IPv6-based smart home heterogeneous network adaptive fusion system

Abstract

The invention relates to the technical field of smart home, solves the technical problem that a traditional smart home solution still has defects in the aspect of heterogeneous network convergence, and particularly relates to an IPv6-based smart home heterogeneous network adaptive convergence system. The system is composed of a network topology structure module, a communication protocol selection module and a home sensing network deployment module. Wherein the network topology structure module is used for fusing heterogeneous network infrastructures with different communication protocols in the smart home, and adaptively and dynamically adjusting the network topology structure according to the change of the environment and the application scene. The invention provides a network topology structure capable of fusing different communication technologies and protocols, and ensures that various devices and services can cooperatively work in the same network system. In the aspect of the adaptive network, the topology network structure module can be adaptively adjusted according to the change of the environment and the application scene so as to maintain the optimal performance.

Description

Technical Field

[0001] The present invention relates to the technical field of smart home, and in particular to an IPv6-based smart home heterogeneous network adaptive fusion system. Background Art

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

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

[0004] IPv6's massive addresses, end-to-end connectivity, IPsec and a series of IPv6 security mechanisms provide the preferred solution to the above problems. IPv6 can support more device access, simplify heterogeneous network management and interconnection, promote extensive connections between smart devices, improve the integration and interoperability of smart home systems, and build an autonomous, secure and controllable smart home network.

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

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

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: an IPv6-based smart home heterogeneous network adaptive fusion system, the system comprising:

[0008] 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;

[0009] 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;

[0010] A 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.

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

[0012] 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;

[0013] 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:

[0014] INF={INF1, INF2, … INFk} represents a 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, where network nodes include perception nodes, control nodes, gateway nodes, and edge computing nodes;

[0017] 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:

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

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

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

[0021] S15. Optimize the network topology structure. 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, perception node deployment, control node deployment, and data aggregation node deployment, specifically:

[0025] Initialize the network environment, including:

[0026] 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;

[0027] Sensing node deployment, N is used to represent the number of sensing nodes. i Represents the i-th sensor node, including:

[0028] 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;

[0029] Control node deployment, using C to represent the number of control nodes, C i Represents the i-th control node, including:

[0030] 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;

[0031] 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 iAssign 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 position of the perception nodes in the indoor space area is determined, which specifically includes the following steps:

[0033] 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;

[0034] 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;

[0035] S23, initially deploying the sensing nodes and measuring the signal strength based on the solution of the multi-objective optimization problem;

[0036] S24, optimizing the signal transmission parameters of the sensing nodes, and completing the final deployment of the optimized sensing nodes;

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

[0038] Further, in step S22, the objective function includes sensing node deployment to achieve maximum coverage area and minimize communication energy consumption, where:

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

[0040]

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

[0042]

[0043] Where N is the number of sensing nodes; F is the coverage area of ​​the sensing nodes; k is the proportional constant, is the distance between sensing node i and sensing node j;

[0044] The constraints of the sensing node deployment location planning include the node quantity constraint and the coverage integrity constraint. For the node quantity constraint, the number of deployable sensing nodes is limited, set to M, that is:

[0045]

[0046] For coverage integrity constraints, all sub-areas 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 functions and constraints, the problem of sensor node deployment location planning can be expressed as a multi-objective optimization problem, namely:

[0050]

[0051] where x1,x2,...,x M Indicates 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 sensing node set is recorded as in

[0054] 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 The calculation formula for 0 is:

[0055] x i =x0+i·Δx,y i =y0+j·Δy

[0056] Where (x0, y00 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;

[0057] 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:

[0058]

[0059] 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 ijis the distance between node i and node j;

[0060] S234, for each signal strength P r,ij Perform n measurements to obtain the measurement sequence

[0061] S235: Calculate the average signal strength and use it as the signal strength between the sensing node i and the node j Right now:

[0062]

[0063] in, Represents the signal strength of the kth measurement.

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

[0065] 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:

[0066]

[0067] 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;

[0068] 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:

[0069]

[0070] 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;

[0071] S243, applying the parameter combination to the home perception network to complete the optimization.

[0072] By means of the above technical solution, the present invention provides an IPv6-based smart home heterogeneous network adaptive fusion system, 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 adaptive networks, the topology network structure module can be adaptively adjusted 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 perception nodes to achieve comprehensive and stable network coverage; at the same time, in terms of signal transmission optimization, it analyzes the interference and attenuation problems in the signal transmission process and proposes corresponding optimization strategies; and in terms of energy management, it explores the energy consumption management technology of perception nodes 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 on the present application. In the drawings:

[0076] Figure 1 Schematic diagram of the smart home heterogeneous network fusion application system architecture of the present invention

[0077] Figure 2 This is a schematic diagram of the system architecture of the present invention integrating heterogeneous infrastructure. DETAILED DESCRIPTION

[0078] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific implementation methods, so that the implementation process of how the present application uses technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly.

[0079] Please refer to Figure 1-Figure 2 This embodiment proposes an IPv6-based smart home heterogeneous network adaptive fusion system. This embodiment conducts research on the IPv6 and smart home heterogeneous network adaptive fusion system, focusing on the design of key issues such as perception network coverage in complex environments, so as to establish a home perception network that integrates heterogeneous infrastructure and improve the performance, integrability 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 in the smart home, and dynamically adjust the network topology structure according to changes in the environment and application scenarios. The present invention is based on the system architecture diagram of the integrated heterogeneous infrastructure, such as Figure 1 and Figure 2As shown, it includes a physiological sensing network interconnected with intelligent sensing terminals, a wireless sensor network, new sensors AI-Sound and TOF, and a home information network, which are then integrated with the Internet and interconnected with cloud services. Therefore, by designing a network topology that integrates heterogeneous infrastructure, supports IPv6 address allocation, and ensures high reliability, low latency, and good scalability. The specific implementation steps for building a network topology are as follows:

[0081] S11. List the heterogeneous network infrastructures that may exist 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), maximum transmission rate (such as 100Mbps), typical coverage (such as 30 meters indoors), and average power consumption (such as 5W for Wi-Fi routers). Create a detailed technology comparison table, including additional indicators such as compatibility, cost, and security.

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

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

[0084] Technology Type Frequency range Transfer rate Coverage Power consumption Wi-Fi 2.4 / 5GHz high middle middle ZigBee 2.4GHz Low Small Low Z-Wave 900MHz Low Small Low Bluetooth 2.4GHz Low Small Low

[0085] The following definitions are made based on the characteristics of heterogeneous network infrastructure:

[0086] INF={INF1, INF2,…INFk} represents a 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 node. Network nodes include: sensor nodes (SN), control nodes (CN), gateway nodes (GN), and edge computing nodes (ECN), and are defined as follows:

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

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

[0091] S14. Design the network topology. First, select a suitable topology, such as star, mesh, hybrid, etc. Select the connection topology: T = {mesh, star, ring}, and design the node connection rule: N(i) → N(j) means that node i is connected to node j. Determine the connection relationship between nodes, including determining directly connected node pairs and determining 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, it can be defined as follows:

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

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

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

[0098] S15. Topological network structure optimization. Based on node density and signal strength, the network coverage is calculated, and graph theory algorithms (such as minimum spanning tree, shortest path, etc.) are used to optimize the connection path. Finally, the performance of the topological structure is verified through simulation and experiment. The formula for topological network structure optimization is expressed as:

[0099] MST(V,E)=(V',E') represents the optimized topological network structure obtained by 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 integrated heterogeneous infrastructure and adaptive network. In terms of integrated heterogeneous infrastructure, 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 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 according to the demand analysis results of family members in the smart home, and to 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. In this embodiment, the composition of the communication protocol selection module mainly includes 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 that the aspects included are:

[0104] 1. Demand analysis and protocol selection

[0105] Requirements collection: List all smart home devices and their communication requirements through interviews with family members or smart home system users; define users' daily 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, build data traffic models, predict network load, and determine service quality requirements, priorities, and key performance indicators (KPIs) for different types of data flows.

[0107] Protocol selection: Based on the results of the demand analysis, select appropriate communication protocols, 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: Clarify the various layers 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 the layers.

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

[0111] Protocol specification: Write protocol specifications for each layer, including data format, operation process and exception handling.

[0112] 3. Transmission technology optimization

[0113] Transmission rate optimization: Improve the data transmission rate by adjusting the modulation method (such as QAM) and coding scheme (such as LDPC) to 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 scheduling algorithms for node sleep and wake-up.

[0115] Delay optimization: Reduce the transmission delay of data packets by optimizing routing algorithms and scheduling strategies.

[0116] 4. Security design

[0117] Security requirements analysis: Build threat models to identify potential attack methods, security threats, and privacy risks.

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

[0119] Security protocol implementation: Implement security handshake protocols, such as TLS, for data transmission security. At the same time, perform node authentication and data integrity verification.

[0120] 5. Protocol performance test

[0121] Test environment construction: Use a network simulator to simulate the actual network environment and build a test bed that simulates a home environment, including different types of sensors and network devices.

[0122] Performance indicator definition: Define specific test scenarios, test indicators, test cases, etc. The indicators may include throughput, packet loss rate, response time, etc.

[0123] Test execution and data analysis: Execute protocol performance tests, collect data and perform statistical analysis, generate performance reports, and identify performance bottlenecks.

[0124] 6. Protocol iteration and optimization

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

[0126] Protocol modification: Modify the protocol specification based on the diagnosis results, conduct code review, optimize the protocol implementation, and ensure that the modification does not introduce new problems.

[0127] Iterative testing: Re-perform performance testing to verify the effect of modifications 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 location 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 network nodes includes initializing the network environment, perception node deployment, control node deployment and data aggregation node deployment, specifically:

[0129] Initialize the network environment, including:

[0130] 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;

[0131] Sensing node deployment, N is used to represent the number of sensing nodes. i Represents the i-th sensor node, including:

[0132] 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;

[0133] Control node deployment, using C to represent the number of control nodes, C i Represents the i-th control node, including:

[0134] 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;

[0135] 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.

[0136] In this embodiment, the location deployment of the perception nodes must be completed before the next step of control node deployment and data aggregation node deployment can be carried out. However, in the network topology module, the design of the network topology must be based on the deployment of the perception nodes, and then the corresponding deployment 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 how to make the deployment of perception nodes cover all areas of the indoor space layout, where professional measurement tools (such as laser rangefinders) can be used to obtain the size of the indoor space, including the size, height and layout of the room.

[0137] Therefore, this embodiment is used to deploy corresponding sensing nodes according to the indoor space area of ​​the smart home and determine the position of the sensing node in the indoor space area, which specifically includes the following steps:

[0138] 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;

[0139] S22. Establish the objective function and constraints of the perception node deployment location planning, and express the perception node deployment location planning problem as a multi-objective optimization problem. In this embodiment, genetic algorithms, particle swarm algorithms, taboo search algorithms, and simulated annealing algorithms can be used to solve the perception node deployment location planning problem. This can be achieved by existing technical means and will not be elaborated on here.

[0140] Specifically, the objective function includes maximizing the coverage area and minimizing the communication energy consumption of the sensing node deployment. 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] in,

[0143] The coverage area of ​​the sensing node covers the entire monitoring area as much as possible, that is, the maximization function is:

[0144]

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

[0146]

[0147] Where N is the number of sensing nodes; F is the coverage area of ​​the sensing nodes; k is the proportional constant, is the distance between sensing node i and sensing node j;

[0148] The constraints of the sensing node deployment location planning include the node quantity constraint and the coverage integrity constraint. For the node quantity constraint, the number of deployable sensing nodes is limited, set to M, that is:

[0149]

[0150] For coverage integrity constraints, all sub-areas must 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 functions and constraints, the problem of sensor node deployment location planning can be expressed as a multi-objective optimization problem, namely:

[0154]

[0155] where x1,x2,...,x M Indicates the deployment locations of M nodes.

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

[0157] S23, based on the solution of the multi-objective optimization problem, the sensing nodes are initially deployed and the signal strength is measured; in step S23, the specific process includes the following steps:

[0158] S231. In the candidate node set θ, M nodes are selected for initial deployment according to specific deployment goals (such as coverage, communication capacity, etc.). The selected sensing node set is recorded as in M. This step can be done automatically by an algorithm, such as using a greedy algorithm to select the node with the largest coverage.

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

[0160] x i =x0+i·Δx,y i =y0+j·Δy

[0161] 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;

[0162] In this embodiment, a logarithmic distance path loss model is used to describe the change of signal strength with distance. The model can be expressed as:

[0163]

[0164] Among them, P r (d) is the received signal strength (RSSI) measured at a distance d from the transmitting source; P t is the transmission power; α is the path loss exponent; d0 is the reference distance (usually 1 meter); Xσ is a Gaussian random variable with mean 0 and standard deviation σ, representing the small-scale fading effect.

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

[0166] Determine the distance d between nodes i and j ij ,Right now:

[0167]

[0168] The theoretical signal strength is calculated based on the logarithmic distance path loss model, that is:

[0169]

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

[0171] S234: In order to reduce the impact of random errors and shadow fading, perform multiple measurements on each measurement value and calculate the average value. r,ij Perform n measurements to obtain the measurement sequence

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

[0173]

[0174] in, Represents the signal strength of the kth measurement.

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

[0176] S24, optimizing the signal transmission parameters of the sensing node; in this embodiment, the identification of transmission parameters is the basis for signal transmission optimization. These parameters directly affect the performance of the network, including transmission power, modulation mode, coding scheme, etc. The goal is to accurately identify these parameters so that they can be adjusted in a targeted manner 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 the energy consumption E of each sensing node. i Will not exceed its preset maximum energy consumption limit E max , which can be expressed as:

[0179]

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

[0181] In order to achieve the above goals, the following detailed steps are taken to optimize the parameters, specifically:

[0182] 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 * .

[0183] Transmit power optimization. The adjustment of transmit power is an art of balance, which requires considering the compromise between signal coverage and energy consumption. The optimal transmit power is found through the following formula, namely:

[0184]

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

[0186] Modulation mode selection. Selecting the appropriate modulation mode is crucial to improving the reliability and efficiency of data transmission. The optimal modulation mode is selected based on the following criteria: * ,Right now:

[0187]

[0188] Here, BER is the bit error rate, P r is the received signal power. The modulation method that can minimize the bit error rate under 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. The optimal coding scheme ε is found by the following formula * ,Right now:

[0190]

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

[0192] S243. Optimize the home perception network using a combination of parameters 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 done carefully to ensure that the parameter changes do not have a negative impact on network stability. Verification testing after parameter adjustment is a key step to ensure the optimization effect. The following is a detailed verification process:

[0193] Under the new parameter settings, the key performance indicators of the network are remeasured.

[0194] The measured results were compared with the data before optimization to evaluate the effect of parameter adjustment.

[0195] If the performance indicators do not meet the expected targets, it is necessary to analyze the reasons and re-optimize the parameters based on the analysis results until the network performance reaches or exceeds the established standards.

[0196] In the final deployment phase, it is possible to ensure that all optimized sensing nodes are accurately placed according to the established plan and that the overall performance of the home sensing network meets or exceeds the preset standards. The detailed steps are as follows:

[0197] According to the results of the previous iterative optimization, an accurate physical position P is determined for each node i , where P i =(x i ,y i ), i = 1, 2, ..., M represents different nodes. These locations should maximize the coverage of the network while minimizing signal interference.

[0198] Configure optimal parameters for each sensing node, including optimal transmit power Optimal modulation mode δ * And the optimal coding scheme ε * Etc. These parameters will directly affect the communication range of the sensing nodes and the overall performance of the network.

[0199] When physically deploying a sensing node, operators need to follow these steps:

[0200] Places the node at the correct location based on the provided deployment location coordinates.

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

[0202] Start the nodes and check their status to make sure all nodes are able to join the network successfully.

[0203] Update the network status to:

[0204] S(t)={(p1,P1,f1),(p2,P2,f2),…,(p M ,P M ,f M )}

[0205] Among them, S(t) represents the network status at time t; p M is the optimal parameter combination of the Mth sensing node, that is, the optimal transmission power Optimal modulation mode δ * and the optimal coding scheme ε*;P M is the power consumption of the Mth sensing node; f M is the operating frequency of the Mth sensing node.

[0206] S25. Evaluate the finally deployed sensing nodes. After the sensing nodes are deployed, start the monitoring system immediately to track the following key indicators in real time, 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 throughput T, end-to-end delay D, and energy consumption E of the home sensing network for subsequent analysis.

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

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

[0211]

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

[0213]

[0214] Average value of energy consumption E and 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] A reasonable threshold value θ is set to identify abnormal values ​​in the performance index. The value of the threshold value θ needs to be set in combination with the actual situation, and this embodiment does not make any limitation here. That is:

[0218] if or It is marked as abnormal.

[0219] Anomaly detection helps to promptly identify potential problems in the network. Based on the monitoring and analysis results, perform the following maintenance and adjustment measures:

[0220] Regularly check node hardware to ensure there is no physical damage or aging issues;

[0221] For abnormal nodes detected, conduct on-site inspection and necessary repair or replacement;

[0222] Adjust node configuration parameters, such as transmit power and operating frequency, based on changes in network performance;

[0223] Optimize the network's routing protocols to adapt to environmental changes or node location adjustments.

[0224] In terms of location selection, this embodiment studies the optimal deployment location of perception 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 perception nodes to extend the service life of the network.

[0225] This invention studies and designs the architecture and composition of a home sensing network suitable for complex environments, supports new generation network technologies such as IPv6, and provides a basis for various smart home applications. At the same time, based on the research content, a smart home prototype system for IPv6 is designed and constructed to explore typical application scenarios of smart homes such as smart home care, smart security, and smart home appliances, and verify and optimize the methods and mechanisms proposed in this design.

[0226] Those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, so the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0227] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between 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 implementation methods have been described in detail. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An IPv6-based smart home heterogeneous network adaptive fusion system, It is 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; A 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.

2. The smart home heterogeneous network adaptive fusion system according to claim 1, It 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, It 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, It is characterized in that In the home perception network deployment module, corresponding perception nodes are deployed according to the indoor space area of ​​the smart home, and the position of the perception nodes in the indoor space area is determined, which specifically includes the following steps: 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; S25. Evaluate the finally deployed sensing nodes.

5. The smart home heterogeneous network adaptive fusion system according to claim 4, It is characterized in that In step S22, the objective function includes the deployment of sensing nodes to maximize coverage and minimize communication energy consumption, where: The coverage area of ​​the sensing node covers the entire monitoring area as much as possible, that is, the maximization function is: Minimize communication energy consumption: Assume that the communication energy consumption between sensing nodes is proportional to the square of the distance, that is: Where N is the number of sensing nodes; F is the coverage area of ​​the sensing nodes; k is the proportional constant, is the distance between sensing node i and sensing node j; The constraints of the sensing node deployment location planning include the node quantity constraint and the coverage integrity constraint. For the node quantity constraint, the number of deployable sensing nodes is limited, set to M, that is: For coverage integrity constraints, all sub-areas must be covered by at least one sensing node, that is: Make d(i,j)≤R Where d(i,j) represents the distance between sensing nodes i and j; R is the coverage radius of the sensing node; Combining the above objective functions and constraints, the problem of sensor node deployment location planning can be expressed as a multi-objective optimization problem, namely: Among them, x 1 ,x 2 ,...,x M Indicates the deployment locations of M nodes.

6. The smart home heterogeneous network adaptive fusion system according to claim 4, It 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 =x 0 +i·Δx,y i =y 0 +j·Δy Among them, (x 0 ,y 0 ) 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; 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 transmit power; α is the path loss exponent; d 0 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.

7. The smart home heterogeneous network adaptive fusion system according to claim 4, It is characterized in that In step S24, 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.

Images