Low-duty-ratio sensor network data collection method based on mobile robot

Abstract

The invention discloses a low-duty-ratio sensor network data collection method based on a mobile robot. The method comprises the following steps: S1, initializing a network; s2, when each node forwards data, determining to adopt a static sink routing mode or an aggregation node routing mode; s3, the sensor network node determines data fusion waiting time: after sensing or receiving the data, the node waits for a certain time, a plurality of arrived data messages are fused, the data forwarding frequency is reduced, and the energy consumption is reduced; s4, according to the data sending mode determined in the step S2, the sensor network node selects a next-hop routing node for data forwarding to forward data; and S5, when the mobile robot passes through the aggregation node, the aggregation node sends the cached data to the mobile robot. According to the invention, by dynamically selecting a data sending mode, a data fusion technology and an aggregation node sleep scheduling mechanism, the guarantee of mobile data collection time delay performance and the improvement of data collection energy efficiency of the robot under the working scenes of uncontrolled movement and low duty ratio of nodes are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor network data collection, and particularly to a method for collecting data from a low-duty-cycle sensor network based on a mobile robot. Background Art

[0002] Wireless Sensor Networks (WSNs) are widely used in environmental monitoring in scenarios such as industrial production, ecological protection, and emergency rescue. Network nodes sense environmental data and send it to a control center for convenient background monitoring and decision-making. In traditional wireless sensor networks, the data collected by nodes is generally sent to a static sink node (or called a base station) through multi-hop relaying. However, this multi-hop communication method brings relatively large energy consumption and serious imbalance of network load. Due to the need for long-term monitoring and its extremely limited energy supply, energy is an extremely precious resource for sensor nodes, directly affecting the network lifetime.

[0003] With the application of mobile robots in various scenarios, they can be used as mobile sink nodes for collecting data from sensor networks, thereby reducing the number of hops for data forwarding and extending the network lifetime. Sensor network nodes transmit data to the nearest aggregation node (i.e., the node near the mobile robot's travel path). When the mobile robot arrives, the aggregation node completes the submission of cached data.

[0004] However, the existing methods for collecting data from sensor networks based on mobile robots have the following deficiencies:

[0005] (1) Most assume that the path of the mobile robot is controlled, that is, the trajectory, speed, etc. of the robot can be designed to meet the requirements of data collection such as delay and energy efficiency. However, since the mobile robot needs to perform a given task, its path is usually uncontrolled. Under this condition, the data collection delay based on the mobile robot is uncontrollable.

[0006] (2) Most are aimed at non-sleeping sensor networks. When the network nodes are in a low-duty-cycle working mode (i.e., most of the time they are in a sleeping state and are only awakened when working, where the proportion of the working time slot is called the duty cycle), it is more difficult to guarantee the data collection delay. Summary of the Invention

[0007] Aiming at the above problems of the prior art, the purpose of the present invention is to provide a method for collecting data from a low-duty-cycle sensor network based on multiple robots, mainly aiming at the scenario where the movement of the robot is uncontrolled and the sensor network nodes are in a low-duty-cycle working mode, and designing a data collection method integrating a dynamic routing protocol, a data fusion algorithm, and a sleep scheduling mechanism to reduce energy consumption under the condition of guaranteeing the data collection delay.

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

[0009] S1: Network initialization. The network includes 1 static sink node S, n sensor network nodes, and m mobile robots. The mobile robots travel on a fixed track (path), and their positions are difficult to predict. The communication radius of the nodes is r. The sensor network nodes are in a low-duty-cycle working mode, periodically sense environmental data, and need to transmit the sensed data to the sink node within a certain time (the delay constraint value is Δ).

[0010] S2: The sensor network node determines the data sending mode. When each node sends data, it determines the static sink routing mode or the aggregation node routing mode, so as to minimize energy consumption while ensuring delay performance. The static sink routing mode is to directly send the data to the static sink node; the aggregation node routing mode is to first send it to the aggregation node and then wait for the mobile robot to collect it. Specifically: When there is data to be sent, calculate the data transmission delay from the node to the aggregation node; calculate the probability that the mobile robot passes by the aggregation node R at least once; calculate the probability p of meeting the delay constraint when choosing the aggregation node routing mode; calculate the expected energy consumption when choosing the aggregation node routing mode; if p > threshold p th and the expected energy consumption of the aggregation node routing mode is less than that of the static sink node routing mode, then choose the aggregation node routing mode; otherwise, choose the static sink node routing mode.

[0011] S3: The sensor network node determines the data fusion waiting time. To further improve the energy efficiency of data collection, after the node senses or receives data, it can wait for a certain time to fuse multiple arriving data packets, thereby reducing the number of data forwarding times and reducing energy consumption.

[0012] S4: According to the data sending 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 aggregation node delivers the cached data to the mobile robot. When the mobile robot passes by the aggregation node, the aggregation node sends the cached data to it. In addition, to ensure that the aggregation node can detect the mobile robot in time and has enough time to deliver data, it is necessary to adjust the duty cycle of the corresponding aggregation node.

[0014] Among them, the specific process of the step S1 is as follows:

[0015] S11: Determine the aggregation node. The nodes whose distance from the track is less than the communication radius r are set as aggregation nodes.

[0016] S12: Construct a track routing that sequentially connects the aggregation nodes.

[0017] S13: Construct the route to the static aggregation node (static sink). The static sink establishes the shortest path tree based on the Expected Transmission Count (ETX) through the broadcast method, and all nodes in the network learn the shortest route to the static sink.

[0018] S14: Planarize the network graph. Construct the network plane graph based on the distributed method.

[0019] Among them, the specific process of step S2 is as follows:

[0020] S21: For node i, calculate the data transmission delay from it to the aggregation node R as:

[0021]

[0022] Among them, dist(i, L R ) is the shortest distance from node i to the robot movement path L R . pro avg and ETX avg are the average single-hop forward distance and the expected average single-hop transmission count (Expected Transmission Count, ETX) respectively, and both can be estimated according to historical values. is the estimated remaining number of hops. σ avg is the average sleep delay of nodes in the low-duty-cycle sensor network, and T is the length of the working cycle.

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

[0024] p(X(t)≥1) = 1 - e -λt

[0025] Among them, λ is the average number of times the mobile robot passes through R per unit time. The aggregation node can estimate λ according to historical data.

[0026] S23: For node i, calculate the probability that it satisfies the delay constraint when choosing the aggregation node routing mode to send data as:

[0027]

[0028] Among them, Δr is the remaining maximum transmission time, Δ r = Δ - t elp , t elp is the time that has elapsed for the transmission of this data packet, and Δ is the delay constraint value. In addition, a safety factor ω is introduced.1 (ω 1 > 1), to further increase the probability of meeting the delay constraint.

[0029] S24: When p exceeds the given threshold p th and meets the following conditions, node i selects to send data to the aggregation node:

[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 the data collection delay performance. That is, the above formula requires that the expected energy consumption when selecting the aggregation node routing mode is less than the expected energy consumption when selecting the static aggregation node routing mode.

[0032] Among them, 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, ensure that the probability of data being delivered on time is not less than p th of the waiting time:

[0034]

[0035] S32: Evaluate the data fusion utility of node i. Assume that the arrival frequency of data packets at node i is η i , that is, the number of data packets arriving per unit time (including the data sensed by node i itself and the data to be forwarded). The expected number of hops from i to the aggregation node is h i . Then the energy consumption reduction amount brought by node i waiting for one unit time, that is, the data fusion utility value is η i h i .

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

[0037]

[0038] Among them, (ηh) max is the maximum data fusion utility value on the entire link from node i to the aggregation node.

[0039] Among them, 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, select the next-hop routing node to the static aggregation node and forward the data.

[0041] S42: If the node selects the rendezvous node routing mode, then adopt the geographic routing method, and select the neighbor node with the highest energy efficiency to the rendezvous node as the next-hop routing node. Assume that neighbor node j is selected as the next-hop forwarding node, and the expected remaining number of transmission times is:

[0042]

[0043] where ETX ij is the expected number of transmission times between node i and j, and the smaller the expected remaining number of transmission times value, the higher the energy efficiency; node i selects the neighbor node that satisfies 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, and dist(i, L k ) is the shortest distance from neighbor node j to the robot movement path L k . And L is the set of all mobile robot tracks (paths).

[0046] Among them, the specific process of the said step S5 is as follows:

[0047] S51: After the rendezvous node detects the mobile robot, send a notification message of the arrival of the mobile robot to the next-hop node of the track routing. This message includes the time stamp, the current position and speed of the mobile robot.

[0048] S52: After the next-hop node receives the notification message, calculate the time point when the robot arrives according to the robot position, speed and the node's own position, and adjust the duty cycle, that is, make the node in the wake-up state when the mobile robot arrives within its communication range.

[0049] S53: When detecting that the channel is idle, the rendezvous node transmits the buffered data to the mobile robot.

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

[0051] The advantages of a data collection method for a low-duty-cycle sensor network based on multiple robots according to the present invention are:

[0052] The probability-based delay guarantee method dynamically selects the data sending mode, so as to improve the energy efficiency on the premise of ensuring the delay performance of mobile data collection in the scenarios where the movement of the robot is uncontrolled and the nodes work in a low duty cycle. The data fusion technology further reduces the energy consumption of data forwarding on the premise of ensuring the delay. The rendezvous node sleep scheduling mechanism ensures the timely discovery of the mobile robot by the nodes in the low duty cycle working mode and the timely delivery of the cached data.

[0053] In order to make the above and other objects, features and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 The following shows a schematic diagram of the composition of the network system in the embodiment of the present invention.

[0056] Figure 2 The following shows the overall flowchart of the method in the embodiment of the present invention.

[0057] Figure 3 The following shows the flowchart of the sensor network node determining the data sending mode.

[0058] Figure 4 The following shows the flowchart of the rendezvous node delivering the cached data to the mobile robot. DETAILED EMBODIMENTS

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0060] It should be noted that the terms "first", "second", etc. in the description, claims and the above-mentioned drawings of the present invention are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0061] First, the technical terms in the text are explained as follows:

[0062] Static aggregation node: that is, the base station of the wireless sensor network.

[0063] Mobile aggregation node: The mobile robot is used as the mobile aggregation node in the sensing network;

[0064] Aggregation node: The sensing network nodes near the driving path of the mobile robot, that is, the nodes whose distance from the mobile robot's orbit is less than the communication radius r.

[0065] As Figure 1 , 2 shown, in an embodiment of the present invention, a method for collecting data from a low-duty-cycle sensor network based on a mobile robot includes the following steps:

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

[0067] S11: Determine the aggregation nodes: The nodes whose distance from the robot's driving orbit is less than the communication radius r are set as aggregation nodes.

[0068] S12: Sequentially construct the track routing connecting the aggregation nodes.

[0069] S13: Construct the routing to the static aggregation node (static sink): The static aggregation node establishes a shortest path tree based on the expected transmission count (ETX) through broadcast, and all nodes in the network learn the shortest routing to the static aggregation node.

[0070] S14: Construct a network floor plan in a distributed manner.

[0071] S2: The sensor network nodes determine the data transmission mode. When each node collects environmental data or forwards data, it determines the static sink routing mode or the aggregation node routing mode, so as to minimize energy consumption as much as possible while ensuring the delay performance. The static sink routing mode is to directly send the data to the static aggregation node; the aggregation node routing mode is to first send it to the aggregation node and then wait for the mobile robot to collect it. Specifically: when there is data to be sent, calculate the data transmission delay from the node to the aggregation node; calculate the probability that the mobile robot passes through the aggregation node R at least once; calculate the probability p that satisfies the delay constraint when choosing the aggregation node routing mode; calculate the expected energy consumption when choosing the aggregation node routing mode; if p > threshold p th and the expected energy consumption of the aggregation node routing mode is less than that of the static aggregation node routing mode, then choose the aggregation node routing mode; otherwise, choose the static aggregation node routing mode. The specific process is as Figure 3 shown.

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

[0073]

[0074] where dist(i, L R ) is the shortest distance from node i to the robot's moving path L R . pro avg and ETX avg are the average single-hop forward distance and the expected average single-hop transmission times respectively, and both can be estimated according to historical values.

[0075] 「dist(i, L R ) / pro avg is the estimated remaining number of hops. σ avg is the average sleep delay of the nodes in the low-duty-cycle sensor network, and T is the length of the working cycle.

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

[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 according to historical data.

[0079] S23: For node i, calculate the probability that it meets the delay constraint when sending data in the mode of selecting the aggregation node for routing as follows:

[0080]

[0081] where Δr is the remaining maximum transmission time, and Δ r = Δ - t elp , t elp is the time already spent on transmitting this data packet, and Δ is the delay constraint value. In addition, a safety factor ω 1 (ω 1 > 1) is introduced to further improve the probability of meeting the delay constraint.

[0082] S24: When p exceeds a certain threshold p th and meets the following conditions, node i selects to send the data to the aggregation node:

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

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

[0085] S3: The sensor network node determines the data fusion waiting time. To further improve the energy efficiency of data collection, after the node senses or receives data, it can wait for a certain time to fuse multiple arriving data packets, thereby reducing the number of data forwarding times and reducing energy consumption.

[0086] S31: For node i, calculate its maximum allowable fusion waiting time That is, based on the probability criterion, ensure that the probability of delivering data on time is not less than p th of the waiting time:

[0087]

[0088] where t fuse is the fusion waiting time.

[0089] S32: Evaluate the data fusion utility of node i. Assume that the arrival frequency of data packets at node i is ηi , that is, the number of data packets arriving per unit time (including the data sensed by node i itself and the data to be forwarded). The expected number of hops from i to the aggregation node is h i . Then the energy consumption reduction brought by node i waiting for a unit time, that is, the data fusion utility value is η i h i .

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

[0091]

[0092] Among them, (ηh) max is the maximum data fusion utility value on the entire link from node i to the aggregation node.

[0093] S4: According to the data sending mode determined in step S2, the sensor network node selects the next-hop routing node for data forwarding and forwards the data.

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

[0095] S42: If the node selects the aggregation node routing mode, then adopt the geographic routing method and select the neighbor node with the highest energy efficiency to the aggregation node as the next-hop routing node. Assume that neighbor node j is selected as the next-hop forwarding node, and the expected remaining number of transmissions is:[[]]

[0096]

[0097] Among them, ETX ij is the expected number of transmissions between node i and j, and the smaller the expected remaining number of transmissions value, the higher the energy efficiency; node i selects the neighbor node that satisfies the following conditions as the next-hop routing node and forwards the data:

[0098]

[0099] Among them, N(i) is the set of neighbor nodes of i, and dist(j, L k ) is the shortest distance from neighbor node j to the mobile path L k of the robot. And L is the set of all mobile robot tracks (paths).

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

[0101] S51: After the aggregation node detects the mobile robot, it sends a notification message of the mobile robot's arrival to the next-hop node of the track routing. This message includes a timestamp, the current position of the mobile robot, and its speed.

[0102] S52: After receiving the notification message, the next-hop node calculates the time point t when the robot reaches its communication range based on the robot's position, speed, and the node's own position r ; adjusts the duty cycle so that the node is in the wake-up state after the time point t r .

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

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

[0105] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memories 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), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memories can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0106] It should also be understood that in the embodiments of the present invention, the term "and / or" is only a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present invention generally represents an "or" relationship between the associated objects before and after.

[0107] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

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

[0109] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can also be electrical, mechanical, or other forms of connection.

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

[0111] Specific embodiments of the present invention are used to elaborate on the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A low duty cycle sensor network data collection method based on a mobile robot, comprising the following steps: S1: Network initialization: The network contains 1 static sink node S, n sensor network nodes, m mobile robots, and the communication radius of the node is r; S2: When there is data to be sent, calculate the data transmission delay from the node to the assembly node; calculate the probability that the mobile robot passes through the assembly node R at least once; Calculate the probability p of satisfying the delay constraint when selecting the assembly node routing mode; Calculate the expected energy consumption when selecting the aggregation node routing mode; 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, the aggregation node routing mode is selected; otherwise, the static aggregation node routing mode is selected; S3: The sensor network node determines the data fusion waiting time: After the node senses or receives the data, it waits for a certain period of time and fuses the multiple data messages that arrive to reduce the number of data forwarding times and reduce energy consumption; S4: According to 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; S5: The assembly node delivers the cached data to the mobile robot; when the mobile robot passes by the assembly node, the assembly node sends the cached data to it.

2. The method according to claim 1, characterized in that: The specific process of step S1 is as follows: S11: Determine the assembly node: the node whose distance to the track is less than the communication radius r is set as the assembly node; S12: constructing a track route that sequentially connects the assembly nodes; S13: Building routes to static sink nodes: The static sink node builds a shortest path tree based on the expected number of transmissions through broadcasting, and all nodes in the network learn the shortest routes to the static sink node; S14: Construct a network plan based on a distributed approach.

3. The method according to claim 1, characterized in that: The specific process of step S2 is as follows: S21: For node i, the data transmission delay from node i to the assembly node R is calculated as: Among them, dist(i,L R ) is the moving path L from node i to the robot R The shortest distance of pro avg and ET X avg They are the average single-hop forward distance and the average expected number of single-hop transmission times, respectively; is the estimated number of remaining hops; σ avg is the average sleep delay of nodes in the low duty cycle sensor network, and T is the length of the working cycle; S22: Calculate the probability that the mobile robot passes through the assembly node R at least once within time t: p(X(t)≥1)=1-e -λt Among them, λ is the average number of times the mobile robot passes through R per unit time; S23: For node i, the probability of satisfying the delay constraint when selecting the assembly node routing mode to send data is calculated as: Among them, Δr is the remaining maximum transmission time, Δ r =Δ-t elp , t elp is the time spent on the data message transmission, Δ is the delay constraint value; in addition, a safety factor ω1 (ω1>1) is introduced 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, node i chooses to send data to the assembly node: p·dist(i,L R )+(1-p)·(dist(i,L R )+dist(R,S))<dist(i,S) Otherwise, the static sink routing mode will be selected to ensure the data collection delay performance; the above formula requires the expected energy consumption p·dist(i,L R )+(1-p)·(dist(i,L R )+dist(R,S)) is less than the expected energy consumption dist(i,S) when the static aggregation node routing mode is selected.

4. The method according to claim 1, characterized in that: The specific process of step S3 is as follows: S31: For node i, calculate its maximum allowed fusion waiting time That is, based on the probability criterion, the probability of data delivery on time is guaranteed to be no less than p th Maximum waiting time: where t fuse Waiting time for integration; S32: Evaluate the data fusion utility of node i: Assume that the arrival frequency of data packets at node i is η i , that is, the number of data packets arriving per unit time; the expected number of hops from i to the assembly node is h i , then the energy consumption reduction brought by node i waiting per unit time, that is, the data fusion utility value is η i h i ; S33: Allocate fusion waiting time: For node i, its data fusion waiting time is allocated as follows: Among them, (ηh) max is the maximum data fusion utility value on the entire link from node i to the assembly node.

5. The method according to claim 1, characterized in that: The specific process of step S4 is as follows: S41: If the node selects the static sink routing mode, then according to the shortest path tree, the next hop routing node to the static sink node is selected and the data is forwarded; S42: If the node selects the assembly node routing mode, a geographical routing method is adopted to select a neighbor node with the highest energy efficiency to the assembly node as the next hop routing node.

6. The method according to claim 5, characterized in that: If the node selects the aggregation node routing mode, assuming that the neighbor node j is selected as the next hop forwarding node, the remaining number of transmissions required is expected to be for: Among them, ETX ij is the expected number of transmissions between nodes i and j, and the expected number of remaining transmissions The smaller the value, the higher the energy efficiency; node i selects the neighbor node that meets the following conditions as the next hop routing node and forwards the data: Where N(i) is the set of neighbor nodes of i, dist(j,L k ) is the moving path L from neighbor node j to the robot k The shortest distance, L is the set of all mobile robot trajectories.

7. The method according to claim 1, characterized in that: The step S5 further includes: in order to ensure that the assembly node can detect the mobile robot in time and has sufficient data delivery time, the duty cycle of the corresponding assembly node needs to be adjusted.

8. The method according to claim 7, characterized in that: The specific process of step S5 is as follows: S51: After the assembly node detects the mobile robot, it sends a notification message of the arrival of the mobile robot to the next hop node of the track routing; the message includes a timestamp, the current position and speed of the mobile robot; S52: After receiving the notification message, the next hop node calculates the arrival time of the robot according to the robot position, speed and the node's own position, and adjusts the duty cycle, that is, the node is in the awake state when the mobile robot arrives within its communication range; S53: When detecting that the channel is in an idle state, the assembly node transmits the cached data to the mobile robot; S54: After the cache data transmission is completed, the node switches back to the normal low duty cycle working mode.

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