sensor is calculated using the above formula (23): .

[0132] In the embodiment of the present invention, after obtaining the calculation result of the power of the nth sensor, it is necessary to perform power constraint verification on the calculation result. Specifically, the verification includes:

[0133] Verify the power of the nth sensor belonging to the kth sensor group Whether the transmission power constraint of the sensor is met, where the transmission power constraint indicates that the transmission power of the scheduled sensor is not greater than the maximum power of the scheduled sensor and is not greater than the power that can be provided by the actual energy of the scheduled sensor;

[0134] In the case that the transmission power constraint of the sensor is met, the nth sensor belonging to the kth sensor group is used as the primary sensor. In the case that the kth sensor group is the scheduled sensor group, the power of the nth sensor in the kth sensor group is set as The transmission power of the scheduled sensors is determined.

[0135] If the transmission power constraint of the sensor is not met, the power of the nth sensor in the kth sensor group is reduced. Adjust to the power boundary value of the sensor, the power boundary value ;in, represents the remaining energy of the nth sensor in the previous time slot t-1, represents the energy collected by the nth sensor in time slot t, Indicates the basic energy consumption of the nth sensor.

[0136] When the adjusted power is greater than zero, the nth sensor belonging to the kth sensor group is used as the primary sensor.

[0137] When the kth sensor group is the scheduled sensor group, the power boundary value of the nth sensor of the kth sensor group is Determine the transmission power of the scheduled sensor.

[0138] In a case where the adjusted power is equal to zero, it is determined that the sensor does not meet the transmission power constraint of the sensor.

[0139] In the embodiment of the present invention, after the preliminary selected sensor is determined, the sensor group to which the preliminary selected sensor belongs is used as the preliminary selected sensor group. The preliminary selected sensor group is further optimized with the goal of minimizing the transmission energy consumption of the scheduled sensor and the information age of the information uploaded by the scheduled sensor group. Specifically, according to the time slot of the preliminary selected sensor group, the sensor group to which the preliminary selected sensor belongs is used as the preliminary selected sensor group. Energy consumption of uploading information , the cumulative throughput debt of the primary sensor group in time slot t , the total amount of data uploaded by all the preliminary sensors in the preliminary sensor group within time slot t , Information age importance weight parameter of the primary sensor group , the information age of the information transmitted by the primary sensor group in time slot t , calculate the target value of the primary sensor group , which is defined as ;

[0140] Target values ​​for multiple pre-selected sensor groups Sort in ascending order, select the first Smaller The sensor group corresponding to the value is the scheduled sensor group;

[0141] in, represents the energy consumption of the nth sensor belonging to the kth sensor group uploading information at time sequence t , Belongs to the sensor group A collection of sensors; ; Indicates sensor The transmission rate; , Indicates whether the nth sensor of the kth sensor group is a primary selected sensor; represents a non-negative control parameter used to balance the target and throughput debt.

[0142] In the embodiment of the present invention, the target value of the preliminary selected sensor group comprehensively considers the energy consumption, throughput and information age of the preliminary selected sensor group.

[0143] In the embodiment of the present invention, after determining the target values ​​of the pre-selected sensors and the pre-selected sensor groups in each time slot t, the pre-selected sensors with smaller target values ​​can be selected based on the scheduling constraint (Formula (1)). M The first sensor group is selected as the scheduled sensor group.

[0144] In this embodiment of the present invention, before the next scheduling discussion (i.e., before the start of the next time slot), the following parameter values ​​are updated:

[0145] The remaining energy of each sensor in time slot t ;

[0146] , Indicates the maximum storage energy of the sensor;

[0147] Each sensor group has a time slot Energy consumption of uploading information ;

[0148] The information age of the information transmitted by each sensor group in time slot t ;

[0149] The cumulative throughput debt of each sensor group in time slot t .

[0150] The scheduled sensor group, the scheduled sensors, and the transmission power of the scheduled sensors are re-determined based on the updated parameter values.

[0151] In the embodiment of the present invention, the KKT-based dynamic transmission scheduling and power adjustment algorithm can be expressed as follows:

[0152]

[0153] In step 7, the power constraint condition in formula (5) is checked. feasibility.

[0154] Therefore, in this embodiment of the present invention, the dynamic transmission and power regulation problem of the inner and outer layers is abstracted through analysis and modeling. The original problem is transformed based on Lyapunov optimization techniques to facilitate its solution. A dynamic transmission and power regulation method for the inner and outer layers is designed.

[0155] The present invention innovatively designs an internal and external double-layer dynamic transmission and power adjustment mechanism for sensor group scheduling and intra-group sensor scheduling based on KKT (Karush-Kuhn-Tucker) conditions, which achieves a significant improvement in system performance while ensuring the quality of updated data. Specifically: In terms of AoI optimization: compared with the traditional AoI perception scheduling method, the AoI of the present invention is reduced by more than 20%; compared with the benchmark polling scheduling method, the AoI reduction can reach more than 30%. In terms of energy efficiency: while maintaining excellent AoI performance, the energy efficiency of the present invention is comparable to that of the optimal energy efficiency scheduling method. Through the dynamic power adjustment mechanism, the optimal balance between energy consumption and system performance is achieved.

[0156] Compared to the baseline polling scheduling method, the AoI reduction can reach over 30%. The proposed KKT-based dual-layer dynamic transmission and power adjustment method can reduce the system AoI by over 20% compared to AoI-aware scheduling, and over 30% compared to the traditional update method (polling scheduling), while ensuring the quality of the required update data. Compared to the two aforementioned scheduling methods, the system energy consumption is comparable to the most energy-efficient scheduling method.

[0157] The intelligent sensor selection and power regulation strategy based on KKT conditions proposed in this invention has the following advantages: (1) Accurate sensor selection mechanism: Establishing a multi-dimensional evaluation model, comprehensively considering indicators such as sensor energy status, channel quality, and data importance, and optimizing the selection of the optimal sensor combination through KKT conditions to ensure maximum system update efficiency; (2) Efficient power regulation strategy: Dynamically adjust the transmission power to adapt to real-time changes in channel conditions and energy supply to achieve optimal allocation of energy resources and avoid ineffective energy consumption; (3) Significant performance improvement: Effectively reduce the number of invalid updates, significantly improve update efficiency, and maximize the overall system performance while ensuring data quality and AoI requirements.

[0158] Through innovative algorithm design and system architecture, the present invention achieves the following comprehensive performance advantages: (1) Strong adaptability: It can dynamically adapt to fluctuations in energy supply and changes in channel conditions; (2) High resource utilization: It can achieve optimal allocation of resources such as energy and bandwidth; (3) Good scalability: It is suitable for real-time systems of different scales and application scenarios; (4) Low implementation cost: It does not require additional hardware support and can be implemented through software upgrades.

[0159] These advantages give the present invention broad application prospects and significant practical value in real-time application scenarios such as industrial Internet of Things and smart cities.

[0160] Based on the same inventive concept, the present invention also provides a two-layer dynamic information updating device for energy harvesting network, such as Figure 3 , which shows a structural block diagram of a two-layer dynamic information update device for an energy harvesting network. The two-layer dynamic information update device 300 for an energy harvesting network includes:

[0161] The traversal module 301 is used to traverse K sensor groups from 1 to K according to the following steps:

[0162] For the kth sensor group traversed, traverse the nth sensor belonging to the kth sensor group and calculate the power of the nth sensor belonging to the kth sensor group ;

[0163] The power of the nth sensor belonging to the kth sensor group When the power constraint condition is met, the nth sensor belonging to the kth sensor group is used as the primary sensor, and the kth sensor group is determined as the primary sensor group;

[0164] an optimization module 302 for determining, when multiple preliminary sensor groups are determined, a scheduled sensor group, scheduled sensors within the scheduled sensor group, and transmission power of the scheduled sensors with the goal of minimizing transmission energy consumption of the scheduled sensors and information age of information uploaded by the scheduled sensor group;

[0165] The scheduling module 303 is configured to schedule the scheduled sensor to upload information at the transmission power.

[0166] Optionally, calculate the power of the nth sensor belonging to the kth sensor group ,include:

[0167] According to the cumulative throughput debt of the k-th sensor group in time slot t , the channel gain during the transmission of the nth sensor and the length of time slot t T d , calculate the power of the nth sensor belonging to the kth sensor group .

[0168] Optionally, when calculating the power of the nth sensor belonging to the kth sensor group After that, it also includes:

[0169] Verify the power of the nth sensor belonging to the kth sensor group Whether the transmission power constraint of the sensor is met, where the transmission power constraint indicates that the transmission power of the scheduled sensor is not greater than the maximum power of the scheduled sensor and is not greater than the power that can be provided by the actual energy of the scheduled sensor;

[0170] Under the condition that the transmission power constraint of the sensor is met, the nth sensor belonging to the kth sensor group is selected as the primary sensor;

[0171] When the kth sensor group is the scheduled sensor group, the power of the nth sensor of the kth sensor group is The transmission power of the scheduled sensors is determined.

[0172] Optionally, if the transmission power constraint of the sensor is not met, the power of the nth sensor of the kth sensor group is set to Adjust to the power boundary value of the sensor, the power boundary value ;in, represents the remaining energy of the nth sensor in the previous time slot t-1, represents the energy collected by the nth sensor in time slot t, represents the basic energy consumption of the nth sensor;

[0173] When the adjusted power is greater than zero, the nth sensor belonging to the kth sensor group is used as the primary sensor;

[0174] When the kth sensor group is the scheduled sensor group, the power boundary value of the nth sensor of the kth sensor group is Determine the transmission power of the scheduled sensor;

[0175] In a case where the adjusted power is equal to zero, it is determined that the sensor does not meet the transmission power constraint of the sensor.

[0176] Optionally, the optimization module 302 is configured to:

[0177] According to the primary selection of sensor groups in the time slot Energy consumption of uploading information , the cumulative throughput debt of the primary sensor group in time slot t , the total amount of data uploaded by all the preliminary sensors in the preliminary sensor group within time slot t , Information age importance weight parameter of the primary sensor group , the information age of the information transmitted by the primary sensor group in time slot t , calculate the target value of the primary sensor group , which is defined as ;

[0178] Target values ​​for multiple pre-selected sensor groups Sort in ascending order, select the first Smaller The sensor group corresponding to the value is the scheduled sensor group;

[0179] in, represents the energy consumption of the nth sensor belonging to the kth sensor group uploading information at time sequence t , Belongs to the sensor group A collection of sensors; ; Indicates sensor The transmission rate; , Indicates whether the nth sensor of the kth sensor group is a primary selected sensor; represents a non-negative control parameter used to balance the target and throughput debt.

[0180] Optionally, the device further includes an updating module configured to:

[0181] Before the next round of scheduling, update the following parameter values:

[0182] The remaining energy of each sensor in time slot t ;

[0183] , Indicates the maximum storage energy of the sensor;

[0184] Each sensor group has a time slot Energy consumption of uploading information ;

[0185] The information age of the information transmitted by each sensor group in time slot t ;

[0186] The cumulative throughput debt of each sensor group in time slot t .

[0187] Based on the same inventive concept, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes, the steps in the two-layer dynamic information update method for the energy harvesting network as described in any of the above embodiments are implemented.

[0188] Based on the same inventive concept, the present invention also provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the program implements the steps of the two-layer dynamic information update method for energy harvesting networks described in any of the above embodiments.

[0189] Based on the same inventive concept, the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the two-layer dynamic information update method for an energy harvesting network described in any of the above embodiments.

[0190] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0191] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatuses, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0192] The present invention is described with reference to flowcharts and / or block diagrams of methods, terminal devices (apparatus), and computer program products according to the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable terminal device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0193] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0194] These computer program instructions can also be loaded onto a computer or other programmable terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0195] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0196] Finally, it should be noted that, in the present invention, relational terms such as first and second, etc., are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises", or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article, or terminal device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.

[0197] The above is a detailed introduction to a two-layer dynamic information update method for an energy harvesting network provided by the present invention. Specific examples are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A two-layer dynamic information update method for an energy harvesting network, applied to an edge node, wherein the edge node is used to schedule K sensor groups to upload information, where the K sensor groups include N sensors, characterized in that: The method comprises: From 1 to K, traverse K sensor groups according to the following steps: For the kth sensor group traversed, traverse the nth sensor belonging to the kth sensor group and calculate the power of the nth sensor belonging to the kth sensor group ; The power of the nth sensor belonging to the kth sensor group When the power constraint condition is met, the nth sensor belonging to the kth sensor group is used as the primary sensor, and the kth sensor group is determined as the primary sensor group; When multiple preliminary sensor groups are determined, the scheduled sensor groups, the scheduled sensors within the scheduled sensor groups, and the transmission powers of the scheduled sensors are determined with the goal of minimizing the transmission energy consumption of the scheduled sensors and the information age of the information uploaded by the scheduled sensor groups; The scheduled sensor is scheduled to upload information at the transmission power.

2. The dual-layer dynamic information update method for energy harvesting network according to claim 1 is characterized in that: Calculate the power of the nth sensor belonging to the kth sensor group ,include: According to the cumulative throughput debt of the k-th sensor group in time slot t , the channel gain during the transmission of the nth sensor and the length of time slot t T d , calculate the power of the nth sensor belonging to the kth sensor group .

3. The dual-layer dynamic information update method for energy harvesting network according to claim 2, characterized in that: When calculating the power of the nth sensor belonging to the kth sensor group After that, it also includes: Verify the power of the nth sensor belonging to the kth sensor group Whether the transmission power constraint of the sensor is met, where the transmission power constraint indicates that the transmission power of the scheduled sensor is not greater than the maximum power of the scheduled sensor and is not greater than the power that can be provided by the actual energy of the scheduled sensor; Under the condition that the transmission power constraint of the sensor is met, the nth sensor belonging to the kth sensor group is selected as the primary sensor; When the kth sensor group is the scheduled sensor group, the power of the nth sensor of the kth sensor group is The transmission power of the scheduled sensors is determined.

4. The dual-layer dynamic information update method for energy harvesting networks according to claim 3, characterized in that: The method further comprises: If the transmission power constraint of the sensor is not met, the power of the nth sensor in the kth sensor group is reduced. Adjust to the power boundary value of the sensor, the power boundary value ;in, represents the remaining energy of the nth sensor in the previous time slot t-1, represents the energy collected by the nth sensor in time slot t, represents the basic energy consumption of the nth sensor; When the adjusted power is greater than zero, the nth sensor belonging to the kth sensor group is used as the primary sensor; When the kth sensor group is the scheduled sensor group, the power boundary value of the nth sensor of the kth sensor group is Determine the transmission power of the scheduled sensor; In a case where the adjusted power is equal to zero, it is determined that the sensor does not meet the transmission power constraint of the sensor.

5. The dual-layer dynamic information update method for energy harvesting network according to claim 4, characterized in that: With the goal of minimizing the transmission energy consumption of the scheduled sensors and the information age of the information uploaded by the scheduled sensor group, the scheduled sensor group is determined, including: According to the primary selection of sensor groups in the time slot Energy consumption of uploading information , the cumulative throughput debt of the primary sensor group in time slot t , the total amount of data uploaded by all the preliminary sensors in the preliminary sensor group within time slot t , Information age importance weight parameter of the primary sensor group , the information age of the information transmitted by the primary sensor group in time slot t , calculate the target value of the primary sensor group , which is defined as ; Target values ​​for multiple pre-selected sensor groups Sort in ascending order, select the first Smaller The sensor group corresponding to the value is the scheduled sensor group; in, represents the energy consumption of the nth sensor belonging to the kth sensor group uploading information at time sequence t , Belongs to the sensor group A collection of sensors; ; Indicates sensor The transmission rate; , Indicates whether the nth sensor of the kth sensor group is a primary selected sensor; represents a non-negative control parameter used to balance the target and throughput debt.

6. The dual-layer dynamic information update method for energy harvesting network according to claim 5, characterized in that: The method further comprises: Before the next round of scheduling, update the following parameter values: The remaining energy of each sensor in time slot t ; , Indicates the maximum storage energy of the sensor; Each sensor group has a time slot Energy consumption of uploading information ; The information age of each sensor group transmitting information in time slot t ; The cumulative throughput debt of each sensor group in time slot t .

7. A two-layer dynamic information update device for an energy harvesting network, applied to an edge node, wherein the edge node is used to schedule K sensor groups to upload information, where the K sensor groups include N sensors, and the device comprises: The traversal module is used to traverse K sensor groups from 1 to K according to the following steps: For the kth sensor group traversed, traverse the nth sensor belonging to the kth sensor group and calculate the power of the nth sensor belonging to the kth sensor group ; The power of the nth sensor belonging to the kth sensor group When the power constraint condition is met, the nth sensor belonging to the kth sensor group is used as the primary sensor, and the kth sensor group is determined as the primary sensor group; an optimization module for determining, when multiple preliminary sensor groups are determined, the scheduled sensor groups, the scheduled sensors within the scheduled sensor groups, and the transmission power of the scheduled sensors with the goal of minimizing the transmission energy consumption of the scheduled sensors and the information age of information uploaded by the scheduled sensor groups; The scheduling module is used to schedule the scheduled sensor to upload information at the transmission power.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the two-layer dynamic information updating method for the energy harvesting network according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the double-layer dynamic information updating method for an energy harvesting network according to any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the double-layer dynamic information update method for energy harvesting networks described in any one of claims 1 to 6 are implemented.

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