Low-power-consumption wide-area Internet of Things communication method based on RedCap technology

Abstract

The invention discloses a low-power-consumption wide-area Internet of Things communication method based on a RedCap technology, and belongs to the field of Internet of Things. Comprising resource allocation strategy optimization, signal processing mechanism improvement and working mode dynamic switching. By collecting the state information and the network load data of the Internet of Things equipment, the resource allocation and the working mode of the RedCap equipment are dynamically adjusted by adopting an optimization algorithm, and the overall energy consumption is minimized. Meanwhile, a signal processing algorithm improves signal quality and reduces power consumption by optimizing data retransmission and transmission power. The method is based on a Lagrangian multiplier method and integer linear programming, and self-adaptive optimization configuration of equipment energy consumption and communication quality is achieved. The technology is suitable for a large-scale Internet of Things scene, the equipment endurance is effectively prolonged while the communication requirement is met, and the overall energy efficiency of the network is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of Internet of Things, and more particularly relates to a low-power wide-area Internet of Things communication method based on RedCap technology. BACKGROUND

[0002] With the development of 5G technology and the popularity of Internet of Things (IoT), a large number of low-power devices need to access the 5G network. The 5G network can provide high-speed, large-capacity, low-latency new-generation wireless communication services for IoT terminals. However, the traditional 5G network design does not fully consider the needs of accessing a large number of low-power devices, and how to significantly reduce device power consumption while maintaining communication quality and prolonging battery life is a problem that the 5G communication system needs to solve when facing large-scale access of Internet of Things devices.

[0003] Current low-power wide-area network technologies mainly include LoRa, Sigfox, NB-IoT, etc., which can provide low-power consumption and long-distance communication solutions in specific scenarios, but when facing the changing wireless environment of 5G networks, such as different channel conditions, frequent network interference, and a wide variety of IoT application requirements, it is still very challenging to provide stable, reliable, and efficient communication services. In addition, for large-scale deployment of Internet of Things devices, how to effectively schedule and manage resources to ensure device communication performance while minimizing energy consumption is also a problem that needs to be solved.

[0004] Therefore, in view of the above problems, 3GPP proposes the RedCap (Reduced Capability) device scenario in the evolution of 5G. The goal of RedCap is to provide a low-rate, low-power, high-frequency coverage, and large connection number solution, mainly used to support Internet of Things applications with medium-speed data transmission requirements, such as smart home, industrial automation, etc. However, how to use RedCap technology to efficiently meet the communication needs of low-power Internet of Things devices in 5G networks also needs further research and exploration. SUMMARY

[0005] The present application mainly achieves the goal through three innovative technical measures, aiming to greatly improve the energy efficiency and connection performance of Internet of Things devices in 5G networks. In view of the problems of high power consumption and unstable connection of existing 5G networks in supporting low-power Internet of Things devices, the present application provides a feasible optimization scheme, and it is expected that through the implementation of the present application, large-scale deployment of Internet of Things devices can be supported, which will help to promote the wide application of Internet of Things technology in various fields.

[0006] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows: Resource allocation strategy optimization: design a dynamic resource allocation algorithm to adjust the resource allocation of RedCap devices based on the current state of the Internet of Things devices and the load of the network environment; Including: Collecting data according to the current state of the Internet of Things devices and the load of the network environment; Processing and analyzing the collected data; Adjusting the resource allocation of RedCap devices to minimize energy consumption; Improvement of signal processing mechanism: through signal processing algorithm, reduce the number of data retransmission, reduce the power consumption of the device; Including: Device state collection and feature extraction; Evaluation and optimization of signal quality; Selection and optimization of retransmission strategy; Adaptive adjustment; Dynamic adjustment of RedCap device working mode: design a new working mode switching mechanism, considering the current environmental conditions and task requirements of the device, as well as the energy consumption of the device itself, on the premise of meeting the normal work of the device, reduce the power consumption of the device; Including: Real-time monitoring of the environmental state and current task demand of the device, including: the current battery level of the device, the surrounding environmental network situation, the current task calculation demand information; According to the current environmental state and task demand, find the most suitable working mode through optimization problem; According to the solution of the above optimization problem, the algorithm will automatically switch to the best working mode to meet the task demand and reduce the power consumption target.

[0007] In one scheme, the processing and analysis of the collected data includes: using an optimization problem to express, that is, minimizing the energy consumption of the device under the premise of meeting the network load and device demand constraints; Processing and analyzing the collected device power, network load and device data transmission demand data, under the premise of meeting the lower and upper constraints of network load and device demand, taking the minimum total energy consumption as the target, taking the sum of each device power consumption function as the optimization target, using the Lagrange multiplier method to solve, introducing the Lagrange multiplier through the Lagrange function, substituting the constraint condition into the optimization model, converting into solving related equation groups, thus obtaining the optimal distribution of network load and device demand variables.

[0008] In one scheme, the signal quality evaluation and optimization includes: evaluation of the current signal quality of the device, optimization of the signal quality Q by using the gradient descent method, maximization of the signal-to-noise ratio by adjusting the device transmission power, reduction of communication noise interference, and optimal improvement of signal quality.

[0009] In one scheme, the selection and optimization of the retransmission strategy includes: selecting and optimizing the retransmission strategy for data loss caused by low signal quality, optimizing the overall energy consumption by setting the retransmission interval and the number of retransmissions, and considering the energy consumption loss and network congestion risk to minimize the overall energy consumption.

[0010] In one scheme, the adaptive adjustment includes: dynamically adjusting the transmission power and the number of retransmissions of the device according to the device operation log or real-time feedback information, and realizing flexible optimization of energy consumption and communication quality according to the network environment and device state by introducing a weight coefficient and combining the optimization results and historical parameters.

[0011] In one scheme, the working mode switching mechanism includes: Real-time monitoring of the environmental state and task demand of the device, collecting information such as device battery power, network status and current task computing demand, and presetting multiple working modes, each mode corresponding to different power consumption and performance; According to the collected data, for M working modes, the optimal working mode with the lowest total energy consumption is selected by optimizing the problem under the constraint of meeting the task demand; According to the solution of the optimization problem, the best working mode is automatically switched to, achieving the goal of meeting the task demand and reducing power consumption.

[0012] In one scheme, the optimization problem solving process is as follows: First, set the selection of each working mode as a binary decision variable, and require that only one of all working modes can be selected; Second, for the energy consumption of different working modes, an optimization scheme is developed to reduce the overall energy consumption; Third, for the performance indicators of each working mode, constraint conditions are set to meet the specific task demand; Finally, the integer linear programming method is used, and the branch and bound method is used to solve the above model to automatically select the best working mode that meets the task demand and has the lowest energy consumption, realizing the optimal configuration of device operation energy efficiency.

[0013] The present application has the following advantages: The primary innovation of the present application is to significantly improve the energy efficiency and connection performance of Internet of Things devices in 5G networks. The key to achieving this goal is to propose and apply an optimization scheme based on RedCap technology, which includes three major innovations.

[0014] Firstly, the present application optimizes the device resource allocation strategy. By designing a dynamic resource allocation algorithm, the resource allocation of RedCap devices can be intelligently adjusted according to the current state of the device and the network load, to reduce unnecessary energy consumption. This optimization of device resource allocation strategy can maximize energy consumption while ensuring the normal operation and data transmission quality of Internet of Things devices, thereby prolonging the service life of the device.

[0015] Secondly, the present application improves the signal processing mechanism and develops a new signal processing algorithm, the main goal of which is to improve data transmission efficiency and reduce retransmission times. With the goal of minimizing energy consumption, the algorithm evaluates the feasible transmission power range according to the current network environment and device state, and selects the smallest possible transmission power under the premise of meeting the signal quality requirements. When the network environment or device state changes, the algorithm will re-evaluate the transmission power in real time and make adjustments.

[0016] Finally, the present application proposes a dynamic adjustment method of working mode, which can automatically switch the working mode of RedCap device according to the environment and task demand of the device. This automatic switching of working mode strategy can flexibly adjust according to the environmental state and task demand of the device, and realize automatic optimization of power consumption and performance without human intervention. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 Resource allocation strategy optimization flowchart; Figure 2 Flowchart for dynamically adjusting the working mode of RedCap device; Figure 3 Flowchart of optimization problem solving process for dynamically adjusting the working mode of RedCap device. DETAILED DESCRIPTION

[0018] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the relevant drawings. The drawings show typical embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described in the present application. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive.

[0019] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0020] like Figure 1 As shown, a low-power wide-area IoT communication method based on RedCap technology specifically includes: 1. Resource Allocation Strategy Optimization: First, the system designs a dynamic resource allocation algorithm. This algorithm can intelligently adjust the resource allocation of RedCap devices based on the current status of IoT devices and the network load. Through this optimization, the system can reduce unnecessary energy consumption.

[0021] like Figure 1 As shown, the optimization of the equipment resource allocation strategy is mainly achieved by designing a dynamic resource allocation algorithm. The specific implementation process of this algorithm is as follows: S101. First, the dynamic resource allocation algorithm collects data based on the current status of IoT devices and the load of the network environment. This includes factors such as device battery level, data transmission requirements, signal strength, network congestion, and signal quality.

[0022] S102. Next, the algorithm processes and analyzes the collected data. Taking device battery level as an example, generally, the lower the battery level, the stricter the control over energy consumption. In this case, the algorithm may choose to allocate fewer resources to the device to reduce energy consumption. However, if the device's data transmission demand is very high, the algorithm may prioritize ensuring transmission efficiency and appropriately increase resource allocation.

[0023] This process can be described as an optimization problem: minimizing device energy consumption while satisfying network load and device demand constraints. It can be expressed as the following mathematical formula: minimize , , . Where E represents total energy consumption. Let L be the energy consumption of the i-th device, and L and U be the lower and upper limits of the network load, respectively. D and V be the lower and upper limits of the device demand, respectively. It is the network load of the i-th device. is the data transmission requirement of the ith device.

[0024] For the optimization problem described above, where is the sum of the power consumption functions for each device, and As a constraint, it reflects the upper and lower limits of network load and device requirements.

[0025] The Lagrange Multiplier Method is used to solve the above optimization problem.

[0026] Specifically as follows: The Lagrange function of this optimization problem can be formalized as:

[0027] Where λ, μ, ν, θ are Lagrange multipliers. The optimization problem is transformed into solving the following system of equations:

[0028]

[0029]

[0030]

[0031]

[0032] Through iteration, we can get the values of the variables of interest e1, e2,..., en, which solves our optimization problem.

[0033] S103、Finally, according to the analysis results of the algorithm, the system will adjust the resource allocation of the RedCap device to minimize the energy consumption. This way can effectively avoid resource waste, improve network resource utilization, and reduce device power consumption.

[0034] The optimization of the device resource allocation strategy based on the dynamic resource allocation algorithm can minimize energy consumption while ensuring the normal operation and data transmission quality of the Internet of Things devices, thereby prolonging the service life of the devices and meeting the needs of low-power wide-area Internet of Things communication systems.

[0035] 2、 Improvement of signal processing mechanism: Then, through the signal processing algorithm, the data transmission efficiency of the Internet of Things device can be improved, and the number of data retransmissions can be reduced, which will also help to reduce the power consumption of the device.

[0036] The main goal is to improve data transmission efficiency and reduce the number of retransmissions. Here is the detailed process: The signal processing algorithm first needs to target the least possible energy consumption, which usually means reducing power during transmission to adapt to various network environments, but this may affect transmission quality.

[0037] To solve this problem, the algorithm first evaluates the feasible transmission power range based on the current network environment and device status. This can be achieved by the device sensing the signal-to-noise ratio (SNR) and other information about its environment.

[0038] In the signal processing algorithm, our main goal is to reduce transmission power to save energy while meeting signal quality requirements.

[0039] The following describes the signal processing algorithm involved in the signal processing process in detail: 2.1, Device state collection and feature extraction The device needs to collect and process enough environmental information to determine the current state of the device and predict its future behavior. This includes the device's battery level, data transmission demand, signal strength, and various environmental factors that may affect the device's communication effectiveness. The main task of this stage is to select enough and effective features to reflect the current state of the device.

[0040] 2.2, Signal quality evaluation and optimization Secondly, we need to evaluate the current signal quality of the device and use the gradient descent method to optimize the signal quality Q. Signal quality can be considered as a function of device power and communication environment noise during optimization. The main task is to select the appropriate power, reduce noise interference during communication, and improve signal quality. The goal of this stage is to find the optimal transmission power of the device that maximizes signal quality.

[0041] Assuming the current device's signal-to-noise ratio (SNR) is g, the transmission power is p, and the noise is n, we need to maximize the signal quality Q, .

[0042] Here Q can be considered as signal quality, the larger the value, the better the signal quality. Therefore, our goal is to find the best p that maximizes Q, which can be formalized as the following optimization problem:

[0043]

[0044] This is an optimization problem under constraints. Use the gradient descent method to solve it.

[0045] 2.3, Selection and optimization of retransmission strategy Then, for data loss caused by signal quality problems, an effective retransmission strategy needs to be designed to reduce the data loss rate. Choose the appropriate retransmission interval and retransmission times to minimize energy loss caused by retransmission. At the same time, the impact of retransmission needs to be considered to avoid network congestion caused by frequent retransmission.

[0046] Design an efficient retransmission strategy for data loss caused by low signal quality, and choose appropriate retransmission interval and retransmission times. The optimization goal of the retransmission strategy is to reduce the overall energy consumption E, which can be expressed as follows: The energy consumption of R times retransmission is where Et is the energy consumption of a single transmission.

[0047] Overall energy consumption where E_i is the waiting energy consumption caused by the retransmission interval.

[0048] Find the appropriate R to minimize E, which can be formalized as the following optimization problem: Minimize

[0049] 2.4, adaptive adjustment Finally, we will use an adaptive way to adjust the transmission power of the device and select the retransmission strategy. Through the running log of the device or real-time feedback information, the transmission power of the device and the selection of the retransmission strategy are adjusted in real time to adapt to the changes in the environment of the device and the business needs.

[0050] The adaptive algorithm can be used to adjust the transmission power of the device and select the retransmission strategy. Specifically, the transmission power p and the number of retransmissions R of the device will be dynamically adjusted according to the network environment and the device state. Introducing a weight coefficient w (0 < w < 1), the update rule of the transmission power p and the number of retransmissions R of the device can be described as:

[0051]

[0052] where popt and Ropt are the optimal transmission power and retransmission times obtained by the above optimization problem, pold and Rold are the last parameter settings of the device. w is the weight coefficient, which can be dynamically adjusted according to the device state and network environment, such as device power, network load, etc.

[0053] Through this adaptive adjustment method, the device can automatically adjust its parameter settings according to the current environmental state and device state to minimize energy consumption and maximize communication quality.

[0054] 3、 Dynamically adjust the working mode of RedCap devices: according to the environment and task requirements of Internet of Things devices, the system can automatically switch the working mode of RedCap devices. For example, when the device is in low load, the system can switch the device to sleep mode to reduce the energy consumption of the device; for another example, when the device needs efficient data transmission, the system can switch the device to high efficiency mode to ensure the stability and efficiency of data transmission.

[0055] The dynamic adjustment of device working mode is mainly realized by designing a new working mode switching mechanism. This mechanism mainly considers the current environmental conditions and task requirements of the device, as well as the energy consumption of the device itself, and under the premise of meeting the normal work of the device, reduces the power consumption of the device as much as possible.

[0056] The design process of the working mode switching mechanism is as follows: Dynamic adjustment of working mode: in this invention, RedCap devices may have multiple different working modes, such as low power consumption mode, high performance mode, power saving mode, etc. By automatically switching different working modes, this invention aims to balance the power consumption and performance of the device.

[0057] For the dynamic adjustment of working mode, the current environmental state and task requirements of the device are mainly considered. According to the environmental state and task requirements of the device, the working mode of RedCap device is automatically switched to balance the power consumption and performance.

[0058] Firstly, the system needs to monitor the working condition and environmental information of each RedCap device in real time. The working condition includes the current battery level of the device, task requirement, working mode of the device, etc.; the environmental information includes the network environment where the device is located, signal quality, environmental noise, etc. These information will have an impact on the working mode and energy consumption of the device.

[0059] Secondly, the system needs to process and analyze the collected information. The system can choose a device working mode that minimizes the total energy consumption.

[0060] Then, the system needs to feed back the analysis results to each RedCap device. Each device automatically adjusts its working mode according to the feedback results. For example, when the battery level of the device is low, the task requirement is small, and the network environment is stable, the device can switch to low power consumption mode; on the contrary, when the battery level of the device is high, the task requirement is large, and the network environment is unstable, the device can switch to high performance mode.

[0061] Finally, the system needs to continuously monitor the working condition and environmental information of the device, and adjust the working mode of the device regularly according to the new information. In this way, it can ensure that the device always runs in the optimal working mode, so as to achieve the lowest energy consumption.

[0062] For example,Figure 2 As shown, the process can be broken down into the following steps: S301. First, the algorithm needs to monitor the device's environmental status and current task requirements in real time. This includes information such as the device's current battery level, the surrounding network conditions, and the computational requirements of the current task. Simultaneously, multiple operating modes are preset, each corresponding to specific power consumption and performance under predetermined task requirements and battery levels.

[0063] S302. Then, based on the current environmental state and task requirements, the algorithm will find the most suitable working mode through optimization. Suppose there are M working modes, each corresponding to a performance metric. and power consumption This process involves selecting an optimal operating mode while meeting task requirements and minimizing energy consumption. It can be represented as the following optimization problem: satisfy

[0064] minimize

[0065] Where D represents the current task requirement. It is a performance metric for working mode i. This is the total energy consumption of working mode i.

[0066] S303. Finally, based on the solution to the above optimization problem, the algorithm will automatically switch to the optimal working mode to meet the task requirements and reduce power consumption.

[0067] like Figure 3 The diagram illustrates the solution process for the optimization problem of dynamically adjusting the operating mode of the RedCap device. Since the decision variables involved in this problem are discrete variables, namely the choice of each working mode, more specifically, this is an Integer Linear Programming (ILP) problem. Below is a solution process for an ILP problem: S3021. Define decision variables: The decision variable here is which working mode to choose, which can be defined as a binary variable vector. Here, xi=1 indicates that the i-th working mode is selected, and xi=0 indicates that the i-th working mode is not selected. To ensure that only one working mode is selected, we also need to add a constraint: .

[0068] S3022. Constructing the objective function: The objective function is the total energy consumption that needs to be minimized, which can be expressed as: , where Ei represents the energy consumption in the i-th working mode.

[0069] S3023. Adding constraints: The constraints here are to meet the task requirements, that is, the performance index Pi of the i-th working mode needs to be greater than or equal to the task requirement D. For all working modes, the constraint condition can be expressed as: xiPi ≥ D.

[0070] The above is sorted out, and the entire ILP problem can be expressed as:

[0071] s.t.

[0072]

[0073]

[0074] This problem is solved using methods such as branch and bound. The basic idea of branch and bound is to divide the problem into several smaller sub-problems (branching), and by finding the upper and lower bounds of the sub-problems (bounding), some feasible solutions are excluded, and the scope of the problem is gradually reduced, and finally the global optimal solution is found.

[0075] This automatic switching of working mode strategy not only can adjust flexibly according to the environmental state of the device and the task requirement, but also can realize the automatic optimization of power consumption and performance without human intervention, to meet the needs of low-power wide-area Internet of Things communication system.

[0076] In summary, through the optimization of device resource allocation strategy, the improvement of signal processing mechanism, and the dynamic adjustment of working mode, the present application realizes the significant reduction of device power consumption while ensuring the communication quality of Internet of Things devices in 5G network, and meets the needs of large-scale Internet of Things deployment scenarios.

[0077] Embodiment: In an industrial park, 10 low-power Internet of Things terminals (RedCap devices) are deployed for environmental monitoring (temperature and humidity, gas concentration, etc.). Each device needs to dynamically allocate communication resources and adjust the working mode according to the on-site battery capacity, network status and real-time task requirements. The present application method is used for resource allocation optimization, signal processing, retransmission strategy optimization, adaptive adjustment and working mode switching.

[0078] 1. Device and network initial state data table Table 1 Device and network initial state data table

[0079] 2. Optimization analysis and results (part of key equipment examples) The Lagrange multiplier method and integer linear programming are used to optimize the overall resource allocation and power consumption.

[0080] The signal-to-noise ratio optimization uses gradient descent to adjust the transmission power to improve signal quality.

[0081] In the case of high network load and high device power consumption, the device working mode is dynamically switched, and the retransmission parameters are optimized.

[0082] Table 2 Optimization Analysis and Results

[0083] 3. Working mode selection and constraint description Device constraints: devices with battery power <40% are prohibited from entering high-power mode, and devices with transmission demand >20 KB / h prefer high-performance mode.

[0084] Table 3 Mode Energy Consumption and Performance Index Table

[0085] 4. Optimization process excerpt (Dev01 as an example) Initial selection of mode 2, high performance, high power consumption (50mW), signal quality Q is 18.

[0086] After Lagrange optimization model, gradient adjustment and retransmission strategy optimization, the device switches to mode 1, power consumption is reduced to 42mW, and signal quality is improved to 23. The number of retransmissions is optimized from 2 to 1, and the overall energy consumption is reduced by 16%.

[0087] The optimal solution is obtained by integer linear programming, which meets the dual objectives of transmission demand and minimum energy consumption.

[0088] Through the above automatic optimization process, even in the environment of high network load and insufficient power of some devices, each RedCap device finds the appropriate working mode, realizes the reliable completion of the communication task and effectively reduces the overall energy consumption.

[0089] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0090] It should be understood that the technical solutions of the present application described above with the preferred embodiments are illustrative rather than limiting. Based on the description of the present application, those skilled in the art can modify the technical solutions recorded in each embodiment, or replace some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A low-power wide-area IoT communication method based on RedCap technology, characterized in that: The method includes: Resource allocation strategy optimization: Design a dynamic resource allocation algorithm to adjust the resource allocation of RedCap devices based on the current status of IoT devices and the load of the network environment; include: Data is collected based on the current status of IoT devices and the load of the network environment; The collected data is processed and analyzed. Adjust the resource allocation of RedCap equipment to minimize energy consumption; Improved signal processing mechanism: By using signal processing algorithms, the number of data retransmissions is reduced, thus lowering the power consumption of the device; include: Equipment status collection and feature extraction; Signal quality assessment and optimization; Selection and optimization of retransmission strategies; Adaptive adjustment; Dynamically adjust the working mode of RedCap equipment: Design a new working mode switching mechanism that takes into account the current environmental conditions and task requirements of the equipment, as well as the energy consumption of the equipment itself, and reduces the power consumption of the equipment while ensuring normal operation. include: Real-time monitoring of the device's environmental status and current task requirements, including: the device's current battery level, the surrounding network conditions, and the computational requirements of the current task; Based on the current environmental conditions and task requirements, find the most suitable working mode by optimizing the problem; Based on the solution to the above optimization problem, the algorithm will automatically switch to the optimal working mode to meet the task requirements and reduce power consumption.

2. The low-power wide-area IoT communication method based on RedCap technology according to claim 1, characterized in that: The aforementioned processing and analysis of the collected data includes: expressing it as an optimization problem, namely, minimizing the energy consumption of the device while satisfying network load conditions and device demand constraints; The collected data on device power consumption, network load, and device data transmission requirements are processed and analyzed. Under the premise of meeting the lower and upper limits of network load and device requirements, the goal is to minimize total energy consumption. The sum of the power consumption functions of each device is used as the optimization objective, and the Lagrange multiplier method is used to solve the problem. By introducing the Lagrange multiplier through the Lagrange function, the constraints are substituted into the optimization model and transformed into solving a set of related equations, thereby obtaining the optimal allocation of network load and device requirements variables.

3. The low-power wide-area IoT communication method based on RedCap technology according to claim 1, characterized in that: The signal quality assessment and optimization includes: assessing the current signal quality of the device, optimizing the signal quality Q using the gradient descent method, adjusting the device's transmission power to maximize the signal-to-noise ratio, reducing communication noise interference, and achieving optimal improvement in signal quality.

4. The low-power wide-area IoT communication method based on RedCap technology according to claim 1, characterized in that: The selection and optimization of the retransmission strategy includes: selecting and optimizing the retransmission strategy for data loss caused by poor signal quality; optimizing the overall energy consumption by setting the retransmission interval and the number of retransmissions; and taking into account both energy loss and network congestion risk in order to minimize the overall energy consumption.

5. A low-power wide-area IoT communication method based on RedCap technology according to claim 1, characterized in that: The adaptive adjustment includes: dynamically adjusting the device's transmission power and retransmission count based on device operation logs or real-time feedback information; and flexibly optimizing energy consumption and communication quality based on network environment and device status by introducing weighting coefficients and combining optimization results with historical parameters.

6. The low-power wide-area IoT communication method based on RedCap technology according to claim 1, characterized in that: The aforementioned working mode switching mechanism includes: It monitors the environmental status and task requirements of the device in real time, collects information such as device battery level, network status and current task computing requirements, and presets multiple working modes, each corresponding to different power consumption and performance. Based on the collected data, for M working modes, the optimal working mode with the lowest total energy consumption is selected by optimizing the problem while meeting the constraints of task requirements. Based on the solution to this optimization problem, the system automatically switches to the optimal working mode to achieve the goals of meeting task requirements and reducing power consumption.

7. A low-power wide-area IoT communication method based on RedCap technology according to claim 6, characterized in that: The optimization problem-solving process is as follows: First, the selection of each work mode is set as a binary decision variable, and it is required that only one of the work modes can be selected in all work modes; Secondly, for the energy consumption of different working modes, optimization schemes are formulated with the goal of reducing overall energy consumption; thirdly, for the performance indicators of each working mode, constraints are set to meet the specific task requirements. Finally, an integer linear programming approach is adopted, with branch and bound as the preferred method for solving the problem. The optimal working mode that meets the task requirements and has the lowest energy consumption is automatically selected to achieve the optimal configuration of equipment operating energy efficiency.

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