Low latency li-fi internet of things communication method and system based on udp

Abstract

The application belongs to the technical field of wireless communication, and discloses a low-delay Li-Fi Internet of Things communication method and system based on UDP, which constructs a low-delay, high-reliability and self-adaptive Li-Fi communication system through communication link initialization, dynamic MTU adaptation, data packet priority scheduling, multi-dimensional intelligent scheduling mechanism, layered fault-tolerant architecture and cross-layer collaborative design. The application is suitable for intelligent factories, automatic driving, remote medical treatment and other Internet of Things scenes with extremely high real-time and reliability requirements, and has strong technical innovation and commercial application value.

Description

Technical Field

[0001] This specification relates to the field of wireless communication technology, and in particular to a low-latency Li-Fi Internet of Things communication method and system based on UDP. Background Technology

[0002] As IoT technology develops in depth, high-end applications such as autonomous driving, remote surgery, and Industry 4.0 have upgraded their requirements for communication systems from "high-speed transmission" to a multi-dimensional demand for "low latency + high reliability + strong adaptability." Existing technologies suffer from the following core shortcomings: 1. Traditional 5G networks and TCP-based Li-Fi systems suffer from latency exceeding 50ms due to TCP's three-way handshake and full confirmation mechanism, which cannot meet the real-time requirements of high-end applications. Furthermore, they lack scenario adaptability, with different service types using the same transmission strategy, resulting in insufficient contention for core service resources.

[0003] 2. The basic UDP-Li-Fi solution simply replaces TCP with UDP, failing to address the reliability defects of UDP, such as lack of checksum and retransmission; it lacks dynamic adaptation capabilities, the fixed MTU value leads to low bandwidth utilization, and the rigid thread configuration cannot cope with load fluctuations; at the same time, it does not consider the problem of Li-Fi channels being susceptible to light interference, resulting in weak anti-interference capabilities.

[0004] 3. Existing scheduling mechanisms have limitations, focusing on single-dimensional optimization and failing to achieve a balance between multiple objectives such as latency, load, and reliability. Furthermore, cross-layer parameters are configured independently, making it impossible to achieve a coordinated optimization effect.

[0005] Therefore, there is an urgent need for a Li-Fi communication solution that integrates UDP deep optimization, intelligent scheduling, hierarchical fault tolerance, and cross-layer collaboration to solve the problems of large latency fluctuations, weak scenario adaptability, and insufficient anti-interference capabilities of existing technologies, and to meet the multi-dimensional needs of high-end IoT applications. Summary of the Invention

[0006] This invention addresses the problems existing in the prior art by proposing a low-latency Li-Fi IoT communication method and system based on UDP. Through dynamic MTU adaptation, packet priority scheduling, a multi-dimensional intelligent scheduling mechanism, a layered fault-tolerant architecture, and cross-layer collaborative design, a low-latency, highly reliable, and adaptive Li-Fi communication system is constructed. Transmission latency is reduced by more than 60% compared to traditional solutions, and network congestion rate is controlled within 3%. It is suitable for IoT scenarios with extremely high real-time and reliability requirements, such as smart factories, autonomous driving, and telemedicine, possessing strong technological innovation and commercial application value.

[0007] To achieve the above objectives, this application provides the following technical solution: Firstly, a low-latency Li-Fi IoT communication method based on UDP is characterized by the following steps: S1, the client and server sides of the Li-Fi communication system are respectively configured with enhanced raw sockets, an adaptive virtual serial port cluster is created using the socat tool, dynamic IP masquerading rules, intelligent firewall policies, and PPP multi-link aggregation connections are configured, and an underlying communication channel is established; S2, a dynamic MTU adaptation mechanism is enabled to adjust the data packet size according to the real-time link bandwidth, a data packet priority identification system is established, and differentiated transmission priorities are assigned to different IoT service types; S3, the client collects data through a link quality monitoring unit. The Li-Fi channel's signal-to-noise ratio (SNR), bit error rate (BER), bandwidth fluctuation, and other parameters are monitored. The server side obtains load information such as CPU utilization, memory usage, and concurrent connection count through a load monitoring unit. In S4, the client sends request data packets with priority identifiers. Based on link quality parameters and load information, the server side selects the optimal transmission path and number of concurrent threads through an intelligent scheduling engine, dynamically segments data according to MTU, and sends it continuously without the need for a three-way handshake and full confirmation. In S5, lightweight checksums are used to verify data packet integrity. Combined with a selective retransmission mechanism, lost or erroneous data packets are handled. At the same time, cross-layer collaborative adjustment of transmission parameters ensures communication reliability.

[0008] Optionally, the dynamic MTU adaptation mechanism adjusts the MTU value according to a preset algorithm by monitoring link bandwidth fluctuations in real time.

[0009] Optionally, the data packet priority identification system is divided into 5 levels: remote medical and autonomous driving control data is the highest priority P1, VR / AR interaction data is the high priority P2, smart factory equipment monitoring data is the medium priority P3, smart home environment data is the low priority P4, and log backup data is the lowest priority P5.

[0010] Optionally, the intelligent scheduling engine employs a multi-objective optimization algorithm to dynamically allocate transmission resources, including thread pool size, transmission bandwidth ratio, and path selection strategy, with the goals of minimizing latency, balancing load, and minimizing packet loss rate.

[0011] Optionally, in step S5, the lightweight checksum uses the CRC-64 algorithm, and the selective retransmission mechanism uses the missing data packet index fed back by the client to retransmit only the corresponding data packet on the server side, without triggering a full retransmission.

[0012] Optionally, cross-layer collaborative adjustment includes dynamic adjustment of transmit power at the physical layer, frame structure optimization at the data link layer, and service priority adaptation at the application layer, to achieve coordinated optimization of parameters at each layer.

[0013] Secondly, this invention provides a UDP-based low-latency Li-Fi IoT communication system for implementing the UDP-based low-latency Li-Fi IoT communication method of the first aspect. The system includes a client module, a server module, a Li-Fi communication link module, and an intelligent scheduling center. The client module includes a first enhanced raw socket unit, a link quality monitoring unit, a priority identification unit, a first fault-tolerant processing unit, and a first thread management unit. The server module includes a second enhanced raw socket unit, a dynamic MTU adaptation unit, a data segmentation unit, a load monitoring unit, a second fault-tolerant processing unit, and a second thread management unit. The Li-Fi communication link module includes a visible light transmitting unit, a photoelectric receiving unit, and a channel adaptive adjustment unit, supporting dynamic switching of transmit power and modulation scheme. The intelligent scheduling center includes a scheduling engine, a resource allocation unit, and a cross-layer coordination unit, used to receive link quality and load information and output the optimal transmission strategy. Both the first and second thread management units support dynamic thread pool technology, adjusting the number of concurrent threads according to load changes, and include UDP priority transmission threads, UDP receive verification threads, selective retransmission threads, and serial forwarding threads.

[0014] Optionally, the channel adaptive adjustment unit dynamically adjusts the LED transmission power and modulation scheme based on the SNR and BER parameters collected by the link quality monitoring unit to ensure channel stability.

[0015] The beneficial effects of this invention are as follows: 1. By using the UDP protocol, the three-way handshake and full confirmation are eliminated, reducing latency; dynamic MTU adaptation improves bandwidth utilization; dynamic thread pool reduces thread scheduling latency; transmission latency is ≤10ms, meeting the real-time requirements of autonomous driving and telemedicine; the layered fault-tolerant architecture makes the packet loss rate ≤0.05%, which is more than 90% lower than the basic UDP solution. 2. The channel adaptive adjustment unit dynamically optimizes the transmit power and modulation method to improve anti-interference capability and adapt to complex lighting environments; 3. Technologies such as dynamic MTU adaptation, virtual port clustering, and dynamic thread pools enable on-demand allocation of transmission resources, reducing CPU utilization, energy consumption, and keeping network congestion rate below 3%. Attached Figure Description

[0016] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. The same numbers in the drawings denote the same structures or steps.

[0017] Figure 1 This is a schematic diagram of a low-latency Li-Fi IoT communication method based on UDP, as described in Embodiment 1 of this application.

[0018] Figure 2For the purposes of this application Figure 1 The diagram shown illustrates intelligent scheduling.

[0019] Figure 3 For the purposes of this application Figure 1 The diagram shows a hierarchical fault tolerance and cross-layer collaboration mechanism. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely one preferred embodiment of this application and are only used to explain this application. They do not limit the scope of protection of this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Example

[0021] like Figures 1-3 As shown, a low-latency Li-Fi IoT communication method based on UDP includes the following steps: S1, based on the Li-Fi communication system, the client and server are configured with enhanced raw sockets respectively, and an adaptive virtual serial port cluster is created using the socat tool. Dynamic IP masquerading rules, intelligent firewall policies and PPP multi-link aggregation connections are configured to establish the underlying communication channel. S2 enables a dynamic MTU adaptation mechanism to adjust the data packet size based on real-time link bandwidth, establishes a data packet priority identification system, and assigns differentiated transmission priorities to different IoT service types. S3: The client collects parameters such as signal-to-noise ratio (SNR), bit error rate (BER), and bandwidth fluctuation of the Li-Fi channel through the link quality monitoring unit, while the server obtains load information such as CPU utilization, memory usage, and number of concurrent connections through the load monitoring unit. S4: The client sends a request data packet with a priority identifier. The server selects the optimal transmission path and the number of concurrent threads based on link quality parameters and load information through an intelligent scheduling engine. It dynamically segments the data according to MTU and sends it continuously without the need for a three-way handshake and full confirmation. S5 uses lightweight checksums to verify data packet integrity, combines selective retransmission mechanisms to handle lost or erroneous data packets, and ensures communication reliability by adjusting transmission parameters through cross-layer collaboration.

[0022] By eliminating the three-way handshake and full confirmation through the UDP protocol, latency is reduced; dynamic MTU adaptation improves bandwidth utilization; dynamic thread pools reduce thread scheduling latency, achieving a transmission latency of ≤10ms to meet the real-time requirements of autonomous driving and telemedicine; a layered fault-tolerant architecture reduces packet loss rate to ≤0.05%, more than 90% lower than the basic UDP solution; a channel adaptive adjustment unit dynamically optimizes transmit power and modulation scheme, improving anti-interference capabilities and adapting to complex lighting environments; through a priority identification system and scenario adaptation database, rapid switching of IoT scenarios is supported, with different service types receiving differentiated transmission resources, prioritizing core services (such as autonomous driving control data) and flexibly scheduling non-core services (such as log backup); technologies such as dynamic MTU adaptation, virtual port clusters, and dynamic thread pools enable on-demand allocation of transmission resources, reducing CPU utilization, energy consumption, and keeping network congestion rate below 3%; a dual security mechanism makes it extremely difficult to crack, meeting the security requirements of industrial and medical data transmission.

[0023] In one specific embodiment, in step S1, enhanced raw sockets are deployed on both the client and server sides, integrating packet priority marking and link state awareness functions, and supporting custom header fields. The adaptive virtual serial port cluster contains at least 3-5 virtual ports to achieve load balancing; dynamic IP masquerading rules are configured to automatically expand network segments based on the number of access devices; intelligent firewall policies allow data packets based on service priority; and PPP multi-link aggregation connections aggregate at least 2-4 PPP links to improve bandwidth stability.

[0024] In one specific embodiment, in step S2, the dynamic MTU adaptation mechanism monitors link bandwidth fluctuations in real time and adjusts the MTU value according to a preset algorithm to ensure that the data packet size matches the bandwidth and avoids latency caused by fragmentation. The preset algorithm has the following formula: (1) in, This refers to the bandwidth adaptation factor. For real-time bandwidth; The base offset, ranging from 576 bytes to 9000 bytes, ensures that the packet size matches the bandwidth and avoids latency caused by fragmentation.

[0025] Specifically, real-time bandwidth is collected by the client link quality monitoring unit at 100ms intervals. The collection method involves statistically analyzing the actual amount of valid data transmitted by the Li-Fi link within that interval (excluding retransmissions and failed verification packets). The calculation method is as follows: (2) in, The effective data transmission volume within the period; The sampling period is 8; the coefficient 8 is used to convert bytes to bits, matching the bandwidth unit Mbps.

[0026] In one specific embodiment, the data packet priority identification system is divided into five priority levels based on business importance: remote medical and autonomous driving control data are of the highest priority (P1), VR / AR interaction data are of high priority (P2), smart factory equipment monitoring data are of medium priority (P3), smart home environment data are of low priority (P4), and log backup data is of the lowest priority (P5). A 2-bit priority identifier field is added to the data packet header, where P1 level data packets preempt the highest transmission resources, and P5 level data packets are transmitted when the network is idle.

[0027] In one specific embodiment, step S3 further includes the intelligent scheduling center receiving data and then initiating a dynamic adjustment process based on a threshold; for example, scheduling is triggered when SNR < 20dB and CPU utilization > 80%.

[0028] In one specific embodiment, in step S4, the client adds a priority identifier to the request data packet according to the service type and sends it to the server through an enhanced raw socket. After receiving the request, the server's dynamic MTU adaptation unit adjusts the MTU value according to the latest link bandwidth, and the data segmentation unit segments the target data according to this value, without waiting for client confirmation. The server starts a parallel thread according to the optimal strategy and continuously sends data packets. The Li-Fi communication link module dynamically adjusts the transmission power and modulation method according to the channel state. The intelligent scheduling engine adopts an improved non-dominated sorting genetic algorithm. This algorithm belongs to multi-objective evolutionary algorithms and is suitable for minimizing multi-objective conflict scenarios involving latency, load balancing, and packet loss rate. The specific implementation method is as follows: 1) Population initialization: An initial population is generated using "MTU value, number of threads, bandwidth ratio, and transmit power" as decision variables; 2) Fast Non-Dominated Sort: Based on the objective function, the population is divided into different dominance levels, and individuals that are better at reducing data transmission latency, reducing data loss rate, and improving load balance are given priority. 3) Crowding density calculation: Quantifies the distribution density of individuals of the same level to avoid the population concentrating on local optima; 4) Elite Preservation Strategy: The best individuals from both the parent and offspring generations are preserved to the next generation to ensure algorithm convergence; 5) Iteration Termination: When the number of iterations reaches 50 or the fluctuation of the objective function value is less than 1%, the optimal transmission strategy in the Pareto optimal solution is output.

[0029] The algorithm can complete an optimization calculation within 100ms, meeting the time requirements of real-time scheduling. At the same time, it accurately quantifies the optimization direction through parameterized objective function, taking into account the balanced optimization of multiple objectives and avoiding other performance degradation caused by the optimization of a single objective.

[0030] The objective function is defined as: (3) in, For data transmission delay, take the time difference between the initiation of a single transmission and the completion of data reception; The data packet loss rate is the ratio of the number of lost data packets to the total number of data packets sent. The load balancing efficiency is calculated by normalizing the variance of server-side CPU utilization, memory usage, and concurrent connections, using the following formula: (4) in, Server-side CPU utilization; For memory usage; This represents the number of concurrent connections. To account for the normalized variance of the parameters, the value ranges from 0 to 1, with values ​​closer to 1 indicating a more balanced load. The algorithm outputs the optimal transmission strategy through fast non-dominated sorting, congestion calculation, and an elite retention strategy, dynamically allocating transmission resources including thread pool size, transmission bandwidth ratio, and path selection strategy.

[0031] In one specific embodiment, in step S5, the lightweight checksum uses the CRC-64 algorithm, and the selective retransmission mechanism uses the missing data packet index fed back by the client. The server only retransmits the corresponding data packet, without triggering a full retransmission. Cross-layer collaborative adjustment includes dynamic adjustment of transmit power at the physical layer, frame structure optimization at the data link layer, and service priority adaptation at the application layer, realizing the linkage optimization of parameters at each layer. Example

[0032] A low-latency Li-Fi IoT communication system based on UDP includes a client module, a server module, a Li-Fi communication link module, and an intelligent scheduling center. The client module includes a first enhanced raw socket unit, a link quality monitoring unit, a priority identification unit, a first fault-tolerant processing unit, and a first thread management unit. The first enhanced raw socket unit supports priority marking and link status awareness, and allows for custom header fields (including a 2-bit priority identifier and an 8-bit link quality feedback field). The link quality monitoring unit integrates an SNR sensor and a BER detector to collect channel parameters in real time and report them to the intelligent scheduling center. The priority identification unit automatically adds priority identifiers to data packets based on the service type. The first fault-tolerant processing unit implements CRC-64 checksum and missing index feedback functions. The first thread management unit supports a dynamic thread pool, including a UDP receive verification thread and a missing index feedback thread, with the number of threads dynamically adjusted according to the amount of received data.

[0033] The server module includes a second enhanced raw socket unit, a dynamic MTU adaptation unit, a data segmentation unit, a load monitoring unit, a second fault-tolerant processing unit, and a second thread management unit. The second enhanced raw socket unit matches the client and supports priority identification and link status resolution; the dynamic MTU adaptation unit dynamically adjusts the MTU value based on the link bandwidth, outputting the optimal data packet size; the data segmentation unit segments data according to the MTU value and adds packet header identifiers (including priority, packet sequence number, and checksum); the load monitoring unit monitors server hardware resources and network load in real time and reports to the intelligent scheduling center; the second fault-tolerant processing unit receives the missing index list and performs selective retransmission; the second thread management unit's dynamic thread pool includes UDP priority transmission threads, selective retransmission threads, and serial forwarding threads.

[0034] The Li-Fi communication link module includes a visible light transmitting unit, a photoelectric receiving unit, and a channel adaptive adjustment unit, supporting dynamic switching of transmit power and modulation scheme. The visible light transmitting unit uses high-power LEDs; the photoelectric receiving unit uses high-sensitivity photodiodes; and the channel adaptive adjustment unit receives instructions from the intelligent scheduling center and adjusts the transmit power and modulation scheme according to channel parameters to ensure SNR ≥ 20dB and BER ≤ 10. -6 .

[0035] The intelligent scheduling center comprises a scheduling engine, a resource allocation unit, and a cross-layer coordination unit. It receives link quality and load information and outputs the optimal transmission strategy. The scheduling engine, the core processing unit, runs a multi-objective optimization algorithm, integrates link quality and load information, and outputs the optimal transmission strategy. The resource allocation unit allocates virtual ports, thread pool size, and bandwidth percentage according to the scheduling strategy. The cross-layer coordination unit sends adjustment instructions to each layer, enabling parameter linkage between the physical layer, data link layer, and application layer. The scenario adaptation database includes pre-stored configuration parameters (priority level, initial MTU value, scheduling algorithm weight) for scenarios such as autonomous driving, telemedicine, and smart factories, supporting rapid scenario switching.

[0036] The secure encryption unit uses the AES-256 algorithm to encrypt the data packet payload. The key is pre-allocated offline and dynamically updated periodically to ensure security. Combined with the physical isolation characteristics of Li-Fi visible light transmission, it achieves dual security protection of "physical isolation + encrypted transmission" to prevent data eavesdropping and tampering.

[0037] Both the first and second thread management units support dynamic thread pool technology, which adjusts the number of concurrent threads according to load changes, and includes UDP priority transmission threads, UDP receive verification threads, selective retransmission threads, and serial forwarding threads.

[0038] Both the first and second enhanced raw socket units integrate packet priority marking and link status awareness functions, and support custom packet header fields to carry priority identifiers and link quality feedback information. The channel adaptive adjustment unit dynamically adjusts the LED transmission power and modulation scheme based on the SNR and BER parameters collected by the link quality monitoring unit to ensure channel stability.

[0039] The intelligent scheduling center also includes a scenario adaptation database, which pre-stores priority configurations, initial MTU values, and scheduling algorithm parameters for different IoT scenarios, supporting rapid scenario switching and automatic parameter matching.

[0040] The dynamic thread pool technology adopts an elastic configuration mode of core thread count + maximum thread count. The core thread count is fixed at 5-10, and the maximum thread count is dynamically expanded to 20-50 according to the load. Idle threads are automatically recycled after a timeout of 30 seconds.

[0041] The above-described specific embodiments are preferred embodiments of a low-latency Li-Fi Internet of Things communication method and system based on UDP according to this application, and are not intended to limit the specific scope of this application. The scope of this application includes but is not limited to the specific embodiments described herein. All equivalent changes made in accordance with the shape and structure of this application are within the protection scope of this application.

Claims

1. A low-latency Li-Fi IoT communication method based on UDP, characterized in that, Includes the following steps: S1, based on the Li-Fi communication system, the client and server are configured with enhanced raw sockets respectively, and an adaptive virtual serial port cluster is created using the socat tool. Dynamic IP masquerading rules, intelligent firewall policies and PPP multi-link aggregation connections are configured to establish the underlying communication channel. S2 enables a dynamic MTU adaptation mechanism to adjust the data packet size based on real-time link bandwidth, establishes a data packet priority identification system, and assigns differentiated transmission priorities to different IoT service types. S3: The client collects parameters such as signal-to-noise ratio (SNR), bit error rate (BER), and bandwidth fluctuation of the Li-Fi channel through the link quality monitoring unit, while the server obtains load information such as CPU utilization, memory usage, and number of concurrent connections through the load monitoring unit. S4: The client sends a request data packet with a priority identifier. The server selects the optimal transmission path and the number of concurrent threads based on link quality parameters and load information through an intelligent scheduling engine. It dynamically segments the data according to MTU and sends it continuously without the need for a three-way handshake and full confirmation. S5 uses lightweight checksums to verify data packet integrity, combines selective retransmission mechanisms to handle lost or erroneous data packets, and ensures communication reliability by adjusting transmission parameters through cross-layer collaboration.

2. The low-latency Li-Fi IoT communication method based on UDP according to claim 1, characterized in that, The dynamic MTU adaptation mechanism adjusts the MTU value according to a preset algorithm by monitoring link bandwidth fluctuations in real time.

3. The low-latency Li-Fi IoT communication method based on UDP according to claim 1, characterized in that, The data packet priority identification system is divided into 5 levels: remote medical and autonomous driving control data are of the highest priority P1, VR / AR interaction data are of high priority P2, smart factory equipment monitoring data are of medium priority P3, smart home environment data are of low priority P4, and log backup data are of the lowest priority P5.

4. The low-latency Li-Fi IoT communication method based on UDP according to claim 3, characterized in that, The intelligent scheduling engine employs a multi-objective optimization algorithm, aiming to minimize latency, balance load, and minimize packet loss rate, and dynamically allocates transmission resources, including thread pool size, transmission bandwidth ratio, and path selection strategy.

5. The low-latency Li-Fi IoT communication method based on UDP according to claim 1, characterized in that, In step S5, the lightweight checksum uses the CRC-64 algorithm, and the selective retransmission mechanism uses the missing data packet index fed back by the client. The server only retransmits the corresponding data packet and does not trigger a full retransmission.

6. The low-latency Li-Fi IoT communication method based on UDP according to claim 1, characterized in that, The cross-layer collaborative adjustment includes dynamic adjustment of transmit power at the physical layer, frame structure optimization at the data link layer, and service priority adaptation at the application layer, thereby achieving coordinated optimization of parameters at each layer.

7. A low-latency Li-Fi Internet of Things (IoT) communication system based on UDP, characterized in that, The method for implementing the UDP-based low-latency Li-Fi IoT communication method according to any one of claims 1-6 includes a client module, a server module, a Li-Fi communication link module, and an intelligent scheduling center. The client module includes a first enhanced raw socket unit, a link quality monitoring unit, a priority identification unit, a first fault tolerance processing unit, and a first thread management unit; The server module includes a second enhanced raw socket unit, a dynamic MTU adaptation unit, a data segmentation unit, a load monitoring unit, a second fault-tolerant processing unit, and a second thread management unit. The Li-Fi communication link module includes a visible light transmitting unit, a photoelectric receiving unit, and a channel adaptive adjustment unit, which supports dynamic switching of transmission power and modulation mode; The intelligent scheduling center includes a scheduling engine, a resource allocation unit, and a cross-layer collaboration unit, which are used to receive link quality and load information and output the optimal transmission strategy. Both the first thread management unit and the second thread management unit support dynamic thread pool technology, which adjusts the number of concurrent threads according to load changes, and includes UDP priority transmission threads, UDP receive verification threads, selective retransmission threads and serial forwarding threads.

8. The low-latency Li-Fi IoT communication system based on UDP according to claim 7, characterized in that, The channel adaptive adjustment unit dynamically adjusts the LED transmission power and modulation scheme based on the SNR and BER parameters collected by the link quality monitoring unit to ensure channel stability.

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