Access Control Method and System for Internet of Things Sensing Devices

Abstract

This application relates to the field of Internet of Things access control technology, and specifically relates to an access control method and system applicable to Internet of Things sensing devices. The method includes: for each sensing device in the Internet of Things, obtaining relevant data; analyzing the degree of data change difference between the data sequence to be uploaded by the sensing device and its respective historical uploaded data sequences, and constructing a historical difference index; analyzing the correlation degree of the data sequence to be uploaded among each sensing device and other sensing devices in its respective clustering cluster, and combining the historical difference index of each sensing device and the deviation of the historical difference index within the clustering cluster where each sensing device is located, to construct the key cooperation coefficient of each sensing device; according to the total number of uploads of each sensing device and the prominence of the size of the uploaded data packets, constructing the priority index of each sensing device; and combining the priority index of each sensing device to perform access control on the Internet of Things sensing devices. This application can improve the access effect of the sensing devices.

Description

Technical Field

[0001] This application relates to the technical field of Internet of Things access control, and specifically to an access control method and system applicable to Internet of Things sensing devices. Background Art

[0002] The access of Internet of Things sensing devices is the basis for realizing data collection and processing, intelligent control, and remote management. However, due to the large amount and diverse types of data of Internet of Things sensing devices, the sensing devices may cause channel congestion during access, resulting in high access latency and affecting the data transmission efficiency.

[0003] To improve the access effect of the Internet of Things on sensing devices, the existing technology has proposed an ACB control mechanism, which uses an ACB factor to control the access of devices. However, in the existing ACB control technology, a random number between 0 and 1 is generated for the devices that need to access, and the devices that generate a random number less than or equal to the ACB factor are allowed to access, so as to achieve the purpose of access control and reduce channel congestion. However, ACB control is a random access control technology and does not consider the importance or urgency of the data of sensing devices. Therefore, it may lead to the phenomenon that important data is delayed in access, thus affecting the access effect. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an access control method and system applicable to Internet of Things sensing devices, and the specific technical solutions adopted are as follows:

[0005] The embodiment of this application provides an access control method applicable to Internet of Things sensing devices, including the following steps:

[0006] For each sensing device in the Internet of Things, obtain the transmission data of the sensing device at each acquisition moment to form the data sequence to be uploaded of the sensing device, count the total number of data uploads and the size of uploaded data packets of the sensing device within the historical period, and obtain each historical upload data sequence of the sensing device;

[0007] Analyze the degree of data change difference between the data sequence to be uploaded of the sensing device and its each historical upload data sequence, and construct the historical difference index of the sensing device;

[0008] Cluster the historical difference indexes of all sensing devices, analyze the correlation degree of the data sequence to be uploaded between each sensing device and other sensing devices in its cluster, and combine the historical difference index of each sensing device and the deviation situation of the historical difference index within the cluster where each sensing device is located to construct the key cooperation coefficient of each sensing device;

[0009] Construct the priority index of each sensing device according to the total number of uploads of each sensing device within the historical period and its prominence in terms of the size of the uploaded data packets among all sensing devices.

[0010] Combined with the priority index of each sensing device, perform access control on the IoT sensing devices.

[0011] Preferably, the calculation method of the historical difference index of the sensing device is as follows:

[0012] In the formula, A i is the historical difference index of the i-th type of sensing device; n is the total number of data uploads of the i-th type of sensing device within the historical period; B i，a is the absolute value of the difference between the peak-valley difference index of the data sequence to be uploaded of the i-th type of sensing device and the a-th historical uploaded data sequence. The peak-valley difference indices of the data sequence to be uploaded and the historical uploaded data sequence are obtained respectively according to the data change situations of the peak values and valley values in the data sequence to be uploaded and the historical uploaded data sequence; K i，a is the absolute value of the difference between the change rates of the data sequence to be uploaded of the i-th type of sensing device and the a-th historical uploaded data sequence; P i，a is the similarity index between the i-th type of sensing device and the a-th historical uploaded data sequence; τ is a constant to avoid the denominator being zero.

[0013] Preferably, the obtaining of the peak-valley difference indices of the data sequence to be uploaded and the historical uploaded data sequence further includes:

[0014] For the data sequence to be uploaded, extract the peak values and valley values in the data sequence to be uploaded, calculate the average value of the absolute differences between each peak value and its previous and subsequent data, and denote it as the mutation index of each peak value. Calculate the product of the variance and the mean of the mutation indices of all peak values, and denote it as the mutation difference index of the peak values. Correspondingly, adopt the calculation method of the mutation difference index of the peak values to obtain the mutation difference index of the valley values. Take the average value of the mutation difference index of the peak values and the mutation difference index of the valley values as the peak-valley difference index of the data sequence to be uploaded;

[0015] For each historical uploaded data sequence, adopt the obtaining method of the peak-valley difference index of the data sequence to be uploaded to calculate the peak-valley difference index of the historical uploaded data sequence.

[0016] Preferably, the obtaining of the change rate further includes:

[0017] For the data sequence to be uploaded of the sensing device and each historical uploaded data sequence, obtain the first-order difference sequences respectively, and take the average value of the absolute values of all elements in the first-order difference sequences as the change rates of the data sequence to be uploaded and each historical uploaded data sequence respectively.

[0018] Preferably, the calculation method of the key cooperation coefficient of each sensing device is as follows:

[0019] In the formula, C i is the key cooperation coefficient of the i-th sensing device; A i is the historical difference index of the i-th sensing device; is the intra-cluster cooperation index of the i-th sensing device, which is calculated by the correlation degree of the data sequence to be uploaded between the i-th sensing device and other sensing devices in its clustering cluster; G is the ratio of the number of historical difference indexes in the clustering cluster where the i-th sensing device is located to the number of all historical difference indexes; D i is the mean value of the historical difference indexes in the clustering cluster where the i-th sensing device is located; D max is the maximum value of the mean values of the intra-cluster historical difference indexes in all clustering clusters.

[0020] Preferably, the calculation method of the intra-cluster cooperation index of the i-th sensing device further includes:

[0021] Calculate the mutual information value of the data sequence to be uploaded between the i-th sensing device and other sensing devices in its clustering cluster, and record the mean value of all mutual information values as the intra-cluster cooperation index of the i-th sensing device.

[0022] Preferably, the calculation method of the priority index of each sensing device is as follows:

[0023] In the formula, F i is the priority index of the i-th sensing device; C i is the key cooperation coefficient of the i-th sensing device; S ci is the upload significance index of the total number of uploads of the i-th sensing device; S bi is the upload significance index of the upload data packet size of the i-th sensing device.

[0024] Preferably, the calculation method of the upload significance index includes:

[0025] For the total number of uploads of the sensing device in the historical period, calculate the average value of the absolute difference between the total number of uploads of the sensing device and the total number of uploads of all other sensing devices, and record it as the upload outlier index of the sensing device. Record the ratio of the total number of uploads of the sensing device to the maximum value among all the total number of uploads as the upload maximum ratio. Take the product of the upload outlier index and the upload maximum ratio as the upload significance index of the total number of uploads of the sensing device;

[0026] For the upload data packet size of the sensing device in the historical period, adopt the calculation method of the upload significance index of the total number of uploads to obtain the upload significance index of the upload data packet size of the sensing device.

[0027] Preferably, the access control for the Internet of Things sensing devices further includes:

[0028] Taking the reciprocal of the priority index of each sensing device in the Internet of Things as the delay coefficient of each sensing device, normalizing the delay coefficients of each sensing device, replacing the random number between 0 and 1 generated for the sensing device in the ACB mechanism with the normalized delay coefficient, and sending an access request to the sensing device whose normalized delay coefficient is less than or equal to the ACB factor in the ACB mechanism.

[0029] The embodiment of the present application also provides an access control system applicable to Internet of Things sensing devices, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the access control method applicable to Internet of Things sensing devices described in any one of the above are implemented.

[0030] As can be seen from the above, the access control method and system applicable to Internet of Things sensing devices provided by the present application have at least the following beneficial effects:

[0031] The present application constructs a historical difference index to calculate whether there is a large difference between the latest data collected by the sensing device and the historical uploaded data, evaluate the abnormality degree of the latest data, and thus initially determine its priority; then constructs a key cooperation coefficient to calculate the cooperative change between the latest data collected by the sensing device, and further evaluate the importance of the collected data; then constructs a priority index, and comprehensively evaluates the access priority of the sensing device by comparing the upload differences of different sensing devices;

[0032] Aiming at the problem that the existing ACB access control technology only generates random numbers for the sensing devices that need to upload, and the important data is delayed in access due to the lack of consideration of the priority of the sensing devices; the present application constructs a priority index, comprehensively evaluates the access priority of various sensing devices, and replaces the generated random number with this, so as to realize that while ensuring that important data is preferentially accessed, it can also control the access of the sensing devices of the Internet of Things, and improve the access effect of the sensing devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1The flowchart of the access control method applicable to Internet of Things (IoT) sensing devices provided by this application. Detailed implementation manners

[0035] To further elaborate on the technical means and effects adopted by this application to achieve the intended invention purpose, the following describes in detail the access control method and system applicable to IoT sensing devices proposed according to this application, their specific implementation manners, structures, features, and effects in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0036] Unless otherwise specified and limited, terms such as "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the article or device including the said element. In addition, the term "and / or" used herein includes any and all combinations of one or more of the related listed items. All technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs.

[0037] The following specifically describes the specific solutions of the access control method and system applicable to IoT sensing devices provided by this application in conjunction with the accompanying drawings.

[0038] Please refer to Figure 1 , which shows the flowchart of the steps of the access control method applicable to IoT sensing devices provided by an embodiment of this application, including the following steps:

[0039] Step 1: For each sensing device in the IoT, obtain the transmission data of the sensing device at each acquisition moment to form the data sequence to be uploaded of the sensing device, count the total number of data uploads and the size of uploaded data packets of the sensing device within the historical period, and obtain each historical upload data sequence of the sensing device.

[0040] Since there are also various sensing devices in a local IoT system, and if the sensing devices immediately transmit the relevant data to the IoT system as soon as they are collected, there will be a large number of device accesses and data transmissions at the same time, which will cause the channel to be blocked. Therefore, in the IoT system, usually after the sensing devices collect a certain amount of data, they access the IoT system and transmit the data.

[0041] In this embodiment, the i-th type of sensing device is taken as an example for detailed analysis. After the transmission data of the i-th type of sensing device is collected and needs to be connected to the Internet of Things system again for data transmission, the data to be uploaded this time is arranged in the order of data collection time to construct the data sequence to be uploaded of the i-th type of sensing device.

[0042] Furthermore, this embodiment statistically analyzes the data transmission situations of each sensing device in the historical period. Specifically, from the database of the Internet of Things system, within the historical period, in this embodiment, within 5 hours before the data collection this time, the total number of data uploads, the size of the uploaded data packets, and the historical data of each upload of the i-th type of sensing device are obtained; among them, the historical data of each upload is also arranged in the order of data collection time to construct each historical upload data sequence.

[0043] So far, the relevant data of the i-th type of sensing device is obtained. By using the above process of this embodiment, the relevant data of all sensing devices in the same Internet of Things is obtained. In order to eliminate the influence of the dimension between the data, all data is processed by Z-score standardization. The specific normalization process is a well-known prior art and will not be elaborated in this embodiment.

[0044] Step 2: Analyze the degree of data change difference between the data sequence to be uploaded of the sensing device and its respective historical upload data sequences, and construct a historical difference index.

[0045] In the Internet of Things system, when a sensing device has collected a certain amount of data, it will establish a connection with the Internet of Things system for data transmission. If at the same time point, the i-th type of sensing device is performing data transmission and other sensing devices also try to initiate data transmission requests, it will lead to competition for network channel resources. When multiple sensing devices simultaneously compete for limited bandwidth, it will cause channel congestion, thereby affecting the data transmission efficiency and delaying the access and data transmission of subsequent sensing devices, which may cause emergency data to be delayed in transmission. Therefore, it is necessary to calculate the upload priorities of all sensing devices to ensure that critical data is transmitted first.

[0046] Under normal circumstances, if the data collected by all sensing devices has not occurred abnormally, then regardless of what type of sensing device it is, there is a certain correlation between the latest collected data and the uploaded historical data, and the sizes of the uploaded data packets are relatively consistent. However, if an abnormal situation occurs, such as a fire due to environmental abnormalities, resulting in an increase in temperature, an increase in smoke concentration, and a decrease in humidity, it will further cause a large difference between the data collected by some sensing devices and the data uploaded in the past. Therefore, it is necessary to upload the data with a large difference to the Internet of Things system in a timely manner to detect whether an abnormal situation has really occurred.

[0047] Further, in this embodiment, the differences between the collected data and the historical data are analyzed, and the differences between the data sequence to be uploaded of the i-th type of sensing device and the historical uploaded data sequence that has been uploaded are analyzed, so as to preliminarily evaluate whether the data collected by the i-th type of sensing device this time has changed significantly and whether it needs to be connected to the Internet of Things system in time for data transmission.

[0048] Specifically, taking the a-th historical uploaded data sequence of the i-th type of sensing device uploaded to the Internet of Things system as an example, the degree of difference between the data sequence to be uploaded and the historical uploaded data sequence a is calculated.

[0049] All the peaks and valleys in the data sequence to be uploaded are obtained through the peak-valley detection algorithm, the average value of the absolute differences between each peak and its previous and subsequent data is calculated, which is denoted as the mutation index of each peak, the product of the variance and the mean of the mutation indices of all peaks is calculated, and it is denoted as the peak mutation difference index. Correspondingly, using the calculation method of the peak mutation difference index, the valley mutation difference index is obtained, and the average value of the peak mutation difference index and the valley mutation difference index is used as the peak-valley difference index of the data sequence to be uploaded. The larger the peak-valley difference index, the greater and more drastic the change difference between the data in the data sequence to be uploaded.

[0050] Correspondingly, for each historical uploaded data sequence, the method for obtaining the peak-valley difference index of the data sequence to be uploaded is used to calculate the peak-valley difference index of the historical uploaded data sequence.

[0051] Further, the first-order difference sequence of the data sequence to be uploaded is obtained, and the mean value of the absolute values of all elements in the first-order difference sequence is calculated, which is denoted as the change rate of the data sequence to be uploaded. In the same way, the change rate of the historical uploaded data sequence a is obtained. Further, according to the deviation of the peak-valley difference index and the deviation of the change rate between the data sequence to be uploaded of each sensing device and each historical uploaded data sequence, the historical difference index of each sensing device is constructed. In this embodiment, the specific calculation formula is:

[0052] In the formula, A i is the historical difference index of the i-th type of sensing device; n is the total number of data uploads of the i-th type of sensing device; B i，a is the absolute value of the difference between the peak-valley difference index of the data sequence to be uploaded of the i-th type of sensing device and the a-th historical uploaded data sequence; K i，a is the absolute value of the difference between the change rate of the data sequence to be uploaded of the i-th type of sensing device and the a-th historical uploaded data sequence; P i，aIt is the similarity index between the i-th sensing device and the a-th historical uploaded data sequence, expressed as the Pearson correlation coefficient between the data sequence to be uploaded and the a-th historical uploaded data sequence; τ is a constant to avoid a zero denominator, with a value range from 0 to 0.1, and in this implementation, the value is 0.01.

[0053] Among them, B i，a reflects the difference in data fluctuations between the current data to be uploaded and the historical data uploaded for the a-th time. If B i，a is larger, it indicates that the fluctuation characteristics of the current data have changed significantly compared with the historical data. K i，a reflects the difference in the degree of data change between the data to be uploaded and the historical data uploaded for the a-th time. If K i，a is larger, it represents that the difference in the degree of data change between the current data and the historical data uploaded for the a-th time is greater.

[0054] In the above formula, the similarity index P i，a reflects the overall correlation between the data to be uploaded and the historical data uploaded for the a-th time. If P i，a is smaller, the data trend between the current data and the historical data is more inconsistent. It should be noted that considering that the calculation result of the Pearson similarity coefficient may be negative, 1 is added to the denominator to ensure that the characteristic meaning reflected by the similarity index will not be changed and to ensure that the denominator is non - negative, which will not affect the overall calculation.

[0055] Therefore, if the historical difference index is larger, it can reflect that the difference between the latest data collected by the i-th sensing device and the previous data is larger, and the possibility that the substance monitored by the i-th sensing device has changed is greater. At this time, it is necessary to upload the data collected by the i-th sensing device to the Internet of Things system in time to detect whether an abnormal situation has occurred. Therefore, if the historical difference index is larger, the access priority of the i-th sensing device is higher when accessing the Internet of Things.

[0056] Step 3: Cluster the historical difference indices of all sensing devices, analyze the correlation degree of the data sequences to be uploaded between each sensing device and other sensing devices in its cluster, and construct the key cooperation coefficient of each sensing device by combining the historical difference index of each sensing device and the deviation of the historical difference index within the cluster where each sensing device is located.

[0057] For IoT sensing devices in the same monitoring environment, when an abnormal change occurs in the monitoring environment, it will simultaneously cause changes in the data collected by multiple sensing devices. That is, in the event of an abnormality, it will cause co-variation among the data collected by multiple sensing devices. If there is a strong co-variation between the i-th sensing device and multiple sensing devices, it indicates that the possibility of a change in the environment monitored by the i-th sensing device is greater, and data upload needs to be carried out in a timely manner.

[0058] Calculate the historical difference index of all sensing devices using the calculation method of the historical difference index of the above-mentioned i-th sensing device; and use the historical difference index of all sensing devices as the input of the k-means clustering algorithm for clustering. The number of clustering clusters is obtained by the elbow method. The specific clustering process is a well-known technology in the art and will not be elaborated in this embodiment.

[0059] In this embodiment, the mutual information value of the data sequence to be uploaded between the i-th sensing device and the remaining sensing devices in the clustering cluster where the i-th sensing device is located is calculated by the mutual information method (MI), and the mean value of all mutual information values is recorded as the intra-cluster cooperation index of the i-th sensing device. The larger the intra-cluster cooperation index, the stronger the correlation dependence and the greater the co-variation trend between the data sequence to be uploaded between the i-th sensing device and the remaining sensing devices in the clustering cluster where the i-th sensing device is located.

[0060] Therefore, in this embodiment, according to the intra-cluster cooperation index corresponding to each sensing device, combined with the historical difference index of each sensing device and the deviation of the historical difference index within the clustering cluster where each sensing device is located, calculate the key cooperation coefficient of each sensing device. In this embodiment, the calculation formula is:

[0061] In the formula, C i is the key cooperation coefficient of the i-th sensing device; A i is the historical difference index of the i-th sensing device; is the intra-cluster cooperation index of the i-th sensing device; G is the ratio of the number of historical difference indexes within the clustering cluster where the i-th sensing device is located to the number of all historical difference indexes; D i is the mean value of the historical difference indexes within the clustering cluster where the i-th sensing device is located; D max is the maximum value of the mean values of the intra-cluster historical difference indexes in all clustering clusters.

[0062] If the historical difference index A i is larger, it reflects that the difference between the latest data and historical data collected by the i-th sensing device is greater. If the intra-cluster cooperation index The larger the value is, and the larger the proportion G is, the stronger the dependence between the latest collected data of all sensing devices in the clustering cluster where the i-th sensing device is located is reflected, and the larger the proportion of the number of elements in the clustering cluster where the i-th sensing device is located is, that is, the more devices that have a strong cooperative relationship with the i-th sensing device. If D max -D i has a smaller value, it reflects that the average historical difference of the clustering cluster where the i-th sensing device is located is closer to the maximum value among all clustering clusters where the i-th sensing device is located, and the greater the possibility that the latest data collected by the sensing devices in the clustering cluster where the i-th sensing device is located is significantly different from the historical data.

[0063] Therefore, if the key cooperation coefficient C i is larger, it indicates that the difference between the latest collected data and the historical data of the i-th sensing device is greater, and there are more devices that have a strong cooperative change with the latest collected data of the i-th sensing device; it reflects that the importance of the latest data collected by the i-th sensing device is higher, and it is more necessary to transmit it in a timely manner.

[0064] Step 4: Construct the priority index of each sensing device according to the total number of uploads and the prominence of the upload data packet size of each sensing device during the historical period among all sensing devices.

[0065] Furthermore, in this embodiment, the access priority of the i-th sensing device is analyzed by calculating the data upload difference between the i-th sensing device and other sensing devices. Within a fixed time range, if the upload frequency and the number of uploads of a sensing device are higher, it usually indicates that the substance or environmental parameters monitored by the sensing device are more important. Therefore, the importance of the sensing device can be further measured by analyzing the upload frequency.

[0066] Therefore, in this embodiment, the mean value of the absolute difference between the total number of uploads of the i-th sensing device and the total number of uploads of all other sensing devices is calculated, and the mean value is recorded as the upload outlier index. The ratio of the total number of uploads of the i-th sensing device to the maximum value among all total number of uploads is recorded as the maximum upload proportion of the i-th sensing device.

[0067] The product of the upload outlier index and the maximum upload proportion is recorded as the upload significant index of the total number of uploads of the i-th sensing device. The larger the upload significant index is, the greater the possibility that the monitoring data collected by the i-th sensing device is more important compared to other sensing devices, and the higher the upload priority is required.

[0068] Further, in this embodiment, the size of the uploaded data packet of the i-th type of sensing device is used to reflect its upload priority; the larger the data packet that the i-th type of sensing device needs to upload, the longer the time required for its data upload, the greater the impact on other sensing devices that need to upload data, and the lower the priority of data access.

[0069] Similarly, in this embodiment, for the size of the uploaded data packets of each sensing device within the historical period, the calculation method and process of the upload significance index of the total number of uploads are adopted to obtain the upload significance index of the size of the uploaded data packet of the i-th type of sensing device.

[0070] According to the upload significance index of the total number of uploads of each sensing device and the upload significance index of the size of the uploaded data packet, combined with the key cooperation coefficient of each sensing device, the priority index of each sensing device is calculated. In this embodiment, the specific calculation formula is:

[0071] In the formula, F i is the priority index of the i-th type of sensing device; C i is the key cooperation coefficient of the i-th type of sensing device; S ci is the upload significance index of the total number of uploads of the i-th type of sensing device; S bi is the upload significance index of the size of the uploaded data packet of the i-th type of sensing device.

[0072] If the key cooperation coefficient C i is larger, it indicates that the importance of the latest data collected by the i-th type of sensing device is greater, and it is more necessary to upload the data in time for further detection; if the upload significance index S ci of the total number of uploads is larger, it indicates that the upload frequency of the i-th type of sensing device is higher compared to other sensing devices, reflecting that the importance of the monitoring data collected by the i-th type of sensing device is higher; if the upload significance index S bi of the uploaded data packet is smaller, it indicates that the uploaded data packet of the i-th type of sensing device is smaller compared to other sensing devices, and the degree of occupation of system resources is smaller.

[0073] Therefore, if the priority index F i is larger, it indicates that the relative importance and urgency of the i-th type of sensing device in the IoT system are higher. Then, the data collected by the i-th type of sensing device not only has significant changes compared with historical data, but also has a higher upload frequency, and the data is relatively important; at the same time, the uploaded data packet is relatively small, and the occupation of system resources is less. Therefore, if the priority index F i is larger, it represents that the priority of the i-th type of sensing device during access is higher.

[0074] Step Five: Combine the priority indices of each sensing device to perform access control on the IoT sensing devices.

[0075] According to the above process of this embodiment, use the calculation method and process of the priority index of the i-th sensing device to obtain the priority indices of all sensing devices in the same IoT system, and use the reciprocal of the priority index as the delay coefficient of the sensing device.

[0076] Perform sigmoid normalization on the delay coefficients of various sensing devices, replace the random numbers between 0 and 1 generated for the sensing devices in the traditional ACB mechanism with the normalized delay coefficients, and compare the normalized delay coefficients of each sensing device with the ACB factor of the ACB mechanism. Send access requests to the sensing devices whose normalized delay coefficients are less than or equal to the ACB factor, so as to optimize the ACB control technology, which can not only achieve access control of IoT sensing devices and avoid network congestion, but also ensure that important data can be timely accessed into the IoT system. It should be noted that the ACB mechanism in this embodiment is the ACB access control technology, and the specific process is prior art, so it will not be elaborated in this embodiment.

[0077] Based on the same inventive concept as the above method, an embodiment of the present application also provides an access control system applicable to IoT sensing devices, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above access control methods applicable to IoT sensing devices.

[0078] It can be understood that: the above sequence of embodiments of the present application is only for description and does not represent the advantages or disadvantages of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. The key point of each embodiment is to illustrate the differences from other embodiments.

[0080] The above content is only the implementation manner of the present application and is not used to limit the scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied to other related technical fields, shall be included in the protection scope of the present application by the same token.

Claims

1. An access control method applicable to Internet of Things sensing devices, characterized in that, It includes the following steps: For each sensing device in the Internet of Things, obtain the transmission data of the sensing device at each collection moment to form the data sequence to be uploaded of the sensing device, count the total number of data uploads and the size of the uploaded data packets of the sensing device within the historical period, and obtain each historical uploaded data sequence of the sensing device; Analyze the degree of data change difference between the data sequence to be uploaded of the sensing device and its each historical uploaded data sequence, and construct the historical difference index of the sensing device; Cluster the historical difference indexes of all sensing devices, analyze the correlation degree of the data sequence to be uploaded between each sensing device and other sensing devices in its clustering cluster, and combine the historical difference index of each sensing device and the deviation of the historical difference index within the clustering cluster where each sensing device is located to construct the key cooperation coefficient of each sensing device; Construct the priority index of each sensing device according to the prominence of the total number of uploads and the size of the uploaded data packets of each sensing device within the historical period among all sensing devices; Combine the priority index of each sensing device to perform access control on the sensing devices of the Internet of Things.

2. The access control method applicable to Internet of Things sensing devices according to claim 1, characterized in that, The calculation method of the historical difference index of the sensing device is: ; wherein, is the historical difference index of the i-th sensing device; n is the total number of data uploads of the i-th sensing device during the historical period; is the absolute value of the difference between the peak-valley difference index of the data sequence to be uploaded of the i-th sensing device and the a-th historical upload data sequence. The peak-valley difference indices of the data sequence to be uploaded and the historical upload data sequence are obtained respectively according to the data change conditions of the peak values and valley values in the data sequence to be uploaded and the historical upload data sequence; is the absolute value of the difference between the change rate of the data sequence to be uploaded of the i-th sensing device and the a-th historical upload data sequence; is the similarity index between the i-th sensing device and the a-th historical upload data sequence; is a constant to avoid a zero denominator.

3. The access control method applicable to the Internet of Things sensing devices according to claim 2, wherein The acquisition of the peak-valley difference index of the data sequence to be uploaded and the historical uploaded data sequence further includes: For the data sequence to be uploaded, extract the peak values and valley values in the data sequence to be uploaded, calculate the average value of the absolute differences between each peak value and its previous and subsequent data, and record it as the mutation index of each peak value. Calculate the product of the variance and the mean of the mutation indexes of all peak values, and record it as the mutation difference index of the peak values. Correspondingly, adopt the calculation method of the mutation difference index of the peak values to obtain the mutation difference index of the valley values, and take the average value of the mutation difference index of the peak values and the mutation difference index of the valley values as the peak-valley difference index of the data sequence to be uploaded; For each historical uploaded data sequence, adopt the acquisition method of the peak-valley difference index of the data sequence to be uploaded to calculate the peak-valley difference index of the historical uploaded data sequence.

4. The access control method applicable to the Internet of Things sensing device according to claim 2, characterized in that The acquisition of the change rate further includes: For the data sequence to be uploaded of the sensing device and each historical uploaded data sequence, respectively obtain the first-order difference sequence, and take the average value of the absolute values of all elements in the first-order difference sequence as the change rate of the data sequence to be uploaded and each historical uploaded data sequence, respectively.

5. The access control method applicable to Internet of Things sensing devices according to claim 1, wherein, The corresponding calculation method of the key cooperation coefficient of each sensing device is: ; where, is the key cooperation coefficient of the i-th sensing device; is the historical difference index of the i-th sensing device; is the in-cluster cooperation index of the i-th sensing device, which is obtained by calculating the correlation degree of the data sequence to be uploaded between the i-th sensing device and other sensing devices in its clustering cluster; G is the ratio of the number of historical difference indexes in the clustering cluster where the i-th sensing device is located to the number of all historical difference indexes; is the mean value of the historical difference indexes in the clustering cluster where the i-th sensing device is located; is the maximum value of the mean values of the in-cluster historical difference indexes in all clustering clusters.

6. The access control method applicable to Internet of Things sensing devices according to claim 5, characterized in that The calculation method of the intra-cluster cooperation index of the i-th sensing device further includes: Calculate the mutual information value of the data sequence to be uploaded between the i-th sensing device and other sensing devices in its clustering cluster, and record the average value of all mutual information values as the intra-cluster cooperation index of the i-th sensing device.

7. The access control method applicable to Internet of Things sensing devices according to claim 1, characterized in that The calculation method of the priority index of each sensing device is: ; where, is the priority index of the i-th sensing device; is the key cooperation coefficient of the i-th sensing device; is the upload significance index of the total number of uploads of the i-th sensing device; is the upload significance index of the size of the upload data packet of the i-th sensing device.

8. The access control method applicable to Internet of Things sensing devices according to claim 7, characterized in that, The calculation method of the upload significance index includes: For the total number of uploads of the sensing device within the historical period, calculate the average value of the absolute difference between the total number of uploads of the sensing device and the total number of uploads of all other sensing devices, which is denoted as the upload outlier index of the sensing device. Denote the ratio of the maximum value between the total number of uploads of the sensing device and all uploads as the maximum upload proportion of the sensing device. Take the product of the upload outlier index and the maximum upload proportion as the upload significance index of the total number of uploads of the sensing device; For the upload data packet size of the sensing device within the historical period, adopt the calculation method of the upload significance index of the total number of uploads to obtain the upload significance index of the upload data packet size of the sensing device.

9. The access control method applicable to Internet of Things sensing devices according to claim 1, characterized in that The access control for the IoT sensing device further includes: Take the reciprocal of the priority index of each sensing device in the IoT as the delay coefficient of each sensing device. Normalize the delay coefficient of each sensing device, and replace the random number between 0 and 1 generated for the sensing device in the ACB mechanism with the normalized delay coefficient. Send an access request to the sensing device whose normalized delay coefficient is less than or equal to the ACB factor in the ACB mechanism.

10. An access control system applicable to Internet of Things sensing devices, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the access control method for IoT sensing devices as described in any one of claims 1-9.

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