A Smart Home Heterogeneous Network Adaptive Fusion System Based on IPv6

Abstract

The present invention relates to the technical field of smart home, and solves the technical problem that the traditional smart home solutions still have deficiencies in the aspect of heterogeneous network fusion. In particular, it relates to a smart home heterogeneous network adaptive fusion system based on IPv6, which is composed of a network topology structure module, a communication protocol selection module, and a home perception network deployment module. Among them, the network topology structure module is used to fuse heterogeneous network infrastructures with different communication protocols located within the smart home, and adaptively and dynamically adjust the network topology structure according to changes in the environment and application scenarios. The present invention provides a network topology structure that can fuse different communication technologies and protocols, ensuring that various devices and services can work together in the same network system. In terms of the adaptive network, the topology network structure module can adaptively adjust according to changes in the environment and application scenarios to maintain optimal performance.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and particularly to a smart home heterogeneous network adaptive fusion system based on IPv6. Background Art

[0002] Smart home is a key development direction of the smart home appliance industry, and its goal is to create a convenient, comfortable, healthy, safe and environmentally friendly home living environment. The smart home industry has received high attention from the state and governments at all levels, and is an important industrial field for the country to build a new engine for the digital economy and lead the upgrading of the consumption structure.

[0003] Smart home is a complex system composed of diverse smart devices, sensors and heterogeneous networks. The IPv4 network cannot meet the needs of a large number of device accesses in smart home applications. IPv4 network address conversion may lead to problems such as increased network latency, unstable connections or difficulty in establishing external connections.

[0004] The massive addresses, end-to-end penetration ability, IPsec and a series of IPv6 security mechanisms of IPv6 provide a preferred solution to solve the above problems. IPv6 can support more device accesses, simplifies the management and interconnection of heterogeneous networks, promotes the extensive connection between smart devices, improves the integration and interoperability of the smart home system, and constructs an autonomous, secure and controllable smart home network.

[0005] With the rapid development of smart home technology, there may be multiple different types of networks inside a home, such as Zigbee, Z-Wave, Wi-Fi, etc. The problem of interconnection between these heterogeneous networks has become a bottleneck in the development of smart home. At the same time, with the wide application of IPv6 technology, its massive address space and efficient data transmission ability provide new possibilities for the network fusion of smart home. The smart home solutions on the market still have deficiencies in heterogeneous network fusion, such as low network conversion efficiency and unstable data transmission. Therefore, it is particularly important to develop a smart home system that can adaptively fuse multiple heterogeneous networks. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a smart home heterogeneous network adaptive fusion system based on IPv6, which solves the technical problem that the traditional smart home solutions still have deficiencies in heterogeneous network fusion.

[0007] To solve the above technical problems, the present invention provides the following technical solution: A smart home heterogeneous network adaptive fusion system based on IPv6, the system includes:

[0008] A network topology structure module, which is used to integrate heterogeneous network infrastructures with different communication protocols within a smart home and adaptively and dynamically adjust the network topology according to changes in the environment and application scenarios;

[0009] A communication protocol selection module, which is used to select a communication protocol compatible with all current heterogeneous devices based on the demand analysis results of family members within the smart home and improve the data transmission rate by optimizing transmission parameters. The communication protocols include IPv6, 6LoWPAN, and CoAP, and the transmission parameters include transmit power, modulation method, and coding scheme;

[0010] A home perception network deployment module, which is used to deploy corresponding network nodes according to the indoor space area of the smart home and determine the positions of the network nodes in the indoor space area. The network nodes include sensing nodes, control nodes, and data aggregation nodes.

[0011] Furthermore, in the network topology structure module, the steps of constructing the network topology structure are as follows:

[0012] S11. List the possible heterogeneous network infrastructures in the home network, including Wi-Fi, ZigBee, Z-Wave, and Bluetooth Low Energy, and record the frequency bands, maximum transmission rates, typical coverage ranges, and average power consumptions of the heterogeneous network infrastructures with different transmission protocol technologies;

[0013] S12. Determine and define the characteristics of each heterogeneous network infrastructure according to S11. The characteristics include frequency range, coverage range, transmission rate, and power consumption, where:

[0014] INF = {INF1, INF2,... INFk} represents the set of heterogeneous network infrastructures;

[0015] CHAR(INFi) = {Cov, BW, Pwr, Lat} represents the characteristics of the heterogeneous network infrastructure INFk;

[0016] S13. Classify and define network nodes. The network nodes include sensing nodes, control nodes, gateway nodes, and edge computing nodes;

[0017] S14. Design the network topology structure based on the above S11 - S13 and use an adaptive fusion algorithm to dynamically adjust the network topology, including selecting a suitable topology structure, selecting a connection topology, and designing node connection rules, where:

[0018] Nodes = {N1, N2,..., Nn} represents all network nodes in the network;

[0019] Connections = {C1, C2, …, Cm} represents the connection relationships between network nodes;

[0020] optimizedTopology = {V', E'} represents the optimized network topology structure;

[0021] S15. Optimize the network topology structure, and the optimization formula is expressed as:

[0022] MST(V, E) = (V', E') represents the optimized topological network structure obtained by the minimum spanning tree algorithm.

[0023] Dijkstra(V, E, s) = P represents the set of shortest paths from the source node s to all other nodes.

[0024] Furthermore, in the home perception network deployment module, the deployment of network nodes includes initializing the network environment, deploying sensing nodes, deploying control nodes, and deploying data aggregation nodes, specifically as follows:

[0025] Initializing the network environment includes:

[0026] Setting the IPv6 addresses of various devices in the smart home heterogeneous network, and determining the locations and functions of sensing nodes, control nodes, and data aggregation nodes;

[0027] Deploying sensing nodes. Using N to represent the number of sensing nodes, N i represents the i-th sensing node, including:

[0028] Determining the deployment locations of sensing nodes according to the indoor space layout to ensure coverage of all monitoring areas, and assigning a unique IPv6 address to each sensing node N i denoted as N i IPv6;

[0029] Deploying control nodes. Using C to represent the number of control nodes, C i represents the i-th control node, including:

[0030] Determining the deployment locations of control nodes to keep a certain communication distance from sensing nodes, and assigning a unique IPv6 address to each control node C i denoted as C i IPv6;

[0031] Deploying data aggregation nodes. Using D to represent the number of data aggregation nodes, D i represents the i-th data aggregation node, including: Selecting appropriate locations to deploy data aggregation nodes so that they can receive data from control nodes, and assigning a unique IPv6 address to each data aggregation node D iAllocate a unique IPv6 address, denoted as D i IPv6

[0032] Furthermore, in the home perception network deployment module, corresponding perception nodes are deployed according to the indoor space area of the smart home, and the positions of the perception nodes in the indoor space area are determined. Specifically, the following steps are included:

[0033] S21. Define the indoor space area as A and divide it into Q sub-areas, and the area of each sub-area is S i , i = 1, 2, …, Q;

[0034] S22. Establish the objective function and constraint conditions for the perception node deployment position planning, and represent the perception node deployment position planning problem as a multi-objective optimization problem;

[0035] S23. Based on the solution results of the multi-objective optimization problem, perform initial deployment of the perception nodes and measure the signal strength;

[0036] S24. Optimize the signal transmission parameters of the perception nodes to complete the final deployment of the optimized perception nodes;

[0037] S25. Evaluate the finally deployed perception nodes.

[0038] Furthermore, in step S22, the objective function includes maximizing the coverage area and minimizing the communication energy consumption for the perception node deployment. Among them:

[0039] The coverage area of the perception nodes covers the entire monitoring area as much as possible, that is, the maximization function is:

[0040]

[0041] Minimize the communication energy consumption: Assume that the communication energy consumption between perception nodes is proportional to the square of the distance, that is:

[0042]

[0043] Among them, N is the number of perception nodes; F is the coverage area of the perception nodes; k is a proportionality constant, is the distance between perception node i and perception node j;

[0044] The constraint conditions for the perception node deployment position planning include node number constraint and coverage integrity constraint. Among them, for the node number constraint, the number of deployable perception nodes is limited, set as M, that is:

[0045]

[0046] For the coverage integrity constraint, all sub-regions must be covered by at least one sensing node, that is:

[0047] Make d(i,j) ≤ R

[0048] where d(i,j) represents the distance between sensing nodes i and j; R is the coverage radius of the sensing node;

[0049] Combining the above objective function and constraints, the problem of planning the deployment location of sensing nodes can be expressed as a multi-objective optimization problem, that is:

[0050]

[0051] where x 1 , x 2 ,..., x M represent the deployment locations of M nodes.

[0052] Furthermore, in step S23, the specific process includes the following steps:

[0053] S231. Select M nodes from the candidate node set θ for initial deployment, and the selected set of sensing nodes is denoted as where

[0054] S232. Determine a specific deployment location for each sensing node in the set of sensing nodes . The deployment location (x i , y i 0 of node i is calculated as follows:

[0055] x i = x 0 + i·Δx, y i = y 0 + j·Δy

[0056] where (x 0 , y 0 0 is the lower left corner coordinate of the deployment area; Δx and Δy are the grid spacings in the x-axis and y-axis directions respectively, and i and j are the grid indices of the sensing nodes;

[0057] S233. Conduct signal strength tests on adjacent nodes for each sensing node. For any two sensing nodes i and j, measure the signal strength P r,ij received by sensing node i from sensing node j, that is:

[0058]

[0059] where P tis the transmission power; α is the path loss exponent; d 0 is the reference distance; X σ,ij represents the small-scale fading effect between the sensing nodes i and j; d ij is the distance between node i and node j;

[0060] S234. For each signal strength P r,ij make n measurements to obtain the measurement sequence

[0061] S235. Calculate the average signal strength and use it as the signal strength between the sensing nodes i and j That is:

[0062]

[0063] where represents the signal strength of the k-th measurement.

[0064] Further, in step S24, the specific process includes the following steps:

[0065] S241. Determine the optimization objective for improving the total throughput T of the home sensing network and define the energy consumption E of each sensing node i shall not exceed its preset maximum energy consumption limit E max , expressed as:

[0066]

[0067] where P t represents the transmission power; δ represents the modulation method; ε represents the coding scheme; T and E i are respectively functions of the total throughput and the energy consumption of the sensing node;

[0068] S242. Optimize the transmission power, modulation method, and coding scheme respectively to obtain a parameter combination, including the optimal transmission power the optimal modulation method δ * and the optimal coding scheme ε * , where:

[0069]

[0070] In the above formula, λ is a weighting factor used to find the best balance between throughput and energy consumption; BER is the bit error rate; P r is the received signal power; C(ε) is the transmission capacity of the coding scheme; E c (ε) is the energy consumption of the coding scheme;

[0071] S243. Apply the parameter combination to the home sensing network to complete the optimization.

[0072] With the above technical solution, the present invention provides a self-adaptive fusion system for a smart home heterogeneous network based on IPv6, which has at least the following beneficial effects:

[0073] 1. The present invention provides a network topology structure that can integrate different communication technologies and protocols, ensuring that various devices and services can work together in the same network system. In terms of the self-adaptive network, the topology network structure module can adaptively adjust according to changes in the environment and application scenarios to maintain optimal performance.

[0074] 2. The present invention can achieve the optimal deployment position of the sensing nodes to achieve comprehensive and stable network coverage; at the same time, in terms of signal transmission optimization, the interference and attenuation problems in the signal transmission process are analyzed, and corresponding optimization strategies are proposed; moreover, in terms of energy management, the energy consumption management technology of the sensing nodes is explored to extend the service life of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation to the present application. In the drawings:

[0076] Figure 1 is a schematic diagram of the architecture of the integrated application system of the smart home heterogeneous network of the present invention

[0077] Figure 2 is a schematic diagram of the architecture of the integrated heterogeneous infrastructure system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0078] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments. Thereby, a full understanding of the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be obtained and implemented accordingly.

[0079] Please refer to Figure 1 - Figure 2 , this embodiment proposes a self-adaptive fusion system for a smart home heterogeneous network based on IPv6. This embodiment conducts research on the self-adaptive fusion system of IPv6 and the smart home heterogeneous network, and focuses on designing key issues such as sensing network coverage in complex environments to establish a home sensing network integrating heterogeneous infrastructures and improve the performance, integratability and reliability of the smart home system. The system includes:

[0080] The network topology module is used to integrate heterogeneous network infrastructures with different communication protocols within a smart home and adaptively and dynamically adjust the network topology according to changes in the environment and application scenarios. The present invention is based on the system architecture diagram of the integrated heterogeneous infrastructure, as shown in Figure 1 and Figure 2 which includes a physiological perception network, a wireless sensor network, new sensors AI-Sound and TOF, and a home information network interconnected with intelligent perception terminals, and then interconnected and integrated with the Internet and interconnected to cloud services. Therefore, by designing a network topology that integrates heterogeneous infrastructures, supports IPv6 address allocation, and ensures high reliability, low latency, and good scalability. The specific implementation steps for constructing the network topology are as follows:

[0081] S11. List the possible heterogeneous network infrastructures in the home network, such as Wi-Fi (Wi-Fi), ZigBee (ZB), Z-Wave (ZW), Bluetooth Low Energy (BLE), etc. For each heterogeneous network infrastructure with different transmission protocol technologies, record the specific frequency band (such as 2.4GHz and 5GHz for Wi-Fi), the maximum transmission rate (such as 100Mbps), the typical coverage range (such as 30 meters indoors), and the average power consumption (such as 5W for a Wi-Fi router). Create a detailed technical comparison table including additional metrics such as compatibility, cost, and security.

[0082] Selection criteria: B = {Wi-Fi, ZB, ZW, BLE}, where B is the set of communication standards.

[0083] S12. Determine the characteristics of each heterogeneous network infrastructure, including frequency range, coverage range, transmission rate, power consumption, etc., as shown in the following table:

[0084] Technology type Frequency range Transmission rate Coverage range Power consumption Wi-Fi 2.4 / 5GHz High Medium Medium ZigBee 2.4GHz Low Small Low Z-Wave 900MHz Low Small Low Bluetooth 2.4GHz Low Small Low

[0085] For the characteristics of the heterogeneous network infrastructure, make the following definitions:

[0086] INF = {INF1, INF2,... INFk} represents the set of heterogeneous network infrastructures.

[0087] CHAR(INFi) = {Cov, BW, Pwr, Lat} represents the characteristics of the heterogeneous network infrastructure INFk.

[0088] S13. Classify network nodes and determine the functions, processing capabilities, storage capabilities, etc. of each type of node. The network nodes include: Sensor Nodes (SN), Control Nodes (CN), Gateway Nodes (GN), and Edge Computing Nodes (ECN), etc., and the following definitions are made:

[0089] NT = {NT1, NT2, … NTm} represents the set of network node types.

[0090] FUNC(NTj) = {Proc, Mem, …} represents the functions and resources of network node type NTm.

[0091] S14. Design the network topology structure. First, select a suitable topology structure, such as star, mesh, hybrid, etc. Select the connection topology: T = {mesh, star, ring}, and design the node connection rule: N(i) → N(j) represents that node i is connected to node j. Determine the connection relationships between nodes, including determining the directly connected node pairs and determining the multi-hop connection paths.

[0092] Implement an adaptive fusion algorithm to dynamically adjust the network topology. The pseudo-code of the adaptive fusion algorithm is as follows:

[0093]

[0094] Among them, the following can be defined:

[0095] Nodes = {N1, N2, …, Nn} represents all network nodes in the network.

[0096] Connections = {C1, C2, …, Cm} represents the connection relationships between network nodes.

[0097] optimizedTopology = {V', E'} represents the optimized network topology structure.

[0098] S15. Optimize the topology network structure. Based on the node density and signal strength, calculate the network coverage range, and use graph theory algorithms (such as minimum spanning tree, shortest path, etc.) to optimize the connection paths. Finally, verify the performance of the topology structure through simulation and experiments. The optimization formula of the topology network structure is expressed as:

[0099] MST(V, E) = (V', E') represents the optimized topology network structure obtained through the minimum spanning tree algorithm.

[0100] Dijkstra(V, E, s) = P represents the set of shortest paths from the source node s to all other nodes.

[0101] In this embodiment, the constructed topology network structure module mainly includes a heterogeneous infrastructure integration and an adaptive network. In terms of heterogeneous infrastructure integration, a network topology structure that can integrate different communication technologies and protocols is designed to ensure that various devices and services can work together in the same network system. In terms of the adaptive network, the topology network structure module can adaptively adjust according to changes in the environment and application scenarios to maintain optimal performance.

[0102] The communication protocol selection module is used to select a communication protocol compatible with all current heterogeneous devices based on the demand analysis results of family members in the smart home and improve the data transmission rate by optimizing transmission parameters. The communication protocols include IPv6, 6LoWPAN, and CoAP, and the transmission parameters include transmission power, modulation method, and coding scheme. In this embodiment, the composition of the communication protocol selection module mainly includes aspects such as demand analysis and protocol selection, protocol stack design, transmission technology optimization, security design, protocol performance testing, and protocol iteration and optimization.

[0103] It should be noted here that the specific aspects included are as follows:

[0104] 1. Demand analysis and protocol selection

[0105] Demand collection: Through interviews with family members or smart home system users, list all smart home devices and their communication requirements lists; define daily user operation scenarios, such as remote control, automatic alarm, etc.; collect communication requirements, such as data transmission rate, latency, reliability, etc.

[0106] Demand analysis: Use statistical methods to analyze demand data, establish a data traffic model, predict network load, and determine the quality of service requirements, priorities, and key performance indicators (KPIs) for different types of data streams.

[0107] Protocol selection: Based on the demand analysis results, select a suitable communication protocol, such as IPv6, 6LoWPAN, CoAP, etc., ensure that the selected protocol is compatible with existing home network devices, and compare the performance indicators of different protocols through simulation experiments.

[0108] 2. Protocol stack design

[0109] Layer definition: Clearly define each layer of the protocol stack, such as the physical layer, link layer, network layer, transport layer, and application layer, and define the interface standards and data exchange formats between layers.

[0110] Function allocation: Allocate specific functions to each layer. For example, the link layer is responsible for neighbor discovery and data frame transmission.

[0111] Protocol Specification: Write the protocol specifications for each layer, including data formats, operation procedures, and exception handling.

[0112] 3. Transmission Technology Optimization

[0113] Transmission Rate Optimization: Improve the data transmission rate by adjusting modulation methods (such as QAM) and coding schemes (such as LDPC), and determine the optimal modulation and coding combination.

[0114] Power Consumption Optimization: Adopt energy-saving transmission strategies, such as dynamic power control, to reduce node energy consumption. At the same time, design a scheduling algorithm for node sleep and wake-up.

[0115] Latency Optimization: Reduce the transmission latency of data packets by optimizing routing algorithms and scheduling strategies.

[0116] 4. Security Design

[0117] Security Requirement Analysis: Build a threat model to identify potential attack methods, security threats, and privacy risks.

[0118] Encryption Scheme Selection: Select appropriate encryption algorithms (such as AES) and key management schemes. At the same time, select an appropriate key length to balance security and computational overhead.

[0119] Security Protocol Implementation: Implement a secure handshake protocol, such as TLS, for secure data transmission. At the same time, conduct node authentication and data integrity verification.

[0120] 5. Protocol Performance Testing

[0121] Test Environment Setup: Use a network simulator to simulate the actual network environment and build a test bed for simulating a home environment, including different types of sensors and network devices.

[0122] Performance Metric Definition: Define specific test scenarios, test metrics, test cases, etc. Metrics can include throughput, packet loss rate, response time, etc.

[0123] Test Execution and Data Analysis: Execute the protocol performance test, collect data and conduct statistical analysis, generate a performance report, and identify performance bottlenecks.

[0124] 6. Protocol Iteration and Optimization

[0125] Problem Diagnosis: Use performance profiling tools to analyze test results and diagnose performance bottlenecks and security vulnerabilities.

[0126] Protocol Modification: Modify the protocol specifications according to the diagnosis results, conduct code reviews, and optimize the protocol implementation to ensure that the modifications do not introduce new problems.

[0127] Iterative testing: Re-perform the performance test to verify the modification effect until the design requirements are met.

[0128] The home perception network deployment module is used to deploy corresponding network nodes according to the indoor space area of the smart home and determine the positions of the network nodes in the indoor space area. The network nodes include perception nodes, control nodes, and data aggregation nodes. In this embodiment, the deployment of the network nodes includes initializing the network environment, deploying perception nodes, deploying control nodes, and deploying data aggregation nodes, specifically as follows:

[0129] Initializing the network environment includes:

[0130] Setting the IPv6 addresses of various devices in the heterogeneous network of the smart home and determining the positions and functions of the perception nodes, control nodes, and data aggregation nodes;

[0131] Deployment of perception nodes. Let N represent the number of perception nodes, and N i represent the i-th perception node, including:

[0132] Determining the deployment positions of the perception nodes according to the indoor space layout to ensure coverage of all monitoring areas, and assigning a unique IPv6 address to each perception node N i denoted as N i IPv6;

[0133] Deployment of control nodes. Let C represent the number of control nodes, and C i represent the i-th control node, including:

[0134] Determining the deployment positions of the control nodes to keep a certain communication distance from the perception nodes, and assigning a unique IPv6 address to each control node C i denoted as C i IPv6;

[0135] Deployment of data aggregation nodes. Let D represent the number of data aggregation nodes, and D i represent the i-th data aggregation node, including: Selecting a suitable position to deploy the data aggregation node so that it can receive data from the control nodes, and assigning a unique IPv6 address to each data aggregation node D i denoted as D i IPv6.

[0136] In this embodiment, it is necessary to complete the location deployment of the sensing nodes first before proceeding with the deployment of the control nodes and data aggregation nodes in the next step. However, in the network topology structure module, the design of the network topology structure must be based on the deployment of the sensing nodes, and then the corresponding deployments can be completed by conventional technical means according to the standard requirements of the control node deployment and data aggregation node deployment. The focus of this embodiment is on how to make the deployment of the sensing nodes cover all areas of the indoor space layout. Professional measurement tools (such as laser rangefinders) can be used to obtain the dimensions of the indoor space, including the size, height, and layout of the rooms.

[0137] Therefore, this embodiment is used to implement the deployment of corresponding sensing nodes according to the indoor space areas of the smart home and determine the positions of the sensing nodes in the indoor space areas, specifically including the following steps:

[0138] S21. Define the indoor space area as A and divide it into Q sub-areas, and the area of each sub-area is S i , i = 1, 2, …, Q;

[0139] S22. Establish the objective function and constraints for the sensing node deployment location planning, and represent the sensing node deployment location planning problem as a multi-objective optimization problem; in this embodiment, genetic algorithms, particle swarm algorithms, tabu search algorithms, simulated annealing algorithms, etc. can be used to solve the sensing node deployment location planning problem, which can be achieved by existing technical means here and will not be elaborated too much.

[0140] Specifically, the objective function includes maximizing the coverage area of the sensing node deployment and minimizing the communication energy consumption. Assuming that the coverage radius of each sensing node is R, the coverage area of the sensing node can be expressed by the area formula of a circle:

[0141] F = πR 2

[0142] Among them,

[0143] The coverage area of the sensing nodes should cover the entire monitoring area as much as possible, that is, the maximization function is:

[0144]

[0145] Minimize the communication energy consumption: Assume that the communication energy consumption between nodes is proportional to the square of the distance, that is:

[0146]

[0147] Among them, N is the number of sensing nodes; F is the coverage area of the sensing nodes; k is a proportionality constant, is the distance between sensing node i and sensing node j;

[0148] The constraint conditions for the deployment location planning of sensing nodes include the node quantity constraint and the coverage integrity constraint. Among them, for the node quantity constraint, the number of deployable sensing nodes is limited, set as M, that is:

[0149]

[0150] For the coverage integrity constraint, all sub-regions should be covered by at least one sensing node, that is:

[0151] Make d(i,j) ≤ R

[0152] where d(i,j) represents the distance between sensing nodes i and j; R is the coverage radius of the sensing node;

[0153] Combining the above objective function and constraint conditions, the deployment location planning problem of sensing nodes can be expressed as a multi-objective optimization problem, that is:

[0154]

[0155] where x 1 , x 2 ,..., x M represents the deployment locations of M nodes.

[0156] Through the above representation, the node location planning problem can be described more precisely and provide a basis for subsequent algorithm design and optimization.

[0157] S23. Perform initial deployment of sensing nodes based on the solution results of the multi-objective optimization problem and measure the signal strength; in step S23, the specific process includes the following steps:

[0158] S231. In the candidate node set θ, select M nodes for initial deployment according to specific deployment objectives (such as coverage range, communication capacity, etc.), and the selected sensing node set is denoted as where M. This step can be automatically completed by an algorithm, for example, using a greedy algorithm to select nodes with the largest coverage range.

[0159] S232. For the selected sensing node set it is necessary to determine a specific deployment location for each sensing node. This usually involves considering factors such as terrain, obstacles, communication requirements, etc. Assuming a uniform grid deployment strategy, the deployment location (x i , y i ) of node i can be calculated by the following formula:

[0160] x i = x 0+i·Δx,y i = y 0 +j·Δy

[0161] where (x 0 , y 0 ) are the coordinates of the lower left corner of the deployment area; Δx and Δy are the grid spacings in the x-axis and y-axis directions respectively, and i and j are the grid indices of the sensing nodes;

[0162] In this embodiment, a log-distance path loss model is adopted to describe the variation of signal strength with distance, and this model can be expressed as:

[0163]

[0164] where P r (d) is the received signal strength (RSSI) measured at a distance d from the transmitter; P t is the transmit power; α is the path loss exponent; d 0 is the reference distance (usually taken as 1 meter); X σ is a Gaussian random variable with a mean of 0 and a standard deviation of σ, representing the small-scale fading effect.

[0165] S233. To accurately measure the signal strength, after deployment, the signal strength of adjacent nodes is tested for each sensing node. For any two nodes i and j, measure the signal strength P r,ij received by node i from node j. The measurement steps are as follows:

[0166] Determine the distance d ij between node i and node j, that is:

[0167]

[0168] Calculate the theoretically signal strength according to the log-distance path loss model, that is:

[0169]

[0170] During the measurement of the signal strength, record the following data: the transmit power P t of each sensing node, the measured received signal strength P r,ij , the distance d ij between nodes and the measurement time, etc. These data will be used for subsequent signal analysis.

[0171] S234. To reduce the influence of random errors and shadow fading, each measurement value is measured multiple times and the average value is calculated. Measure the signal strength P r,ij n times to obtain the measurement sequence

[0172] S235. Calculate the average signal strength and use the average value. As the estimated value of the signal strength between the sensing node i and the node j, that is:

[0173]

[0174] Wherein, represents the signal strength of the k-th measurement.

[0175] Through the above steps, the accuracy of the initial deployment and signal strength measurement can be ensured, and a reliable data basis can be provided for network optimization.

[0176] S24. Optimize the signal transmission parameters of the sensing nodes; in this embodiment, the identification of the transmission parameters is the basis for signal transmission optimization. These parameters directly affect the performance of the network, including the transmission power, modulation method, coding scheme, etc. The goal is to accurately identify these parameters so that targeted adjustments can be made in the subsequent optimization process.

[0177] In step S24, the specific process includes the following steps:

[0178] S241. The main optimization goal is to improve the total throughput T of the home sensing network while ensuring that the energy consumption E of each sensing node i does not exceed its preset maximum energy consumption limit E max , which can be expressed as:

[0179]

[0180] Wherein, P t represents the transmission power; δ represents the modulation method; ε represents the coding scheme; T and E i are respectively functions of the total throughput and the energy consumption of the sensing node.

[0181] To achieve the above goals, the following detailed steps are taken for parameter optimization, specifically:

[0182] S242. Optimize the transmission power, modulation method, and coding scheme respectively to obtain a parameter combination, including the optimal transmission power the optimal modulation method δ * and the optimal coding scheme ε * .

[0183] Transmission power optimization. Adjusting the transmission power is an art of balance, which requires considering the trade-off between the signal coverage range and the energy consumption. The optimal transmission power is found through the following formula, that is:

[0184]

[0185] Among them, λ is a weight factor used to find the best balance between throughput and energy consumption.

[0186] Modulation mode selection. Selecting an appropriate modulation mode is crucial for improving the reliability and efficiency of data transmission. Select the optimal modulation mode δ based on the following criteria * , that is:

[0187]

[0188] Here, BER is the bit error rate, and P r is the received signal power. The modulation mode that can minimize the bit error rate at a given signal strength can be obtained.

[0189] Coding scheme optimization: The choice of coding scheme will directly affect the efficiency and reliability of data transmission. Find the optimal coding scheme ε through the following formula * , that is:

[0190]

[0191] Among them, C(ε) is the transmission capacity of the coding scheme, and E c (ε) is the energy consumption of the coding scheme. In this way, the coding scheme that can provide the maximum ratio of transmission capacity to minimum energy consumption can be found.

[0192] S243. Optimize the home perception network using the parameter combination and complete the final deployment of the optimized perception nodes. After determining the optimal parameter combination, implement the adjustment of these parameters in the home perception network. This step needs to be carried out carefully to ensure that the change of parameters will not have a negative impact on the network stability. The verification test after parameter adjustment is the key step to ensure the optimization effect. The following is the detailed verification process:

[0193] Under the new parameter settings, re-measure the key performance indicators of the network.

[0194] Compare the measurement results with the data before optimization to evaluate the effect of parameter adjustment.

[0195] If the performance indicators do not meet the expected goals, the reasons need to be analyzed, and parameter optimization should be carried out again according to the analysis results until the network performance reaches or exceeds the established standards.

[0196] In the final deployment stage, it can be ensured that all optimized perception nodes can be accurately placed according to the established plan, and the overall performance of the home perception network reaches or exceeds the preset standards. The detailed steps are as follows:

[0197] Determine an accurate physical location P for each node according to the results obtained from the previous iterative optimization i , where P i=(x i , y i ), where \(i = 1, 2, \ldots, M\) represents different nodes. These positions should maximize the network coverage while minimizing signal interference.

[0198] Configure optimal parameters for each sensing node, including the optimal transmission power the optimal modulation method \(\delta\) * and the optimal coding scheme \(\varepsilon\) * etc. These parameters will directly affect the communication range of the sensing nodes and the overall performance of the network.

[0199] When physically deploying the sensing nodes, the operator needs to follow the following steps:

[0200] Place the nodes at the correct positions according to the provided deployment location coordinates.

[0201] Use professional tools to calibrate the direction and height of the nodes to ensure the consistency of signal propagation.

[0202] Start the nodes and check their status to ensure that all nodes can successfully join the network.

[0203] Update the network status to:

[0204] \(S(t)=\{(p 1 , P 1 , f 1 ), (p 2 , P 2 , f_2), \ldots, (p M , P M , f M )\}\)

[0205] where \(S(t)\) represents the network status at time \(t\); \(p M is the optimal parameter combination of the \(M\)th sensing node, i.e., the optimal transmission power the optimal modulation method \(\delta\) * and the optimal coding scheme \(\varepsilon^*\); \(P M is the power consumption of the \(M\)th sensing node; \(f M is the operating frequency of the \(M\)th sensing node.

[0206] S25. Evaluate the finally deployed sensing nodes; After the deployment of the sensing nodes is completed, immediately start the monitoring system and real-time track the following key metrics, including:

[0207] Sensing node status: Monitor the online status of each sensing node through heartbeat signals or other mechanisms, and record it as \(a i .

[0208] Performance indicators: Continuously collect data such as the throughput T, end-to-end delay D, and energy consumption E of the home perception network for subsequent analysis.

[0209] Regularly conduct in-depth analysis of the monitoring data and calculate the average value and standard deviation of the performance indicators, including the average value of the throughput T and the standard deviation σ T , the average value of the end-to-end delay D and the standard deviation σ D , the average value of the energy consumption E and the standard deviation σ E . To evaluate the stability and efficiency of the network, that is:

[0210] The average value of the throughput T and the standard deviation σ T The calculation formula is:

[0211]

[0212] The average value of the end-to-end delay D and the standard deviation σ D The calculation formula is:

[0213]

[0214] The average value of the energy consumption E and the standard deviation σ E The calculation formula is:

[0215]

[0216] In the above formula, M is the number of sensing nodes; T i , D i , E i are the throughput, end-to-end delay, and energy consumption of the i-th sensing node respectively.

[0217] Set a reasonable threshold θ to identify outliers in the performance indicators. The value of the threshold θ needs to be set in combination with the actual situation and is not limited in this embodiment. That is:

[0218] If or Then it is marked as an anomaly.

[0219] Anomaly detection helps to timely discover potential problems in the network. According to the monitoring and analysis results, perform the following maintenance and adjustment measures:

[0220] Regularly check the node hardware to ensure there are no physical damage or aging problems;

[0221] For the detected abnormal nodes, conduct on-site inspections and necessary repairs or replacements;

[0222] Adjust the configuration parameters of the nodes, such as transmission power and operating frequency, according to the changes in network performance;

[0223] Optimize the routing protocol of the network to adapt to environmental changes or node location adjustments.

[0224] In terms of location selection, this embodiment studies the optimal deployment locations of the sensing nodes to achieve comprehensive and stable network coverage; in terms of signal transmission optimization, it analyzes the interference and attenuation problems in the signal transmission process and proposes corresponding optimization strategies; in terms of energy management, it explores the energy consumption management technology of the sensing nodes to extend the service life of the network.

[0225] The present invention studies and designs a home sensing network architecture and composition applicable to complex environments, supports new generation network technologies such as IPv6, and provides a basis for carrying out various smart home applications. At the same time, based on the research content, it designs and constructs a smart home prototype system oriented to IPv6, explores typical smart home application scenarios such as smart home care, smart security, and smart appliances, and verifies and optimizes the methods and mechanisms proposed in this design.

[0226] Those of ordinary skill in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program. Therefore, this application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0227] Each embodiment in this specification is described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For the above embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0228] The above embodiments have introduced the present invention in detail. Specific examples are used in this article to elaborate on the principles and implementation methods of the present invention. The descriptions of the above embodiments are only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. An IPv6-based smart home heterogeneous network adaptive fusion system, characterized in that: The system includes: A network topology module, which is used to integrate heterogeneous network infrastructures with different communication protocols in a smart home and dynamically adjust the network topology structure according to changes in the environment and application scenarios; A communication protocol selection module, which is used to select a communication protocol compatible with all current heterogeneous devices according to the needs analysis results of family members in the smart home, and improve the data transmission rate by optimizing transmission parameters, wherein the communication protocols include IPv6, 6LoWPAN, and CoAP, and the transmission parameters include transmission power, modulation mode, and coding scheme; The home perception network deployment module is used to deploy corresponding network nodes according to the indoor space area of ​​the smart home and determine the location of the network nodes in the indoor space area. The network nodes include perception nodes, control nodes and data aggregation nodes. Specifically, the following steps are included: S21. Define the indoor space area as A and divide it into Q sub-areas. The area of ​​each sub-area is S i , i=1,2,…,Q; S22, establishing the objective function and constraint conditions of the sensing node deployment location planning, and expressing the sensing node deployment location planning problem as a multi-objective optimization problem; S23, initially deploying the sensing nodes and measuring the signal strength based on the solution of the multi-objective optimization problem; S24, optimizing the signal transmission parameters of the sensing nodes, and completing the final deployment of the optimized sensing nodes. The specific process includes the following steps: S241, determine the optimization goal for improving the total throughput T of the home sensing network, and define the energy consumption E of each sensing node i Will not exceed its preset maximum energy consumption limit E max , expressed as: Among them, P t represents the transmission power; δ represents the modulation method; ε represents the coding scheme; T and E i They are functions of total throughput and energy consumption of sensing nodes, respectively; S242, respectively optimize the transmission power, modulation mode and coding scheme to obtain a parameter combination, including the optimal transmission power Optimal modulation mode δ * And the optimal coding scheme ε * ,in: In the above formula, λ is a weight factor used to find the best balance between throughput and energy consumption; BER is the bit error rate; P r is the received signal power; C(ε) is the transmission capacity of the coding scheme; E c (ε) is the energy consumption of the coding scheme; S243, applying the parameter combination to the home perception network to complete the optimization; S25. Evaluate the finally deployed sensing nodes.

2. The smart home heterogeneous network adaptive fusion system according to claim 1 is characterized in that: In the network topology structure module, the steps of constructing the network topology structure are as follows: S11. List the heterogeneous network infrastructures that may exist in the home network, including Wi-Fi, ZigBee, Z-Wave, and Bluetooth Low Energy, and record the frequency band, maximum transmission rate, typical coverage, and average power consumption of each heterogeneous network infrastructure with different transmission protocol technologies; S12. Determine and define the characteristics of each heterogeneous network infrastructure according to S11, the characteristics including frequency range, coverage, transmission rate, and power consumption, wherein: INF={INF1, INF2, … INFk} represents a set of heterogeneous network infrastructures; CHAR(INFi)={Cov,BW,Pwr,Lat} represents the characteristics of the heterogeneous network infrastructure INFk; S13, classify and define network nodes, where network nodes include perception nodes, control nodes, gateway nodes, and edge computing nodes; S14. Design a network topology structure based on S11-S13 above, and use an adaptive fusion algorithm to dynamically adjust the network topology, including selecting a suitable topology structure, selecting a connection topology, and designing node connection rules, wherein: Nodes = {N1, N2, ..., Nn} represents all network nodes in the network; Connections = {C1, C2, ..., Cm} represents the connection relationship between network nodes; optimizedTopology={V',E'} represents the optimized network topology; S15. Optimize the network topology structure. The optimization formula is expressed as: MST(V,E)=(V',E') represents the optimized topological network structure obtained by the minimum spanning tree algorithm'; Dijkstra(V, E, s) = P represents the set of shortest paths from the source node s to all other nodes.

3. The smart home heterogeneous network adaptive fusion system according to claim 1 is characterized in that: In the home perception network deployment module, the deployment of network nodes includes initializing the network environment, perception node deployment, control node deployment, and data aggregation node deployment, specifically: Initialize the network environment, including: Set the IPv6 addresses of various devices in the smart home heterogeneous network, and determine the location and function of the sensing nodes, control nodes, and data aggregation nodes; Sensing node deployment, N is used to represent the number of sensing nodes. i Represents the i-th sensor node, including: Determine the deployment location of the sensing node according to the indoor space layout to ensure coverage of all monitoring areas, and i Assign a unique IPv6 address, denoted as N i IPv6; Control node deployment, using C to represent the number of control nodes, C i Represents the i-th control node, including: Determine the deployment location of the control node so that it maintains a certain communication distance with the sensing node, and set a i Assign a unique IPv6 address, denoted as C i IPv6; Data aggregation node deployment, D is used to represent the number of data aggregation nodes, D i represents the i-th data aggregation node, including: selecting a suitable location to deploy the data aggregation node so that it can receive data from the control node, and for each data aggregation node D i Assign a unique IPv6 address, denoted as D i IPv6.

4. The smart home heterogeneous network adaptive fusion system according to claim 1 is characterized in that: In step S23, the specific process includes the following steps: S231, select M nodes from the candidate node set θ for initial deployment, and the selected sensing node set is recorded as in S232, is a set of sensing nodes Each sensing node in the system determines a specific deployment location. The deployment location of node i (x i ,y i ) is calculated as: x i =𝑥0+𝑖·Δ𝑥,𝑦 i =y0+j·Δy Where (x0, y0) is the coordinate of the lower left corner of the deployment area; Δx and Δy are the spacing of the grid in the x-axis and y-axis directions respectively, and i and j are the grid indexes of the sensing nodes; S233: Perform a signal strength test of the adjacent nodes on each sensing node. For any two sensing nodes i and j, measure the signal strength P received by the sensing node i from the sensing node j. r,ij ,Right now: Among them, P t is the transmission power; α is the path loss exponent; d0 is the reference distance; X σ,ij represents the small-scale fading effect between sensing nodes i and j; d ij is the distance between node i and node j; S234, for each signal strength P r,ij Perform n measurements to obtain the measurement sequence S235: Calculate the average signal strength and use it as the signal strength between the sensing node i and the node j Right now: in, Represents the signal strength of the kth measurement.

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