A sensor adaptive sampling rate control method, device, computer equipment and storage medium

Abstract

The embodiment of the present invention discloses a sensor adaptive sampling rate control method, characterized in that the method includes obtaining the short-term average information age a of the sensor in the previous control cycle. k‑1 and the sampling rate u k‑1 , according to the interruption threshold δ of the short-term average information age and the sampling rate u in the previous control cycle k‑1 , the short-term average information age a k‑1 The sampling rate within the current control period is calculated, and sensor data within the current control period is sampled according to the sampling rate within the current control period. The sensor adaptive sampling rate control method of the present invention ensures the real-time performance of sensor data collection under non-stationary service time conditions, significantly improving the timeliness of sensor data in actual networks.

Description

Technical Field

[0001] The present invention relates to the field of sensors and signal processing technology, and in particular to a sensor adaptive sampling rate control method, computer equipment and storage medium. Background Art

[0002] Sensors are widely used in areas such as autonomous vehicles, smart manufacturing, and smart homes. In these scenarios, sensors sample physical processes such as temperature, humidity, speed, and position, encapsulate the sampled data into packets, and transmit them via wireless links to monitors, ensuring that the monitors have real-time access to the latest status of the physical process. Due to the varying value of different packets, outdated and older packets are often less valuable and fail to reflect the latest sensor measurements. Therefore, traditional performance metrics such as throughput and latency cannot measure the real-time nature of sensor data. For example, maximizing throughput requires increasing the sensor sampling rate, but this can lead to network congestion and packet aging. Conversely, minimizing latency requires reducing the sensor sampling rate as much as possible, but this can result in the monitor being unable to obtain sensor data for extended periods, making it difficult to effectively monitor the latest status and thus failing to ensure the real-time nature of sensor data. To address this problem, the real-time nature of sensor data can be characterized by a new performance metric: Age of Information (AoI). This performance metric compromises throughput and latency, representing the duration from data generation to the current moment. It can fully describe the real-time nature of sensor data. A smaller AoI value indicates higher real-time nature of the data.

[0003] Because sensor-generated data packets must be queued on a first-in-first-out (FIFO) basis before transmission, the sensor's sampling rate is crucial to ensuring the timeliness of its data, known as the AoI performance metric. Furthermore, the sampling rate must be adjusted to fully account for the distribution of wireless network service times—the time it takes for a data packet to leave the sender's queue and arrive at the receiver's monitor. For example, when the network's average service time is low, sensors should avoid adopting excessively high sampling rates, as this would cause them to generate data packets too frequently, leading to queues and network congestion.

[0004] Existing sensor sampling rate control technologies for Age of Information (AoI) metrics are primarily designed based on two assumptions: a known service time distribution or a stationary service time distribution. However, in real-world communication systems, the service time distribution is often unknown and non-stationary. For example, each time a user joins or leaves the network, the service time distribution changes, making it difficult to accurately predict. In systems with unknown and non-stationary service time distributions, sampling rate control becomes more critical and challenging, but existing technologies have not been able to address this challenge. Summary of the Invention

[0005] The present invention provides a sensor adaptive sampling rate control method that can significantly improve the timeliness of sensor data in actual networks. The specific technical solution is as follows:

[0006] The first aspect of the present invention provides a sensor adaptive sampling rate control method, the control method includes obtaining the short-term average information age of the sensor in the previous control cycle and sampling rate ,

[0007] At least a preset interruption threshold based on the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age Calculate the sampling rate within the current control cycle ,

[0008] The sensor data in the current control period is sampled according to the sampling rate in the current control period.

[0009] In one possible implementation of the present invention, The sampling rate in the current control cycle Calculation and The sampling rate in the current control cycle The calculation method is different.

[0010] In a possible implementation of the embodiment of the present invention, the interruption threshold based on at least the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age Calculate the sampling rate within the current control cycle , specifically including calculating the sampling rate in the current control period according to the following:

[0011]

[0012] in, is the time-varying pseudo partial derivative parameter, and is the weight parameter, is the Lambert W function, It is calculated as follows:

[0013]

[0014]

[0015] Where, , , is the weight parameter, is the initial value of the time-varying pseudo partial derivative parameter, is the small value judgment threshold set in the sensor estimation value calculation, so that .

[0016] In a possible implementation of the embodiment of the present invention, the short-term average information age Calculated by the following formula:

[0017]

[0018] in For the The first successfully received The system time of a data packet is calculated using the following formula: ;

[0019] , Respectively represent In the control cycle The generation time and reception time of a successfully received data packet, where , To control the number of packets successfully received during the period, Indicates the control cycle index;

[0020] It is The time interval between successfully received data packets in a control cycle is calculated according to the following formula:

[0021]

[0022] In a possible implementation manner of the embodiment of the present invention, the interruption threshold according to the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age After calculating the sampling rate in the current control period, the control method further includes:

[0023] Calculate the upper limit of the sampling rate update based on the packet loss rate, the average value and variance of the service time;

[0024] When the interruption threshold according to the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age When it is calculated that the sampling rate in the current control period is greater than the sampling rate update upper limit, the sampling rate in the current control period is set as the sampling rate update upper limit.

[0025] In a possible implementation of the embodiment of the present invention, sampling the sensor data in the current control period according to the sampling rate in the current control period specifically includes:

[0026] The sensor data of the current control period is sampled according to the sampling rate within the current control period set as the sampling rate update upper limit.

[0027] In a possible implementation manner of the embodiment of the present invention, calculating the sampling rate update upper limit based on the packet loss rate and the average and variance of the service time specifically includes calculating the sampling rate update upper limit according to the following steps:

[0028]

[0029] in, Representing the The packet loss rate, mean value and variance of service time in a control cycle are calculated as follows:

[0030]

[0031]

[0032]

[0033] in, is the weight parameter.

[0034] A second aspect of the present invention further discloses a sensor adaptive sampling rate control device, the device comprising:

[0035] Acquisition unit, used to obtain the short-term average information age of the sensor in the previous control cycle and sampling rate ,

[0036] A calculation unit for determining a preset interruption threshold based on at least a short-term average information age , the sampling rate in the previous control cycle , the short-term average information age Calculate the sampling rate within the current control cycle ,

[0037] The sampling unit is used to sample the sensor data in the current control period according to the sampling rate in the current control period.

[0038] The third aspect of the present invention further discloses a computer device, which includes a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the steps of the sensor adaptive sampling rate control method as claimed in any one of the first aspects of the present invention.

[0039] The fourth aspect of the present invention further discloses a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the sensor adaptive sampling rate control method as described in any one of the first aspects of the present invention is implemented.

[0040] Beneficial effects of the present invention: The sensor adaptive sampling rate control method of the present invention includes obtaining the short-term average information age of the sensor in the previous control cycle and the sampling rate , based on the interruption threshold of the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age The sampling rate in the current control cycle is calculated, and the sensor data in the current control cycle is sampled according to the sampling rate in the current control cycle. The technical solution of the present invention is to calculate the average information age of the sensor in the previous control cycle. and the sampling rate and the interruption threshold of the short-term average information age The adaptive sampling rate of the current control cycle is calculated, which ensures the adaptive adjustment of the sampling rate in units of control cycle. Compared with the existing technology, it can also ensure the real-time performance of sensor sensing data under non-stationary service time conditions, and significantly improves the timeliness of sensor data in actual networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A flow chart of a sensor adaptive sampling rate control method according to an embodiment of the present invention;

[0042] Figure 2 is the sensor system model in the embodiment of the present invention;

[0043] Figure 3 is the evolution of AoI in each control cycle in the embodiment of the present invention;

[0044] Figure 4a is the average round trip time of each control cycle in two scenarios in the actual system experiment of the embodiment of the present invention

[0045] Figure 4b The short-term average AoI of the ASC of the embodiment of the present invention and other existing technologies in scenario 1 in the actual system experiment is

[0046] Figure 4c The short-term average AoI of the ASC proposed by this invention and other existing technologies in scenario 2 in the actual system experiment

[0047] Figure 5 Schematic diagram of the computer device structure according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention will be further described in detail below with reference to the accompanying drawings. It is apparent that the embodiments described are only some, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0049] It should be noted that the terms "first," "second," etc. are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0050] In the above description, the reference terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. 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 can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0051] In the field of sensor data transmission, existing techniques for selecting and adjusting sampling rates primarily include three approaches. The first is fixed sampling (FS). This technique eliminates the need for acknowledging the transmission success of each data packet, typically an ACK or NACK message. Instead, the sensor consistently samples at a fixed rate. When the statistical distribution of service times is known, FS can minimize the long-term average AoI by finding the optimal sampling rate. However, when the statistical distribution of service times is unknown and non-stationary, FS, without dynamic sampling rate adjustment, cannot effectively reduce the AoI and ensure the timeliness of sensor data. The second approach is threshold waiting (TW). Unlike FS, TW requires acknowledging the transmission success of each data packet. After each data packet is transmitted, i.e., when the transmitting sensor receives the feedback message, it waits for a period of time before sampling and transmitting the next data packet. The optimal waiting time can be determined by combining the time the feedback message is received and the current instantaneous AoI of the monitor. TW technology can minimize the long-term average AoI under the condition of a stable service time distribution, but it struggles with non-stationary service times. Furthermore, the confirmation messages required by TW technology consume additional communication resources, impacting the transmission and real-time performance of sensor data. A third method is the Age Control Protocol (ACP). ACP technology also requires confirmation messages to confirm the success or failure of each data packet transmission. ACP estimates the congestion level of the current sensor sender queue based on confirmation message feedback over a period of time. It selects the highest sampling rate possible without causing congestion, thereby reducing the AoI of sensor data and ensuring its timeliness. ACP technology is relatively aggressive in selecting the sampling rate. While ACP can achieve extremely low AoI when the service time distribution is stable, its AoI performance fluctuates significantly when the service time distribution is non-stationary, making it unable to cope with dynamic network changes.

[0052] Since existing technologies cannot be used in scenarios where the service time distribution is unknown and non-stationary, the present invention focuses on this pain point and mainly addresses the following two specific shortcomings.

[0053] Disadvantage 1: Existing technologies struggle to guarantee short-term average AoI under non-stationary service time conditions. Existing technologies often focus on minimizing the long-term average AoI, which is the system's average AoI when the number of received packets approaches infinity. However, when the service time distribution is non-stationary, meaning it is constantly changing, the system should prioritize the short-term average AoI over the long-term average AoI. The short-term average AoI is defined as the average AoI of recently received packets monitored by the receiver. This is because under non-stationary service time conditions, the short-term average AoI can experience significant fluctuations. Therefore, even if the long-term average AoI remains low, the short-term average AoI can still be too high during periods of poor service, leading to system failures. Under non-stationary service time conditions, ensuring the system's short-term average AoI throughout its entire operational period is particularly important.

[0054] Disadvantage 2: Existing technologies lack a clear model to describe the relationship between sampling rate and short-term average AoI. Existing technologies are usually based on queuing theory, characterizing the relationship between sampling rate and long-term average AoI under the condition of known service time distribution. For example, when the service time is exponentially distributed, a closed-form expression between the long-term average AoI and the sampling rate is given. However, these expressions are no longer applicable when the service time is unknown and non-stationary, and cannot be used to further adjust the sensor sampling rate. Therefore, under the condition of unknown and non-stationary service time, it is necessary to characterize the relationship between the sensor sampling rate and the short-term average AoI without relying on any prior information assumptions about the service time in order to further adjust the sampling rate.

[0055] To address the aforementioned technical issues in sensor signal processing, the present invention proposes an adaptive sampling rate control (ASC) technology for sensors, which can be effectively applied to practical communication systems with unknown and non-stationary service times. To address the existing difficulty in ensuring short-term average AoI under non-stationary service time conditions, the proposed ASC technology adjusts the sensor sampling rate in units of a control period. This design eliminates the need for any prior knowledge or assumptions about service time distribution, making the proposed ASC technology applicable to any practical scenario.

[0056] The first embodiment of the present invention discloses a method for controlling the adaptive sampling rate of a sensor. Figure 1 As shown, the sensor adaptive sampling rate control method includes:

[0057] Step S1: Obtain the short-term average information age of the sensor in the previous control cycle and the sampling rate ;

[0058] Step S2: based on the interruption threshold of the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age Calculate the sampling rate within the current control cycle;

[0059] Step S3: sampling the sensor data in the current control period according to the sampling rate in the current control period.

[0060] The embodiment of the present invention uses the average information age of the sensor in the previous control cycle and the sampling rate and the interruption threshold of the short-term average information age The adaptive sampling rate of the current control cycle is calculated, which ensures the adaptive adjustment of the sampling rate in units of control cycle. Compared with the existing technology, it can also ensure the real-time performance of sensor sensing data under non-stationary service time conditions, and significantly improves the timeliness of sensor data in actual networks.

[0061] In one implementation of the embodiment of the present invention, the interruption threshold based on at least the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age Calculate the sampling rate within the current control cycle , specifically including calculating the sampling rate in the current control period according to the following:

[0062]

[0063] in, is the time-varying pseudo partial derivative parameter, and is the weight parameter, is the Lambert W function, It is calculated as follows:

[0064]

[0065] ;

[0066] Where, , , is the weight parameter, is the initial value of the time-varying pseudo partial derivative parameter, is the small value judgment threshold set in the sensor estimation value calculation, so that .

[0067] In one possible implementation of the present invention, The sampling rate in the current control cycle Calculation and The sampling rate in the current control cycle The calculation method is different.

[0068] In a possible implementation of the embodiment of the present invention, the short-term average information age Calculated by the following formula:

[0069]

[0070] in, For the The first successfully received The system time of a data packet is calculated using the following formula: ;

[0071] , Respectively represent In the control cycle The generation time and reception time of a successfully received data packet, where , To control the number of packets successfully received during the period, Indicates the control cycle index;

[0072] It is The time interval between successfully received data packets in a control cycle is calculated according to the following formula:

[0073]

[0074] In a possible implementation manner of the embodiment of the present invention, the interruption threshold according to the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age After calculating the sampling rate in the current control period, the control method further includes:

[0075] Calculate the upper limit of the sampling rate update based on the packet loss rate, the average value and variance of the service time;

[0076] When the interruption threshold according to the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age When it is calculated that the sampling rate in the current control period is greater than the sampling rate update upper limit, the sampling rate in the current control period is set as the sampling rate update upper limit.

[0077] In a possible implementation of the embodiment of the present invention, sampling the sensor data in the current control period according to the sampling rate in the current control period specifically includes:

[0078] The sensor data of the current control period is sampled according to the sampling rate within the current control period set as the sampling rate update upper limit.

[0079] In a possible implementation manner of the embodiment of the present invention, calculating the sampling rate update upper limit based on the packet loss rate and the average and variance of the service time specifically includes calculating the sampling rate update upper limit according to the following steps:

[0080]

[0081] in, Representing the The packet loss rate, mean value and variance of service time in a control cycle are calculated as follows:

[0082]

[0083]

[0084]

[0085] in, is the weight parameter.

[0086] In a possible implementation of the embodiment of the present invention, the preset interruption threshold is related to at least one of the sensor signal change amplitude, sensor system accuracy, and sensor system response time, or the interruption threshold is set to one of a fixed value, an adaptive value, or a multi-level threshold.

[0087] In one possible implementation of the present invention, The sampling rate in the current control cycle Calculation and The sampling rate in the current control cycle The calculation method is different.

[0088] In order to illustrate that the sampling rate in the current control cycle can be obtained based on the above calculation, the short-term average information age in the control cycle unit is within the interrupt threshold. Under the constraint of , the long-term average AoI of the system can be minimized, and the real-time performance of sensor data can be guaranteed under non-stationary service time conditions, which significantly improves the timeliness of sensor data in the actual network, as described in detail below.

[0089] The present invention calculates the adaptive sampling rate that can minimize the system's short-term average AoI from the unknown and non-stationary service time of the sensing system as follows.

[0090] Refer to the attached Figure 2 is the sensor system model in the embodiment of the present invention. The embodiment of the present invention considers Figure 2 In the sensing system shown, the sensor at the transmitting end samples the physical process it observes, and encapsulates the sampled data into data packets and sends them to the monitor at the receiving end via the transmission network. Considering that the sensing equipment is usually connected to the monitor through the UDP / TCP protocol and adopts a FIFO queue, the present invention assumes that the generated data packets will be stored in a FIFO queue and transmitted on a first-come, first-served basis. In the proposed ASC technology, each control cycle consists of N successfully received data packets. At the end of each control cycle, the receiving end determines the sampling rate that the sending end should adopt in the next control cycle and feeds it back to the sending end through the UDP / TCP protocol. Whether each control cycle ends or not is determined by the counter at the receiving end. Whenever the counter reaches N, the current control cycle ends and the next control cycle begins.

[0091] Because the service time of transmission networks in real systems is often unknown and non-stationary, this paper considers a general piecewise short-term stationary service time model when constructing the system model. In this model, the service time distribution can vary between different control cycles. Furthermore, the system model considered in this paper does not make any a priori assumptions about the expression of the service time distribution.

[0092] Since the sensor system considered in the present invention adopts AoI as a performance indicator, the mathematical expression of this performance indicator will be further explained below. Represents the index of each control cycle, The sampling rate of each control cycle is One data packet is sampled per second. Figure 2 As shown, , Respectively represent In the control cycle The generation time and reception time of a successfully received data packet, where .

[0093] The present invention is used and Indicates the index of the latest received data packet, The instantaneous AoI at a moment can be defined as:

[0094]

[0095] In order to further characterize the short-term average AoI in each control cycle, let For the The first successfully received The system time of a data packet can be expressed as

[0096]

[0097] set up It is The generated time interval between successfully received data packets in a control cycle can be expressed as:

[0098]

[0099] like Figure 3 As shown, through the area You can get the The short-term average AoI of the system over a control period can be expressed as:

[0100]

[0101]

[0102]

[0103] In the formula It is not difficult to find that the receiver monitor can be based on The method is based only on the reception time of each data packet. and generation time , calculate the short-term average AoI for each cycle .in, can be obtained directly at the receiving end, and Can be encapsulated in the data packet generated by the sender.

[0104] Existing sampling rate control technologies primarily minimize the system's long-term average AoI under the premise of stable service time. However, in real-world scenarios, the service time of transmission networks is often non-stationary. Minimizing the long-term average AoI cannot guarantee the system's short-term average AoI, resulting in significant jitter in the short-term average AoI and even system stagnation. To ensure the real-time performance of sensor data collection under non-stationary service time conditions, the present invention reduces the probability of interruption of the short-term average AoI by addressing the following issues:

[0105]

[0106]

[0107]

[0108] Where, is the number of control cycles, For the interrupt judgment function, when It is equal to 1 when it is positive, and 0 otherwise. is the interruption threshold of the short-term average AoI. Aims to minimize the target threshold The short-term AoI outage probability is . In addition, the constraint Indicates the sampling rate of the current control cycle It can only be determined by the sampling rate of the previous control cycle and the short-term average AoI, which meets the actual system requirements.

[0109] The present invention proposes a sensor adaptive sampling rate control (ASC) technology. This technology aims to minimize the interruption probability of the system's short-term average AoI, that is, the above-mentioned problem (5), and ensure the real-time performance of sensor data acquisition under non-stationary service time conditions. At the end of each control cycle, the ASC technology proposed in this invention will determine the sampling rate of the sensor in the next control cycle based on the system's historical sampling rate and short-term average AoI. First, it is necessary to establish a dynamic linear data model based on the historical acquisition data to characterize the mathematical relationship between the sampling rate and the short-term average AoI. Next, the optimal sampling rate for the next control cycle is determined based on the dynamic linear data model. The following will specifically explain the proposed ASC technology from two aspects: the dynamic linear data model and the sampling rate update, and further indicate its deployment in the actual system.

[0110] Dynamic Linear Data Model

[0111] ASC technology uses the short-term average AoI in each control cycle and sampling rate The unknown dynamic relationship between them is transformed into the following equivalent dynamic linear data model:

[0112]

[0113] In the formula , , is the time-varying pseudo-partial derivative (PPD) parameter. Based on formula (6), and The complex relationship between and Through time-varying The non-stationary and unknown service time characteristics of the considered sensor system can be expressed as The dynamic changes of . Estimate in each control cycle It can be established and The relationship between them is estimated as follows:

[0114]

[0115] Where, is the weight parameter.

[0116] To further improve estimation accuracy and ensure stability, the proposed ASC technology follows the following reset scheme to cope with erroneous estimation when service time abruptly changes:

[0117]

[0118] In the formula is the initial value of PPD, A small positive number to ensure .

[0119] Sampling rate update

[0120] Adopting the estimation formula of formula (7) can establish the formula (6) and Next, we will further solve the sampling rate control problem in (5) based on this relationship, with the goal of minimizing the short-term average AoI interruption probability.

[0121] In the embodiment of the present invention, The sampling rate in the current control cycle Calculation and The sampling rate in the current control cycle The calculation method is different.

[0122] The sampling rate for each control cycle can be updated as follows:

[0123]

[0124] Where, and is the weight parameter, is the Lambert W function.

[0125] The embodiments of the present invention address the problem that existing sensor sampling rate calculation and adjustment algorithms cannot be used in scenarios with unknown and non-stationary service times. The proposed ASC technology first constructs a dynamic linear data model between the sampling rate and the short-term average AoI. This model does not require a priori assumptions about the service time distribution and can be used in any actual system. Based on this dynamic linear data model, the embodiments of the present invention propose an ASC algorithm with the goal of minimizing the interruption probability and provides a dynamic adjustment control scheme for the sampling rate. The technical solution of the embodiments of the present invention can significantly reduce the interruption probability of the short-term average AoI of the sensor system under conditions of unknown and non-stationary service times, and the performance improvement compared to the existing technology reaches 100%, providing an efficient sampling control technology for actual sensor systems.

[0126] To further illustrate the sensor adaptive sampling rate control method and its reduction of the short-term average AoI outage probability under unknown and non-stationary service time conditions in an embodiment of the present invention, the following is a detailed description from the perspective of deployment and verification in an actual system.

[0127] The following is a description of the Adaptive Sampling Control (ASC) technology deployment in an embodiment of the present invention:

[0128] Common input parameters for all control cycles: interrupt threshold , weight coefficient The number of packets per control cycle N

[0129] Input of the kth control cycle:

[0130] 1: Packet reception and generation time

[0131] Output of the kth control cycle:

[0132] 2: Sampling rate

[0133] 3: null

[0134] 4: Initialization:

[0135] 5: for each control cycle do

[0136] 6: Update PPD

[0137] 7: if or or then

[0138] 8:

[0139] 9: end if

[0140] 10:if then

[0141] 11: Update sampling rate

[0142] 12:else

[0143] 13: Update sampling rate

[0144] 14:end if

[0145] 15:

[0146] 16: Will Feedback to the sender, inverse mapping

[0147] 17: Received from the sender Data packets

[0148] 18: Calculate the average AoI of this control cycle

[0149] 19: Calculation , update the sampling rate upper limit

[0150] 20: end for

[0151] The pseudo code of ASC technology is given above. The pseudo code can be deployed in Figure 1 The receiver monitor of the system shown in the figure. The sensor and the monitor can be connected via UDP / TCP protocol. In the initialization phase, the sensor can adopt a random sampling rate in the first two control cycles, and the receiver can calculate the initialization parameters required by the algorithm based on the first two control cycles. packages, according to 、 、 、 and The short-term average AoI and sampling rate upper limit of the current control cycle are calculated by the formula, and then and The PPD of the next control cycle is calculated by the formula, and finally according to and The sampling rate of the next control cycle is calculated and fed back to the sending end. After receiving the update, the sending end adopts the new sampling rate, and the cycle continues.

[0152] To demonstrate the benefits of the present invention's technical solution, we conducted a real-world system experiment comparing the short-term average AoI outage probability of the proposed ASC technology with that of existing FS, TW, and ACP technologies under non-stationary service time conditions. Since existing FS, TW, and ACP technologies have been introduced above, they will not be repeated here.

[0153] The present invention uses three micro hosts to build an experimental platform. These micro hosts are equipped with Ubuntu 20.04 operating system, Intel Core i7-4510U CPU and 4G memory. The data between the micro hosts are transmitted through the Atheros AR9382 wireless network card, adopting the IEEE 802.11 link layer and physical layer protocols. Among them, one micro host serves as an access point (AP) and the other two serve as terminals (STA). In the experiment, AP represents the receiving end monitor, one STA represents the sensor, and the other STA will generate a data stream of random size, representing the unknown impact of the remaining devices in the network on the sensor link. Each data packet generated by the sensor is 25 bytes in size and is transmitted by the UDP transport layer protocol. Finally, each control cycle contains The weight coefficients of the proposed ASC technology are shown below.

[0154]

[0155] This experiment considers two different scenarios. In scenario 1, another STA used to generate data flow provides 0.1Mbps of traffic, which represents a scenario with a small load and a basically stable service time distribution. In scenario 2, the traffic generated by another STA varies randomly between 0 and 0.8Mbps, which represents a scenario with a dynamic change in network load and a non-stationary service time distribution. In order to better reflect the difference in service time in the two considered scenarios, Figure 3 The average round-trip time for each control cycle is shown in Figure 2. Since average service time cannot be directly measured in real systems, we measured the average round-trip time (ART) in our experiments, which is highly correlated with average service time. As can be seen, in Scenario 1, the average round-trip time across different control cycles is almost 5ms, while in Scenario 2, the average round-trip time varies dynamically between 10ms and 20ms.

[0156] Figure 4b The short-term average AoI of each control cycle in scenario 1 is shown. Since the average RTT in scenario 1 is low, a relatively strict interruption threshold is adopted in this experiment. .like Figure 4bAs shown in Figure 2, around the 80th, 200th, and 300th control cycles, the short-term average AoI of the ASC technique fails to reach the expected interruption threshold. This is due to the high RTT in these time periods, as shown in Figure 2. Figure 3 As shown in the figure, the short-term average AoI cannot reach 10ms. In addition, because the FS technology adopts a fixed sampling rate, its short-term average AoI deviates significantly from the expected interruption threshold when the service time distribution changes, that is, when the average RTT changes. Finally, although the TW and ACP technologies can achieve extremely low short-term average AoI when the average RTT is low, their short-term average AoI increases significantly when the average RTT increases, far exceeding the target interruption threshold. This is because, on the one hand, TW and ACP technologies rely on the confirmation message feedback of each data packet, which incurs high overhead in actual systems. On the other hand, ACP will aggressively select a higher sampling rate. Although such a strategy can achieve an extremely low short-term average AoI under stable service time conditions, it is very likely to cause network congestion when the service time changes.

[0157] Figure 4c The short-term average AoI of each control cycle in scenario 2 is shown. Since the average RTT of each control cycle in scenario 2 is high and changes dynamically, the experiment adopts As the interrupt threshold. Figure 5 It can be seen that the ASC technology proposed in the present invention can effectively control the short-term average AoI of the sensor below the interruption threshold under non-stationary and unknown service time distribution conditions. However, the short-term average AoI of the existing FS, TW and ACP technologies deviates significantly from the interruption threshold required by the system when the service time is non-stationary, and cannot guarantee the real-time performance of the sensor data. Finally, Table 3 shows the short-term average AoI interruption probability of different sampling technologies in the experiment. Compared with the existing technology, the ASC technology proposed in the present invention can reduce the interruption probability by about 100%, significantly improving the timeliness of sensor data in the actual network. The specific short-term average AoI interruption probability of different sampling technologies is as follows:

[0158]

[0159] Another embodiment of the present invention further discloses a sensor adaptive sampling rate control device, characterized in that the device includes:

[0160] The acquisition unit is used to obtain the short-term average information age and sampling rate of the sensor in the previous control cycle.

[0161] A calculation unit, configured to calculate the sampling rate in the current control cycle based on at least a preset interruption threshold of the short-term average information age, the sampling rate in the previous control cycle, and the short-term average information age. ,

[0162] The sampling unit is used to sample the sensor data in the current control period according to the sampling rate in the current control period.

[0163] Another embodiment of the present invention further discloses a computer storage medium having a computer program stored thereon. When the computer program is executed by a processor, the sensor adaptive sampling rate control method as described in any one of the first aspects of the present invention is implemented.

[0164] Example 4

[0165] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the computer program is executed by the processor, it implements the sensor adaptive sampling rate control method of any implementation method of the above embodiment. Since any implementation method of each specific step has been described in detail in the above embodiment and each implementation method, it will not be repeated here.

[0166] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0167] The sensor adaptive sampling rate control method of the present invention, as well as the computer device and computer storage medium of the above-mentioned embodiments, all calculate the adaptive sampling rate of the current control period through the average information age and sampling rate of the sensor in the previous control period and the interruption threshold of the short-term average information age, thereby ensuring the adaptive adjustment of the sampling rate in units of control periods. Compared with the existing technology, it can also ensure the real-time performance of sensor sensing data under non-stationary service time conditions, significantly improving the timeliness of sensor data in actual networks.

[0168] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0169] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A sensor adaptive sampling rate control method, characterized in that: The method comprises, Get the short-term average information age of the sensor in the last control cycle and sampling rate , At least a preset interruption threshold based on the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age Calculate the sampling rate within the current control cycle , The sensor data in the current control period is sampled according to the sampling rate in the current control period.

2. A sensor adaptive sampling rate control method according to claim 1, characterized in that: The sampling rate in the current control cycle Calculation and The sampling rate in the current control cycle The calculation method is different.

3. A sensor adaptive sampling rate control method according to claim 2, characterized in that: The interruption threshold based on at least the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age Calculate the sampling rate within the current control cycle , specifically including calculating the sampling rate in the current control period according to the following: in, is the time-varying pseudo partial derivative parameter, and is the weight parameter, is the Lambert W function, It is calculated as follows: Where, , , is the weight parameter, is the initial value of the time-varying pseudo partial derivative parameter, is the small value judgment threshold set in the sensor estimation value calculation, so that .

4. The sensor adaptive sampling rate control method according to claim 1, wherein: The short-term average information age Calculated by the following formula: in For the The first successfully received The system time of a data packet is calculated using the following formula: ; , Respectively represent In the control cycle The generation time and reception time of a successfully received data packet, where , To control the number of packets successfully received during the period, Indicates the control cycle index; It is The time interval between successfully received data packets in a control cycle is calculated according to the following formula:

5. The sensor adaptive sampling rate control method according to claim 4, characterized in that: The interruption threshold based on the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age After calculating the sampling rate in the current control period, the control method further includes: Calculate the upper limit of the sampling rate update based on the packet loss rate, the average value and variance of the service time; When the interruption threshold according to the short-term average information age , the sampling rate in the previous control cycle , the short-term average information age When it is calculated that the sampling rate in the current control period is greater than the sampling rate update upper limit, the sampling rate in the current control period is set as the sampling rate update upper limit.

6. The sensor adaptive sampling rate control method according to claim 5, characterized in that: The sampling of sensor data in the current control period according to the sampling rate in the current control period specifically includes: The sensor data of the current control period is sampled according to the sampling rate within the current control period set as the sampling rate update upper limit.

7. The sensor adaptive sampling rate control method according to claim 5, characterized in that: The updating upper limit of the sampling rate is calculated based on the packet loss rate and the average and variance of the service time, specifically including calculating the updating upper limit of the sampling rate based on the following steps: in, Representing the The packet loss rate, mean value and variance of service time in a control cycle are calculated as follows: in, is the weight parameter.

8. A sensor adaptive sampling rate control device, characterized in that: The device comprises: Acquisition unit, used to obtain the short-term average information age of the sensor in the previous control cycle and sampling rate , A calculation unit for determining a preset interruption threshold based on at least a short-term average information age , the sampling rate in the previous control cycle , the short-term average information age Calculate the sampling rate within the current control cycle , The sampling unit is used to sample the sensor data in the current control period according to the sampling rate in the current control period.

9. A computer device, characterized in that: The computer device includes a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the steps of the sensor adaptive sampling rate control method according to any one of claims 1 to 8.

10. A computer storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the sensor adaptive sampling rate control method according to any one of claims 1 to 8 is implemented.

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