Large-scale sensor data aggregation method based on multiple paths

Abstract

The invention discloses a multi-path-based large-scale sensor data aggregation method, which comprises the following steps of: deploying a sensor to acquire environment data, randomly clustering sensor nodes, and establishing a communication link among a sensor source node, a cluster head node and an aggregation node; the optimal transmission path is selected by using a multi-path selection algorithm, so that the total benefit of the system is maximized; the cluster head node binds the data packets by using a dynamic data packet binding (DPB) algorithm and then sends the data packets to the sink node; after the sink node receives the data, data packet reconstruction is carried out, and original data information is obtained; according to the wireless sensor network data aggregation method and system based on the multiple paths, data collection and aggregation can be effectively carried out, the transmission rate and the delivery rate of the data are increased through a multi-path transmission protocol, the delay constraint of the data is ensured, the energy requirement of the system is reduced, and the system reliability is improved. And the resource utilization rate and the reliability of the system are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of large-scale sensor data aggregation, and in particular to a large-scale sensor data aggregation method based on multi-path. Background Art

[0002] In large-scale wireless sensor networks, the data aggregation solution is realized by sensing data at the sensor end, the central base station acts as an aggregation node, and the cluster head node acts as a relay node for communication between multiple terminals. This means that the sensor nodes will sense and collect a large amount of data in the surrounding environment and transmit it to the aggregation node. Depending on the transmission method or other methods, the existing data aggregation methods are mainly of the following types:

[0003] Data aggregation method based on communication protocol: When the distance between sensor node and aggregation node is close, ZigBee and other protocols are used, while LoRa and NB-IoT protocols are used when the distance is far; in addition, SigFox protocol is suitable for scenarios with low data transmission rate requirements, while scenarios with high data transmission rate requirements use protocols such as WiFi and 5G.

[0004] Data aggregation methods based on data processing methods: This type of method can be divided into real-time data aggregation and non-real-time data aggregation. In the real-time data aggregation method, the sensor nodes collect data in real time and send it to the aggregation node immediately after preprocessing. In the non-real-time data aggregation method, the sensor nodes store a certain amount of data locally and send it to the aggregation node when conditions are right. It can be sent by scheduled transmission or event triggering.

[0005] Data aggregation methods based on network topology: This type of method includes flat network and hierarchical network data aggregation. In flat network data aggregation, all sensor nodes are at the same level, and data is transmitted to the aggregation node through neighbor nodes. In hierarchical network data aggregation, sensor nodes are divided into one or more aggregation layers and a common layer, and are aggregated to the aggregation node layer by layer.

[0006] Existing large-scale wireless sensor aggregation methods have the following disadvantages: 1) Data aggregation methods based on communication protocols: Due to the different coverage and capabilities of different communication protocols, this may result in ineffective operation in certain environments. In addition, the wireless communication module is usually the part with the highest energy consumption in the sensor node. Some protocols may consume too much energy during data transmission, affecting the service life of the node. 2) Data aggregation methods based on data processing methods: Real-time data aggregation methods require high-speed data processing and transmission capabilities, but in resource-constrained sensor networks, this may make it difficult to ensure real-time performance, especially when the network load is heavy. Non-real-time data aggregation methods require data to be stored locally at the node, which increases the risk of data leakage and corruption. At the same time, the storage space is limited and long-term data may not be stored. 3) Data aggregation methods based on network topology: When the number of nodes in a flat network structure increases, the network performance will significantly decrease. Although the hierarchical network structure can improve the scalability of the network, the complexity of maintaining the network structure increases when the node density is large or the network changes dynamically.

[0007] In addition, another disadvantage of the existing technology is that when the scale of sensor nodes is large, the amount of data transmitted will be extremely large, causing network congestion, data loss, and system life cycle crisis problems.

[0008] The invention application with application number 202411222399.8 discloses a method and device for collecting and transmitting data from multiple types of sensors. The application scheme controls the hardware channel switching through the MCU to match the access channel with the sensor type. This enables real-time online remote monitoring of disasters in difficult and complex mountainous areas, ensuring the safety of railway operations along the line. However, the scheme also has the problem of consuming too much energy during data transmission, which affects the service life of the node.

[0009] Therefore, there is a need for a method that can increase the data aggregation rate while reducing the energy consumption of the sensor network, extending the life cycle of the sensor network, and enhancing the user experience. Summary of the invention

[0010] The purpose of the present invention is to provide a large-scale sensor data aggregation method based on multipath, which realizes the data aggregation effect in a large-scale wireless sensor network through multipath transmission technology, and can extend the life cycle of the sensor network and achieve load balancing.

[0011] The present invention provides a large-scale sensor data aggregation method based on multi-path, comprising the steps of:

[0012] S1. Deploy sensor nodes in the monitoring area to collect environmental data and assign a unique identifier to each sensor node;

[0013] S2, randomly cluster the sensor nodes and assign a unique identifier to each cluster;

[0014] S3, based on the identifier, establish the communication link between the sensor source node, the cluster head node and the sink node, and construct a multi-path transmission network based on the MPQUIC protocol;

[0015] S4, using the multi-path selection algorithm to select the best transmission path to maximize the total system benefit of data transmission from the source node to the sink node;

[0016] S5, the cluster head node uses the dynamic packet bundling (DPB) algorithm to bundle the data packets and then send them to the sink node;

[0017] S6. After receiving the data, the aggregation node reconstructs the data packet and obtains the original data information.

[0018] Furthermore, the environmental data set collected in S1 is represented as J={j 1,1 ... 1,i ... m,i}, each data feature is represented by a four-tuple:

[0019]

[0020] in, is the collection time, is the data size, is the data delay constraint, The number of CPU cycles required to process each KB of data.

[0021] Furthermore, the sensor nodes are divided into sleep, standby and running states according to the current remaining energy and storage size of the nodes, and whether they are source nodes is determined according to the current state of each sensor node.

[0022] Furthermore, the communication links between the source node, the cluster head node and the aggregation node include two paths: 5G and WiFi.

[0023] Furthermore, the best transmission path in S4 is selected according to a path weight function, which is expressed as follows:

[0024]

[0025] Where B is the bandwidth, T RTT is the delay of each path, P loss is jitter and packet loss rate.

[0026] Furthermore, the dynamic data packet bundling (DPB) algorithm in S5 comprises the following steps:

[0027] S51. Create an empty data slice p binding ;

[0028] S52, receiving buffer D pq Packet p in m,i ;

[0029] S53, according to data packet p m,i The delay constraint and the set delay threshold determine whether data needs to be bundled. The formula is:

[0030]

[0031] in, is the delay constraint, T max is the delay threshold;

[0032] S54: When the delay constraint is greater than the delay threshold, the data packet p m,i Assemble to empty data slice p binding Middle Bundle;

[0033] S55: If the delay constraint is less than the delay threshold, it is directly added to the sending buffer

[0034] S56, repeat S12-S15 to iterate, when the empty data slice p binding When full, insert into send buffer And create a new empty data shard p binding .

[0035] Furthermore, the empty data slice p binding For data packet p m,i When bundling, header information is added, including source address, destination address, data packet type, and data packet quantity information.

[0036] Furthermore, the total benefit of the system in S4 is maximized, and the formula is expressed as:

[0037]

[0038] Among them, j m,i For data, is the total transmission delay, is the total energy consumption of the system, is the trade-off coefficient;

[0039]

[0040]

[0041] in, is the transmission delay, For the bundling delay, is the transmission energy consumption, For bundled energy consumption.

[0042] Furthermore, the data packet is reconstructed in S6 to obtain original data information, including the steps of:

[0043] S61, the aggregation node establishes a mapping table Y (PSN, SSN) to associate the fragment sequence number PSN with the original data stream SSN;

[0044] S62, check whether all data fragments have arrived, if there are data fragments that have not arrived, but the current time has exceeded the delay constraint of the data, then do not retransmit, if it does not exceed the delay constraint, then retransmit the data from the data buffer of the cluster head node;

[0045] S63. Reassemble the data fragments into the original data packet according to the fragment sequence numbers, and check the data integrity through CRC.

[0046] Furthermore, the S61 establishes a mapping table Y (PSN, SSN) to associate the fragment sequence number PSN with the original data stream SSN;

[0047] Based on the header information of each data slice, the sensor identifier, timestamp, and slice sequence number information are extracted to determine its position in the original data.

[0048] Beneficial effects of the present invention:

[0049] 1. The wireless sensor network data aggregation method and system based on multi-path of the present invention can effectively collect and aggregate data. Through the multi-path transmission protocol, the data transmission rate and delivery rate are accelerated, the data delay constraint is guaranteed, the system energy demand is reduced, and the resource utilization rate and reliability of the system are greatly improved.

[0050] 2. The multi-path-based wireless sensor network data aggregation method and system of the present invention also utilizes a data bundling (DPB) algorithm to adaptively perform data bundling, thereby reducing the amount of data during transmission and increasing the upper limit of the number of sensors that the system can accommodate. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a structural schematic diagram of a large-scale sensor data aggregation method based on multi-path according to the present invention;

[0052] Figure 2 It is a schematic diagram of the process of a large-scale sensor data aggregation device based on multi-paths of the present invention;

[0053] Figure 3 It is a flow chart of the dynamic data packet bundling algorithm of the present invention. DETAILED DESCRIPTION

[0054] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0055] The existing large-scale wireless sensor aggregation methods have the problems of highest node energy consumption, affecting the service life of nodes, limited storage space, and may not be able to store long-term data. When the scale of sensor nodes is large, the amount of data transmitted will be extremely large, which will also cause network congestion and data loss, resulting in a system life cycle crisis.

[0056] In view of the above problems, the present invention provides a large-scale sensor data aggregation method based on multi-path. Figure 2 A flow chart of a convergence method provided by an embodiment of the present invention, the method comprising the steps of:

[0057] S1. Deploy sensor nodes in the monitoring area to collect environmental data, ensure that the nodes cover the monitoring area, and assign a unique identifier to each sensor node.

[0058] like Figure 1 As shown in the figure, the monitoring area is divided into grids of equal size, and each sensor node is randomly distributed in a grid. Each node can collect environmental data of the monitoring area, including one or several attributes, such as temperature, humidity, light, decibel, etc.; a unique identifier is assigned to each sensor to identify and track each node in the subsequent data collection and transmission process.

[0059] When collecting environmental data, use S = {S1, S2, S3, ..., S M} represents a sensor node in a cluster, and the collected data set is represented by J = {j 1,1 ... 1,i ... m,i}, each data feature is represented by a four-tuple:

[0060]

[0061] in, is the collection time, is the data size, is the data delay constraint, The number of CPU cycles required to process each KB of data.

[0062] S2. Randomly cluster the sensor nodes and assign a unique identifier to each cluster. Data transmission is divided into intra-cluster transmission and inter-cluster transmission.

[0063] All sensor nodes can be randomly divided into P clusters. According to the current remaining energy and storage size of the sensor node, it can be set to sleep, standby and running state.

[0064] In each cluster, the current state of each sensor node is monitored, such as monitoring the energy level, storage size, etc. to determine whether its working state is sleep, standby or running. And according to the set parameter threshold, the sensor node with the best state is selected as the source node to collect data. The source node collects data, and the non-source node does not collect data.

[0065] The data aggregation process is modeled as a two-hop transmission, namely intra-cluster transmission and inter-cluster transmission. Intra-cluster transmission data is transmitted step by step from the cluster to the cluster head node of the cluster, while inter-cluster transmission is when the cluster head node sends the data to the aggregation node.

[0066] S3. Establish communication links between sensor source nodes, cluster head nodes and sink nodes, and construct a multi-path transmission network based on the MPQUIC protocol.

[0067] End-to-end transmission is carried out between the source node and the cluster head node and the sink node, and each transmission link has at least two available paths, including 5G and WiFi.

[0068] Implement the MPQUIC protocol stack on each node to support multi-path transmission of data on MPQUIC; configure MPQUIC-related stream multiplexing, congestion control, connection migration, etc., establish a connection through MPQUIC, and establish a connection on each path for data transmission. After a series of negotiations, an end-to-end connection is established between the aggregation node and the sensor node, which transmits data through multiple paths during the data aggregation process; deploy the MPQUIC protocol on the sensor node and the cluster head node, configure congestion control as CUBIC, and enable connection migration.

[0069] S4. Use the multi-path selection algorithm to select the best transmission path to maximize the overall efficiency of the data transmission system from the source node to the sink node.

[0070] The sensor nodes sense the surrounding environment and collect environmental data such as temperature, humidity, and light, divide the data into multiple data packets, and send them to the sink node through different paths. The data transmission on each path is encapsulated using the MPQUIC protocol.

[0071] The best transmission path is selected based on the delay, bandwidth, packet loss rate and jitter indicators of each path. X is used to represent the path selection scheme of all nodes, where The value is 0 or 1, indicating that the data j is transmitted m,iThe selected path, 0 means selecting the first path, 1 means selecting the second path; the node divides the data, and each data packet is sent to the aggregation node through a different path. When data packet loss occurs, the data packet is retransmitted according to the urgency of the data. If it has not exceeded the data delay constraint, the path can be reselected for sending. If the data has timed out, there is no need to retransmit.

[0072] The best transmission path is selected based on the path weight function, and the formula is expressed as:

[0073]

[0074] Where B is the bandwidth, T RTT为 The delay of each path, P loss is jitter and packet loss rate.

[0075] Continuously monitor the network status and maximize the overall system benefits, including link quality and node energy consumption, dynamically adjust the path selection of MPQUIC connections, and optimize transmission performance.

[0076] The total benefit of the system is maximized, and the formula is expressed as:

[0077]

[0078] Among them, j m,i For data, is the total transmission delay, is the total energy consumption of the system, is the trade-off coefficient;

[0079]

[0080] in, is the transmission delay, For the bundling delay, is the transmission energy consumption, For bundled energy consumption.

[0081] By balancing the minimization of the total energy consumption and total transmission delay of the system, the total benefit of the system is maximized and the optimal transmission path is selected.

[0082] S5. The cluster head node uses the dynamic packet bundling (DPB) algorithm to bundle the data packets and then send them to the sink node.

[0083] The cluster head node collects data from each sensor node, compresses and preprocesses the collected raw data, and sets a receiving buffer at the cluster head node to temporarily store the preprocessed data packets; the cluster head node uses the dynamic packet bundling (DPB) algorithm to process the data before sending it to the sink node.

[0084] This includes setting a delay threshold based on the delay constraint of the data packet. For data packets with a delay constraint greater than the delay threshold, a new data fragment is created, and the data packets that meet the requirements are inserted as a payload bundle. The necessary header information is added to the new bundled data packets, including the source address, destination address, data packet type, number of data packets, etc., and the transmission order of the bundled data packets is ensured; and for data packets with a delay constraint less than the delay threshold, they are directly sent to the aggregation node through the sending buffer.

[0085] Specifically, Figure 3 As shown, the steps include:

[0086] S11. Create an empty data shard p binding ;

[0087] S12, receiving buffer D pq Packet p in m,i ;

[0088] S13, according to data packet p m,i The delay constraint and the set delay threshold determine whether data needs to be bundled. The formula is:

[0089]

[0090] in, is the delay constraint, T max is the delay threshold;

[0091] S14. When the delay constraint is greater than the delay threshold, the data packet p m,i Assemble to empty data slice p binding Middle Bundle;

[0092] S15: If the delay constraint is less than the delay threshold, it is directly added to the sending buffer

[0093] S16, repeat S12-S15 to iterate, when the empty data slice p binding When full, insert into send buffer And create a new empty data shard p binding .

[0094] S6. After receiving the data, the aggregation node reconstructs the data packet and obtains the original data information.

[0095] The sink node receives the data packet from the cluster head node, parses the header information of the data packet, including the source address, destination address, data packet type, number of data packets, etc., and extracts the sensor identifier, timestamp, and fragment sequence number information.

[0096] The aggregation node determines the position of each data fragment in the original data based on the header information of each data fragment, establishes a mapping table Y (PSN, SSN), and associates the fragment sequence number PSN with the original data stream SSN.

[0097] Check whether all data fragments have arrived. If a data fragment has not arrived, but the current time has exceeded the data delay constraint, it will not be retransmitted. If it does not exceed the delay constraint, the data will be retransmitted from the data buffer of the cluster head node;

[0098] The bundled data packets are unpacked into the original multiple data packets, and the integrity of the unpacked data is verified through a cyclic redundancy check (CRC) to ensure that the data packets have not been tampered with during transmission.

[0099] The data is then sorted and reorganized according to the metadata (serial number, timestamp) in the data packet to restore the original order of the data. The reorganized data packet is then decompressed and parsed into specific sensor readings or values.

[0100] The present invention combines multipath transmission and large-scale sensor data aggregation to increase the data aggregation rate while reducing the energy consumption of the sensor network, extending the life cycle of the sensor network, and enhancing the user experience, thus having high innovation and practical value.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A large-scale sensor data aggregation method based on multipath, characterized in that: include: S1. Deploy sensor nodes in the monitoring area to collect environmental data and assign a unique identifier to each sensor node; S2, randomly cluster the sensor nodes and assign a unique identifier to each cluster; S3, based on the identifier, establish the communication link between the sensor source node, the cluster head node and the sink node, and construct a multi-path transmission network based on the MPQUIC protocol; S4, using the multi-path selection algorithm to select the best transmission path to maximize the total system benefit of data transmission from the source node to the sink node; S5, the cluster head node uses the dynamic packet bundling (DPB) algorithm to bundle the data packets and then send them to the sink node; S6. After receiving the data, the aggregation node reconstructs the data packet and obtains the original data information.

2. The aggregation method according to claim 1, characterized in that: The environmental data set collected in S1 is represented by J={j 1,1 ... 1,i ... m,i }, each data feature is represented by a four-tuple: in, is the collection time, is the data size, is the data delay constraint, The number of CPU cycles required to process each KB of data.

3. The aggregation method according to claim 1, characterized in that: The sensor nodes are divided into sleep, standby and running states according to the current remaining energy and storage size of the nodes, and whether they are source nodes is determined according to the current state of each sensor node.

4. The aggregation method according to claim 1, characterized in that: The communication links between the source node, the cluster head node and the aggregation node include two paths: 5G and WiFi.

5. The aggregation method according to claim 1, characterized in that: The best transmission path in S4 is selected according to the path weight function, which is expressed as follows: Where B is the bandwidth, T RTT is the delay of each path, P loss is jitter and packet loss rate.

6. The aggregation method according to claim 1, characterized in that: The steps of the dynamic data packet bundling (DPB) algorithm in S5 are as follows: S51. Create an empty data slice p binding ; S52, receiving buffer D pq Packet p in m,i ; S53, according to data packet p m,i The delay constraint and the set delay threshold determine whether data needs to be bundled. The formula is: in, is the delay constraint, T max is the delay threshold; S54: When the delay constraint is greater than the delay threshold, the data packet p m,i Assemble to empty data slice p binding Middle Bundle; S55: If the delay constraint is less than the delay threshold, it is directly added to the sending buffer S56, repeat S12-S15 to iterate, when the empty data slice p binding When full, insert into send buffer And create a new empty data shard p binding .

7. The aggregation method according to claim 6, characterized in that: The empty data slice p binding For data packet p m,i Header information is added during bundling, including source address, destination address, data packet type, and data packet quantity information.

8. The aggregation method according to claim 1, characterized in that: The total benefit of the system in S4 is maximized, and the formula is expressed as: Among them, j m,i For data, is the total transmission delay, is the total energy consumption of the system, is the trade-off coefficient; in, is the transmission delay, For the bundling delay, is the transmission energy consumption, For bundled energy consumption.

9. The aggregation method according to claim 1, characterized in that: The step S6 is to reconstruct the data packet and obtain the original data information, including the following steps: S61, the aggregation node establishes a mapping table Y (PSN, SSN) to associate the fragment sequence number PSN with the original data stream SSN; S62, check whether all data fragments have arrived, if there are data fragments that have not arrived, but the current time has exceeded the delay constraint of the data, then do not retransmit, if it does not exceed the delay constraint, then retransmit the data from the data buffer of the cluster head node; S63. Reassemble the data fragments into the original data packet according to the fragment sequence numbers, and check the data integrity through CRC.

10. The aggregation method according to claim 9, characterized in that: The S61 establishes a mapping table Y (PSN, SSN) to associate the fragment sequence number PSN with the original data stream SSN; Based on the header information of each data slice, the sensor identifier, timestamp, and slice sequence number information are extracted to determine its position in the original data.

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