Energy-saving communication method and system for 5.5G Internet of Things devices

Abstract

The present invention relates to the field of Internet of Things communication technology, specifically to an energy-saving communication method and system for 5.5G Internet of Things devices, which analyzes the time characteristics of historical data collected by Internet of Things devices, builds and trains a scheduling model, and adaptively controls the data collection frequency of the device; trains a communication strategy calculation model based on the historical status information and communication data of the device, comprehensively considers the communication time, frequency, data volume and power factor, and weighs the communication performance and energy consumption by setting a reward function to obtain the optimal strategy; constructs a data cache in the device, sets the data transmission interval and upload threshold, and determines whether to upload data according to the time and occupancy; encrypts and compresses the collected data, and uploads it to the edge node through the 5.5G network. The method of the present invention effectively reduces the energy consumption of Internet of Things devices and improves communication efficiency and data security.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things communication technology, and in particular to an energy-saving communication method and system for 5.5G Internet of Things equipment. Background Art

[0002] IoT devices are usually powered by batteries with limited energy. Therefore, how to reduce energy consumption and extend the service life of the device while ensuring communication performance has become an urgent problem to be solved.

[0003] Traditional IoT device communication methods usually adopt fixed data collection and transmission strategies, which do not fully consider the dynamic changes of device energy consumption and communication environment. For example, devices collect and upload data at a fixed frequency. When the data changes little or the communication conditions are poor, frequent communication not only wastes energy but also may cause network congestion and data loss. At the same time, due to the limited computing and storage resources of IoT devices, complex communication optimization algorithms are difficult to implement on the device side.

[0004] Existing adaptive communication methods have some shortcomings. On the one hand, most of these methods assume that IoT devices collect data at a fixed frequency and do not consider strategies for dynamically adjusting the sampling rate. On the other hand, since IoT devices usually use low-power microprocessors, it is difficult to run complex optimization algorithms directly on the devices. Therefore, studying a lightweight, end-to-end adaptive communication framework remains an urgent problem to be solved.

[0005] In addition, with the maturity and evolution of 5G technology, 5.5G, as an extension of 5G, has higher spectrum efficiency, lower transmission latency and more flexible business support capabilities, providing new opportunities and challenges for energy-saving communication of IoT devices. Therefore, it is of great theoretical and practical significance to give full play to the technical advantages of 5.5G and design energy-saving communication solutions for IoT devices suitable for 5.5G networks.

[0006] In view of this, the present invention proposes an energy-saving communication method and system for 5.5G Internet of Things devices. Summary of the invention

[0007] To achieve the above objectives, the present invention provides an energy-saving communication method and system for 5.5G IoT devices, and the specific technical solutions are as follows:

[0008] An energy-saving communication method for a 5.5G Internet of Things device, comprising:

[0009] According to the time characteristics of historical data collected by IoT devices, a frequency scheduling model for data collection of IoT devices is constructed and trained to control the data collection frequency of IoT devices;

[0010] Based on the historical status information and historical communication data of IoT devices, the communication strategy calculation model is trained. The communication strategy includes multiple decision variables such as communication time, frequency, data volume and power. The communication strategy calculation model balances the communication performance and energy consumption by setting a reward function.

[0011] Build a data cache in the IoT device to temporarily store the data collected by the IoT device, set the data transmission interval and upload threshold of the IoT device to the edge node, and determine whether to upload data based on the interval and upload threshold;

[0012] The data collected by IoT devices is encrypted and compressed before uploading, and uploaded to the edge node through the 5.5G network.

[0013] Preferably, according to the time characteristics of the historical data collected by the IoT devices, a data collection frequency scheduling model for the IoT devices is constructed and trained to control the data collection frequency of the IoT devices;

[0014] Assume the historical data set is:

[0015]

[0016] in, Indicates the amount of data in the dataset. Indicates The amount of data in a data item, Indicates the data type, Indicates the timestamp of data transmission. Indicates data priority, Indicates the device type. Indicates the device ID. Indicates the power level of the device;

[0017] Using the sliding window method For feature extraction, set the window length to , the sliding step length is , then The feature vector of a time window is:

[0018]

[0019] in It means taking the mean value, represents the standard deviation, Indicates count;

[0020] The feature vectors of all windows form a temporal feature dataset , using the time feature training set and the corresponding target value Train the LSTM model, is the data collection frequency, ;

[0021] Build the LSTM model structure and input A continuous time window feature vector, the output is The optimal data collection frequency after time windows is expressed as:

[0022]

[0023] in, is the LSTM model function, For the The optimal data collection frequency after a time window;

[0024] Based on the trained LSTM model, the data collection frequency of IoT devices is obtained:

[0025]

[0026] ;

[0027] in, is a calculation function of the data acquisition frequency, Indicates The optimal data collection frequency for a time window.

[0028] Preferably, the communication strategy calculation model is trained based on the historical status information and historical communication data of the IoT device. The communication strategy calculation model is trained based on the historical status information and historical communication data of the IoT device. The communication strategy includes multiple decision variables such as communication time, frequency, data volume and power. The communication strategy calculation model balances the communication performance and energy consumption by setting a reward function.

[0029] Define the state feature vector of IoT devices , including CPU utilization , indicating the CPU usage and memory utilization of IoT devices at all times , indicating the The memory usage of IoT devices at all times; battery power , indicating the The remaining battery power of IoT devices at all times; signal strength , indicating the The wireless signal strength received by the IoT device at all times; state feature vector Expressed as ;

[0030] Defining the communication eigenvectors of IoT devices , including communication time , indicating the Timestamp of each communication transmission; data volume , indicating the The amount of data transmitted in each communication; power , indicating the The transmission power used for the communication transmission; the communication characteristic vector Expressed as ;

[0031] Defining Communication Strategy Action Vectors , including communication approval , indicating whether to approve the Communication request, value is 0 or 1; time slot allocation , expressed as The number of time slot resources allocated for each communication; bandwidth allocation , expressed as The amount of bandwidth resources allocated for each communication; action vector Expressed as ;

[0032] Build Status - Communication Dataset ,in, is the data volume of the state-communication dataset, each sample Indicates in status Next Action , and generate communication characteristics ; Define the reward function , used to evaluate the state Next Action The pros and cons of communication performance and energy consumption: ,in, represents the communication utility, represents the energy consumption cost, and is a trade-off factor;

[0033] Using the reinforcement learning algorithm DQN neural network, based on the state-communication dataset Training the Communication Strategy Computational Model ;

[0034] The input of the model is the state , the output is each action of value , indicating that in the state Next Action The long-term cumulative reward expectation;

[0035] During training, use Strategy exploration, with probability Randomly select actions; at the same time, use the experience replay mechanism to transfer samples of each step Store them in the playback buffer and randomly extract some samples from them for training to reduce the correlation between samples;

[0036] After training is completed, the optimal communication strategy is obtained , for any state , select the action with the largest Q value as the optimal decision:

[0037]

[0038]

[0039] in, is the action space, is the next step, is the next state, is the discount factor, is the symbolic representation of mathematical expectation, Indicates that in a given state Next Action After that, the expected future cumulative rewards obtained;

[0040] Get the optimal communication strategy ,in, is the state space, for any state , select action Make maximum.

[0041] Preferably, a data cache area is constructed in the IoT device to temporarily store the data collected by the IoT device, and a data transmission strategy is formulated;

[0042] Dynamically adjust the data collection frequency of IoT devices based on the output results of the IoT device data collection frequency scheduling model ;

[0043] During the data collection process of IoT devices, monitor the occupancy rate of the cache area , occupancy rate Indicates the ratio of the used space in the cache to the total space;

[0044] Optimal communication strategy for IoT devices Extract the data transmission interval duration As the optimal data upload interval, according to the data transmission interval length Set a timer for IoT devices to upload data;

[0045] When the IoT device reaches the scheduled time of uploading data, read the storage status of the current cache area of ​​the IoT device and calculate the occupancy rate of the current cache area , set the upload threshold of the cache area ;

[0046] When the scheduled time is reached, if the occupancy rate of the buffer area is calculated Greater than or equal to the upload threshold of the cache When the cache data of the IoT device is uploaded to the upper edge node, if the occupancy rate of the cache is calculated Less than the upload threshold of the cache When the IoT device cache data is not uploaded to the upper edge node.

[0047] Preferably, during the data collection process of the IoT device, the occupancy rate of the buffer area is monitored , which is shorter than the data transmission interval cycle, calculate the occupancy rate of the cache area ;

[0048] Set the upper threshold of the cache occupancy rate and lower threshold , detect the cache occupancy rate in the gap when the IoT device uploads data to the upper edge node. When the occupancy rate is greater than the upper threshold When the occupancy rate is less than the lower threshold, the buffer area data upload is triggered. When , the cache area is expanded;

[0049] When the cache occupancy rate Greater than upper threshold When the IoT device sends a data transmission instruction to the edge node, all the data in the cache area is uploaded to the edge node, and the cache area is expanded at the same time. The size calculation formula is:

[0050]

[0051] in, is the size of the cache before expansion, is the expansion coefficient, which controls the expansion range;

[0052] When the cache occupancy rate Less than the lower threshold When the cache area is reduced, the storage space of the cache area is reduced; the size of the reduced cache area The calculation formula is:

[0053]

[0054] in, is the shrinkage coefficient, which controls the shrinkage range.

[0055] Preferably, the data collected by the IoT device is encrypted and uploaded to the upper edge node;

[0056] Assume that the data collected by IoT devices is original plaintext data , the encrypted data is ciphertext , the key is ; The encryption process is expressed as: ;in, For encryption function, receive plaintext data and key As input, generate ciphertext As output;

[0057] Design an encryption algorithm based on XOR operation and permutation operation. The process is as follows:

[0058] Plaintext data Divide into plaintext blocks by bytes, each plaintext block length is Bytes, less than The bytes are filled with 0; let the plaintext block after division be ;

[0059] For each plaintext block , , For the total number of plaintext blocks, perform the following steps:

[0060] Step a: With key Perform bitwise XOR operation to obtain the intermediate result : ;in, Represents a bitwise exclusive OR operation;

[0061] Step b: Perform a permutation operation to obtain the permutation result : ;in, is the permutation function, The byte order of the byte is rearranged according to predefined rules;

[0062] Step c: Replace the result As the encryption result of the current plaintext block, that is, the ciphertext block : ;

[0063] Step d: All ciphertext blocks Splice them together to get the final ciphertext : ;in, Represents a splicing operation;

[0064] Before uploading data, IoT devices use encryption algorithms to encrypt plaintext data. Encrypt and get the ciphertext .

[0065] Preferably, the encrypted ciphertext data is compressed, and the encrypted ciphertext data is , the compressed data is , the compression algorithm is ; The compression process is expressed as: ;in, is the compression function, receiving the ciphertext As input, generate compressed data As output;

[0066] Design a compression algorithm based on a combination of dictionary encoding and run-length encoding. The process is as follows:

[0067] Ciphertext Divide into ciphertext blocks by bytes, each ciphertext block length is bytes; let the ciphertext block after division be , is the total number of ciphertext blocks;

[0068] Build an initial dictionary , which contains the encoding of all individual bytes; for each ciphertext block , , perform the following steps:

[0069] In the dictionary Search The longest matching substring of , and its corresponding code is recorded as ;

[0070] Will As The compression code is added to the compression result;

[0071] if and If it does not match exactly, The unmatched part is used as a new substring ,Will Add to Dictionary and assign a new code to it ;

[0072] Run-length encoding is performed on the compression result, and the continuously repeated codes are expressed in the form of codes and the number of repetitions to reduce the amount of data;

[0073] The result of run-length encoding is used as the final compressed data;

[0074] IoT devices upload compressed ciphertext data to edge nodes through the 5.5G network.

[0075] An energy-saving communication system for a 5.5G Internet of Things device, which is used to implement the energy-saving communication method for a 5.5G Internet of Things device, comprises: a data acquisition frequency control module, a communication strategy module, a data upload control module and an encryption compression module;

[0076] The data collection frequency control module constructs and trains an IoT device data collection frequency scheduling model based on the time characteristics of historical data collected by physical IoT devices, and controls the data collection frequency of IoT devices;

[0077] The communication strategy module trains a communication strategy calculation model based on historical status information and historical communication data of IoT devices. The communication strategy includes multiple decision variables such as communication time, frequency, data volume, and power. The communication strategy calculation model balances communication performance and energy consumption by setting a reward function.

[0078] The data upload control module constructs a data cache area in the IoT device to temporarily store data collected by the IoT device, sets the data transmission interval and upload threshold of the IoT device to the edge node, and determines whether to upload data according to the interval and upload threshold;

[0079] The encryption and compression module encrypts and compresses the data collected by the IoT device before uploading, and uploads it to the edge node through the 5.5G network.

[0080] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the energy-saving communication method of a 5.5G Internet of Things device by calling the computer program stored in the memory.

[0081] A computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the energy-saving communication method for a 5.5G Internet of Things device.

[0082] Beneficial effects of the present invention: By training the IoT device data collection frequency scheduling model according to the time characteristics of historical data, the present invention can adaptively adjust the data collection frequency, reduce unnecessary sampling, effectively reduce the energy consumption of the device, and extend the battery life.

[0083] The present invention can obtain the optimal communication strategy by training the communication strategy calculation model, comprehensively considering multiple decision variables such as communication time, frequency, data volume and power, and weighing communication performance and energy consumption, thereby minimizing energy consumption while ensuring communication quality.

[0084] The present invention constructs a data buffer area in the IoT device and sets the data transmission interval time, which can reduce frequent data uploads and reduce communication energy consumption. At the same time, when the buffer area is full, data upload is triggered again, which can improve transmission efficiency and reduce data loss.

[0085] The present invention encrypts and compresses the data collected by the IoT device and then uploads it through the 5.5G network, which can improve the security and transmission efficiency of the data, reduce the risk of data leakage and bandwidth occupancy during transmission, and save energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 A flow chart of an energy-saving communication method for a 5.5G Internet of Things device provided by the present invention;

[0087] Figure 2 This is a structural diagram of an energy-saving communication system for a 5.5G Internet of Things device provided by the present invention. DETAILED DESCRIPTION

[0088] In order to better understand the present invention, a more detailed description will be made of various aspects of the present invention with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0089] In the accompanying drawings, the size, dimensions and shapes of the elements have been slightly adjusted for ease of illustration. The accompanying drawings are for illustration only and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms of approximation, not as terms of degree, and are intended to illustrate the inherent deviations in measurements or calculations that will be recognized by those of ordinary skill in the art. In addition, in the present invention, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0090] It should also be understood that expressions such as "comprises", "including", "having", "includes" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention". And, the term "exemplary" is intended to refer to an example or illustration.

[0091] Unless otherwise defined, all terms (including engineering terms and scientific and technological terms) used in this document have the same meaning as commonly understood by ordinary technicians in the field to which the present invention belongs. It should also be understood that unless otherwise clearly stated in the present invention, words defined in commonly used dictionaries should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0092] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0093] Example 1

[0094] Reference Figure 1 , which is the first embodiment of the present invention, provides an energy-saving communication method for a 5.5G Internet of Things device.

[0095] S1: Based on the time characteristics of historical data collected by physical IoT devices, a data collection frequency scheduling model for IoT devices is constructed and trained to control the data collection frequency of IoT devices.

[0096] According to the time characteristics of the historical data collected by IoT devices, a frequency scheduling model for data collection of IoT devices is constructed and trained to control the data collection frequency of IoT devices. The historical data set is assumed to be:

[0097]

[0098] in, Indicates the amount of data in the dataset, Indicates The amount of data in a data item, Indicates the data type, Indicates the timestamp of data transmission. Indicates data priority, Indicates the device type. Indicates the device ID. Indicates the device battery level.

[0099] Using the sliding window method For feature extraction, set the window length to , the sliding step length is The first The feature vector of a time window is:

[0100]

[0101] in It means taking the mean value, represents the standard deviation, Indicates count.

[0102] The feature vectors of all windows form a temporal feature dataset , using the time feature training set and the corresponding target value Train the LSTM model, is the data collection frequency, .

[0103] Build the LSTM model structure and input A continuous time window feature vector, the output is The optimal data collection frequency after time windows is expressed as:

[0104]

[0105] in, is the LSTM model function, For the The optimal data collection frequency after a time window.

[0106] Based on the trained LSTM model, the data collection frequency of IoT devices is obtained:

[0107]

[0108]

[0109] in, is a calculation function of the data acquisition frequency, Indicates The optimal data collection frequency for a time window.

[0110] S2: Based on the historical status information and historical communication data of IoT devices, the communication strategy calculation model is trained. The communication strategy includes multiple decision variables such as communication time, frequency, data volume and power. The communication strategy calculation model balances the communication performance and energy consumption by setting a reward function.

[0111] Define the state feature vector of IoT devices , including CPU utilization , indicating the CPU usage and memory utilization of IoT devices at all times , indicating the The memory usage of IoT devices at all times; battery power , indicating the The remaining battery power of IoT devices at all times; signal strength , indicating the The wireless signal strength received by the IoT device at all times; state feature vector Expressed as .

[0112] Defining the communication eigenvectors of IoT devices , including communication time , indicating the Timestamp of each communication transmission; data volume , indicating the The amount of data transmitted in each communication; power , indicating the The transmission power used for the communication transmission; the communication characteristic vector It can be expressed as .

[0113] Defining Communication Strategy Action Vectors , including communication approval , indicating whether to approve the Communication request, value is 0 or 1; time slot allocation , expressed as The number of time slot resources allocated for each communication; bandwidth allocation , expressed as The amount of bandwidth resources allocated for each communication; action vector Expressed as .

[0114] Build Status - Communication Dataset ,in, is the data volume of the state-communication dataset, each sample Indicates in status Communication Action , and generate communication characteristics ; Define the reward function , used to evaluate the state Next Action The pros and cons of communication performance and energy consumption: ,in, represents the communication utility, which is calculated based on the communication characteristics. ; Indicates the cost of energy consumption; based on power and communication duration To calculate, ;in, and is a trade-off factor that controls the balance between communication utility and energy consumption cost.

[0115] Using the reinforcement learning algorithm DQN neural network, based on the state-communication dataset Training the Communication Strategy Computational Model .

[0116] The input of the model is the state , the output is each action of value , indicating that in the state Next Action The long-term cumulative reward expectation.

[0117] During training, use Strategy exploration, with probability Randomly select actions; at the same time, use the experience replay mechanism to transfer samples of each step The samples are stored in the playback buffer and some samples are randomly selected for training to reduce the correlation between samples.

[0118] During the training process, the model parameters are continuously optimized to make the predicted Q value as close as possible to the actual cumulative reward; the loss function can be defined as the mean square error (MSE) between the predicted Q value and the target Q value:

[0119]

[0120] in, are model parameters, is the target network parameter, For the experience replay buffer, is the discount factor.

[0121] After training is completed, the optimal communication strategy is obtained , for any state , select the action with the largest Q value as the optimal decision:

[0122]

[0123]

[0124] in, is the action space, is the next step, is the next state, is the discount factor, is the symbolic representation of mathematical expectation, Indicates that in a given state Next Action After that, the expected future cumulative rewards are obtained.

[0125] Get the optimal communication scheduling strategy ,in, is the state space, for any state , select action Make maximum.

[0126] S3: Build a data cache area in the IoT device to temporarily store the data collected by the IoT device, set the data transmission interval and upload threshold of the IoT device to the edge node, and determine whether to upload the data based on the time and upload threshold.

[0127] Build a data cache area in the IoT device to temporarily store the data collected by the IoT device and formulate a data transmission strategy.

[0128] Dynamically adjust the data collection frequency of IoT devices based on the output results of the IoT device data collection frequency scheduling model .

[0129] During the data collection process of IoT devices, monitor the occupancy rate of the cache area , occupancy rate Indicates the ratio of the used space in the cache to the total space.

[0130] Optimal communication strategy for IoT devices Extract the data transmission interval duration As the optimal data upload interval, according to the data transmission interval length Set a timer for IoT devices to upload data.

[0131] When the IoT device reaches the scheduled time of uploading data, read the storage status of the current cache area of ​​the IoT device and calculate the occupancy rate of the current cache area , set the upload threshold of the cache area .

[0132] When the scheduled time is reached, if the occupancy rate of the buffer area is calculated Greater than or equal to the upload threshold of the cache When the cache data of the IoT device is uploaded to the upper edge node, if the occupancy rate of the cache is calculated Less than the upload threshold of the cache When the IoT device cache data is not uploaded to the upper edge node.

[0133] During the data collection process of IoT devices, monitor the occupancy rate of the cache area , which is shorter than the data transmission interval Cycle ,For example ; Calculate the occupancy of the cache area .

[0134] Set the upper threshold of the cache occupancy rate and lower threshold , detect the cache occupancy rate in the gap when the IoT device uploads data to the upper edge node. When the occupancy rate is greater than the upper threshold When the occupancy rate is less than the lower threshold, the buffer area data upload is triggered. , triggers the cache expansion.

[0135] When the cache occupancy rate Greater than upper threshold When the IoT device sends a data transmission instruction to the edge node, all the data in the cache area is uploaded to the edge node, and the cache area is expanded at the same time. The size calculation formula is:

[0136]

[0137] in, is the size of the cache before expansion, is the expansion coefficient, which controls the expansion range.

[0138] When the cache occupancy rate Less than the lower threshold When the cache area is reduced, the storage space of the cache area is reduced; the size of the reduced cache area The calculation formula is:

[0139]

[0140] in, is the shrinkage coefficient, which controls the shrinkage range.

[0141] In the process of cache management, adaptive management of the cache is achieved by dynamically adjusting the data collection frequency and cache size, reducing data overflow and storage space waste. At the same time, by setting up a cache, the number of times the IoT device transmits data to the superior edge node is reduced, thereby achieving energy-saving effects.

[0142] S4: The data collected by IoT devices is encrypted and compressed before uploading, and uploaded to the edge node through the 5.5G network.

[0143] The data collected by the IoT devices is encrypted and uploaded to the upper-level edge node.

[0144] Assume that the data collected by IoT devices is original plaintext data , the encrypted data is ciphertext , the key is ; The encryption process can be expressed as: ;in, For encryption function, receive plaintext data and key As input, generate ciphertext as output.

[0145] Design an encryption algorithm based on XOR operation and permutation operation. The process is as follows:

[0146] Plaintext data Divide into several plaintext blocks by bytes, each plaintext block length is Bytes, less than The bytes are filled with 0; let the plaintext block after division be ; .

[0147] For each plaintext block , , For the total number of plaintext blocks, perform the following steps:

[0148] Step a: With key Perform bitwise exclusive OR (XOR) operation to obtain the intermediate result : ;in, Represents a bitwise exclusive-or operation.

[0149] Step b: Perform a permutation operation to obtain the permutation result : ;in, is the permutation function, The byte order of the data is rearranged according to predefined rules.

[0150] Step c: Replace the result As the encryption result of the current plaintext block, that is, the ciphertext block : .

[0151] Step d: All ciphertext blocks Splice them together to get the final ciphertext : ;in, Represents a concatenation operation.

[0152] Before uploading data, IoT devices use encryption algorithms to encrypt plaintext data. Encrypt and get the ciphertext ; and continue to compress the encrypted ciphertext. Key The key is generated through negotiation between the device and the edge node through a key exchange protocol (such as ECDH).

[0153] The security of this encryption algorithm is based on the following points: XOR operation has good obfuscation characteristics, which can effectively hide the statistical characteristics of plaintext; permutation operation can disrupt the order of data and increase the diffusion of encryption; key Regular updates can prevent the decryption of historical data after the key is leaked.

[0154] Compress the encrypted ciphertext data to reduce the amount of data transmission and save the energy consumption of IoT devices; suppose the encrypted ciphertext data is , the compressed data is , the compression algorithm is ; The compression process can be expressed as: ;in, is a compression function that accepts ciphertext As input, generate compressed data as output.

[0155] Design a compression algorithm based on a combination of dictionary encoding and run-length encoding. The process is as follows:

[0156] Ciphertext Divide into several ciphertext blocks by bytes, each ciphertext block length is bytes; let the ciphertext block after division be , is the total number of ciphertext blocks.

[0157] Build an initial dictionary It contains the encoding of all individual bytes; for each ciphertext block , , perform the following steps:

[0158] In the dictionary Search The longest matching substring of , and its corresponding code is recorded as ;

[0159] Will As The compression code is added to the compression result ;

[0160] if and If it does not match exactly, The unmatched part is used as a new substring ,Will Add to Dictionary and assign a new code to it .

[0161] The compression result is run-length encoded, and the continuously repeated codes are represented in the form of codes and the number of repetitions to reduce the amount of data.

[0162] The result of run-length encoding is used as the final compressed data.

[0163] IoT devices efficiently upload compressed ciphertext data to edge nodes through the 5.5G network.

[0164] The key to the compression algorithm lies in the dictionary By dynamically adding new substrings to the dictionary, the compression effect can be continuously optimized; at the same time, run-length encoding can effectively compress data with high repetitiveness.

[0165] By combining encryption and compression, data security can be guaranteed while significantly reducing the amount of data transmission, reducing the energy consumption of IoT devices, and achieving safe and efficient data upload; at the same time, the computing and storage loads can be shared to edge nodes, reducing the workload of IoT devices and further reducing device energy consumption.

[0166] Example 2

[0167] Reference Figure 2 , which is the second embodiment of the present invention, provides an energy-saving communication system for a 5.5G Internet of Things device.

[0168] The system comprises: a data acquisition frequency control module, a communication strategy module, a data upload control module and an encryption compression module.

[0169] The data collection frequency control module constructs and trains an IoT device data collection frequency scheduling model based on the time characteristics of historical data collected by physical IoT devices, and controls the data collection frequency of IoT devices.

[0170] The communication strategy module trains a communication strategy calculation model based on historical status information and historical communication data of the IoT device. The communication strategy includes multiple decision variables such as communication time, frequency, data volume and power. The communication strategy calculation model balances communication performance and energy consumption by setting a reward function.

[0171] The data upload control module constructs a data cache area in the IoT device to temporarily store data collected by the IoT device, sets the data transmission interval and upload threshold of the IoT device to the edge node, and determines whether to upload data according to the interval and upload threshold.

[0172] The encryption and compression module encrypts and compresses the data collected by the IoT device before uploading, and uploads it to the edge node through the 5.5G network.

[0173] Example 3

[0174] The present invention also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable codes, and when the computer-readable codes are executed by the one or more processors, the energy-saving communication method of a 5.5G Internet of Things device as described above may be executed.

[0175] The method or system according to the embodiment of the present invention may also be implemented with the aid of the architecture of the electronic device of the present invention.

[0176] An electronic device may include a bus, one or more CPUs, read-only memory (ROM), random access memory (RAM), a communication port for connecting to a network, input / output components, a hard disk, and the like.

[0177] A storage device in an electronic device, such as a ROM or a hard disk, can store an energy-saving communication method for a 5.5G Internet of Things device provided by the present invention.

[0178] An energy-saving communication method for 5.5G Internet of Things devices comprises: constructing and training an Internet of Things device data collection frequency scheduling model according to the time characteristics of historical data collected by physical Internet of Things devices to control the data collection frequency of the Internet of Things devices; training a communication strategy calculation model based on historical status information and historical communication data of the Internet of Things devices, wherein the communication strategy includes multiple decision variables such as communication time, frequency, data volume and power, and the communication strategy calculation model balances communication performance and energy consumption by setting a reward function; constructing a data cache area in the Internet of Things device to temporarily store data collected by the Internet of Things device, setting a data transmission interval time and an upload threshold value of the Internet of Things device to an edge node, and judging whether to upload data according to the interval time and the upload threshold value; encrypting and compressing the data collected by the Internet of Things device before uploading, and uploading it to the edge node through a 5.5G network.

[0179] Furthermore, the electronic device may also include a user interface. Of course, the architecture of the present invention is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed in the present invention may be omitted according to actual needs.

[0180] Example 4

[0181] The invention also discloses a computer-readable storage medium.

[0182] The computer-readable storage medium has computer-readable instructions stored thereon.

[0183] When the computer-readable instructions are executed by the processor, an energy-saving communication method for a 5.5G Internet of Things device according to an embodiment of the present invention described with reference to the above drawings may be executed.

[0184] The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. In addition, according to an embodiment of the present invention, the process described above with reference to the flowchart may be implemented as a computer software program.

[0185] For example, the present invention provides a non-temporary machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be executed by a processor to execute instructions corresponding to the method steps provided by the present invention, for example: based on the time characteristics of historical data collected by physical Internet of Things devices, construct and train an Internet of Things device data collection frequency scheduling model to control the data collection frequency of the Internet of Things devices; based on the historical status information and historical communication data of the Internet of Things devices, train a communication strategy calculation model, the communication strategy includes multiple decision variables such as communication time, frequency, data volume and power, and the communication strategy calculation model balances communication performance and energy consumption by setting a reward function; construct a data cache area in the Internet of Things device to temporarily store data collected by the Internet of Things device, set the data transmission interval and upload threshold of the Internet of Things device to the edge node, and determine whether to upload data based on the interval time and upload threshold; encrypt and compress the data collected by the Internet of Things device before uploading, and upload it to the edge node through the 5.5G network.

[0186] When the computer program is executed by the central processing unit (CPU), the above functions defined in the method of the present invention are performed. The method, apparatus, and device of the present invention may be implemented in many ways. For example, the method, apparatus, and device of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware.

[0187] The above sequence for the steps of the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless otherwise specifically stated.

[0188] In addition, in some embodiments, the present invention can also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Therefore, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0189] In addition, the parts of the above technical solutions provided in the embodiments of the present invention that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0190] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An energy-saving communication method for a 5.5G Internet of Things device, characterized in that: include: According to the time characteristics of historical data collected by IoT devices, a frequency scheduling model for data collection of IoT devices is constructed and trained to control the data collection frequency of IoT devices; Based on the historical status information and historical communication data of IoT devices, the communication strategy calculation model is trained. The communication strategy includes multiple decision variables such as communication time, frequency, data volume and power. The communication strategy calculation model balances the communication performance and energy consumption by setting a reward function. Build a data cache in the IoT device to temporarily store the data collected by the IoT device, set the data transmission interval and upload threshold of the IoT device to the edge node, and determine whether to upload data based on the interval and upload threshold; The data collected by IoT devices is encrypted and compressed before uploading, and uploaded to the edge node through the 5.5G network.

2. The energy-saving communication method for a 5.5G Internet of Things device according to claim 1, characterized in that: According to the time characteristics of historical data collected by IoT devices, a frequency scheduling model for data collection of IoT devices is constructed and trained to control the data collection frequency of IoT devices; Assume the historical data set is: ； in, Indicates the amount of data in the dataset, Indicates The amount of data in a data item, Indicates the data type, Indicates the timestamp of data transmission. Indicates data priority, Indicates the device type. Indicates the device ID. Indicates the power level of the device; Using the sliding window method For feature extraction, set the window length to , the sliding step length is , then The feature vector of a time window is: ； in It means taking the mean value, represents the standard deviation, Indicates count; The feature vectors of all windows form a temporal feature dataset , using the time feature training set and the corresponding target value Train the LSTM model, is the data collection frequency, ; Build the LSTM model structure and input A continuous time window feature vector, the output is The optimal data collection frequency after time windows is expressed as: ； in, is the LSTM model function, For the The optimal data collection frequency after a time window; Based on the trained LSTM model, the data collection frequency of IoT devices is obtained: ； in, is a calculation function of the data acquisition frequency, Indicates The optimal data collection frequency for a time window.

3. The energy-saving communication method for a 5.5G Internet of Things device according to claim 2, characterized in that: Based on the historical status information and historical communication data of IoT devices, the communication strategy calculation model is trained. The communication strategy includes multiple decision variables such as communication time, frequency, data volume and power. The communication strategy calculation model balances the communication performance and energy consumption by setting a reward function. Define the state feature vector of IoT devices , including CPU utilization , indicating the CPU usage of IoT devices at all times; Memory Utilization , indicating the The memory usage of IoT devices at all times; battery power , indicating the The remaining battery power of IoT devices at all times; signal strength , indicating the The wireless signal strength received by the IoT device at all times; state feature vector It is expressed as: ; Defining the communication eigenvectors of IoT devices , including communication time , indicating the Timestamp of each communication transmission; data volume , indicating the The amount of data transmitted in each communication; power , indicating the The transmission power used for the secondary communication transmission; Communication feature vector It is expressed as: ; Defining Communication Strategy Action Vectors , including communication approval , indicating whether to approve the Communication request, value is 0 or 1; time slot allocation , expressed as The number of time slot resources allocated for each communication; bandwidth allocation , expressed as The amount of bandwidth resources allocated for each communication; action vector Expressed as ; Build Status - Communication Dataset ,in, is the data volume of the state-communication dataset, each sample Indicates in status Next Action , and generate communication characteristics ; Define the reward function , used to evaluate the state Next Action The pros and cons of communication performance and energy consumption: ,in, represents the communication utility, represents the energy consumption cost, and is a trade-off factor; Using the reinforcement learning algorithm DQN neural network, based on the state-communication dataset Training the Communication Strategy Computational Model ; The input of the model is the state , the output is each action of value , indicating that in the state Next Action The long-term cumulative reward expectation; During training, use Strategy exploration, with probability Randomly select actions; at the same time, use the experience replay mechanism to transfer samples of each step Store them in the playback buffer and randomly extract some samples from them for training to reduce the correlation between samples; After training is completed, the optimal communication strategy is obtained , for any state , select the action with the largest Q value as the optimal decision: ； in, is the action space, is the next step, is the next state, is the discount factor, is the symbolic representation of mathematical expectation, Indicates that in a given state Next Action After that, the expected future cumulative rewards obtained; Get the optimal communication strategy ,in, is the state space, for any state , select action Make maximum.

4. The energy-saving communication method for a 5.5G Internet of Things device according to claim 3, characterized in that: Build a data cache in IoT devices to temporarily store data collected by IoT devices and formulate a data transmission strategy; Dynamically adjust the data collection frequency of IoT devices based on the output results of the IoT device data collection frequency scheduling model ; During the data collection process of IoT devices, monitor the occupancy rate of the cache area , occupancy rate Indicates the ratio of the used space in the cache to the total space; Optimal communication strategy for IoT devices Extract the data transmission interval duration As the optimal data upload interval, according to the data transmission interval length Set a timer for IoT devices to upload data; When the IoT device reaches the scheduled time of uploading data, read the storage status of the current cache area of ​​the IoT device and calculate the occupancy rate of the current cache area , set the upload threshold of the cache area ; When the scheduled time is reached, if the occupancy rate of the buffer area is calculated Greater than or equal to the upload threshold of the cache When the cache data of the IoT device is uploaded to the upper edge node, if the occupancy rate of the cache is calculated Less than the upload threshold of the cache When the IoT device cache data is not uploaded to the upper edge node.

5. The energy-saving communication method for a 5.5G Internet of Things device according to claim 4, characterized in that: During the data collection process of IoT devices, monitor the occupancy rate of the cache area , which is shorter than the data transmission interval cycle, calculate the occupancy rate of the cache area ; Set the upper threshold of the cache occupancy rate and lower threshold , detect the cache occupancy rate in the gap when the IoT device uploads data to the upper edge node. When the occupancy rate is greater than the upper threshold When the occupancy rate is less than the lower threshold, the buffer area data upload is triggered. When , the cache area is triggered to shrink; When the cache occupancy rate Greater than upper threshold When the IoT device sends a data transmission instruction to the edge node, all the data in the cache area is uploaded to the edge node, and the cache area is expanded at the same time. The size calculation formula is: ； in, is the size of the cache before expansion, is the expansion coefficient, which controls the expansion range; When the cache occupancy Less than the lower threshold When the cache area is reduced, the storage space of the cache area is reduced; the size of the reduced cache area The calculation formula is: ； in, is the shrinkage coefficient, which controls the shrinkage range.

6. The energy-saving communication method for a 5.5G Internet of Things device according to claim 5, characterized in that: Encrypt and upload the data collected by IoT devices to the upper-level edge node; Assume that the data collected by IoT devices is original plaintext data , the encrypted data is ciphertext , the key is ; The encryption process is expressed as: ;in, For encryption function, receive plaintext data and key As input, generate ciphertext As output; Design an encryption algorithm based on XOR operation and permutation operation. The process is as follows: Plaintext data Divide into plaintext blocks by bytes, each plaintext block length is Bytes, less than The bytes are filled with 0; let the plaintext block after division be ; For each plaintext block , , For the total number of plaintext blocks, perform the following steps: Step a: With key Perform bitwise XOR operation to obtain the intermediate result : in, Represents a bitwise exclusive OR operation; Step b: Perform a permutation operation to obtain the permutation result : ;in, is the permutation function, The byte order of the byte is rearranged according to predefined rules; Step c: Replace the result As the encryption result of the current plaintext block, that is, the ciphertext block : ; Step d: All ciphertext blocks Splice them together to get the final ciphertext : ;in, Represents a splicing operation; Before uploading data, IoT devices use encryption algorithms to encrypt plaintext data. Encrypt and get the ciphertext .

7. The energy-saving communication method for a 5.5G Internet of Things device according to claim 6, characterized in that: Compress the encrypted ciphertext data. Suppose the encrypted ciphertext data is , the compressed data is , the compression algorithm is ; The compression process is expressed as: ;in, is the compression function, receiving the ciphertext As input, generate compressed data As output; Design a compression algorithm based on a combination of dictionary encoding and run-length encoding. The process is as follows: Ciphertext Divide into ciphertext blocks by bytes, each ciphertext block length is bytes; let the ciphertext block after division be , is the total number of ciphertext blocks; Build an initial dictionary , which contains the encoding of all individual bytes; for each ciphertext block , , perform the following steps: In the dictionary Search The longest matching substring of , and its corresponding code is recorded as ; Will As The compression code is added to the compression result; if and If it does not match exactly, The unmatched part is used as a new substring ,Will Add to Dictionary and assign a new code to it ; Run-length encoding is performed on the compression result, and the continuously repeated codes are expressed in the form of codes and the number of repetitions to reduce the amount of data; The result of run-length encoding is used as the final compressed data; IoT devices upload compressed ciphertext data to edge nodes through the 5.5G network.

8. An energy-saving communication system for a 5.5G Internet of Things device, which is used to implement an energy-saving communication method for a 5.5G Internet of Things device according to any one of claims 1 to 7, characterized in that: include: Data collection frequency control module, communication strategy module, data upload control module and encryption compression module; The data collection frequency control module constructs and trains a data collection frequency scheduling model for IoT devices according to the time characteristics of historical data collected by IoT devices, and controls the data collection frequency of IoT devices; The communication strategy module trains a communication strategy calculation model based on historical status information and historical communication data of IoT devices. The communication strategy includes multiple decision variables such as communication time, frequency, data volume, and power. The communication strategy calculation model balances communication performance and energy consumption by setting a reward function. The data upload control module constructs a data cache area in the IoT device to temporarily store data collected by the IoT device, sets the data transmission interval and upload threshold of the IoT device to the edge node, and determines whether to upload data according to the interval and upload threshold; The encryption and compression module encrypts and compresses the data collected by the IoT device before uploading, and uploads it to the edge node through the 5.5G network.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the energy-saving communication method of a 5.5G Internet of Things device as described in any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes an energy-saving communication method for a 5.5G Internet of Things device as described in any one of claims 1 to 7.

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