A wireless optimized transmission method based on smart home

Abstract

The present invention discloses a wireless optimization transmission method based on smart home, which relates to the field of smart home technology. The wireless optimization transmission method includes the following steps: constructing a two-layer architecture of regional edge gateways and cloud centers, managing terminal devices through dynamic grouping, embedding a localized decision-making module in the regional edge gateways, and adopting a dual-mode adaptive transmission protocol to optimize path selection. The advantages of the present invention are: constructing a two-layer architecture of regional edge gateways and cloud centers, sinking data processing tasks to local gateways, avoiding the remote transmission bottleneck of traditional centralized architectures, embedding a lightweight decision-making module in the regional edge gateways, and completing localized route optimization and data compression without relying on cloud-based iterative calculations. The dual-mode adaptive transmission protocol switches the transmission mode in real time according to data priority and channel quality, solving the high delay problem caused by fixed iteration and rigid transmission in the existing technology.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and in particular to a wireless optimization transmission method based on smart home. Background Art

[0002] With the popularization of smart home devices, the application of wireless transmission technology in home environments faces severe challenges. Existing solutions generally have core problems such as high latency, high energy consumption, poor anti-interference and network congestion. Taking application number: 201810635338.2 as an example, it adopts a centralized core layer scheduler architecture and requires a fixed 50 iterations to process requests, resulting in an average end-to-end delay of more than 200 milliseconds. This design not only fails to meet the needs of real-time scenarios such as security monitoring, but also lacks dynamic adaptability due to the rigid transmission mode, further exacerbating response delays. In addition, the solution relies on a single transmission protocol and cannot flexibly switch transmission strategies according to data priority or channel status. It is susceptible to interference in complex home environments and has serious lack of stability.

[0003] The shortcomings of existing technologies are even more pronounced in high-load scenarios. When the number of nodes increases to more than 50 or when bursty traffic occurs, traditional solutions lack a distributed optimization mechanism, resulting in a sharp drop in throughput. For example, compressed sensing technology with a fixed dictionary struggles to adapt to dynamic sparse features, resulting in a reconstruction error rate as high as 35%. Furthermore, the computational complexity of the path planning algorithm reaches O(n²), and global backtracking is required after node failure, seriously affecting network robustness. These limitations not only restrict the scalability of smart home systems but also lead to excessive energy consumption at sensor nodes. Therefore, a distributed solution that integrates edge computing, dynamic routing optimization, and adaptive transmission is urgently needed to comprehensively improve the system's real-time performance, energy efficiency, and reliability. Summary of the Invention

[0004] The purpose of the present invention is to provide a wireless optimization transmission method based on smart home.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a wireless optimization transmission method based on smart home, including a wireless optimization transmission method, wherein the wireless optimization transmission method comprises the following steps:

[0006] Step 1: Build a two-tier architecture of regional edge gateways and cloud centers to manage terminal devices through dynamic grouping.

[0007] Step 2: Embed a localized decision-making module in the regional edge gateway to implement route optimization and data compression;

[0008] Step 3: Use a dual-mode adaptive transmission protocol to dynamically switch transmission modes based on data priority and channel quality;

[0009] Step 4: Implement dynamic routing decisions based on reinforcement learning algorithms to optimize path selection;

[0010] Step 5: Dynamically update the dictionary and perform sparse coding through the lightweight compressed sensing module to reduce the amount of data transmission.

[0011] As a further solution of the present invention: the regional edge gateway dynamic grouping includes:

[0012] Divide gateway groups based on the physical location of terminal devices ,in Indicates the kth gateway group, and the coverage radius of the terminal devices managed by each group does not exceed 10 meters;

[0013] The grouping is optimized by minimizing the distance between the terminal device and the group centroid, as follows: ;

[0014] Where G is the candidate grouping set, which refers to all device grouping methods (such as by room, by coverage radius, etc.). A single device node (such as a camera, sensor, etc.) in a smart home system is the basic unit of grouping. For device nodes The physical location coordinates of is the geometric centroid coordinate of all device nodes in group G, is the Euclidean distance;

[0015] After the grouping is completed, the lightweight decision-making module embedded in the gateway independently processes the data in this group, and the decision-making time is less than 10 milliseconds.

[0016] As a further solution of the present invention: the dual-mode adaptive transmission protocol includes:

[0017] The default mode is low power consumption, using the ZigBee protocol to transmit data, with a transmission power not exceeding -25dBm;

[0018] When data priority Exceeding the preset threshold and the channel quality Q is below the threshold When one of the two conditions is met, it switches to 5G NR high-speed transmission mode, and the channel quality The calculation formula is: ;

[0019] Where SHR is the signal-to-noise ratio, which reflects the ratio of the received signal strength to the noise strength (needs to be normalized to the interval [0, 1]) and is the core indicator for measuring channel reliability. The packet loss rate (value range 0-1) refers to the ratio of unsuccessfully received data packets to the total sent data packets per unit time. and Both are empirical weights, with values ​​of 0.7 and 0.3 respectively, and the high-speed transmission mode switching time does not exceed 20 milliseconds.

[0020] As a further solution of the present invention: the dynamic routing algorithm based on reinforcement learning includes:

[0021] The state set is defined as , where E is the remaining energy percentage, Score the link quality, is the queue length;

[0022] The action set is defined as , the reward function R is: ;

[0023] Where, The total time it takes for a data packet to be transmitted from the source node to the destination node, including transmission time, processing time, and queue waiting time. is the total energy consumption of the source node, relay node and destination node when the data packet passes through the current path. The total energy consumption includes the energy consumption of transmission, reception and processing. 、 and The importance weights of the three dimensions of delay, energy consumption, and packet loss rate are respectively corresponding, and , , ;

[0024] The Q-table of all edge gateways is updated synchronously through the cloud center every 5 minutes.

[0025] As a further solution of the present invention: the lightweight compressed sensing module includes:

[0026] Initialize the dictionary Using the DCT basis matrix, the formula for dynamically updating the dictionary for each frame of data is: ;

[0027] Where, is the dynamic dictionary of the current time step, is the dictionary of the previous time step, , X is the input data matrix, is the sparse coding coefficient matrix;

[0028] Sparse coding is solved by the Orthogonal Matching Pursuit (OMP) algorithm: ;

[0029] Where D is the sparse dictionary matrix, For constraints, is the sparsity constraint, ;

[0030] When the compression ratio is 15:1, the peak signal-to-noise ratio (PSNR) is not less than 35dB.

[0031] As a further solution of the present invention: the localization decision module includes:

[0032] Real-time monitoring of data queue length ,If it exceeds the threshold of 50, the compression module is activated;

[0033] For periodic data, only 70% of the changes are transmitted;

[0034] Combined with the Q-Learning routing engine, it selects the path with the peak reward.

[0035] As a further solution of the present invention: the cloud center includes:

[0036] Collect Q-table and network status data of all edge gateways;

[0037] Analyze node failures and link congestion anomalies to generate global optimization strategies;

[0038] The policy is synchronized to the edge gateway every 5 minutes.

[0039] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. This invention builds a two-tier architecture of regional edge gateways and cloud centers, shifting data processing tasks to local gateways. This avoids the remote transmission bottleneck of traditional centralized architectures. The regional edge gateways have built-in lightweight decision-making modules that can perform localized routing optimization and data compression without relying on cloud-based iterative computing. The dual-mode adaptive transmission protocol switches transmission modes in real time based on data priority and channel quality. Urgent data is transmitted directly through high-speed channels, resolving the high latency problem caused by fixed iterations and rigid transmission in existing technologies and meeting the stringent requirements of real-time security monitoring.

[0041] 2. This invention innovatively introduces a dual-mode adaptive transmission protocol and a dynamic routing algorithm. It uses the low-power ZigBee mode under normal conditions. Combined with the Q-Learning routing engine, it actively avoids low-power nodes, balancing network loads. It switches to high-speed mode only when urgent data is needed or when the channel is degraded, effectively reducing ineffective energy consumption. Furthermore, the lightweight compressed sensing module uses sparse coding to reduce the amount of periodic data transmission. This comprehensive optimization reduces node energy consumption and significantly extends battery life, addressing the high standby power consumption and insufficient battery life of traditional solutions.

[0042] 3. This invention uses dynamic routing algorithm and distributed compressed sensing to optimize and overcome the problem of throughput drop in high concurrency scenarios in existing technologies. The Q-Learning routing engine is used to The optimal path is selected in real time based on complexity. Combined with the edge gateway queue monitoring mechanism, the compression module is immediately activated when congestion risk is detected, and only key change data is transmitted. The dynamic dictionary learning technology adaptively updates the sparse basis to avoid the problem of fixed dictionary reconstruction error rate under burst traffic, significantly improving network anti-destruction and scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is an overall flow chart of the wireless optimization transmission method based on smart home. DETAILED DESCRIPTION

[0044] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0045] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0046] Please see the attached Figure 1 The present invention provides a wireless optimization transmission method based on smart home, including a wireless optimization transmission method, the wireless optimization transmission method including the following steps:

[0047] Step 1: Build a two-tier architecture of regional edge gateways and cloud centers to manage terminal devices through dynamic grouping.

[0048] Step 2: Embed a localized decision-making module in the regional edge gateway to implement route optimization and data compression;

[0049] Step 3: Use a dual-mode adaptive transmission protocol to dynamically switch transmission modes based on data priority and channel quality;

[0050] Step 4: Implement dynamic routing decisions based on reinforcement learning algorithms to optimize path selection;

[0051] Step 5: Dynamically update the dictionary and perform sparse coding through the lightweight compressed sensing module to reduce the amount of data transmission.

[0052] In one embodiment of the present invention, the dynamic grouping of regional edge gateways includes:

[0053] Divide gateway groups based on the physical location of terminal devices ,in Indicates the kth gateway group, and the coverage radius of the terminal devices managed by each group does not exceed 10 meters;

[0054] The grouping is optimized by minimizing the distance between the terminal device and the group centroid, as follows: ;

[0055] Where, The candidate set for grouping refers to all device grouping methods (such as by room, by coverage radius, etc.). A single device node (such as a camera, sensor, etc.) in a smart home system is the basic unit of grouping. For device nodes The physical location coordinates of For grouping The geometric center coordinates of all device nodes within, is the Euclidean distance;

[0056] After the grouping is completed, the lightweight decision-making module embedded in the gateway independently processes the data in this group, and the decision-making time is less than 10 milliseconds.

[0057] In one embodiment of the present invention: the dual-mode adaptive transmission protocol includes: adopting a low-power mode by default, using the ZigBee protocol to transmit data, with a transmission power not higher than -25dBm;

[0058] When data priority Exceeding the preset threshold and channel quality Below threshold When one of the two conditions is met, it switches to 5G NR high-speed transmission mode, and the channel quality The calculation formula is: ;

[0059] Where SHR is the signal-to-noise ratio, which reflects the ratio of the received signal strength to the noise strength (needs to be normalized to the interval [0, 1]) and is the core indicator for measuring channel reliability. The packet loss rate (value range 0-1) refers to the ratio of unsuccessfully received data packets to the total sent data packets per unit time. and Both are empirical weights, with values ​​of 0.7 and 0.3 respectively, and the high-speed transmission mode switching time does not exceed 20 milliseconds.

[0060] In one embodiment of the present invention, the dynamic routing algorithm based on reinforcement learning includes:

[0061] The state set is defined as , where E is the remaining energy percentage, LQ is the link quality score, is the queue length;

[0062] The action set is defined as , the reward function R is: ;

[0063] Where, The total time it takes for a data packet to be transmitted from the source node to the destination node, including transmission time, processing time, and queue waiting time. is the total energy consumption of the source node, relay node and destination node when the data packet passes through the current path. The total energy consumption includes the energy consumption of transmission, reception and processing. 、 and The importance weights of the three dimensions of delay, energy consumption, and packet loss rate are respectively corresponding, and , , ;

[0064] The Q-table of all edge gateways is updated synchronously through the cloud center every 5 minutes.

[0065] In one embodiment of the present invention, the lightweight compressed sensing module includes:

[0066] Initialize the dictionary Using the DCT basis matrix, the formula for dynamically updating the dictionary for each frame of data is: ;

[0067] Where, is the dynamic dictionary of the current time step, is the dictionary of the previous time step, , X is the input data matrix, is the sparse coding coefficient matrix;

[0068] Sparse coding is solved by the Orthogonal Matching Pursuit (OMP) algorithm: ;

[0069] Where D is the sparse dictionary matrix, For constraints, is the sparsity constraint, ;

[0070] When the compression ratio is 15:1, the peak signal-to-noise ratio (PSNR) is not less than 35dB;

[0071] Require, The row dimension of Column dimensions are consistent.

[0072] In one embodiment of the present invention: the localization decision module includes:

[0073] Real-time monitoring of data queue length ,If it exceeds the threshold of 50, the compression module is activated;

[0074] For periodic data, only the changes are transmitted, reducing the data transmission volume by 70%;

[0075] Combined with the Q-Learning routing engine, it selects the path with the peak reward.

[0076] In one embodiment of the present invention: the cloud center includes:

[0077] Collect Q-table and network status data of all edge gateways;

[0078] Analyze node failures and link congestion anomalies to generate global optimization strategies;

[0079] The policy is synchronized to the edge gateway every 5 minutes.

[0080] In one embodiment of the present invention: security video transmission optimization includes:

[0081] Emergency trigger ( When the level is emergency, the gateway switches to 5G NR mode within 20 milliseconds;

[0082] Dynamic routing avoids nodes with remaining energy less than 20%;

[0083] Video compression uses dynamic dictionary update, step size .

[0084] In one embodiment of the present invention, high-load scenario optimization includes:

[0085] Edge gateway detection queue length Activate the compression module when

[0086] Sparse coding only transmits the amount of data change;

[0087] The routing engine selects paths with peak reward and end-to-end latency of no more than 50 milliseconds.

[0088] Example 1: Security Video Transmission Optimization

[0089] Scenario Description

[0090] Twenty 1080P@30fps high-definition cameras are deployed in residential environments for real-time monitoring of security events (such as intrusion detection). The system uses regional edge gateways to group them by physical space (living room group G1, bedroom group G2), with each group's coverage radius not exceeding 10 meters, ensuring low latency for local decision-making.

[0091] Implementation steps

[0092] 1. Initialization and grouping

[0093] The gateway dynamically groups cameras based on their locations and optimizes the layout by minimizing the distance between the devices and the group centroid: ;

[0094] in For camera position, the decision time within the group is less than 10ms.

[0095] set up Parameter: Discount factor , exploration rate .

[0096] 2. Event triggering and mode switching

[0097] Access sensor detects intrusion (data priority ), the gateway switches to 5G NR high-speed mode within 20ms;

[0098] Real-time calculation of channel quality :

[0099] ;

[0100] like , force high-speed mode to be enabled.

[0101] 3. Dynamic routing and video compression

[0102] The Q-Learning routing engine selects the optimal path (e.g. ), avoid nodes with residual energy < 20%;

[0103] Video data is compressed by dynamic dictionary update (step size ):

[0104] ;

[0105] Sparse coding constraints , PSNR ≥35dB when the compression ratio is 15:1.

[0106] Technical Effects

[0107] End-to-end latency is ≤50ms, emergency response speed is increased by 75%, dynamic routing avoids low-power nodes, energy consumption is reduced by 30%, and video compression reduces bandwidth usage by 70%, meeting the real-time and reliability requirements of security scenarios.

[0108] Example 2: Adaptive Optimization of High-Load Temperature and Humidity Sensors

[0109] Scenario Description

[0110] 100 temperature and humidity sensors concurrently report data when the environment changes suddenly (such as an air conditioning failure causing a sudden change in temperature and humidity). The system needs to handle high concurrency load and maintain low latency.

[0111] Implementation steps

[0112] 1. Queue monitoring and compression activation

[0113] Edge gateway monitors data queue length in real time , if > 50, immediately activate the compression module;

[0114] Sparse coding only transmits changes ,in, is the temperature change, The humidity change is the amount of data reduced by 70%.

[0115] 2. Dynamic routing optimization

[0116] State Set Dynamically evaluate node status:

[0117] Remaining energy E (percentage);

[0118] Link quality LQ (score);

[0119] Queue length (number of packets);

[0120] Reward function weight , , :

[0121] ;

[0122] Select the path with the maximum reward value and optimize the computational complexity to .

[0123] 3. Dual-mode transmission collaboration

[0124] The default is ZigBee low power mode (transmit power ≤ -25dBm), and burst data switches to 5G NR mode;

[0125] The global policy is synchronized to the edge gateway through the cloud center every 5 minutes to adapt to network changes.

[0126] Technical Effects

[0127] The throughput remains ≥95% and the end-to-end latency is ≤50ms under 100 concurrent nodes. Energy consumption is reduced by 40%, and the sensor battery life is extended to more than 12 months. Dynamic compression and routing avoid the problem of reconstruction error rate >35%.

[0128] Example 3: Multi-protocol smart home collaborative management

[0129] Scenario Description

[0130] Smart home systems run multiple protocol devices simultaneously, such as ZigBee sensors, Wi-Fi cameras, and Bluetooth door locks, requiring coordinated transmission and optimized resource allocation.

[0131] Implementation steps

[0132] 1. Gateway Grouping and Local Decision Making

[0133] Divide gateway groups by room (kitchen group G3, balcony group G4), with a coverage radius of ≤10 meters;

[0134] The lightweight decision module independently processes the data within the group:

[0135] ZigBee sensor data local compression (sparse coding )

[0136] Wi-Fi video streams are dynamically routed to less-loaded paths

[0137] 2. Dynamic switching of dual-mode protocols

[0138] When the door lock Bluetooth signal wall penetration loss is greater than 20dB, it switches to 5G NR mode to transmit key commands;

[0139] The channel quality model calculates Q value and weight in real time Prioritize signal stability.

[0140] 3. Global collaboration and exception handling

[0141] The cloud center collects the status of the entire network and analyzes node failure events (such as gateway failure);

[0142] Generate a global policy:

[0143] Dynamically adjust routing tables to avoid faulty areas;

[0144] Update compression dictionary step size Adapt to changes in data characteristics;

[0145] The policy is synchronized to the edge gateway every 5 minutes to improve the robustness of the system.

[0146] Technical Effects

[0147] The collaborative delay of multi-protocol devices is ≤50ms, and the bit error rate in wall penetration scenarios is reduced by 90%;

[0148] Global policy synchronization ensures that the throughput remains ≥95% after node failure;

[0149] Balanced energy consumption increases the overall endurance of all home equipment by 40%.

[0150] Although the present invention is disclosed above with reference to preferred embodiments, this is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, any modifications, equivalent variations, and modifications made to the above embodiments in accordance with the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection defined by the claims of the present invention.

Claims

1. A wireless optimization transmission method based on smart home, comprising a wireless optimization transmission method, characterized in that: The wireless optimized transmission method comprises the following steps: Step 1: Build a two-tier architecture of regional edge gateways and cloud centers to manage terminal devices through dynamic grouping. Step 2: Embed a localized decision-making module in the regional edge gateway to implement route optimization and data compression; Step 3: Use a dual-mode adaptive transmission protocol to dynamically switch transmission modes based on data priority and channel quality; Step 4: Implement dynamic routing decisions based on reinforcement learning algorithms to optimize path selection; Step 5: Dynamically update the dictionary and perform sparse coding through the lightweight compressed sensing module to reduce the amount of data transmission; The regional edge gateway dynamic grouping includes: Divide gateway groups based on the physical location of terminal devices ,in Indicates the kth gateway group, and the coverage radius of the terminal devices managed by each group does not exceed 10 meters; The grouping is optimized by minimizing the distance between the terminal device and the group centroid, as follows: ； In the formula, G is the candidate grouping set, which refers to all device grouping methods. A single device node in a smart home system is the basic unit of grouping. For device nodes The physical location coordinates of is the geometric centroid coordinate of all device nodes in group G, is the Euclidean distance; After grouping is completed, the lightweight decision module embedded in the gateway processes the data of this group independently, and the decision time is less than 10 milliseconds; The default mode is low power consumption, using the ZigBee protocol to transmit data, with a transmission power not exceeding -25dBm; When data priority Exceeding the preset threshold and the channel quality Q is below the threshold When one of the above conditions is met, the system switches to 5G NR high-speed transmission mode. The channel quality Q is calculated as follows: ; Where SHR is the signal-to-noise ratio, is the packet loss rate, and Both are empirical weights, with values ​​of 0.7 and 0.3 respectively, and the high-speed transmission mode switching time does not exceed 20 milliseconds; The dynamic routing algorithm based on reinforcement learning includes: The state set is defined as ; The action set is defined as , the reward function R is: ; Where, The total time it takes for a data packet to be transmitted from the source node to the destination node, including transmission time, processing time, and queue waiting time. is the total energy consumption of the source node, relay node and destination node when the data packet passes through the current path. The total energy consumption includes the energy consumption of transmission, reception and processing. 、 and The importance weights of the three dimensions of delay, energy consumption, and packet loss rate are respectively corresponding, and , , .

2. The wireless optimization transmission method based on smart home according to claim 1, characterized in that: The lightweight compressed sensing module includes: Initialize the dictionary Using the DCT basis matrix, the formula for dynamically updating the dictionary for each frame of data is: ; Where, is the dynamic dictionary of the current time step, is the dictionary of the previous time step, , X is the input data matrix, is the sparse coding coefficient matrix; Sparse coding is solved by the orthogonal matching pursuit algorithm: ; Where D is the sparse dictionary matrix, For constraints, is the sparsity constraint, ; When the compression ratio is 15:1, the peak signal-to-noise ratio is not less than 35dB.

3. The wireless optimization transmission method based on smart home according to claim 2, characterized in that: The localization decision module includes: Real-time monitoring of data queue length ,If it exceeds the threshold of 50, the compression module is activated; For periodic data, only the changes are transmitted, reducing the data transmission volume by 70%; Combined with the Q-Learning routing engine, it selects the path with the peak reward.

4. The wireless optimization transmission method based on smart home according to claim 3, characterized in that: The cloud center includes: Collect Q-table and network status data of all edge gateways; Analyze node failures and link congestion anomalies to generate global optimization strategies; The policy is synchronized to the edge gateway every 5 minutes.

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