Node discovery method and system based on distributed network and computer program product
By using the method of initial scanning and iterative selection of long-distance nodes as relay nodes in distributed networks, the problem of excessive time-consuming node discovery under dense node layout is solved, efficient network construction and dynamic adaptation are achieved, and the network's self-healing ability is improved.
Patent Information
- Application Number
- CN202510611305.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-13
- Publication Date
- 2025-07-04
AI Technical Summary
In distributed networks, under dense node layout, traditional node discovery methods take too long to dynamically adapt to network topology changes, resulting in a long time for network self-healing.
Using a node discovery method based on a distributed network, the nodes are initially scanned and classified by the main node, and the long-distance nodes are randomly selected as the relay nodes, and iteratively scans until the missing node list is empty, combining the signal strength indicator value and the rescue signal processing of unconnected nodes.
It improves node discovery efficiency, shortens network network construction time, and enhances the dynamic adaptability and self-healing performance of the network.
Smart Images
Figure CN120264334A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of distributed systems, and particularly to a node discovery method, system and computer program product based on a distributed network. Background Art
[0002] In a distributed network, the communication and connection status between each node are crucial to the stability and functionality of the network. However, due to factors such as wide node distribution, signal attenuation, and environmental interference, some nodes may lose connection with the master node and become "missing nodes". Traditional node discovery methods usually rely on centralized scanning by the master node, which has problems such as low efficiency, limited coverage, and inability to dynamically adapt to network topology changes.
[0003] In related technologies, in order to avoid the problems brought by only using the master node for scanning, a path planning algorithm, such as the MPR algorithm, is usually used to implement node discovery. It is necessary to separately count the information of each child node and the neighbor information of each child node, and then transmit them to the master node. The master node uniformly plans the nodes with high reachability as relay nodes, and then the relay nodes perform node discovery.
[0004] However, for the occasion of a densely laid-out distributed network, there are hundreds or more neighbor nodes around each node. Summarizing the information of hundreds of neighbor nodes and the neighbors of the neighbors results in a very large amount of data, and it takes too long to summarize all of them, the network construction time is long, and when the connections between network nodes change, the time required for network self-healing is also relatively long. Summary of the Invention
[0005] Object of the Invention: The object of the present invention is to provide a node discovery method, system and computer program product based on a distributed network, so as to avoid consuming too much time collecting the information of all neighbor nodes and the neighbors of the neighbors in the case of a densely laid-out node, and improve the node discovery efficiency.
[0006] The present invention provides a node discovery method based on a distributed network, including: S1 In the first scan, the master node is used as the first scan node to scan the secondary nodes within the communication range, and the first feedback information of each secondary node is obtained; according to the first feedback information, the secondary nodes are classified into the first long-distance node list, the first short-distance node list, and the missing node list; S2 In the second scan, a secondary node randomly selected from the first long-distance node list is used as the second scan node for scanning, and the second feedback information of other secondary nodes within the communication range of the second scan node is obtained. According to the second feedback information, the other secondary nodes are classified into the second long-distance node list, the second short-distance node list, and the missing node list; S3 In the nth scan, the process of S2 is iterated. Each time, a secondary node classified into the (n - 1)th long-distance node list after (n - 1) scans is randomly selected as the nth scan node, and the missing node list is updated according to the nth feedback information after the nth scan until the missing node list is empty, where n is an integer greater than 2.
[0007] In addition, the received signal strength indication value RSSI when the secondary node replies to the corresponding scan node;
[0008] The relationship between the received signal strength indication value RSSI and the actual communication distance d is as follows:
[0009] d = 10^((abs(RSSI) - A) / (10 * m)),
[0010] where the actual communication distance d represents the actual distance between the secondary node and the corresponding scan node, abs represents the absolute value calculation function, A represents the signal strength when the secondary node and the corresponding scan node are 1 meter apart, and m represents the environmental attenuation factor.
[0011] In addition, the secondary nodes in the long-distance node list are the secondary nodes with the received signal strength indication value within the preset indication value range;
[0012] The secondary nodes in the short-distance node list are the secondary nodes with the received signal strength indication value less than the preset indication value;
[0013] The secondary nodes in the missing node list are the secondary nodes that do not reply.
[0014] In addition, in step S3, selecting the nth scan node includes: selecting the nth scan node from the secondary nodes that are repeated in the (n - 1)th long-distance node list and the (n - 2)th long-distance node list.
[0015] In addition, in step S3, when the number of iterations exceeds the preset threshold and there are still missing secondary nodes in the missing node list, the missing secondary nodes are made to send out a distress signal by themselves;
[0016] After receiving the distress signal, the secondary nodes in the list of the nearest non-missing nodes around the missing secondary node establish a connection with the missing secondary node.
[0017] The present invention also provides a node discovery system based on a distributed network, including: a primary node scanning and classification module for performing the above-mentioned node discovery method based on a distributed network, a secondary node scanning and classification module, and an iteration module;
[0018] The primary node scanning and classification module is used to, in the first scan, use the primary node as the first scanning node to scan the secondary nodes within the communication range, and obtain the first feedback information of each secondary node; classify the secondary nodes into a first long-distance node list, a first short-distance node list, and a missing node list according to the first feedback information;
[0019] The secondary node scanning and classification module is used to use a secondary node randomly selected from the first long-distance node list as the second scanning node to scan, obtain the second feedback information of other secondary nodes within the communication range of the second scanning node, and classify the other secondary nodes into a second long-distance node list, a second short-distance node list, and a missing node list according to the second feedback information;
[0020] The iteration module is used to, in the nth scan, iterate the process of S2. Each time, randomly select a secondary node that was classified into the (n - 1)th long-distance node list after the (n - 1)th scan as the nth scanning node, and update the missing node list according to the nth feedback information after the nth scan until the missing node list is empty, where n is an integer greater than 2.
[0021] The present invention also provides a computer program product, including a computer program, which implements the node discovery method based on a distributed network as described above when executed by a processor.
[0022] Compared with the prior art, the present invention has the following remarkable effects: In the present invention, first, the first scan is performed through the primary node. However, it is not a centralized scan of all nodes, but a scan of the nodes within the communication range, which can avoid errors caused by nodes that cannot be scanned because they are outside the communication range; and the scanned nodes are classified, and the points farther away in the effective communication distance coverage range are preferentially searched, and a randomly selected point farther away is used as the relay node for the next scan. In this way, it is possible to avoid spending too much time collecting information about all neighbor nodes and the neighbors of neighbor nodes, and achieve the effect of spreading to a distance and selecting effective relay nodes in a shorter time. Description of the Drawings
[0023] Figure 1 It is a flowchart of a node discovery method based on a distributed network according to an embodiment of the present invention;
[0024] Figure 2 Schematic diagram of the scanning communication range of the master node and the second scanning node according to an embodiment of the present invention;
[0025] Figure 3 Schematic diagram of the scanning communication range of multiple scanning nodes according to an embodiment of the present invention;
[0026] Figure 4 Schematic diagram of the structure of a node discovery system for a distributed network according to another embodiment of the present invention. Detailed implementation manners
[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will elaborate on each embodiment of the present invention with reference to the accompanying drawings. However, those of ordinary skill in the art can understand that in each embodiment of the present invention, many technical details are presented for the better understanding of the readers. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented. The division of the following embodiments is for convenience of description and should not constitute any limitation on the specific implementation manners of the present invention. The various embodiments can be combined and cross-referenced with each other on the premise of no contradiction.
[0028] The following further describes the present invention in detail with reference to the accompanying drawings of the specification and specific implementation manners.
[0029] An embodiment of the present invention relates to a node discovery method based on a distributed network, and its implementation steps are as Figure 1 shown.
[0030] In step S1, in the first scan, the master node is used as the first scanning node to scan the secondary nodes within the communication range, and the first feedback information of each secondary node is obtained; according to the first feedback information, the secondary nodes are classified into the first long-distance node list, the first short-distance node list, and the missing node list.
[0031] In an example, let the master node be R. The master node R initiates a scan to the secondary nodes within the communication range. The communication range is the maximum distance D that the signal transmission of the node can reach, and the feedback information of each secondary node is obtained. Here, the feedback information is the received signal strength indication value (RSSI) when each secondary node replies. The received signal strength indication value can be obtained through the following formula: RSSI = transmit power txPower + path loss pathloss + receive gain rxGain + system gain SystemGain, which can generally be read from the radio frequency chip of the node. When the communication is successful, the RSSI value of each communication can be obtained, that is, the signal strength. The RSSI value of the secondary node close to the master node R is higher, and the RSSI value of the secondary node far from the master node R is smaller. When the communication is not successful, there is no RSSI value.
[0032] The relationship between the signal strength indication value RSSI and the actual communication distance d is as follows:
[0033] d = 10^((abs(RSSI) - A) / (10*m)),
[0034] where the actual communication distance d represents the actual distance between the secondary node and the corresponding scanning node, abs represents the absolute value calculation function, A represents the signal strength when the secondary node and the corresponding scanning node are 1 meter apart, m represents the environmental attenuation factor, and RSSI represents the signal strength indication value.
[0035] In this embodiment, according to the RSSI value, the secondary nodes within the communication range of the master node R are divided into three lists:
[0036] The first near-node list R-near list, that is, the nodes that have successfully communicated with R and whose RSSI value is less than the preset indication value. In this embodiment, since there is a corresponding relationship between RSSI and distance, the range of the RSSI value is limited by the range of the distance. The preset indication value is the RSSI value when the distance d is equal to 0.7D.
[0037] The first far-node list R-far list, that is, the nodes that have successfully communicated with R and whose RSSI value is within the preset indication interval. The preset indication interval is the RSSI interval when the distance d is within the range of 0.7D to 0.9D.
[0038] The missing node list Missing list (nodes without replies).
[0039] This step is equivalent to drawing a circle with the master node R as the center, and the nodes outside the circle will be processed in the following steps.
[0040] In step S2, in the second scan, a secondary node randomly selected from the first far-node list is used as the second scanning node for scanning, and the second feedback information of other secondary nodes within the communication range of the second scanning node is obtained. According to the second feedback information, other secondary nodes are divided into the second far-node list, the second near-node list, and the missing node list.
[0041] In one example, a secondary node randomly selected from the first long-distance node list is used as the second scanning node for scanning. That is, a node A is randomly selected from the R-far list as the next scanning initiation node. Similarly, after node A performs scanning within its communication range, the secondary nodes within the communication range of node A are classified according to the same classification method to obtain the second long-distance node list A-nearlist and the second long-distance node list A-farlist. This step is equivalent to drawing a circle with node A as the center. Since node A is on the edge (equivalent to the circumference) of the master node R, the communication ranges of the two are similar to the intersection of two circles. The schematic diagram is as Figure 2 shown. Among them, the red solid circular part represents the R-farlist, the blue dashed part represents the A-farlist, and the yellow part is the A-nearlist in the R-far list, that is, the points that are simultaneously in the second short-distance node list of node A and the first long-distance node list of the master node R. The intersection of the two circles is the A-farlist in the R-far list, that is, the secondary nodes that are simultaneously in the second long-distance node list of node A and the first long-distance node list of the master node R. Node A also scans the nodes that are simultaneously within its own communication range and in the Missing list of the master node R, and removes the secondary nodes of node A that can be connected from the Missing list to complete the update of the Missing list.
[0042] In step S3, in the nth scan, the process of step S2 is iterated. Each time, a secondary node that was classified into the (n - 1)th long-distance node list after (n - 1) random selections is used as the nth scanning node, and the missing node list is updated according to the feedback information of the nth scan until the missing node list is empty, where n is an integer greater than 2.
[0043] In one example, selecting the nth scanning node includes: selecting the nth scanning node from the secondary nodes that are repeated in the (n - 1)th long-distance node list and the (n - 2)th long-distance node list, as Figure 2 shown. In the third scan, the third scanning node B can be selected from the intersection of the two circles of the master node R and node A, that is, the secondary nodes that are simultaneously in the second long-distance node list of node A and the first long-distance node list of the master node R (A-far list in the R-far list). By obtaining the intersection of the two circles as the starting point for the next scan, in addition to being able to preferentially find the points farther away in the effective communication range and avoiding consuming too much time collecting information about all neighbor nodes and the neighbors of neighbors, it can also ensure that the next scan can cover as many points outside the previous communication range and the points in the missing node list as possible, ensuring the integrity of the overall scan of the distributed network.
[0044] After selecting the third scanning node B, the secondary nodes within the communication range of the third scanning node are classified according to the same classification method. The fourth scanning node C is a random point of the intersection of the B-far list in the R-far list. The seventh scanning node F is a random point of the intersection of the E-far list in the R-far list. Through 6 scans from 2 to 7, the surrounding area of the node R can be covered. After that, the eighth scanning node G is a random point of the intersection of the F-far list in the A-far list. The schematic diagram of the obtained scanning range is as follows: Figure 3 As shown, finally, iterate continuously according to this rule, and in the iterative process, continuously update the Missing list until the Missing list is empty.
[0045] In some special cases, some secondary nodes in the Missinglist may not be scanned. For example, there may be an irregular layout at the edge of the distributed network, which makes it impossible to find the secondary nodes. At this time, when the number of iterations exceeds the preset threshold, in this implementation, the iteration time may exceed the agreed network establishment time (this time is determined by the total number and density of nodes). If there are still missing secondary nodes in the missing node list, the missing secondary node itself starts to transmit a distress signal. After the surrounding networked secondary nodes receive it, they establish a new connection with the missing secondary node.
[0046] In another embodiment of the present invention, a node discovery system for a distributed network is provided. Figure 4 As shown, it includes: a preliminary node scanning classification module, a secondary node scanning classification module, and an iteration module; the preliminary node scanning classification module is used to use the main node as the first scanning node to scan the secondary nodes within the communication range in the first scanning, and obtain the first feedback information of each secondary node; according to the first feedback information, the secondary nodes are divided into a first long-distance node list, a first short-distance node list, and a missing node list;
[0047] The secondary node scanning classification module is used to scan a secondary node randomly selected from the first long-distance node list as a second scanning node, obtain the second feedback information of other secondary nodes within the communication range of the second scanning node, and classify the other secondary nodes into a second long-distance node list, a second short-distance node list, and a missing node list according to the second feedback information;
[0048] The iterative module is used to iterate the process of S2 in the nth scan. Each time, a secondary node randomly selected from the secondary node list of the (n - 1)th long-distance node list after the (n - 1)th scan is used as the nth scan node, and the missing node list is updated according to the nth feedback information after the nth scan until the missing node list is empty, where n is an integer greater than 2.
[0049] In another embodiment of the present invention, a computer program product is further provided, including a computer program which, when executed by a processor, implements the node discovery method based on a distributed network as described above.
[0050] Those of ordinary skill in the art can understand that the above embodiments are specific embodiments for implementing the present invention, and in practical applications, various changes can be made in form and details without departing from the spirit and scope of the present invention.
Claims
1. A node discovery method based on a distributed network, characterized in that, Including: S1 In the first scan, use the master node as the first scanning node to scan the secondary nodes within the communication range, and obtain the first feedback information of each secondary node; Classify the secondary nodes into the first long-distance node list, the first short-distance node list, and the missing node list according to the first feedback information; S2 In the second scan, use a secondary node randomly selected from the first long-distance node list as the second scanning node to scan, obtain the second feedback information of other secondary nodes within the communication range of the second scanning node, and classify the other secondary nodes into the second long-distance node list, the second short-distance node list, and the missing node list according to the second feedback information; S3 In the nth scan, iterate the process of S2. Each time, randomly select a secondary node classified into the (n - 1)th long-distance node list after the (n - 1)th scan as the nth scanning node, and update the missing node list according to the nth feedback information after the nth scan until the missing node list is empty, where n is an integer greater than 2.
2. The node discovery method based on a distributed network according to claim 1, wherein The feedback information includes: the received signal strength indication value RSSI when the secondary node replies to the corresponding scanning node; The relationship between the received signal strength indication value RSSI and the actual communication distance d is as follows: d = 10^((abs(RSSI) - A) / (10 * m)), where the actual communication distance d represents the actual distance between the secondary node and the corresponding scanning node, abs represents the absolute value calculation function, A represents the signal strength when the secondary node and the corresponding scanning node are 1 meter apart, and m represents the environmental attenuation factor.
3. The node discovery method based on a distributed network according to claim 2, wherein The secondary nodes in the long-distance node list are the secondary nodes with the received signal strength indication value within the preset indication value range; The secondary nodes in the short-distance node list are the secondary nodes with the received signal strength indication value less than the preset indication value; The secondary nodes in the missing node list are the secondary nodes that do not reply.
4. The node discovery method based on a distributed network according to claim 1, wherein Selecting the nth scanning node in step S3 includes: selecting the nth scanning node from the secondary nodes that are repeated in the (n - 1)th long-distance node list and the (n - 2)th long-distance node list.
5. The node discovery method based on a distributed network according to claim 1, wherein In step S3, when the number of iterations exceeds the preset threshold and there are still missing secondary nodes in the missing node list, make the missing secondary nodes send out a distress signal by themselves; After the secondary nodes in the nearest non-missing node list around the missing secondary node receive the distress signal, establish a connection with the missing secondary node.
6. A node discovery system based on a distributed network, characterized in that, Including: A preliminary node scan and classification module, a secondary node scan and classification module, and an iteration module that execute the node discovery method based on a distributed network described in any one of claims 1 - 5 above; The preliminary node scan and classification module is used to, in the first scan, use the master node as the first scanning node to scan the secondary nodes within the communication range, and obtain the first feedback information of each secondary node; classify the secondary nodes into the first long-distance node list, the first short-distance node list, and the missing node list according to the first feedback information; The secondary node node scanning and classification module is used to select a secondary node randomly selected from the first long-distance node list as the second scanning node for scanning, obtain the second feedback information of other secondary nodes within the communication range of the second scanning node, and classify other secondary nodes into the second long-distance node list, the second short-distance node list, and the missing node list according to the second feedback information; The iteration module is used to iterate the process of S2 in the nth scan. Each time, a secondary node randomly selected from the secondary nodes classified into the (n - 1)th long-distance node list after the (n - 1)th scan is used as the nth scanning node, and the missing node list is updated according to the nth feedback information after the nth scan until the missing node list is empty, where n is an integer greater than 2.
7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the node discovery method based on a distributed network according to any one of claims 1 to 5.