Dynamic scheduling mode of narrowband Internet of Things MAC layer

Abstract

The invention discloses a dynamic scheduling mode of a narrowband Internet of Things MAC layer, which comprises the following steps: S1, after an Internet of Things terminal is powered on and successfully registered in a network, the Internet of Things terminal performs dynamic scheduling; s2, performing parameter estimation according to historical data stored in the Internet of Things terminal to obtain parameters theta and lambda; s3, according to the parameters theta and lambda obtained in the step S2, calculating a frame interval schemN [mu] m; s4, setting a start frame for starting scheduling as n; s5, checking whether the current frame conflicts with the set initial frame or not; if not, executing the step S6; if yes, updating the starting frame of scheduling starting; s6, outputting the scheduling of the Internet of Things terminal, setting the frame number corresponding to the scheduling to be used, and archiving the information of the current terminal, the arrived frame number and the transmitted data volume; s7, judging whether the current frame number accumulatively reaches N frames or not; if yes, returning to the step S2 and restarting; and if not, returning to the step S3 and restarting. According to the scheme, time domain scheduling of multiple terminals is realized, and meanwhile, data backlog caused by a scheduling mode is avoided.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things data, and in particular to a dynamic scheduling method of a narrowband Internet of Things MAC layer. Background Art

[0002] Low-orbit narrowband IoT terminals are an important system based on licensed spectrum low-power wide area networks. They have four key features: strong coverage, low power consumption, large connections, and low cost. The data transmission of IoT terminals is characterized by small amounts of data, discontinuity, and entering a sleep state after transmission. Therefore, the MAC layer protocol supports dynamic scheduling, allowing the terminal to use frequency domain resources only in certain frames. As the number of IoT terminals increases, a single satellite will have multiple terminals transmitting on the same frequency domain resources. At this time, it is necessary to consider a scheduling method to achieve time domain scheduling of multiple terminals while avoiding data backlogs caused by the scheduling method. Summary of the invention

[0003] Purpose of the invention: In order to solve the above-mentioned problems, the present invention proposes a MAC layer dynamic scheduling method based on Bayesian estimation.

[0004] A technical solution, a dynamic scheduling method of a narrowband Internet of Things MAC layer, includes the following steps:

[0005] Step S1: After the IoT terminal is powered on and successfully registered, the IoT terminal is dynamically scheduled according to the low-orbit narrowband MAC layer protocol rules;

[0006] Step S2: perform parameter estimation based on historical data stored in the IoT terminal to obtain parameters θ and λ;

[0007] Step S3: After the IoT terminal arrives, the frame interval scheNum is calculated according to the parameters θ and λ obtained in step S2;

[0008] Step S4, setting the start frame of scheduling to n;

[0009] Step S5, check whether the current frame conflicts with the start frame set in step S4; if not, execute step S6; if so, update the start frame for scheduling.

[0010] Step S6: Output the IoT terminal schedule, set the frame number corresponding to the schedule as used, and archive the information of the current terminal, the frame number arrived, and the amount of data transmitted;

[0011] Step S7, determine whether the current frame number has accumulated to N frames; if so, return to step S2 and start again; if not, return to step S3 and start again.

[0012] According to another aspect of the present application, determining the parameters θ and λ in step S2 includes:

[0013] Step S21: Determine the parameters of the prior estimation based on the historical data of the IoT terminal And count the number of all terminals that need to transmit data in K frame realTotalPhone i-1 The amount of data that needs to be transmitted to all terminals in the K frame

[0014]

[0015] Step S22: The size of the data packet obeys the lognormal distribution, and the parameters of the lognormal distribution are determined by linear Bayesian estimation. ,remember Represents the estimated parameters of the i-th normal distribution. The specific calculation formula is as follows:

[0016]

[0017] in,

[0018] Step S23: The arrival of data packets follows Poisson distribution, and the parameters of Poisson distribution are determined by Bayesian estimation. Among them, t1 and t2 are sample mean and sample variance respectively. Parameter estimation is performed every K frames to update the parameter value.

[0019] According to another aspect of the present application, step S3 updates the distribution parameters through historical data, estimates the data load in the future, and thereby determines the frame interval between scheduling, reserving transmission opportunities for subsequent service data; specifically includes:

[0020] Reservation mechanism for data packet scheduling: When the IoT terminal transmits data, the data volume x in the data packet that the current IoT terminal needs to transmit is obtained. When x is to the right of the median and the distance is farther, x is determined to be a data size with a small probability. At the same time, it is believed that the subsequent business data volume will be distributed near the median. Therefore, the scheduling of this data packet needs to reserve transmission opportunities for subsequent data packets to prevent long waiting times for subsequent data. The specific method is as follows:

[0021] The lognormal distribution is left-skewed and inverted bell-shaped, with the median coordinate of the distribution being μ. * =e μ , multiple standard deviation σ * =e σ , similar to the normal distribution, the coordinate interval of 1σ is The coordinate interval of 2σ is The coordinate interval of 3σ is

[0022] Prediction of terminal data arrival: The probability of terminal data arrival follows Poisson distribution, with a confidence interval of 95%. Based on the estimated parameters , determine the number of terminals that may be reached within the i-th K frame range procastTotalPhone i , and count the number of terminals that have arrived within the i-th K frame range arrivedPhone i , according to the number of terminals that may be reached procastTotalPhone i and the number of terminals that have arrived i Calculate the number of remaining terminals that may arrive within the i-th K frame range procastRemainPhone i .

[0023] According to another aspect of the present application, in step S6, a scheduling table of the MAC layer is generated according to the amount of data obtained in steps S2 and S3, and the content of the table is the number of interval frames between two scheduling packets, and the value range is 1 to maxScheNum.

[0024] According to another aspect of the present application, in step S7, according to the number of interval frames determined in step S6, the waiting time for non-conflicting scheduling of time domain resources is determined by enumeration, and traversal is started from the Nth frame where the data request is received, and it is determined from which frame the data can be transmitted without conflict with the number of interval frames determined in step 4; the specific steps are:

[0025] Step S71: After the IoT terminal data arrives, the interval frame scheNum is determined according to steps S2 and S3;

[0026] Step S72, starting from the Nth frame, check whether there is a conflict. If there is a conflict, continue to check backward from the first frame without conflict. After checking that the least common multiple [scheNum, maxScheNum] frames do not conflict, determine that the current schedule will not conflict in the subsequent schedules;

[0027] Step S73: After storing the IoT terminal information, the dynamic scheduling of the IoT terminal is completed.

[0028] Beneficial effects:

[0029] This scheduling method estimates parameters based on the historical data of IoT terminals and can dynamically adjust the scheduling strategy. This adaptability enables the system to better cope with changes in network load and improve resource utilization;

[0030] By dynamically calculating the frame interval, the system can flexibly adjust the scheduling strategy according to the current network conditions, avoiding resource waste or network congestion that may be caused by fixed scheduling methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a dynamic scheduling flow chart of the present invention.

[0032] Figure 2 It is a dynamic configuration flow chart of the present invention. DETAILED DESCRIPTION

[0033] like Figure 1 As shown, a dynamic scheduling method for the MAC layer of a narrowband Internet of Things is shown. The dynamic scheduling rules use the rules described by the low-orbit narrowband MAC layer protocol, and use historical data to update the distribution parameters through Bayesian estimation. The data load of the MAC layer in the future is estimated based on the estimated parameters, so as to determine the frame interval between scheduling and reserve transmission opportunities for subsequent business data. During the scheduling calculation, the data of the previous [a, b] (least common multiple) frames are calculated to determine whether the new scheduling will conflict with the existing scheduling, and the scheduling is obtained, thereby completing a MAC data packet frame scheduling.

[0034] The steps include:

[0035] Step S1: After the IoT terminal is powered on and successfully registered, the IoT terminal is dynamically scheduled according to the low-orbit narrowband MAC layer protocol rules;

[0036] Step S2: perform parameter estimation based on historical data stored in the IoT terminal to obtain parameters θ and λ;

[0037] Step S3: After the IoT terminal arrives, the frame interval scheNum is calculated according to the parameters θ and λ obtained in step S2;

[0038] Step S4, setting the start frame of scheduling to n;

[0039] Step S5, check whether the current frame conflicts with the start frame set in step S4; if not, execute step S6; if so, update the start frame for scheduling.

[0040] Step S6: Output the IoT terminal schedule, set the frame number corresponding to the schedule as used, and archive the information of the current terminal, the frame number arrived, and the amount of data transmitted;

[0041] Step S7, determine whether the current frame number has accumulated to N frames; if so, return to step S2 and start again; if not, return to step S3 and start again.

[0042] This scheduling method estimates parameters based on the historical data of IoT terminals and can dynamically adjust the scheduling strategy. This adaptability enables the system to better cope with changes in network load and improve resource utilization; by dynamically calculating the frame interval, the system can flexibly adjust the scheduling strategy according to the current network conditions, avoiding resource waste or network congestion that may be caused by fixed scheduling methods.

[0043] The system will check whether the current frame conflicts with the start frame, and update the start frame if there is a conflict. This mechanism effectively reduces scheduling conflicts, improves the success rate of data transmission and the overall efficiency of the network; by dynamically adjusting the frame interval and start frame, the system can allocate network resources more reasonably, avoid resource waste, and improve network throughput.

[0044] The system uses historical data stored in IoT terminals to estimate parameters. This data-driven approach makes scheduling decisions more scientific and accurate. When the cumulative number of frames reaches N, the system will re-estimate the parameters. This continuous optimization mechanism ensures that the scheduling strategy can be continuously improved as the network conditions change.

[0045] The entire scheduling process is automated, reducing the need for manual intervention and lowering the complexity of management and maintenance; the system archives scheduling information, which helps with subsequent analysis and optimization, as well as facilitates troubleshooting and performance monitoring.

[0046] Through dynamic scheduling, the system can respond to requests from IoT terminals more quickly, reduce data transmission delays, and improve user experience; reduce conflicts and optimize resource allocation, improve the reliability of data transmission, and ensure that data from IoT terminals can be transmitted in a timely and accurate manner.

[0047] This scheduling method can adapt to network environments of different sizes, and can operate effectively in both small and large IoT networks; This scheduling method is based on the standard low-orbit narrowband MAC layer protocol, which is easy to integrate with other network devices and systems and has good compatibility and scalability.

[0048] In summary, this dynamic scheduling method of the narrowband IoT MAC layer significantly improves the efficiency, reliability and user experience of the network through its advantages such as adaptability, flexibility, data-driven and automated management, while simplifying management and maintenance work, and has broad application prospects.

[0049] According to another aspect of the present application, determining the parameters θ and λ in step S2 includes:

[0050] Step S21: Determine the parameters of the prior estimation based on the historical data of the IoT terminal And count the number of all terminals that need to transmit data in K frame realTotalPhone i-1The amount of data that needs to be transmitted to all terminals in the K frame

[0051]

[0052] Step S22: The size of the data packet obeys the lognormal distribution, and the parameters of the lognormal distribution are determined by linear Bayesian estimation. ,remember Represents the estimated parameters of the i-th normal distribution. The specific calculation formula is as follows:

[0053]

[0054] in,

[0055] Step S23: The arrival of data packets follows Poisson distribution, and the parameters of Poisson distribution are determined by Bayesian estimation. Among them, t1 and t2 are sample mean and sample variance respectively. Parameter estimation is performed once every K frames to update the parameter value.

[0056] According to another aspect of the present application, step S3 updates the distribution parameters through historical data, estimates the data load in the future, and thereby determines the frame interval between scheduling, reserving transmission opportunities for subsequent service data; specifically includes:

[0057] Reservation mechanism for data packet scheduling: When the IoT terminal transmits data, the data volume x in the data packet that the current IoT terminal needs to transmit is obtained. When x is to the right of the median and the distance is farther, x is determined to be a data size with a small probability. At the same time, it is believed that the subsequent business data volume will be distributed near the median. Therefore, the scheduling of this data packet needs to reserve transmission opportunities for subsequent data packets to prevent long waiting times for subsequent data. The specific method is as follows:

[0058] The lognormal distribution is left-skewed and inverted bell-shaped, with the median coordinate of the distribution being μ. * =e μ , multiple standard deviation σ * =e σ , similar to the normal distribution, the coordinate interval of 1σ is The coordinate interval of 2σ is The coordinate interval of 3σ is

[0059] Prediction of terminal data arrival: The probability of terminal data arrival follows Poisson distribution, with a confidence interval of 95%. Based on the estimated parameters , determine the number of terminals that may be reached within the i-th K frame range procastTotalPhone i, and count the number of terminals that have arrived within the i-th K frame range arrivedPhone i , according to the number of terminals that may be reached procastTotalPhone i and the number of terminals that have arrived i Calculate the number of remaining terminals that may arrive within the i-th K frame range procastRemainPhone i .

[0060] According to another aspect of the present application, in step S6, according to the amount of data obtained in step S2 and step S3, a scheduling table of the MAC layer is generated, and the content of the table is the number of interval frames between two packet schedulings, and the value range is 1 to maxScheNum; specifically, the following table:

[0061] Table 1

[0062]

[0063] According to another aspect of the present application, in step S7, according to the number of interval frames determined in step S6, the waiting time for non-conflicting scheduling of time domain resources is determined by enumeration, and traversal is started from the Nth frame where the data request is received, and it is determined from which frame the data can be transmitted without conflict with the number of interval frames determined in step 4; the specific steps are:

[0064] Step S71: After the IoT terminal data arrives, the interval frame scheNum is determined according to steps S2 and S3;

[0065] Step S72, starting from the Nth frame, check whether there is a conflict. If there is a conflict, continue to check backward from the first frame without conflict. After checking that the least common multiple [scheNum, maxScheNum] frames do not conflict, determine that the current schedule will not conflict in the subsequent schedules;

[0066] Step S73: After storing the IoT terminal information, the dynamic scheduling of the IoT terminal is completed.

[0067] According to another embodiment of the present application, it also includes:

[0068] Precondition: The terminal is powered on normally and the IoT terminal is successfully registered. The terminal is dynamically scheduled according to the rules described by the low-orbit narrowband MAC layer protocol.

[0069] 1. According to the characteristics of IoT data packets, it is believed that the size of data packets obeys the log-normal distribution. According to the characteristics of IoT terminal data transmission, it is believed that the arrival of data packets obeys the Poisson distribution. Bayesian parameter estimation is performed based on the data stored during program operation. First, the parameters of the prior estimation are determined based on the terminal historical data. Count the number of all terminals that need to transmit data in K frame realTotalPhone i-1 , count the amount of data that needs to be transmitted by all terminals in K frame:

[0070] Determine the parameters of the lognormal distribution using linear Bayesian estimation ,remember Represents the estimated parameters of the i-th normal distribution. The specific calculation formula is as follows:

[0071]

[0072] in:

[0073]

[0074] Determine the parameters of the Poisson distribution using Bayesian estimation

[0075] Among them, t1 and t2 are sample mean and sample variance respectively;

[0076] Parameter estimation is performed every K frames to update the parameter values.

[0077] 2. The log-normal distribution is left-skewed and inverted bell-shaped, and the median coordinate of the distribution is μ * =e μ , multiple standard deviation σ * =e σ , similar to the normal distribution, the coordinate interval of 1σ is The coordinate interval of 2σ is The coordinate interval of 3σ is When a terminal needs to transmit data, the amount of data x that the current terminal needs to transmit can be obtained. When x is to the right of the median and the farther the distance is, the more it can be considered that this is a data size with a small probability. At the same time, it is believed that the subsequent business data volume will be distributed near the median, so the scheduling of this packet data needs to reserve transmission opportunities for subsequent data to prevent long waiting times for subsequent data.

[0078] 3. The probability of terminal data arrival is assumed to follow Poisson distribution, with a confidence interval of 95%. Based on the estimated parameters Determine the number of terminals that may be reached within the i-th K frame range procastTotalPhone i , count the number of terminals that have arrived within the i-th K frame range arrivedPhone i Based on this, the number of possible remaining terminals in the i-th K frame range is calculated procastRemainPhone i .

[0079] 4. According to the data volume obtained in step 2 and step 3, a scheduling table of the MAC layer is generated. The table content is the number of interval frames between two scheduling packets, and the value range is 1 to maxScheNum, as shown in Table 1.

[0080] 5. According to the number of interval frames determined in step 4, the waiting time for non-conflicting scheduling of time domain resources is determined by enumeration, and the traversal starts from the Nth frame after receiving the data request, and determines from which frame the data can be transmitted without conflict with the number of interval frames determined in step 4. The specific steps are:

[0081] After the terminal data arrives, the interval frame scheNum is determined according to steps 3 and 4;

[0082] Check for conflicts starting from the Nth frame. If there is a conflict, continue to check backwards from the first frame without conflict. It can be proved that after checking that [scheNum, maxScheNum] (least common multiple) frames do not conflict, it is considered that the current schedule will not conflict in the future schedules.

[0083] This completes the dynamic scheduling of the Internet of Things.

[0084] It should be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

Claims

1. A dynamic scheduling method for a narrowband Internet of Things MAC layer, characterized in that: The steps include: Step S1: After the IoT terminal is powered on and successfully registered, the IoT terminal is dynamically scheduled according to the low-orbit narrowband MAC layer protocol rules; Step S2: perform parameter estimation based on historical data stored in the IoT terminal to obtain parameters θ and λ; Step S3: After the IoT terminal arrives, the frame interval scheNum is calculated according to the parameters θ and λ obtained in step S2; Step S4, setting the start frame of scheduling to n; Step S5, check whether the current frame conflicts with the start frame set in step S4; if not, execute step S6; if so, update the start frame for scheduling. Step S6: Output the IoT terminal schedule, set the frame number corresponding to the schedule as used, and archive the information of the current terminal, the frame number arrived, and the amount of data transmitted; Step S7, determining whether the current frame number has accumulated to N frames; If reached, return to step S2 and start again; If not reached, return to step S3 and start again.

2. A MAC layer dynamic scheduling method based on Bayesian estimation as claimed in claim 1, characterized in that: Determining parameters θ and λ in step S2 includes: Step S21: Determine the parameters of the prior estimation based on the historical data of the IoT terminal And count the number of all terminals that need to transmit data in K frame realTotalPhone i-1 The amount of data that needs to be transmitted to all terminals in the K frame Step S22: The size of the data packet obeys the lognormal distribution, and the parameters of the lognormal distribution are determined by linear Bayesian estimation. remember Represents the estimated parameters of the i-th normal distribution. The specific calculation formula is as follows: in, Step S23: The arrival of data packets follows Poisson distribution, and the parameters of Poisson distribution are determined by Bayesian estimation. Among them, t1 and t2 are sample mean and sample variance respectively. Parameter estimation is performed once every K frames to update the parameter value.

3. A MAC layer dynamic scheduling method based on Bayesian estimation as claimed in claim 1, characterized in that: The step S3 updates the distribution parameters through historical data, estimates the data load in the future, and thus determines the frame interval between scheduling, and reserves transmission opportunities for subsequent service data; specifically includes: Reservation mechanism for data packet scheduling: When the IoT terminal transmits data, the data volume x in the data packet that the current IoT terminal needs to transmit is obtained. When x is to the right of the median and the distance is farther, x is determined to be a data size with a small probability. At the same time, it is believed that the subsequent business data volume will be distributed near the median. Therefore, the scheduling of this data packet needs to reserve transmission opportunities for subsequent data packets to prevent long waiting times for subsequent data. The specific method is as follows: The lognormal distribution is left-skewed and inverted bell-shaped, with the median coordinate of the distribution being μ. * =e μ , multiple standard deviation σ * =e σ , similar to the normal distribution, the coordinate interval of 1σ is The coordinate interval of 2σ is The coordinate interval of 3σ is Prediction of terminal data arrival: The probability of terminal data arrival follows Poisson distribution, with a confidence interval of 95%. Based on the estimated parameters Determine the number of terminals that may be reached within the i-th K frame range procastTotalPhone i , and count the number of terminals that have arrived within the i-th K frame range arrivedPhone i , according to the number of terminals that may be reached procastTotalPhone i and the number of terminals that have arrived i Calculate the number of remaining terminals that may arrive within the i-th K frame range procastRemainPhone i .

4. The MAC layer dynamic scheduling method based on Bayesian estimation as claimed in claim 1, characterized in that: In step S6, a scheduling table of the MAC layer is generated according to the data amount obtained in step S2 and step S3. The content of the table is the number of interval frames between two scheduling packets, and the value range is 1 to maxScheNum.

5. The MAC layer dynamic scheduling method based on Bayesian estimation as claimed in claim 1, characterized in that: In step S7, according to the number of interval frames determined in step S6, the waiting time for non-conflicting scheduling of time domain resources is determined by enumeration, and traversal is started from the Nth frame where the data request is received to determine from which frame data can be transmitted without conflict with the number of interval frames determined in step 4; the specific steps are: Step S71: After the IoT terminal data arrives, the interval frame scheNum is determined according to steps S2 and S3; Step S72, starting from the Nth frame, check whether there is a conflict. If there is a conflict, continue to check backward from the first frame without conflict. After checking that the least common multiple [scheNum, maxScheNum] frames do not conflict, determine that the current schedule will not conflict in the subsequent schedules; Step S73: After storing the IoT terminal information, the dynamic scheduling of the IoT terminal is completed.

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