Wireless sensor network adaptive routing method and device based on intelligent decision

Abstract

The invention relates to industrial wireless sensor network communication, in particular to a wireless sensor network self-adaptive routing method and device based on intelligent decision making. The data transmission stability and the operation life of the industrial Internet of Things system in a complex environment are effectively improved. Comprising the following steps: S1, performing periodic and event-driven data acquisition on each monitoring node in the industrial wireless sensor network; and S2, calculating a cluster head opportunity value of the node, and electing to generate a cluster head set according to the cluster head opportunity value. And S3, by taking the minimum residual energy of the path, the hop count and the link quality as optimization targets, constructing a multi-hop transmission path from the cluster head to the sink node through a routing discovery mechanism based on multi-target cost evaluation. And S4, dynamic switching of node states is controlled according to data characteristics and task importance, and energy consumption of the nodes is monitored in real time to be fed back to subsequent cluster head election and path optimization, so that global energy balance is realized.

Description

Technical Field

[0001] This invention relates to industrial wireless sensor network communication, and more particularly to an adaptive routing method and apparatus for wireless sensor networks based on intelligent decision-making. Background Technology

[0002] Industrial wireless sensor networks (ICS) form a complex, self-organizing communication environment by deploying numerous sensor nodes in industrial settings, enabling environmental monitoring, equipment status awareness, and data exchange. However, with the increasing number of nodes and dynamic changes in network topology, existing routing mechanisms face severe challenges in energy balancing, data transmission reliability, and real-time performance. In this context, problems such as rapid node energy consumption, link congestion, and delays in critical data transmission frequently occur, leading to shortened network lifetime, decreased communication stability, and insufficient system responsiveness. In complex and ever-changing industrial environments, traditional static clustering and fixed-path strategies are insufficient for dynamic management of node energy and data. Therefore, how to reduce overall energy consumption while ensuring data transmission quality has become a critical issue that urgently needs to be addressed in the field of industrial IoT communication.

[0003] Typically, data transmission in industrial wireless sensor networks must simultaneously consider multi-dimensional constraints such as energy consumption, link quality, and node load to support reliable communication between devices and data transmission from field nodes to the aggregation node. Current routing methods still face many challenges, especially in high-density and highly dynamic topology environments. Existing routing strategies based on fixed cluster structures or shortest paths are mostly used in static network scenarios. When applied to complex industrial wireless environments, their energy management and link adaptation capabilities are insufficient, easily leading to uneven node energy distribution, cluster head overload, and path instability, thereby increasing data packet loss rate and latency. Summary of the Invention

[0004] This invention addresses the shortcomings of existing technologies by providing an adaptive routing method and apparatus for wireless sensor networks based on intelligent decision-making.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: an adaptive routing method for wireless sensor networks based on intelligent decision-making, comprising the following steps:

[0006] S1. Perform periodic and event-driven data acquisition on each monitoring node in the industrial wireless sensor network.

[0007] S2. Based on the node data collected in S1, obtain the node's remaining energy, neighbor density, and average distance to neighboring nodes as input features, calculate the cluster head chance value of the node, and elect a cluster head set based on the cluster head chance value.

[0008] S3. After the cluster head election is completed, with the minimum remaining energy, hop count and link quality of the path as optimization objectives, a multi-hop transmission path from the cluster head to the sink node is constructed through a route discovery mechanism based on multi-objective cost evaluation (improved Bellman-Ford algorithm).

[0009] S4. Data is forwarded according to the path constructed in S3. During this process, the dynamic switching of node states is controlled according to data characteristics and task importance, and the energy consumption of nodes is monitored in real time to feed back to subsequent cluster head election and path optimization, so as to achieve global energy balance.

[0010] Furthermore, in S2, the formula for calculating the cluster head chance value is:

[0011]

[0012] Where FIS() represents the fuzzy inference system function.

[0013] x1 represents the node's remaining energy factor, which is the ratio of the node's remaining energy to the maximum remaining energy of its neighboring nodes.

[0014] x2 represents the neighbor density factor of a node, which is the ratio of the number of neighbors to the ideal number of neighbors.

[0015] x3 represents the distance factor of the node, which is the normalized value of the average communication distance between the node and its neighboring nodes.

[0016] In a preferred embodiment, in S2, the step of generating a cluster head set based on the cluster head chance value specifically involves: sorting the cluster head chance values ​​in descending order and selecting the nodes with the highest rankings by a predetermined percentage as cluster heads. The predetermined percentage is 10%.

[0017] Furthermore, in S3, the implementation of the route discovery mechanism based on multi-objective cost evaluation includes:

[0018] S3.1 Calculate the comprehensive weight W based on the node's remaining energy, available buffer capacity, and connectivity between upper and lower level neighbors. gateway When W gateway If the threshold is exceeded, it will be selected as a candidate gateway node.

[0019] S3.2 For the path from the source node to the sink node, the path priority score S is calculated by comprehensively considering the minimum remaining energy, hop count, and average link quality. path .

[0020] S3.3 Each gateway node retains multiple candidate paths with the highest priority to achieve data forwarding under multi-objective constraints.

[0021] Furthermore, in S3.1, the comprehensive weight W gateway The calculation formula is:

[0022]

[0023] Among them, e i This represents the current remaining energy of the node. The initial energy of the node. and These are the node's current available buffer capacity and the node's maximum data buffer capacity, respectively. and These represent the number of neighboring nodes in the previous hop of the node in the routing path and the number of neighboring nodes in the next hop of the node in the routing path, respectively. This represents the total number of nodes in the wireless sensor network. , and This is the adjustment coefficient, the coefficient... This reflects the impact of node remaining energy on network lifetime. This indicates the role of node buffer capacity in data congestion control. This indicates the impact of neighbor connectivity at different layers on routing connectivity and load distribution. All three conditions must be met. .

[0024] Furthermore, the adjustment coefficient , , The values ​​are 0.5, 0.3, and 0.2.

[0025] Furthermore, in S3.2, the path priority score S path The calculation formula is:

[0026]

[0027] in, , and For adjustment coefficients, Reflecting the importance of the minimum remaining energy of the path, This indicates the impact of path hop count on transmission efficiency. This represents the contribution of average link quality to communication stability. All three conditions must be met. + + =1. E min E0 represents the minimum remaining energy on the path; h represents the initial energy of the node; Q represents the number of hops on the path; avg This indicates the average link quality of the path.

[0028] Optimal scheme, adjustment coefficient =0.4、 =0.4、 =0.2. The adjustment coefficient ratio can achieve a good balance between energy balance, time delay control and path reliability.

[0029] Furthermore, in S4, the dynamic switching of the control node state specifically involves:

[0030] Dual threshold triggering conditions are set based on data characteristics and task importance, and a light wake-up-acknowledgment mechanism is used to control the switching of nodes between deep sleep, light wake-up, and active states. In the light wake-up state, the node listens to the channel and only switches to the active state when it confirms that there is data to be transmitted or the triggering conditions are met; otherwise, it returns to the deep sleep state.

[0031] Furthermore, in S4, when the primary transmission path constructed by S3 fails due to link interruption or insufficient node energy, the system automatically activates the backup path for data forwarding.

[0032] Furthermore, steps S2, S3, and S4 constitute a closed-loop optimization process. The node energy consumption information monitored in S4 is fed back to the cluster head election in S2 and the route path construction in S3 to achieve dynamic adaptive adjustment of the network.

[0033] An adaptive routing device for wireless sensor networks based on intelligent decision-making, used to implement the method, includes:

[0034] The sleep scheduling subunit is used to set dual threshold triggering conditions based on the data characteristics collected by the nodes and the importance of the tasks. It controls the switching of nodes between deep sleep, light wake-up and active states through a light wake-up-acknowledgment mechanism to dynamically adjust network activity.

[0035] The fuzzy decision sub-unit is used to calculate the cluster head chance value, generate a set of candidate cluster heads, and determine the final cluster head by taking the node's remaining energy, neighbor density, and relative distance to the convergence center as inputs.

[0036] The multi-objective routing optimization subunit is used to introduce a routing discovery mechanism based on multi-objective cost evaluation based on inter-cluster connectivity. It comprehensively considers link connectivity, node energy and buffer utilization to construct multi-hop transmission paths that meet the requirements of energy balance and reliability.

[0037] The energy management subunit is used to adaptively adjust the task allocation and sleep strategy between cluster members and the cluster head based on the node's energy consumption status and data load, so as to achieve global energy coordination and long-term stable network operation.

[0038] Compared with the prior art, the present invention has the following advantages.

[0039] This invention fully leverages the communication and energy utilization potential of network nodes. By adaptively adjusting node transmission power and communication radius, dynamically controlling node sleep and wake-up states, and constructing multi-index optimal multi-hop paths, it not only ensures the reliable transmission of critical data but also improves the overall energy efficiency and load balancing of the network, thereby effectively enhancing the data transmission stability and operational lifespan of industrial IoT systems in complex environments. Attached Figure Description

[0040] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The scope of protection of the present invention is not limited to the following description.

[0041] Figure 1 This is a schematic diagram of the system structure for an embodiment.

[0042] Figure 2 This is a schematic diagram of the device structure in an embodiment.

[0043] Figure 3 The diagram illustrates the principle of the fuzzy decision-making and multi-feature collaborative optimization algorithm in the device described in this embodiment.

[0044] Figure 4 This is a schematic diagram of the adaptive routing structure based on the multi-index Bellman-Ford algorithm in the device of the embodiment. Detailed Implementation

[0045] To make the objectives, features, and advantages of the present invention clearer and more understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. In the following description, several specific details are set forth to facilitate a full understanding of the present invention. However, those skilled in the art should understand that the present invention can also be implemented in other ways different from these embodiments, and these embodiments should not be construed as limiting the scope of protection of the present invention. Unless otherwise stated, all technical terms used herein have the same meaning as commonly understood by those skilled in the art.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0047] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0048] To implement the aforementioned adaptive routing method based on intelligent decision-making, Embodiment 1 of this invention provides a corresponding system architecture. See also... Figure 1 The system architecture shown is mainly composed of: a data acquisition device, an adaptive routing device based on intelligent decision-making, and a data receiving device.

[0049] Specifically:

[0050] 1. The data acquisition device consists of a sensor node network and a control and management module. The sensor node network is used to collect monitoring data such as temperature, gas concentration, and pressure in an industrial wireless sensor network environment. The control and management module is used to control the working status of the nodes, coordinate the wake-up and sleep of the nodes, realize efficient energy management, and upload the collected and processed data to an adaptive routing device based on intelligent algorithms.

[0051] 2. The intelligent decision-making-based routing device consists of an embedded multi-core processing system, integrating wireless communication and data processing modules. It receives monitoring data from various sensor nodes and performs comprehensive analysis of node status, energy distribution, and network topology characteristics. This device combines fuzzy logic and a multi-objective routing optimization mechanism to achieve intra-cluster data aggregation, inter-cluster routing selection, and adaptive energy control, ensuring high efficiency and stability of network transmission. The device mainly comprises five subunits: a data acquisition subunit, a sleep scheduling subunit, a fuzzy decision-making subunit, a multi-objective routing optimization subunit, and an energy management subunit.

[0052] 3. The data receiving device is used to centrally receive, analyze, and visualize the monitoring data transmitted by each node in the wireless sensor network. It can reflect the network operating status, routing topology, and node energy distribution in real time. Users can monitor network performance and adjust parameters in real time through an interactive interface, thereby achieving visualized management and intelligent control of the entire wireless sensor network operation process.

[0053] Example 2, as follows Figure 2 As shown, the adaptive routing device for wireless sensor networks based on intelligent decision-making includes: a data acquisition subunit, a sleep scheduling subunit, a fuzzy decision-making subunit, a multi-objective route optimization subunit, and an energy management subunit.

[0054] The data acquisition subunit is responsible for collecting and initially processing the environmental data and operating status of each monitoring node in the wireless sensor network, providing basic information support for subsequent routing decisions and energy optimization.

[0055] The sleep scheduling subunit manages the node status using a dynamic threshold mechanism based on the importance of the node, and rationally arranges the sleep and active states, thereby reducing redundant energy consumption and extending the network lifetime.

[0056] The fuzzy decision-making sub-unit uses fuzzy logic reasoning mechanism to comprehensively evaluate nodes based on parameters such as the node's remaining energy, neighbor density, and average distance, calculates the cluster head chance value, and completes the cluster head election to ensure balanced cluster head distribution and reasonable inter-cluster load.

[0057] The multi-target routing optimization subunit adopts a multi-target cost evaluation routing discovery mechanism (improved Bellman-Ford algorithm) to comprehensively optimize multi-hop paths from cluster head to sink node. It comprehensively considers the minimum remaining energy, hop count and link quality of the path to construct a set of optimal multi-target paths, thereby achieving efficient and stable data forwarding.

[0058] The energy management subunit adaptively adjusts the task allocation and sleep strategy between cluster members and cluster head based on the real-time energy consumption status and data load of the nodes, and performs global energy coordination and dynamic balance to ensure that the system maintains stability and optimal energy efficiency during long-term operation.

[0059] Example 3: The adaptive routing method for wireless sensor networks based on the device of Example 2 includes the following steps:

[0060] S1 is a data acquisition subunit that performs periodic and event-driven data acquisition from each monitoring node in the industrial wireless sensor network. This subunit is responsible for coordinating the sampling frequency and reporting mechanism of each sensing node to avoid duplicate sampling and energy waste.

[0061] The S2 fuzzy decision subunit, based on three input features—remaining energy of a node, neighbor density, and average distance to its neighbors—calculates the cluster head competition value through a fuzzy logic inference system, thereby achieving cluster head election. Its fuzzy inference rules integrate node features with high energy, close proximity, and moderate neighbor density to improve the network's energy balance and cluster structure stability. The cluster head chance value of a node is... The calculation formula is as follows:

[0062]

[0063] Among them, energy factor Neighbor density factor and distance factor These are used to characterize the node's energy capacity, local connectivity density, and average communication distance characteristics, respectively.

[0064] The S3 multi-metric Bellman-Ford routing optimization subunit is used to construct a set of multi-objective optimal paths from the cluster head to the sink node. By comprehensively considering the minimum remaining energy, hop count, and link quality of the path, it achieves efficient and reliable multi-hop data forwarding.

[0065] Based on the optimal path set generated by the multi-target routing optimization subunit, S4 enables the system to perform multi-hop data forwarding within and between clusters. To reduce overall energy consumption and extend network lifetime, nodes employ a light wake-up mechanism and adaptive power control strategy during transmission. Non-critical nodes remain dormant, participating in communication only when woken up or receiving a forwarding request. The energy management subunit monitors node energy consumption in real time and feeds back energy distribution information to the fuzzy decision subunit to support subsequent cluster head election and path optimization, achieving dynamic energy balance and efficient collaborative operation of the network.

[0066] During system operation, the S5's subunits work collaboratively, adaptively adjusting the cluster structure and routing paths based on real-time network status monitoring, energy changes, link quality fluctuations, and node load conditions. This closed-loop optimization mechanism ensures that the network maintains optimal energy utilization and data transmission reliability at different stages, thereby significantly extending network lifespan and improving overall stability.

[0067] Example 4: To ensure the stability and energy efficiency of the network cluster structure, the adaptive routing device employs an intelligent decision-making method based on fuzzy logic to complete cluster head election. For example... Figure 3 As shown, this is the principle of the cluster head election mechanism based on fuzzy logic in the device of the present invention. The mechanism includes an initialization stage, an input stage, a fuzzification stage, a fuzzy inference stage, and a result output stage.

[0068] First, nodes are pre-screened by setting a threshold function. Then, characteristic indicators such as the remaining energy, neighbor node density, and average distance to neighbor nodes of candidate nodes are calculated, and fuzzification is performed using a membership function.

[0069] Next, based on parameter fuzzification, fuzzy inference calculations are performed according to a preset rule base to comprehensively evaluate the cluster head competition capability of the nodes.

[0070] Finally, the cluster head chance value of each node is output, and the nodes with the top 10% chance values ​​are selected as the optimal cluster heads to achieve coordinated optimization of energy balance and communication efficiency.

[0071] After cluster head election and a stable network cluster structure are established through the aforementioned fuzzy logic mechanism, the network enters a phase of efficient data transmission. This process relies on a routing model that combines cluster management and multi-hop transmission mechanisms. Figure 4As shown, the model combines cluster management with a multi-hop transmission mechanism. Cluster member nodes complete data aggregation and uplink forwarding through the cluster head node, thereby achieving efficient data collection and energy balancing. For intra-cluster communication, member nodes send data to their respective cluster head; while in inter-cluster communication, the cluster head node uses a multi-hop forwarding method to transmit data step by step to the aggregation node, reducing energy consumption for long-distance communication and extending network lifetime. Gateway nodes establish relay connections between different clusters for path optimization and load balancing, improving network connectivity and robustness. Especially in industrial wireless sensor network environments, this structure can effectively reduce energy consumption differences between nodes, avoid communication congestion and data loss, and achieve synergistic optimization of energy utilization and data transmission efficiency.

[0072] This invention can efficiently manage node data of varying importance in a network and accurately adapt to complex and ever-changing industrial environments. The device effectively ensures high-reliability transmission of critical data while optimizing energy consumption and transmission efficiency of non-critical data through node communication capability awareness, a dynamic wake-up mechanism driven by data importance, fuzzy logic cluster head election, and a multi-index Bellman-Ford routing optimization strategy, significantly improving the overall network performance.

[0073] It is understood that the above detailed description of the present invention is for illustrative purposes only and is not intended to limit the technical solutions described in the embodiments of the present invention. Those skilled in the art should understand that modifications or equivalent substitutions can still be made to the present invention to achieve the same technical effects. As long as the usage requirements are met, they are all within the protection scope of the present invention.

Claims

1. A method for self-adapting routing in wireless sensor networks based on intelligent decision making, characterized in that, The method comprises the following steps: S1, periodically and event-driven data collection is performed on each monitoring node in the industrial wireless sensor network; S2, based on the node data collected in S1, the residual energy, neighbor density and average distance to neighbor nodes of the node are obtained as input features, the cluster head opportunity value of the node is calculated, and a cluster head set is generated according to the cluster head opportunity value; S3, after the cluster head election is completed, a multi-hop transmission path from the cluster head to the sink node is constructed by a routing discovery mechanism based on multi-objective cost evaluation, taking the minimum residual energy, hop count and link quality of the path as optimization objectives; S4, data forwarding is performed according to the path constructed in S3, in the process, the dynamic switching of the node state is controlled according to the data features and task importance, and the energy consumption of the node is monitored in real time to feed back to the subsequent cluster head election and path optimization, so that global energy balance is achieved.

2. The method of claim 1, wherein, In S2, the calculation formula of the cluster head opportunity value chance is: ； Wherein, FIS() represents a fuzzy inference system function; x1 represents the residual energy factor of the node, x1 is the ratio of the residual energy of the node to the maximum residual energy among its neighbor nodes; x2 represents the neighbor density factor of the node, which is the ratio of the number of neighbor nodes to the ideal number of neighbor nodes; x3 represents the distance factor of the node, which is the normalized value of the average communication distance between the node and its neighbor nodes.

3. The method of claim 1, wherein, In S3, the implementation of the routing discovery mechanism based on multi-objective cost evaluation includes: S3.1, based on the residual energy of the node, the available buffer capacity and the upper and lower layer neighbor connectivity, calculate its comprehensive weight W gateway When W gateway is selected as a candidate gateway node when it exceeds the set threshold value; S3.2, for the path from the source node to the sink node, considering the path minimum residual energy, hop count and average link quality, calculate the path priority score S path ; S3.3, each gateway node retains multiple candidate paths with the highest priority, and data forwarding is realized under the multi-objective constraint.

4. The method of claim 3, wherein, In S3.1, the comprehensive weight W gateway The calculation formula is: ； where e i is the current residual energy of the node, is the initial energy of the node, is the current available buffer capacity of the node and is the maximum data buffer capacity of the node, and respectively represent the number of neighbor nodes of the node at the previous hop in the routing path and the number of neighbor nodes of the node at the next hop in the routing path, is the total number of nodes in the wireless sensor network; , and are the adjustment coefficients, and the coefficients reflect the influence of the residual energy of the node on the network lifetime, represent the effect of the buffer capacity of the node on the data congestion control, represent the influence of the upper and lower layer neighbor connectivity on the routing connectivity and load distribution; and the three satisfy .

5. The method of claim 4, wherein, the adjustment coefficient , , the values of the adjustment coefficients are 0.5, 0.3, 0.2, respectively.

6. The method of claim 3, wherein, In S3.2, the path priority score S path The calculation formula is: ； wherein, , and are adjustment coefficients, reflecting the importance of the minimum residual energy of the path, representing the influence of the hop count of the path on the transmission efficiency, representing the contribution of the average link quality to the communication stability; the three satisfy + + = 1; E min represents the minimum residual energy on the path; E0represents the initial energy of the node; h represents the hop count; Q avg represents the average link quality of the path.

7. The method of claim 1, wherein, In S4, the dynamic switching of the node state is specifically: Double threshold trigger conditions are set according to the data features and task importance, and a light wake-up-confirmation mechanism is used to control the node to switch between deep sleep, light wake-up and active states; the node listens to the channel in the light wake-up state, and only switches to the active state when it is confirmed that there is data to be transmitted or the trigger condition is met, otherwise it returns to the deep sleep state.

8. The method of claim 1, wherein, In S4, when the main transmission path constructed in S3 fails due to link interruption or insufficient node energy, the system automatically enables the backup path to forward data.

9. The method of claim 1, wherein, The steps S2, S3 and S4 constitute a closed-loop optimization process, and the node energy consumption information monitored in S4 is fed back to the cluster head election in S2 and the routing path construction in S3, so as to realize the dynamic self-adaptive adjustment of the network.

10. An apparatus for adaptive routing in a wireless sensor network based on intelligent decision making for implementing the method of any one of claims 1 to 9, characterized by It comprises: a sleep scheduling subunit, which is used to set double threshold trigger conditions based on the data features and task importance collected by the node, and control the node to switch between deep sleep, light wake-up and active states through a light wake-up-confirmation mechanism, so as to dynamically adjust the network activity; a fuzzy decision subunit, which is used to calculate the cluster head opportunity value by taking the residual energy, neighbor density and relative distance to the sink center of the node as input, generate a candidate cluster head set and determine the final cluster head; a multi-objective routing optimization subunit, which is used to introduce a routing discovery mechanism based on multi-objective cost evaluation based on the inter-cluster connectivity, comprehensively consider link connectivity, node energy and buffer utilization, and construct a multi-hop transmission path meeting the requirements of energy balance and reliability; An energy management subunit is configured to adaptively adjust the task allocation and sleep strategy between the cluster members and the cluster head according to the node energy consumption state and the data load condition, so as to realize global energy coordination and long-term stable operation of the network.

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