A low-duty-cycle sensor network data collection method based on mobile robots

Abstract

The application discloses a low-duty-cycle sensor network data collection method based on a mobile robot, which comprises the following steps: S1, network initialization; S2, when each node forwards data, determining to adopt a static sink routing mode or a cluster node routing mode; S3, a sensor network node determines a data fusion waiting time: after the node senses or receives data, waiting for a certain time, fusing multiple data packets that arrive, reducing the number of data forwarding times and reducing energy consumption; S4, according to the data sending mode determined in the step S2, a sensor network node selects a next hop routing node for data forwarding, and forwards data; and S5, when the mobile robot passes through the cluster node, the cluster node sends the buffered data to the mobile robot. Through dynamic selection of a data sending mode, a data fusion technology and a cluster node sleep scheduling mechanism, the application realizes the guarantee of a mobile data collection delay performance and the improvement of data collection energy efficiency of the robot under the working scene of uncontrolled movement and low-duty-cycle nodes.

Description

Technical Field

[0001] This invention relates to the field of sensor network data collection technology, and in particular to a low duty cycle sensor network data collection method based on a mobile robot. Background Technology

[0002] Wireless Sensor Networks (WSNs) are widely used in environmental monitoring across industrial production, environmental protection, and emergency rescue scenarios. Network nodes sense environmental data and transmit it to a control center for backend monitoring and decision-making. In traditional WSNs, data collected by nodes is typically transmitted to a static aggregation node (or base station) via multi-hop relays. However, this multi-hop communication method leads to significant energy consumption and severe imbalances in network load. Due to the need for long-term monitoring and their extremely limited energy supply, energy is a precious resource for sensor nodes, directly impacting the network's lifetime.

[0003] With the application of mobile robots in various scenarios, they can be used as mobile aggregation nodes for sensor network data collection, thereby reducing the number of hops in data forwarding and extending the network lifespan. Sensor network nodes transmit data to the nearest aggregation node (i.e., a node near the mobile robot's path). When the mobile robot arrives, the aggregation node completes the delivery of the buffered data.

[0004] However, existing methods for collecting sensor network data based on mobile robots have the following shortcomings:

[0005] (1) Most assumptions assume that the path of a mobile robot is controlled, meaning that the requirements for data collection latency and energy efficiency can be met by designing the robot's trajectory and speed. However, because mobile robots need to perform a given task, their paths are usually uncontrolled. Under these conditions, the data collection latency based on mobile robots is uncontrollable.

[0006] (2) Most of them are for non-sleep sensor networks. When the network nodes are in a low duty cycle working mode (i.e., they are in a sleep state most of the time and are only woken up when working, and the proportion of working time slots is called duty cycle), the data collection latency is even more difficult to guarantee. Summary of the Invention

[0007] To address the aforementioned problems in the prior art, the present invention aims to provide a low duty cycle sensor network data collection method based on multiple robots. This method is primarily designed for scenarios where robot movement is uncontrolled and sensor network nodes operate in a low duty cycle mode. It integrates a dynamic routing protocol, a data fusion algorithm, and a sleep scheduling mechanism to reduce energy consumption while ensuring data collection latency.

[0008] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows: A low duty cycle sensor network data collection method based on a mobile robot, comprising the following steps:

[0009] S1: Network Initialization. The network consists of one static aggregation node S, n sensor network nodes, and m mobile robots. The mobile robots travel on fixed tracks (paths), and their positions are difficult to predict. The communication radius of each node is r. The sensor network nodes operate in a low duty cycle mode, periodically sensing environmental data and needing to transmit the sensed data to the aggregation node within a certain time (delay constraint value is Δ).

[0010] S2: Sensor network nodes determine data transmission modes. When each node transmits data, it determines either a static sink routing mode or a rally node routing mode to minimize energy consumption while ensuring latency performance. The static sink routing mode sends data directly to the static sink node; the rally node routing mode sends data to the rally node first and then waits for the mobile robot to collect it. Specifically: when data needs to be transmitted, the data transmission latency from the node to the rally node is calculated; the probability that the mobile robot passes through the rally node R at least once is calculated; the probability p that satisfies the latency constraint when selecting the rally node routing mode is calculated; the expected energy consumption when selecting the rally node routing mode is calculated; if p > threshold p th If the expected energy consumption of the aggregation node routing mode is less than that of the static aggregation node routing mode, then the aggregation node routing mode is selected; otherwise, the static aggregation node routing mode is selected.

[0011] S3: Sensor network nodes determine data fusion waiting time. To further improve the energy efficiency of data collection, after a node senses or receives data, it can wait for a certain period of time to fuse multiple arriving data packets, thereby reducing the number of data forwardings and lowering energy consumption.

[0012] S4: Based on the data transmission mode determined in step S2, the sensor network node selects the next-hop routing node for data forwarding and performs data forwarding.

[0013] S5: The rallying node delivers cached data to the mobile robot. When the mobile robot passes by the rallying node, the rallying node sends the cached data to it. Additionally, to ensure that the rallying node can detect the mobile robot in a timely manner and has sufficient time to deliver data, the duty cycle of the corresponding rallying node needs to be adjusted.

[0014] The specific process of step S1 is as follows:

[0015] S11: Determine the assembly node. Nodes whose distance from the track is less than the communication radius r are designated as assembly nodes.

[0016] S12: Construct a track route that connects the aggregation nodes in sequence.

[0017] S13: Construct routes to the static sink node (static sink). The static sink establishes a shortest path tree based on the Expected Transmission Count (ETX) via broadcast, so that all nodes in the network know the shortest route to the static sink.

[0018] S14: Network Graph Flattening. Constructing a network graph using a distributed approach.

[0019] The specific process of step S2 is as follows:

[0020] S21: For node i, calculate its data transmission delay to cluster node R as follows:

[0021]

[0022] Where, dist(i,L) R Let L be the path from node i to the robot. R The shortest distance. avg and ETX avg These are the average single-hop forward distance and the expected single-hop transmission count (ETX), respectively, both of which can be estimated based on historical values. σ represents the estimated number of remaining hops. avg This represents the average sleep latency of nodes in a low duty cycle sensor network, where T is the length of the duty cycle.

[0023] S22: Under the condition that the movements of each robot are independent, using the Poisson distribution, calculate the probability that the mobile robot passes through the aggregation node R at least once within time t:

[0024] p(X(t)≥1)=1-e -λt

[0025] Where λ is the average number of times the mobile robot passes through R per unit time. The aggregation node can estimate λ based on historical data.

[0026] S23: For node i, calculate the probability that it satisfies the delay constraint when sending data in the selected cluster node routing mode:

[0027]

[0028] Where Δr is the remaining maximum transmission time, Δ r =Δ-t elp , t elpThe time spent transmitting the data packet is denoted by Δ, which represents the delay constraint value. Additionally, a safety factor ω1 (ω1>1) is introduced to further increase the probability of meeting the delay constraint.

[0029] S24: When p exceeds a given threshold p th Node i will choose to send data to the aggregation node if the following conditions are met:

[0030] p·dist(i,L R )+(1-p)·(dist(i,L R )+dist(R,S))<dist(i,S)

[0031] Otherwise, the static sink routing mode will be selected to ensure data collection latency performance. That is, the above formula requires that the expected energy consumption when selecting the aggregation node routing mode be less than the expected energy consumption when selecting the static sink node routing mode.

[0032] The specific process of step S3 is as follows:

[0033] S31: For node i, calculate its maximum allowable fusion waiting time. That is, based on the probability criterion, the probability of timely data submission is guaranteed to be no less than p. th Waiting time:

[0034]

[0035] S32: Evaluate the data fusion utility of node i. Assume the arrival frequency of data packets at node i is η. i This refers to the number of data packets arriving per unit time (including data perceived by node i and data that needs to be forwarded). The expected hop count from i to the aggregation node is h. i The energy reduction resulting from node i waiting per unit time, i.e., the data fusion utility value, is η. i h i .

[0036] S33: Allocate fusion waiting time. To maximize the energy reduction brought about by data fusion across the entire transmission link, the data fusion waiting time for node i is allocated as follows:

[0037]

[0038] Wherein, (ηh) max This represents the maximum data fusion utility value along the entire link from node i to the aggregation node.

[0039] The specific process of step S4 is as follows:

[0040] S41: If the node selects the static sink routing mode, then according to the shortest path tree constructed in step S13, the next-hop routing node to the static sink node is selected and the data is forwarded.

[0041] S42: If a node selects the cluster node routing mode, then geographical routing is used, selecting the neighbor node with the highest energy efficiency to the cluster node as the next-hop routing node. Assuming neighbor node j is selected as the next-hop forwarding node, the expected number of remaining transmissions is... for:

[0042]

[0043] Among them, ETX ij Let i be the expected number of transmissions between nodes i and j, and j be the expected number of remaining transmissions. The smaller the value, the higher the energy efficiency; Node i selects a neighbor node that meets the following conditions as the next-hop routing node and forwards the data:

[0044]

[0045] Where N(i) is the set of neighbor nodes of i, dist(i,L) k ) represents the robot's movement path L from neighbor node j. k The shortest distance. And L is the set of all mobile robot tracks (paths).

[0046] The specific process of step S5 is as follows:

[0047] S51: After detecting the mobile robot, the assembly node sends a notification message about the mobile robot's arrival to the next-hop node in the track route. This message includes a timestamp, the mobile robot's current position, and its speed.

[0048] S52: After receiving the notification message, the next-hop node calculates the arrival time of the robot based on the robot's position, speed, and its own position, and adjusts the duty cycle so that the node is in a wake-up state when the mobile robot arrives within its communication range.

[0049] S53: When the channel is detected to be idle, the assembly node transmits the buffered data to the mobile robot.

[0050] S54: After the cached data transmission is complete, the node switches back to the normal low duty cycle working mode.

[0051] This invention discloses a low duty cycle sensor network data collection method based on multiple robots, the advantages of which are:

[0052] A probabilistic latency-guaranteed approach dynamically selects the data transmission mode, thereby improving energy efficiency while ensuring mobile data collection latency performance in scenarios with uncontrolled robot movement and low node duty cycles. Data fusion technology further reduces data forwarding energy consumption while maintaining latency. A node sleep scheduling mechanism ensures timely discovery of the mobile robot by nodes and timely delivery of cached data in low duty cycle operating modes.

[0053] To make the above and other objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0054] 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.

[0055] Figure 1 The diagram shown is a schematic representation of the network system composition according to an embodiment of the present invention.

[0056] Figure 2 The diagram shown is an overall flowchart of the method according to an embodiment of the present invention.

[0057] Figure 3 The diagram shown is a flowchart of a sensor network node determining its data transmission mode.

[0058] Figure 4 The diagram shows a flowchart of the assembly node submitting cached data to the mobile robot. Specific Implementation

[0059] 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.

[0060] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0061] First, let's explain the technical terms used in the text:

[0062] Static aggregation node: i.e., wireless sensor network base station.

[0063] Mobile aggregation node: Using mobile robots as mobile aggregation nodes in the sensor network;

[0064] Assembly node: A sensor network node near the mobile robot's travel path, that is, a node whose distance from the mobile robot's track is less than the communication radius r.

[0065] like Figure 1 , 2 As shown in the figure, one embodiment of the present invention provides a low duty cycle sensor network data collection method based on a mobile robot, comprising the following steps:

[0066] S1: Network initialization. (e.g., ...) Figure 1 As shown, the network includes one static aggregation node S, n sensor network nodes, and m mobile robots. The mobile robots travel on fixed tracks, and their positions are difficult to predict. The communication radius of each node is r. The sensor network nodes operate in a low duty cycle mode, periodically sensing environmental data and needing to transmit the sensed data to the aggregation node within a certain time (with a delay constraint value of Δ). The specific process is as follows:

[0067] S11: Determine the assembly node: Nodes whose distance from the robot's travel track is less than the communication radius r are set as assembly nodes.

[0068] S12: Construct track routes connecting the aggregation nodes in sequence.

[0069] S13: Constructing routes to static sink nodes: Static sink nodes establish shortest path trees based on Expected Transmission Count (ETX) through broadcasting, so that all nodes in the network know the shortest routes to static sink nodes.

[0070] S14: Construct a network plane graph in a distributed manner.

[0071] S2: Sensor network nodes determine data transmission modes. When each node collects environmental data or forwards data, it determines either a static sink routing mode or a rally node routing mode to minimize energy consumption while ensuring latency performance. The static sink routing mode sends data directly to the static sink node; the rally node routing mode sends data to the rally node first and then waits for the mobile robot to collect it. Specifically: when data needs to be sent, the data transmission latency from the node to the rally node is calculated; the probability that the mobile robot passes through the rally node R at least once is calculated; the probability p that satisfies the latency constraint when selecting the rally node routing mode is calculated; the expected energy consumption when selecting the rally node routing mode is calculated; if p > threshold p... th If the expected energy consumption of the aggregation node routing mode is less than that of the static aggregation node routing mode, then the aggregation node routing mode is selected; otherwise, the static aggregation node routing mode is selected. The specific process is as follows: Figure 3 As shown.

[0072] S21: For node i, calculate its data transmission delay to cluster node R as follows:

[0073]

[0074] Where, dist(i,L) R Let L be the path from node i to the robot. R The shortest distance. avg and ETX avg These are the average single-hop travel distance and the expected average single-hop transmission count, respectively, both of which can be estimated based on historical values.

[0075] σ represents the estimated number of remaining hops. avg This represents the average sleep latency of nodes in a low duty cycle sensor network, where T is the length of the duty cycle.

[0076] S22: Under the condition that the movements of each robot are independent, using the Poisson distribution, calculate the probability that the mobile robot passes through the aggregation node R at least once within time t:

[0077] p(X(t)≥1)=1-e -λt

[0078] Where λ is the average number of times the mobile robot passes through R per unit time. The aggregation node can estimate λ based on historical data.

[0079] S23: For node i, calculate the probability that it satisfies the delay constraint when sending data in the selected cluster node routing mode:

[0080]

[0081] Where Δr is the remaining maximum transmission time, Δ r =Δ-t elp , t elp The time spent transmitting the data packet is denoted by Δ, which represents the delay constraint value. Additionally, a safety factor ω1 (ω1>1) is introduced to further increase the probability of meeting the delay constraint.

[0082] S24: When p exceeds a certain threshold p th Node i will choose to send data to the aggregation node if the following conditions are met:

[0083] p·dist(i,L R )+(1-p)·(dist(i,L R )+dist(R,S))<dist(i,S)

[0084] Otherwise, a static sink routing mode will be selected to ensure data collection latency performance. The above formula requires the expected energy consumption p·dist(i,L) when selecting the cluster node routing mode. R )+(1-p)·(dist(i,L R The threshold p is less than the expected energy consumption dist(i,S) when selecting the static aggregation node routing mode. th The value is set according to the probability required to satisfy the time delay constraint; for example, in this embodiment, it can be 0.9.

[0085] S3: Sensor network nodes determine data fusion waiting time. To further improve the energy efficiency of data collection, after a node senses or receives data, it can wait for a certain period of time to fuse multiple arriving data packets, thereby reducing the number of data forwardings and lowering energy consumption.

[0086] S31: For node i, calculate its maximum allowable fusion waiting time. That is, based on the probability criterion, the probability of timely data submission is guaranteed to be no less than p. th Waiting time:

[0087]

[0088] Where t fuse This is the fusion waiting time.

[0089] S32: Evaluate the data fusion utility of node i. Assume the arrival frequency of data packets at node i is η. i This refers to the number of data packets arriving per unit time (including data perceived by node i and data that needs to be forwarded). The expected hop count from i to the aggregation node is h. iThe energy reduction resulting from node i waiting per unit time, i.e., the data fusion utility value, is η. i h i .

[0090] S33: Allocate fusion waiting time. To maximize the energy reduction brought about by data fusion across the entire transmission link, the data fusion waiting time for node i is allocated as follows:

[0091]

[0092] Wherein, (ηh) max This represents the maximum data fusion utility value along the entire link from node i to the aggregation node.

[0093] S4: Based on the data transmission mode determined in step S2, the sensor network node selects the next-hop routing node for data forwarding and performs data forwarding.

[0094] S41: If the node selects the static sink routing mode, then according to the shortest path tree constructed in step S13, the next-hop routing node to the static sink node is selected and the data is forwarded.

[0095] S42: If a node selects the cluster node routing mode, then geographical routing is used, selecting the neighbor node with the highest energy efficiency to the cluster node as the next-hop routing node. Assuming neighbor node j is selected as the next-hop forwarding node, the expected number of remaining transmissions is... for:

[0096]

[0097] Among them, ETX ij Let i be the expected number of transmissions between nodes i and j, and j be the expected number of remaining transmissions. The smaller the value, the higher the energy efficiency; Node i selects a neighbor node that meets the following conditions as the next-hop routing node and forwards the data:

[0098]

[0099] Where N(i) is the set of neighbor nodes of i, dist(j,L) k ) represents the robot's movement path L from neighbor node j. k The shortest distance. And L is the set of all mobile robot tracks (paths).

[0100] S5: The rallying node delivers cached data to the mobile robot. When the mobile robot passes by the rallying node, the rallying node sends the cached data to it. Additionally, to ensure that the rallying node can detect the mobile robot in a timely manner and has sufficient data delivery time, the duty cycle of the corresponding rallying node needs to be adjusted. The specific process is as follows: Figure 4 As shown.

[0101] S51: After detecting the mobile robot, the assembly node sends a notification message about the mobile robot's arrival to the next-hop node in the track route. This message includes a timestamp, the mobile robot's current position, and its speed.

[0102] S52: After receiving the notification message, the next-hop node calculates the time t when the robot arrives within its communication range, based on the robot's position, speed, and the node's own position. r Adjust the duty cycle so that the node is at time point t. r It is then in a wake-up state.

[0103] S53: When the channel is detected to be idle, the assembly node transmits the buffered data to the mobile robot.

[0104] S54: After the cached data transmission is complete, the node switches back to the normal low duty cycle working mode.

[0105] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0106] It should also be understood that, in the embodiments of the present invention, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the present invention, the character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0107] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0108] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0109] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, or may be electrical, mechanical, or other forms of connection.

[0110] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention, depending on actual needs.

[0111] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A low duty cycle sensor network data collection method based on mobile robots, comprising the following steps: S1: network initialization: the network contains 1 static aggregation node S , n sensing network nodes, m mobile robots, the communication radius of the nodes is r ; S2: when there is data to be sent, the data transmission delay of the computing node to the aggregation node; Computing a mobile robot to pass at least through a rendezvous node R There is a 1 in chance; Probability of meeting latency constraints when computing a set of rendezvous node routing patterns p ; calculating the expected energy consumption when selecting the cluster node routing mode; If p threshold value p th If the expected energy consumption of the aggregation node routing mode is less than that of the static sink node routing mode, the aggregation node routing mode is selected; otherwise, the static sink node routing mode is selected; the specific process of step S2 is as follows: S21: to the node i , calculate its data transmission delay to the aggregation node R : ； wherein, is the node i to the robot movement path of the shortest distance; and are the average single-hop progress distance and the average single-hop transmission number expectation, respectively; is the estimated remaining hop count; is the average sleep latency of a node in a low duty cycle sensor network, T is the length of the duty cycle; S22: Calculate the time within which the mobile robot has to pass at least the rendezvous node t has a probability of 1 that R has a probability of 1 that ； wherein, λ is the average number of times the mobile robot passes through R in a unit of time; S23: The node i , calculates the probability that the delay constraint is met when it selects the set of aggregation nodes to route the data: ； Wherein, Δ r is the remaining maximum transmission time, Δ r = Δ- t elp , t elp is the time spent by the data packet transmission, Δ is the delay constraint value; in addition, the safety factor ω 1( ω 1>1), to further improve the probability of meeting the delay constraint; S24: When p exceeds a given threshold p th , and the following conditions are met, the node i selects to send data to the rendezvous node: ； Otherwise, the static sink routing mode is selected to guarantee the data collection latency performance; the above equation requires the expected energy consumption when the aggregation node routing mode is selected is less than the expected energy consumption when the static sink routing mode is selected ; S3: The sensor network node determines the data fusion waiting time: after the node perceives or receives data, it waits for a certain time to fuse multiple data packets arriving to reduce the number of data forwarding times and reduce energy consumption; S4: According to the data sending mode determined in step S2, the sensor network node selects the next hop routing node for data forwarding; S5: The cluster node submits the buffered data to the mobile robot; when the mobile robot passes through the cluster node, the cluster node sends the buffered data to the mobile robot; step S5 further comprises: in order to ensure that the cluster node can timely detect the mobile robot and has sufficient data submission time, the duty cycle of the corresponding cluster node needs to be adjusted; The specific process of step S5 is as follows: S51: After the cluster node detects the mobile robot, it sends a notification message to the next hop node of the track route that the mobile robot has arrived; the message includes a timestamp, the current position of the mobile robot, and the speed; S52: After the next hop node receives the notification message, it calculates the time point of the robot's arrival according to the robot's position, speed, and the node's own position, and adjusts the duty cycle, i.e. makes the node in the wake-up state when the mobile robot arrives within its communication range; S53: When detecting that the channel is in an idle state, the cluster node transmits the buffered data to the mobile robot; S54: After the buffered data transmission is completed, the node switches back to the normal low duty cycle working mode.

2. The method of claim 1, wherein: The specific process of step S1 is as follows: S11: Determine the rendezvous node: the distance between the rendezvous node and the track is less than the communication radius r the node is set as the rendezvous node; S12: Construct a track route that connects the cluster nodes in sequence; S13: Construct a route to the static sink node: the static sink node establishes a shortest path tree based on the expected number of transmissions through broadcast, and all nodes in the network learn the shortest route to the static sink node; S14: Based on a distributed manner, construct a network plan.

3. The method of claim 1, wherein: The specific process of step S3 is as follows: S31: For a node i , compute its maximum allowed fusion latency , i.e. based on the probabilistic criterion, guarantee that the data is delivered on time with a probability not less than maximum latency: ； wherein t fuse is the fusion latency; S32: evaluation node i data fusion utility: assuming the data packet arrival frequency of node i is , that is, the number of data packets arriving per unit time; i the expected value of the hop number to the aggregation node is , then the energy consumption reduction brought by the node i waiting per unit time, that is, the data fusion utility value is ; S33: Assign fusion latency: Assign the data fusion latency to the node i with the data fusion latency assigned as follows: ； wherein, the maximum data fusion utility value on the whole link from the node i to the rendezvous node.

4. The method of claim 1, wherein: The specific process of step S4 is as follows: S41: If the node selects the static sink node routing mode, it selects the next hop routing node to the static sink node according to the shortest path tree and forwards the data; S42: If the node selects the cluster node routing mode, it selects the neighbor node with the highest energy efficiency to the cluster node as the next hop routing node in a geographic routing manner.

5. The method of claim 4, wherein: If the node chooses the rendezvous node routing mode, assume a neighbor node is chosen j as the next hop forwarding node, the expected number of remaining transmissions needed is: ； wherein, is the transmission number expectation between the nodes i and j the remaining transmission number expectation The smaller the value, the higher the energy efficiency; the nodes i select a neighbor node that satisfies the following condition as the next hop routing node and forwards the data: ； wherein is i a set of neighbor nodes, is a neighbor node j to the robot movement path is the shortest distance, L is a set of all moving robot trajectories.

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