Industrial wireless sensor network topology control method for data collection

Abstract

The invention provides an industrial wireless sensor network topology control method for data collection, which comprises the following steps of: uniformly dividing a task period into a plurality of continuous time periods, and calculating an initial network topology sequence according to whether each industrial sensor node is active or not in each time period and dynamic position information, constructing a space-time diagram according to the initial network topology sequence; calculating an energy consumption index and a reliability index of each link in the space-time diagram, and fusing the energy consumption index and the reliability index into a comprehensive link weight; and searching an optimal space-time path from each industrial sensor node to a control node by using an improved shortest path algorithm according to the space-time diagram and the comprehensive link weight, and combining link sets of all the shortest space-time paths to form a final network topology of the IWSNs. According to the method, the energy consumption and reliability of data transmission in a dynamic network are balanced while the influence of node mobility on industrial wireless communication is considered.

Description

Technical Field

[0001] This invention relates to the technical field of wireless communication technology, and in particular to a method for topology control of industrial wireless sensor networks. Background Technology

[0002] With the intelligent development of various industrial products in manufacturing and production processes, technologies in Industrial Wireless Sensor Networks (IWSNs) have attracted widespread attention from industry and academia. To achieve intelligent decision-making and control in industrial systems, various industrial sensor nodes need to transmit collected data to control nodes via multi-hop transmission. To further reduce the energy consumption of sensor nodes, they enter a sleep state during inactive periods, i.e., choosing whether to be active within each time period of the task cycle. The activity status or dynamic positional changes of sensor nodes cause dynamic changes in the network topology, rendering traditional network topology control methods ineffective. Furthermore, the reliability of data transmission is directly related to the quality of industrial tasks and even the security of industrial control systems.

[0003] For IWSNs, PR Desai et al. proposed a programmable network architecture and designed various low-latency data collection routing algorithms in their paper "Edge-based optimal routing in SDN-enabled industrial internet of things". Wang Heng et al. studied scheduling methods to improve the real-time performance of data delivery in their paper "Hybrid Data Scheduling Method for Industrial Wireless Sensor Networks Based on Information Age". Currently, there are few research results on topology control methods for IWSNs. Z. Ding et al. proposed a clustering-based and programmable topology control method in their paper "Energy-efficient topology control mechanism for IoT-oriented software-defined WSNs", which simplifies network management and adapts to some dynamic scenarios. However, the inventors of this application have found that none of the above existing methods simultaneously consider node mobility and balance the energy consumption and reliability of data transmission in dynamic networks, thus failing to truly meet industrial needs. Therefore, it is necessary to further propose a topology control method for IWSNs. Summary of the Invention

[0004] To address the technical problem that existing methods do not simultaneously consider node mobility and balance energy consumption and reliability of data transmission in dynamic networks, this invention proposes an industrial wireless sensor network topology control method for data collection. This method considers the impact of node mobility on industrial wireless communication while balancing energy consumption and reliability of data transmission in dynamic networks.

[0005] To achieve the above objectives, the technical solution of the present invention is implemented as follows:

[0006] A topology control method for industrial wireless sensor networks for data collection, wherein the control node constructs a space-time path based on a time-segmented dynamic topology, and each industrial sensor node can transmit collected data to the control node through the space-time path within a task cycle, the method comprising the following steps:

[0007] S1: Divide a task cycle evenly into multiple consecutive time periods, calculate the initial network topology sequence based on the activity and dynamic location information of each industrial sensor node in each time period, and construct a space-time graph based on the initial network topology sequence;

[0008] S2: Calculate the energy consumption index and reliability index of each link in the space-time graph, and merge the energy consumption index and reliability index into a comprehensive link weight;

[0009] S3: Based on the space-time map and the comprehensive link weights, the improved shortest path algorithm is used to find the optimal space-time path from each industrial sensor node to the control node, and the link set of all shortest space-time paths is merged to form the final network topology of IWSNs.

[0010] Furthermore, the network topology sequence is calculated based on the activity and dynamic location information of each industrial sensor node in each time period, including:

[0011] Obtain each sensor node Acquire the activity status of each sensor node at each time period. Location coordinates at each time period And the sensor node transmission range R;

[0012] For each time period, iterate through all sensor node pairs. Determine whether the sensor node is satisfied. and sensor nodes Simultaneous activity and sensor nodes and sensor nodes If the distance is less than the transmission range R, a spatial link is obtained that satisfies the condition that both nodes are active simultaneously and the distance is less than the transmission range. ;

[0013] Integrating spatial links across all time periods This yields an initial network topology sequence that simultaneously considers node activity status and transmission range.

[0014] Furthermore, constructing a space-time graph based on the initial network topology sequence includes: modeling the initial network topology sequence as a unified space-time graph. , among which, nodes For each industrial sensor node's replica node at different time periods, the spatiotemporal link This includes time links and spatial links.

[0015] Furthermore, the method for constructing the space-time graph is as follows:

[0016] Generate replica nodes: for each industrial sensor node copy A portion, recorded as ,correspond Each time period One time endpoint;

[0017] Build time links: for replica nodes at every two adjacent time endpoints and Add a time link between them, denoted as The timeline set is constructed as follows:

[0018] ;

[0019] Constructing spatial links: Traversing each of the initial network topology sequences Spatial links, if the initial network topology sequence includes time periods There is a link Then there are two replica nodes. Add spatial links between them to construct a set of spatial links as follows:

[0020] ;

[0021] Integrating replica nodes, temporal links, and spatial links yields a spacetime graph. Spatiotemporal links .

[0022] Furthermore, the energy consumption metrics for each link in the space-time graph are calculated, including:

[0023] Calculate the space-time graph Each spatiotemporal link Time-link energy consumption metrics and space link energy consumption indicators ;

[0024] Calculate the space-time graph Each spatiotemporal link Time link reliability metrics and space link reliability indicators .

[0025] Furthermore, the energy consumption index of the space link Energy consumption for transmitting data between transceiver nodes:

[0026]

[0027] in, The number of bits representing data information , These are the circuit power consumptions for data transmission and data reception, respectively. Indicates the distance between the sending and receiving nodes. This represents the output power consumption of the antenna per unit distance. This refers to the path loss factor.

[0028] The time link energy consumption index Energy consumption is required to maintain information.

[0029] Furthermore, the space link reliability index The time link reliability index represents the probability of successful transmission between the transmitting and receiving nodes within a corresponding time period. The value is 1.

[0030] Furthermore, energy consumption and reliability metrics are integrated into a comprehensive link weight, including: for each spatiotemporal link Upload power consumption indicators and reliability indicators The normalized weighting function is used to synthesize the link weights:

[0031]

[0032] in, The overall link weights include either time-based link weights or spatial link weights. Energy consumption indicators The maximum value, For reliability indicators The minimum value, As a weighting factor, energy consumption index Energy consumption index for time link or space link energy consumption index Reliability indicators Time link reliability metrics or space link reliability indicators .

[0033] Furthermore, an improved shortest path algorithm is used to find the optimal space-time path from each industrial sensor node to the control node, including:

[0034] Furthermore, based on the space-time diagram and integrated link weights In each path optimization, the improved Dijkstra's shortest path algorithm is used to find each industrial sensor node. and control node The shortest path between and the shortest path Add to network topology In the middle, for those already added to the network topology spatiotemporal links , to link the time and space The weights of the selected time link or spatial link are set to minimum values. And add it to the set of integrated link weights used in the next path optimization.

[0035] An industrial wireless sensor network topology control system for data collection includes:

[0036] The task cycle division and spatiotemporal map construction module is used to evenly divide a task cycle into multiple consecutive time periods, calculate the initial network topology sequence based on the activity and dynamic location information of each industrial sensor node in each time period, and construct a spatiotemporal map based on the initial network topology sequence.

[0037] The integrated link weight calculation module is used to calculate the energy consumption index and reliability index of each link in the space-time diagram, and integrate the energy consumption index and reliability index into an integrated link weight.

[0038] The path optimization and network topology generation module uses an improved shortest path algorithm to find the optimal space-time path from each industrial sensor node to the control node based on the space-time map and the comprehensive link weights. The link set of all shortest space-time paths is merged to form the final network topology of the IWSNs.

[0039] The beneficial effects of this invention are as follows:

[0040] By discretizing the task cycle into multiple time periods, the activity status and location information of sensor nodes in each time period are captured in real time to generate a static topology sequence. These sequences are then modeled as a spatiotemporal graph containing replica nodes, temporal links, and spatial links, which uniformly describes the connection relationship of nodes in the spatiotemporal dimension. This effectively solves the problem of topology disconnection caused by node movement and dynamic sleep, achieves accurate adaptation to dynamic scenarios, and ensures that all links are actually usable communication links.

[0041] By employing an innovative weight function design and combining it with an improved Dijkstra's algorithm to select the optimal path based on the weight function, a balance between low energy consumption and high reliability is achieved. This completely solves the problem of single-objective bias in existing technologies, avoiding both data loss for power saving and battery drain for reliability, thus adapting to the personalized needs of different industrial scenarios.

[0042] This application achieves intelligent optimization and efficient networking through an improved Dijkstra algorithm. The algorithm assigns minimal weights to links already added to the topology for priority reuse, reducing redundant energy consumption; it iteratively searches for the minimum weight path using the initial sensor replica as the source and the final control node replica as the destination, until all nodes are covered, thus improving the algorithm's efficiency.

[0043] Overall, this ultimately extends sensor battery life and improves data transmission success rate, providing low-energy, highly reliable IWSNs networking support for scenarios such as factory monitoring and production line data acquisition. Attached Figure Description

[0044] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is an example of the network topology for three consecutive time periods in the Industrial Wireless Sensor Networks (IWSNs) of the present invention;

[0046] Figure 2 for Figure 1 The corresponding space-time diagram model;

[0047] Figure 3 This is an example of the initial network topology sequence of the present invention;

[0048] Figure 4 The network topology ultimately generated by the method proposed in this invention;

[0049] Figure 5 This is a graph showing the trend of network topology energy consumption and reliability as a function of weights, generated by the present invention.

[0050] Figure 6 This is a flowchart of the method of the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1

[0053] A method for topology control of industrial wireless sensor networks for data collection, such as Figure 6 As shown, the control node constructs a space-time path based on a time-segmented dynamic topology. Each industrial sensor node can transmit the acquired data to the control node through the space-time path within a task cycle, including the following steps:

[0054] S1: Divide a task cycle evenly into multiple consecutive time periods, calculate the initial network topology sequence based on the activity and dynamic location information of each industrial sensor node in each time period, and construct a space-time map based on the initial network topology sequence.

[0055] In this embodiment of the application, the specific components include:

[0056] The S11 control node will schedule the task cycle. Evenly divided into A continuous time period, denoted as The length of each time period is .

[0057] The S12 control node calculates the network topology sequence based on the activity and dynamic location information of each industrial sensor node in different time periods, specifically including:

[0058] Obtain each sensor node At different times Activity status , Indicates sleep. Indicates activity, acquires each sensor node At different times Position coordinates And the sensor node transmission range R;

[0059] For each time period Traverse all sensor node pairs Determine whether the sensor node is satisfied. and sensor nodes Simultaneous activity and sensor nodes and sensor nodes The distance is less than the transmission range R, that is... , For sensor nodes and sensor nodes The Euclidean distance is used to obtain a spatial link that satisfies the condition that both nodes are active simultaneously and the distance is less than the transmission range. ;

[0060] Integrate all time periods Space Links Obtain the initial network topology sequence that simultaneously considers node activity states and transmission range. ,like Figure 3 As shown, where, This represents a set of control nodes and industrial sensor nodes. Indicates time period The static topology.

[0061] S13 constructs a space-time graph based on the initial network topology sequence, including:

[0062] The network topology sequence Modeled as a unified space-time graph , among which, nodes For each industrial sensor node's replica node at different time periods, the spatiotemporal link It includes two types: time links and spatial links, which are used to capture the connection relationships of nodes in time and space.

[0063] Spacetime diagram The construction method is as follows:

[0064] Generate replica nodes: for each industrial sensor node copy A portion, recorded as ,correspond Each time period One time endpoint;

[0065] Build time links: for replica nodes at every two adjacent time endpoints and Add a time link between them, denoted as The timeline set is constructed as follows:

[0066] ;

[0067] Constructing spatial links: Traversing the initial network topology sequence Each of them Space Links If the initial network topology sequence contains time periods There is a link Then there are two replica nodes. Add spatial links between them to construct a set of spatial links as follows:

[0068] Integrating replica nodes, temporal links, and spatial links yields a spacetime graph. Spatiotemporal links .

[0069] This application discretizes the task cycle into multiple time periods, captures the activity status and location information of sensor nodes in real time within each time period, and then models the dynamic topology sequence as a spatiotemporal graph containing replica nodes, temporal links, and spatial links, achieving a precise characterization of the spatiotemporal dynamic connection relationship of nodes. It completely breaks through the limitations of traditional static graph spatiotemporal separation, ensuring that each link in the spatiotemporal graph corresponds to a node that is simultaneously active and within the transmission range, effectively solving the problems of false link availability, data transmission interruption, and topology incompatibility with mobile devices such as AGVs and robotic arms caused by fixed default nodes and unchanged activity states in existing technologies. This lays a dynamic adaptation foundation for subsequent reliable optimization.

[0070] S2: Calculate the energy consumption and reliability indicators of each link in the space-time graph, and merge them into a comprehensive link weight to reflect the energy efficiency and reliability requirements of data transmission.

[0071] In this embodiment of the application, the specific components include:

[0072] S21. Calculate the spacetime diagram Each spatiotemporal link Energy consumption indicators Including time-link energy consumption metrics and space link energy consumption indicators Each space link The energy consumption index is the energy consumed by the transmitting and receiving nodes when using this link to transmit data information, that is:

[0073]

[0074] in, The number of bits representing data information , These are the circuit power consumptions for data transmission and data reception, respectively. Indicates the distance between the sending and receiving nodes. This represents the output power consumption of the antenna per unit distance. This is the path loss factor; the energy consumption of each time link is a fixed value. Energy consumption is required to maintain information.

[0075] S22, Calculate the spacetime diagram Each spatiotemporal link Reliability indicators Including time link reliability metrics and space link reliability indicators Reliability metrics for each space link The reliability index of a time link is defined as the probability that the transmitting and receiving nodes will successfully transmit data using this link during a given time period. It is related to data encoding, modulation, and channel conditions, and can be obtained statistically through multiple transmission experiments. Since the time link represents the continuation of the node's own state without external interference, the transmission success rate is 100%. Therefore, the reliability index of the time link is defined as follows: .

[0076] S23, Spacetime Diagram Each spatiotemporal link Comprehensive link weight Defined as data information in each spatiotemporal link Upload power consumption indicators and reliability indicators The normalized weighted function is expressed as:

[0077]

[0078] in, The overall link weights include either time-based link weights or spatial link weights. Energy consumption indicators The maximum value of the sum. For reliability indicators The minimum value, It is a weighting factor used to balance energy consumption and reliability.

[0079] This application achieves a flexible trade-off between low energy consumption and high reliability by quantifying link energy consumption and reliability, then normalizing them and introducing weighting factors to fuse them into a comprehensive weight. This eliminates the magnitude difference between the two indicators and allows for adaptation to different industrial needs by adjusting the weighting factors (e.g., increasing μ for temperature and humidity monitoring to emphasize energy consumption, and decreasing μ for fault warning to emphasize reliability). It perfectly solves the pain point of existing technologies' single-objective bias, avoiding the dilemma of forcing nodes to sleep frequently to save power, leading to data loss, or keeping nodes continuously active to ensure reliability, resulting in rapid battery depletion. This ensures data transmission success rate while extending sensor battery life.

[0080] S3: Based on the space-time map and the comprehensive link weights, the improved shortest path algorithm is used to find the optimal space-time path from each industrial sensor node to the control node, and the link set of all shortest space-time paths is merged to form the final network topology of IWSNs.

[0081] In this embodiment of the application, the specific components include:

[0082] According to the space-time diagram and integrated link weights The improved Dijkstra's shortest path algorithm is used to sequentially find each industrial sensor node. and control node The shortest path between This path is then added to the network topology, and the set of all shortest paths constitutes the network topology of IWSNs. .

[0083] The set of links in all shortest paths constitutes the network topology of IWSNs. Initially, Starting with the first industrial sensor node, search... to minimum weight path And add the links contained in that path to In, that is The minimum weight path is searched sequentially and added to... In this process, the final topology is formed.

[0084] The improved Dijkstra's shortest path algorithm dynamically adjusts link weights during path finding, especially for links already added to the topology. spatiotemporal links Its weight is set to a sufficiently small value. The source node is each industrial sensor node. replica node at time endpoint 0 The destination node is the control node. In the replica nodes at each time endpoint The design goals of minimizing energy consumption and maximizing reliability are achieved by finding the space-time path with the minimum weight.

[0085] The specific calculation process of the algorithm is as follows:

[0086] S31. Initialize network topology The selected link set Empty; Initialize the selected link tag set. The selected link tag set is used to record the links that have been added to the topology, and the set of sensor nodes to be processed is initialized. The set of sensor nodes to be processed is used to record nodes where no path was found, and to obtain the initial integrated link weight table. , , The initial composite link weights include the weights of time links or the weights of spatial links.

[0087] S32, in the The next step is path optimization, starting from the set of sensor nodes. Select any current target sensor node The source node is each industrial sensor node. replica node at time endpoint 0 The destination node is the control node. In the replica nodes at each time endpoint .

[0088] S33, Based on Space-Time Diagram and integrated link weights The current target sensor node is obtained through conventional Dijkstra optimization. Shortest spacetime path to the control node .

[0089] S34, Spacetime Path All spatiotemporal links are added to the selected link set. and the set of selected link tags This allows us to obtain the updated network topology. and the updated set of selected link tags .

[0090] S35. Review the integrated link weight table. Update the data for all spatiotemporal links, if... Then let Otherwise The updated comprehensive link weight table is obtained. and from the set of sensor nodes to be processed Remove node The set of sensor nodes to be processed is obtained. ,like If the algorithm terminates, it returns to step S32 to process the next node.

[0091] This application achieves multiple benefits, including reduced redundancy energy consumption, improved optimization efficiency, and full node coverage, by prioritizing the reuse of selected links by setting a minimum value δ, iteratively optimizing using the initial sensor copy as the source and the final control node copy as the destination, and terminating the iteration upon access to the destination node. It significantly reduces the additional energy consumption caused by redundant link construction; the algorithm's time complexity is reduced, making it suitable for long-duration scenarios.

[0092] Example 2

[0093] An industrial wireless sensor network topology control system for data collection includes:

[0094] The task cycle division and spatiotemporal map construction module is used to evenly divide a task cycle into multiple consecutive time periods, calculate the initial network topology sequence based on the activity and dynamic location information of each industrial sensor node in each time period, and construct a spatiotemporal map based on the initial network topology sequence.

[0095] The integrated link weight calculation module is used to calculate the energy consumption index and reliability index of each link in the space-time diagram, and integrate the energy consumption index and reliability index into an integrated link weight.

[0096] The path optimization and network topology generation module uses an improved shortest path algorithm to find the optimal space-time path from each industrial sensor node to the control node based on the space-time map and the comprehensive link weights. The link set of all shortest space-time paths is merged to form the final network topology of the IWSNs.

[0097] The effects of this invention can be further illustrated by the following simulations:

[0098] 1. Simulation conditions:

[0099] In the simulation, one control node (marked as number 0) and several industrial sensor nodes are randomly and uniformly distributed in... m 2 Within a planar region, such as Figure 1 The image shows an example of the network topology of IWSNs for three consecutive time periods. Sensor nodes marked "Zzz…" indicate that they are in a sleep state, and node 0 represents a control node. Figure 2 for Figure 1 The corresponding space-time graph model is replicated four times for each industrial sensor node. The period for each industrial sensor node root is determined before the task begins. The simulation considers whether each time period is active, assuming an active period accounts for 60%. Control nodes are assumed to be stationary, while other sensor nodes randomly move within a 30-meter radius circular area centered on their current location between adjacent time periods. The maximum transmission range of each industrial sensor node is set to 180 m. The reliability of each spatial link is randomly generated from the interval [0.7, 1]. Unless otherwise specified, other simulation parameters are shown in Table 1. Data information is transmitted along the path... Energy consumption during transmission For path The sum of energy consumption of each link, reliability For path The product of the reliability of each link, i.e. , .

[0100] 2. Simulation Content and Results

[0101] First, we examine the network topology generated by the proposed method. Figure 3 , Figure 4 The initial topology and the network topology generated based on the topology control method of this invention are presented respectively. Figure 3It can be seen that the initial topology lacked topology control, and the network contained a large number of redundant links, resulting in a huge energy consumption for network maintenance. Figure 4 In this invention, the network topology produced contains fewer links and can ensure that there is a space-time path between each industrial sensor and the control node (first row node).

[0102] Secondly, the energy consumption and reliability of the network topology generated by the proposed method are examined. The network topology reliability is defined as the average reliability of the spatiotemporal path from each sensor node to the control node. This is achieved by adjusting the weighting factor. As the value increased from 0.1 to 0.9, the algorithm gradually shifted its focus towards low energy consumption. Figure 5 It can be seen that as the weight increases, the energy consumption of the network topology decreases continuously, but the reliability of the topology also gradually decreases.

[0103] Table 1 Simulation parameter values

[0104]

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An industrial wireless sensor network topology control method for data collection, characterized by, In the method, the control node builds the space-time path based on the time-division dynamic topology, each industrial sensor node can transmit the collected data to the control node through the space-time path in a task cycle, and the steps comprise: S1: dividing a task cycle into multiple continuous time periods, calculating an initial network topology sequence according to whether each industrial sensor node is active and dynamic position information in each time period, and constructing a space-time graph according to the initial network topology sequence; S2: calculating an energy consumption index and a reliability index of each link in the space-time graph, and fusing the energy consumption index and the reliability index into a comprehensive link weight value; S3: finding an optimal space-time path for each industrial sensor node to the control node according to the space-time graph and the comprehensive link weight value by using an improved shortest path algorithm, and merging a link set of all shortest space-time paths to form a final network topology of the IWSNs.

2. The industrial wireless sensor network topology control method for data collection according to claim 1, wherein, The method for calculating the network topology sequence according to whether each industrial sensor node is active and dynamic position information in each time period comprises: acquiring each sensor node acquiring each sensor node acquiring each sensor node and a sensor node transmission range R; For each time period, traverse all sensor node pairs , determine whether sensor node and sensor node are active at the same time and the distance between sensor node and sensor node is less than the transmission range R, to obtain a spatial link satisfying the two conditions of simultaneous activity and distance less than the transmission range ; Integrating all time period spatial links An initial network topology sequence is obtained which takes into account both node activity status and transmission range.

3. The industrial wireless sensor network topology control method for data collection of claim 2, wherein, According to the initial network topology sequence, a space-time graph is constructed, including: modeling the initial network topology sequence as a unified space-time graph wherein the nodes corresponding to each industrial sensor node in different time periods, a space-time link including a time link and a space link.

4. The industrial wireless sensor network topology control method for data collection of any one of claims 1-3, wherein, The method for constructing the space-time graph comprises: Generate replica nodes: for each industrial sensor node copy parts, denoted corresponding time period time endpoints; Build time link: add a time link between each pair of replica nodes on two adjacent time endpoints and denoted by , the set of time links is built as: ； Constructing spatial links: traverse each of the initial network topology sequence and construct a spatial link if there exists a link in the initial network topology sequence between the two replica nodes and add the spatial link between the two replica nodes, the set of spatial links is constructed as: ； Integrating the replica nodes, the temporal links and the spatial links results in a space-time graph wherein the space-time links .

5. The industrial wireless sensor network topology control method for data collection of claim 4, wherein, The method for calculating the energy consumption index of each link in the space-time graph comprises: Computing space-time diagrams Energy consumption index for time links in each space-time link Energy consumption index for time links in each space-time link Energy consumption index for space links in each space-time link ; Computing space-time diagrams Time link reliability index in each space-time link Time link reliability index in each space-time link Space link reliability index in each space-time link .

6. The industrial wireless sensor network topology control method for data collection of claim 5, wherein, The space link energy consumption indicator Energy consumption for transmitting data information for the transceiving node: wherein, denotes the number of bits of data information, , denotes the circuit energy consumption of data transmission and data reception, respectively, denotes the distance between the transceiving nodes, denotes the output energy consumption of the antenna per unit distance, is a path loss factor; The time link energy consumption indicator Energy consumption for information maintenance.

7. The industrial wireless sensor network topology control method for data collection of claim 5 or 6, wherein, The space link reliability indicator The time link reliability indicator is a probability of a corresponding time period transmission success of the node is 1.

8. The industrial wireless sensor network topology control method for data collection of claim 7, wherein, The energy consumption index and the reliability index are fused into a comprehensive link weight value, including: taking the energy consumption index of each space-time link uploaded and the reliability index as a normalized weighted function of the comprehensive link weight value: wherein is a value of the comprehensive link weight including a value of the time link or a value of the space link, is an energy consumption index is a maximum value of the energy consumption index, is a reliability index is a minimum value of the reliability index, is a weight factor, the energy consumption index is a time link energy consumption index or a space link energy consumption index , the reliability index is a time link reliability index or a space link reliability index .

9. The industrial wireless sensor network topology control method for data collection of claim 8, wherein, The method for finding the optimal space-time path for each industrial sensor node to the control node according to the space-time graph and the comprehensive link weight value by using the improved shortest path algorithm comprises: According to the space-time diagram and integrated link weights In each path optimization, the improved Dijkstra's shortest path algorithm is used to find each industrial sensor node. and control node The shortest path between and the shortest path Add to network topology In the middle, for those already added to the network topology spatiotemporal links , to link the spatiotemporal links The weights of the selected time link or spatial link are set to minimum values. And add it to the set of integrated link weights used in the next path optimization.

10. A collected industrial wireless sensor network topology control system employing the industrial wireless sensor network topology control method for data collection according to any one of claims 1 to 9, characterized by, The method comprises: The task cycle division and space-time graph construction module is configured to divide a task cycle into multiple continuous time periods, calculate an initial network topology sequence according to whether each industrial sensor node is active and dynamic position information in each time period, and construct a space-time graph according to the initial network topology sequence; The comprehensive link weight value calculation module is configured to calculate an energy consumption index and a reliability index of each link in the space-time graph, and fuse the energy consumption index and the reliability index into a comprehensive link weight value; The path optimization and network topology generation module is configured to find an optimal space-time path for each industrial sensor node to the control node according to the space-time graph and the comprehensive link weight value by using an improved shortest path algorithm, and merge a link set of all shortest space-time paths to form a final network topology of the IWSNs.

Images