Distributed detection method and system for key nodes of wireless multi-hop network

Abstract

The invention discloses a distributed detection method and system for key nodes of a wireless multi-hop network, and the method comprises the steps: each node constructs adjacent information at a set time interval, and the method comprises the steps: broadcasting a neighbor collection frame and a neighborhood collection frame, receiving a neighbor set and a neighborhood set, combining repeated nodes, and calculating the connectivity; broadcasting a degree collection frame, receiving neighbor connectivity and neighborhood information, merging a degree value, and calculating a key performance score; when data are sent, received and forwarded each time, 1 is added to the numerical value of the flow meter; the server traverses all nodes, accesses the nodes in a depth-first mode, collects key performance scores and flowmeter values, and constructs and updates a score set and a flow set; when the maximum flow meter numerical value in the flow set exceeds a set threshold value proportion, the server broadcasts a data contraction frame, the node compresses the flow meter numerical values, and the compressed counting result is uploaded in the next traversal; marking the key nodes according to the score set and the flow set, and completing distributed detection of the key nodes.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless multi-hop networks, and more particularly, relates to a distributed detection method and system for key nodes in a wireless multi-hop network. Background Art

[0002] The application of wireless multi-hop networks is becoming increasingly popular. They are used in many fields, such as mine disaster warning, agricultural information monitoring, drone collaboration, military coordinated command, etc. Wireless multi-hop networks can transmit information between nodes through self-organizing networks, avoiding the pre-fixed laying of centralized networks. They are highly flexible and applicable.

[0003] An example of a wireless multi-hop network is Figure 1 As shown, multiple nodes are interconnected through limited radio coverage, where node 14 is a server and the other nodes 1-13 are sensors or distributed system devices. Each device can use other devices as a springboard to gradually transmit data to the server in a hop-by-hop manner.

[0004] However, wireless multi-hop networks also have certain disadvantages. Since network nodes may fail, move, or shut down, their distribution density varies greatly, and the radio coverage range of a single network node is limited. If some network nodes are interrupted or moved, the entire originally connected network will be disconnected into multiple scattered and isolated networks, which will make it impossible for information to traverse and transmit. These nodes are called key nodes.

[0005] For example Figure 1 If one of the nodes 4-8 in the network were to shut down or fail, the network would be split in two, and data from some nodes would be unable to be transmitted to the server. Furthermore, key nodes act as bridges connecting multiple subnetworks. Information transmission between these subnetworks would be concentrated in these key nodes for data exchange, leading to data congestion and hindering the reliable and efficient operation of the entire network.

[0006] For the above reasons, it is necessary to identify the key nodes of the entire wireless multi-hop network through certain strategies, monitor the data traffic density on the key nodes in real time, and equip the key nodes with redundant devices to avoid the failure, movement, and shutdown of key nodes, which may cause the network to be divided into multiple complementary and interconnected sub-networks, thereby ensuring the efficient and reliable flow of information.

[0007] Traditionally, critical node identification in wireless multi-hop networks is centralized. This centralized approach relies on obtaining the entire network's topology, which is then used for analysis and processing. However, the acquisition and processing of the overall network topology is inefficient, consuming excessive computing and network resources. Furthermore, due to the volatile nature of wireless multi-hop networks, this process needs to be repeated at regular intervals, which places a heavy burden on both network communication and computing.

[0008] Currently, some studies have attempted to use distributed detection methods to identify key nodes in order to reduce the centralized solution's dependence on the entire network topology and consumption of computing resources. However, these methods generally have problems such as low detection accuracy, untimely updates, and weak response mechanisms. They are still unable to meet the actual needs of wireless multi-hop networks for dynamic identification and high-frequency monitoring of key nodes.

[0009] Therefore, there is an urgent need for a distributed detection method for key nodes that does not require centralized control, has real-time update capabilities, and has low resource consumption, so as to adapt to practical application scenarios where the wireless multi-hop network structure changes frequently. Summary of the Invention

[0010] To address the deficiencies in the prior art, the present invention provides a distributed detection method and system for key nodes in a wireless multi-hop network, aiming to quickly and accurately detect key nodes in the wireless multi-hop network. Using a distributed method, each node locally uses its own data and the data of adjacent nodes to complete calculations and detections, thereby providing network managers with fast and effective data support, enabling network managers to perform targeted redundant backup or scheduling adjustments to the network, improving the connection reliability of the wireless multi-hop network and ensuring more efficient data flow.

[0011] The present invention adopts the following technical solutions.

[0012] A first aspect of the present invention provides a distributed detection method for key nodes in a wireless multi-hop network, comprising the steps of detecting key nodes, including statistical processing logic for a single node and statistical processing logic for the entire network;

[0013] The statistical processing logic for a single node includes: each node broadcasts neighbor collection frames and neighborhood collection frames, and the nodes connected by one hop reply frame data. This is used to calculate the degree of each node in a distributed manner. Based on the degree value of each node and the degree values of all nodes connected by one hop, the key performance score of the single node is calculated; and the traffic count value of each node is counted.

[0014] The statistical processing logic of the entire network and the steps of detecting key nodes include: the server traverses each node, iteratively compares the key performance score and traffic count value of each node, and selects the nodes with the top key performance score and traffic count value as key nodes.

[0015] Preferably, each node broadcasts a neighbor collection frame and a neighborhood collection frame, and a node connected by one hop replies with frame data, thereby distributedly calculating the degree value of each node includes:

[0016] Each node in the wireless multi-hop network regularly broadcasts neighbor collection frames. The node that receives the neighbor collection frame replies with the sending node ID and the receiving node ID. The neighbor collection frame replies are counted to form a set containing neighbor node IDs.

[0017] Broadcast the neighborhood collection frame externally, count the neighborhood collection frame replies, and form a neighborhood node ID set that includes the sending node ID, the receiving node ID, and its neighbor node IDs;

[0018] Each node deduplicates the collected neighborhood node IDs and calculates the degree of each node.

[0019] Preferably, the step of calculating the key performance score of a single node based on the degree value of each node and the degree values of all nodes connected to it in one hop comprises:

[0020] After each node calculates its own degree value, it broadcasts a degree collection frame and counts the degree collection frame replies from neighboring nodes.

[0021] First perform maximum value merging on the degree value;

[0022] After merging the maximum values, the degree values of all nodes in the neighbor node set are subtracted by 1 and then summed up. The sum is added by 1 and used as the denominator. The square of the node's degree value is used as the denominator. The ratio of the two is the key performance score of the single node.

[0023] Preferably, the counting of traffic count values of each node includes:

[0024] Whenever data is sent, received, transmitted or forwarded on a node, the traffic count value of the node is increased by 1;

[0025] Whenever a node receives a data contraction frame, it performs square root operation on the traffic count value of the node and updates and replaces the previous traffic count value.

[0026] Preferably, the server traverses all nodes at set time intervals, accesses nodes in a depth-first manner, collects key performance scores and traffic count values, and constructs a score set and a traffic set, including:

[0027] The server sends a traversal statistics frame, which includes three parts: statistical value, key performance score set and traffic count set. The statistical value is the set number of target key nodes, and the key performance score set and traffic count set are initially empty.

[0028] The server visits each node in turn. After receiving the traversal statistics frame, each node determines whether the number of nodes recorded in the key performance score set and the traffic count set is less than the statistical value. If the key performance score set is not full, the data pair consisting of the current node identifier and the key performance score is added to the key performance score set; if the traffic count set is not full, the data pair consisting of the current node identifier and the traffic count value is added to the traffic count set.

[0029] After the traversal statistics frame completes the visit of all nodes, it falls back to the server step by step along the original traversal path. When the intermediate nodes along the way pass through the statistics frame, they update the key performance score set and traffic count set copies stored locally at the node when receiving the fallback frame, which will be used as the initial reference in the next round of traversal.

[0030] Preferably, the updating of the score set and the traffic set according to the key performance scores and traffic count values uploaded by the nodes, when the set is full, replacing the node data with the smallest key performance score value in the set, and when there are multiple nodes with the same score, replacing the node data with the smallest traffic count value, includes:

[0031] Compare the key performance score of the current node with the key performance scores of each data pair in the key performance score set. If the current key performance score is greater, replace the data pair with the smallest key performance score in the key performance score set.

[0032] If there are multiple data pairs with the same key performance score, continue to compare the corresponding flow count values and replace the data pair with the smaller flow count value with the data pair with the larger flow count value;

[0033] The same logic is executed on the traffic count set. If the current traffic count value is greater than the traffic count value of a data pair in the set, the data pair consisting of the current node identifier and the traffic count value is replaced.

[0034] Preferably, when the maximum flow count value in the flow set exceeds a set threshold ratio, the server broadcasts a data contraction frame, including:

[0035] After completing a node traversal statistics, the server extracts the traffic count values of all nodes in the current traffic set and calculates the maximum value among them;

[0036] If the maximum traffic count value exceeds the set ratio threshold of the maximum allowable value of stored data, the server broadcasts the data contraction frame to all nodes in a traversal manner, triggering each node to perform traffic compression processing.

[0037] Preferably, the node performs compression processing on the traffic count value, including:

[0038] After the current node receives the data contraction frame broadcast by the server, it performs a square root operation on the current traffic count value and replaces the original traffic count value of the current node with the operation result; during the next traversal access process of the server, the compressed traffic count value is uploaded.

[0039] Preferably, after the traversal is completed, the server marks the key nodes according to the score set and the traffic set, and completes the distributed detection of the key nodes, including:

[0040] After the traversal statistics frame is returned to the server, the server reads the node identifiers in the score set and the traffic set respectively, confirms all the nodes included in the set; and marks all the nodes in the score set and the traffic set as key nodes;

[0041] After the detection process is completed, a new round of node information construction and traversal process is restarted to form a key node distributed detection mechanism with cyclic execution.

[0042] A second aspect of the present invention provides a distributed detection system for key nodes in a wireless multi-hop network, which runs the distributed detection method for key nodes in a wireless multi-hop network as described in the first aspect, and includes a server and a distributed single-node statistical processing logic module;

[0043] The distributed single-node statistical processing logic module broadcasts neighbor collection frames and neighborhood collection frames. Nodes connected by one hop reply to the frame data. This distributed calculation calculates the degree of each node. Based on the degree value of each node and the degree values of all nodes connected by one hop, the key performance score of each node is calculated. The traffic count value of each node is also counted.

[0044] The server is used to traverse each node, iteratively compare the key performance score and traffic count value of each node, and select the nodes with the top key performance score and traffic count value as key nodes.

[0045] Compared with the prior art, the beneficial effects of the present invention include at least:

[0046] The present invention constructs adjacency information through local communication between nodes, and combines the joint evaluation method of structural characteristics and flow information to improve the representativeness and accuracy of key node identification; adopts the server traversal collection mechanism to construct key performance score sets and flow sets, and dynamically updates the set content based on replacement rules, so that the detection results have continuity and adaptability; combines real-time statistics and threshold judgment of data traffic to trigger data contraction control operations, and timely alleviates the data transmission pressure of high-frequency nodes during network operation, avoiding connection failures caused by local congestion; finally, through periodic traversal and judgment, a distributed key node detection process that can be executed cyclically is constructed, which does not rely on centralized control, realizes low-overhead and high-stability dynamic monitoring capabilities of key nodes, and is suitable for wireless multi-hop network scenarios with frequently changing structures.

[0047] The method of the present invention can quickly and accurately identify key nodes across an entire wireless multi-hop network. Its identification method takes into account both the node's local structural score and traffic data, resulting in a more comprehensive evaluation. Because the structural score uses a distributed approach, it avoids the need to acquire the entire wireless multi-hop network's topology. The overall operation is efficient and concise, with wide applicability. Only a single network-wide traversal and statistical analysis is required at regular intervals, significantly reducing the communication overhead of detecting key nodes across the entire network. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is based on the wireless multi-hop network connection diagram in the prior art;

[0049] Figure 2 is a processing logic flow chart of a single node provided according to an embodiment of the present invention;

[0050] Figure 3 It is a logic flow chart of overall processing of a wireless multi-hop network provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0052] Embodiment 1 of the present invention provides a distributed detection method for key nodes in a wireless multi-hop network, including single-node statistical processing logic and overall network statistical processing logic, and steps for detecting key nodes, wherein the single-node statistical processing logic uses distributed computing to obtain the key performance score of each node. On the basis of the single-node statistical processing logic, the steps of the overall network processing logic statistically analyze traffic and score data. The two cooperate with each other to detect and obtain key nodes, thereby fully realizing distributed detection of key nodes in a multi-hop wireless network.

[0053] Specifically, if Figure 2 As shown, the statistical processing logic of a single node specifically includes:

[0054] Step A.1: Each node in the wireless multi-hop network regularly broadcasts neighbor collection frames. The node that receives the neighbor collection frame replies with the sending node ID and the receiving node ID.

[0055] Specifically, each node in the wireless multi-hop network regularly broadcasts to the neighbor set H connected by one hop. u The node in sends a neighbor collection frame, with node u representing the external broadcast node. The neighbor collection frame contains the ID of the sending node u. The specific frame structure can be of any type, as long as the node receiving this frame can recognize that it is a neighbor collection frame.

[0056] Node v represents the node that receives the neighbor collection frame. Node v only needs to reply but does not need to forward the neighbor collection frame. The reply frame data needs to indicate the ID of the node u that sends the neighbor collection frame and the ID of the node v that replies to the neighbor collection frame.

[0057] Step A.2: Count the neighbor collection frame replies to form a set of neighbor node IDs; broadcast the neighborhood collection frame externally, count the neighborhood collection frame replies, and form a set of neighbor node IDs including the sending node ID, the receiving node ID, and its neighbor node IDs.

[0058] Specifically, node u counts the neighbor set H received u After the neighbor collection frame replies of each one-hop neighbor node, the neighbor node IDs are summarized and a set of neighbor node IDs is formed. u , and then node u broadcasts to the neighbor set H connected in one hop u The node in sends a neighborhood collection frame. The specific frame structure can be of any type. It only needs to be able to identify that the node receiving this frame is a neighborhood collection frame, and the neighborhood collection frame must contain the ID of the sending node u.

[0059] The node v that receives the neighborhood collection frame only needs to reply instead of forwarding the neighborhood collection frame. The reply frame data needs to indicate the ID of the node u that sends the neighborhood collection frame and the ID of the node v that replies to the neighborhood collection frame as well as the set J of its neighbor node IDs. v .

[0060] Step A.3: Each node deduplicates the responses to the collected neighborhood collection frames, that is, deduplicates the neighborhood node ID set and calculates the degree of each node.

[0061] Specifically, traverse the neighbor set J of each neighbor node v v If two neighbor nodes' neighbor sets each contain the other, the two nodes are considered as one node. After deduplication, the degree of node u is calculated, which is expressed as follows:

[0062] DGR u = the number of neighbor nodes of u after deduplication

[0063] Where:

[0064] DGR u represents the degree of node u.

[0065] Step A.4: After each node calculates its own degree, it broadcasts a degree collection frame and counts the degree collection frame replies from neighboring nodes.

[0066] Specifically, after node u calculates its own degree, it broadcasts to the neighbor set H connected in one hop. u The node in sends a degree collection frame. The degree collection frame contains the ID of the sending node u. The specific frame structure can be of any type, as long as the node receiving this frame can recognize it as a degree collection frame.

[0067] The node v that receives the degree collection frame needs to reply instead of forwarding the degree collection frame. The reply frame data needs to indicate the ID of the node u that sends the degree collection frame and the calculated degree DGR of the node v that replies to the degree collection frame. v And the set J of neighbor nodes of node v that replies to the degree collection frame v .

[0068] Step A.5: Each node calculates its own performance score based on the degree of the reply.

[0069] Specifically, after node u receives the degree of its one-hop neighbors, it first performs maximum merging on the degrees and then calculates the score. Maximum merging merges the degrees of nodes that are one-hop neighbors of node u. The two nodes are considered as one node, and the degree value is the maximum value of the degrees of the two one-hop neighbors, which is expressed as the following formula:

[0070] DGR 合并 = max(DGR of nodes that are one-hop neighbors)

[0071] Where:

[0072] DGR 合并 Indicates the degree after maximum value merging.

[0073] Let set M represent the set of neighbor nodes after the maximum value merging process, and calculate the key performance score of node u according to the following formula:

[0074]

[0075] Where:

[0076] SCORE u represents the key performance score of node u.

[0077] Step A.6: Whenever there is data transmission, reception, transmission or forwarding on node u, the traffic count value LL of node u is u Do add 1 processing.

[0078] Step A.7: Whenever node u receives a data contraction frame, the traffic count value of node u is square rooted as follows:

[0079]

[0080] Where:

[0081] LL u Indicates the traffic count value of node u.

[0082] The above steps are executed repeatedly to continuously complete the processing of a single node.

[0083] Specifically, if Figure 3 As shown in the figure, the statistical processing logic of the entire network and the steps of detecting key nodes specifically include:

[0084] Step B.1: The server periodically sends a traversal statistics frame. The initial traversal statistics frame contains the statistical value K, the traffic set L, and the score set P.

[0085] Specifically, the server sends traversal statistics frames regularly at regular intervals. The traversal method can adopt various traversal methods such as depth-first traversal or breadth-first traversal. It is only necessary to add information forward and backward mechanisms during the traversal process. Forward refers to the process of visiting the child node after visiting the parent node, and backward refers to the process of feeding back information to the parent node of the child node after visiting the child node.

[0086] The initial traversal statistics frame sent by the server includes a statistical value K, a traffic set L, and a score set P, wherein the initial values of the traffic set L and the score set P are both empty.

[0087] Step B.2: When the child node u visited in the forward direction receives the traversal statistics frame, it will compare and count the number of tuples in the set traffic set L and the score set P.

[0088] If the number of tuples in the traffic set L is less than the statistical value K, the ID and traffic data of node u are combined into a tuple and added to the traffic set L; if the number of tuples in the score set P is less than the statistical value K, the ID and score data of node u are combined into a tuple and added to the score set P; if the number of tuples in the traffic set L is greater than or equal to the statistical value K, proceed to step B.3.

[0089] Step B.3: Compare the traffic of each tuple in the traffic set L with the traffic of node u. If the traffic of node u is greater than the traffic count value in a tuple in the traffic set L, replace the original tuple in the traffic set L with the tuple consisting of node u's ID and traffic. If the number of tuples in the score set P is greater than or equal to the statistical value K, compare the score of each tuple in the score set P with the score of node u. If the score of node u is greater than the score value in a tuple in the score set P, replace the original tuple in the score set P with the tuple consisting of node u's ID and score.

[0090] Step B.4: During the traversal fallback access process, when the parent node v receives the fallback data of the traversal statistics frame, it directly passes the latest statistical value K, traffic set L, and score set P contained in the fallback access frame, and updates the statistical value K, traffic set L, and score set P data stored locally in the parent node.

[0091] Step B.5: When a traversal statistics is completed, the traversal statistics frame will be returned to the server. The server counts the tuples in the traffic set L and the score set P, and then marks the corresponding nodes of the ID in the traffic set L and the score set P as key nodes. The administrator can query the specific situation of the key nodes on the server.

[0092] Step B.6: When the server finds that a traversal statistics is completed, it will check the tuples in the traffic set L. If the maximum traffic value in the tuple exceeds a certain proportion of the maximum allowable value of the stored data, preferably but not limited to, exceeding 80%, the server sends a data contraction frame in a traversal manner.

[0093] The node that receives the data shrink frame takes the square root of the traffic data. The specific processing logic is the same as step 1.7.

[0094] Steps B.1 to B.6 are executed cyclically to continuously complete the statistical processing of the entire network.

[0095] Example 2 of the present invention provides a distributed detection method for key nodes in a wireless multi-hop network, comprising the following steps:

[0096] Step 1: Broadcast neighbor collection frames and neighborhood collection frames, receive neighbor sets and neighborhood sets, merge duplicate nodes, and calculate connectivity.

[0097] Preferably, step 1 specifically includes:

[0098] Step 1.1: The current node broadcasts a neighbor collection frame to its one-hop neighbor node. After receiving the neighbor response frame, the one-hop neighbor node returns a neighbor response frame containing its one-hop neighbor information. The current node receives multiple neighbor response frames and merges them to form the neighbor set of the current node.

[0099] Specifically, each node in the wireless multi-hop network regularly broadcasts to the neighbor set H connected by one hop. u The node in sends a neighbor collection frame, with u representing the name of the node. The neighbor collection frame contains the ID of the sending node u. The specific frame structure can be of any type, as long as the node receiving this frame can recognize that it is a neighbor collection frame.

[0100] Let v represent the node that receives the neighbor collection frame. Node v only needs to reply but not forward the neighbor collection frame. The reply frame data needs to indicate the ID of the node u that sends the neighbor collection frame and the ID of the node v that replies to the neighbor collection frame. u After the neighbor collection frame replies of each one-hop neighbor node, the neighbor node IDs are summarized and a set of neighbor node IDs is formed. u .

[0101] Step 1.2: The current node broadcasts a neighborhood collection frame to each adjacent node in the current node's neighbor set. After receiving the neighboring node, the adjacent node returns a neighborhood response frame. The neighborhood response frame contains the neighbor node identifier and degree value of the adjacent node. The current node receives multiple neighborhood response frames and merges them to form the neighborhood set of the current node.

[0102] Specifically, node u broadcasts to the neighbor set H connected in one hop. u The node in sends a neighborhood collection frame. The specific frame structure can be of any type. It only needs to be able to identify that the node receiving this frame is a neighborhood collection frame, and the neighborhood collection frame must contain the ID of the sending node u.

[0103] The node v that receives the neighborhood collection frame only needs to reply instead of forwarding the neighborhood collection frame. The reply frame data needs to indicate the ID of the node u that sends the neighborhood collection frame and the ID of the node v that replies to the neighborhood collection frame as well as the set J of its neighbor node IDs. v .

[0104] Step 1.3: After the current node receives the neighbor set, it identifies the neighbor node ID. If there are neighbor nodes with the same ID, they are merged into a one-hop neighbor node. When multiple neighbor nodes have a one-hop neighbor relationship with each other, the neighbor node with the largest degree value is retained, and the connectivity information of the remaining neighbor nodes is discarded. All retained neighbor nodes are used as the first neighbor set, where the connectivity is the number of neighbor nodes retained after the merger.

[0105] Specifically, it is to traverse the neighbor set J of each neighbor node v v If the neighbor sets of two neighbor nodes each contain each other, the two nodes are considered as one node. After deduplication, the connectivity DGR of node u is calculated. u , expressed as follows:

[0106] DGR u = the number of neighbor nodes of node u after deduplication

[0107] Where:

[0108] DGR u represents the connectivity of node u.

[0109] Step 2: Broadcast the degree collection frame, receive neighbor connectivity and neighborhood information, merge the degree values, and calculate the performance score.

[0110] Preferably, step 2 specifically includes:

[0111] Step 2.1: The current node broadcasts a degree collection frame to its one-hop neighboring node, and the neighboring node returns a response message containing its own connectivity and the set of its one-hop neighboring nodes;

[0112] Specifically, after node u calculates its own degree, it broadcasts to the neighbor set H connected in one hop. u The node in sends a degree collection frame. The degree collection frame contains the ID of the sending node u. The specific frame structure can be of any type, as long as the node receiving this frame can recognize it as a degree collection frame. The node v that receives the degree collection frame needs to reply instead of forwarding the degree collection frame. The reply frame data needs to indicate the ID of the node u that sends the degree collection frame and the calculated degree DGR of the node v that replies to the degree collection frame. v And the set J of neighbor nodes of node v that replies to the degree collection frame v .

[0113] Step 2.2: After receiving all response messages, the current node constructs a response adjacency set. If there are multiple nodes in the response adjacency set that are adjacent to each other, the node with the largest degree is retained and the rest are discarded.

[0114] Step 2.3: The key performance score is obtained by the ratio between the square of the current node's connectivity and the sum of the degree values of each node in the response adjacency set minus 1.

[0115] Specifically, after node u receives the degree of its one-hop neighbor, it first performs maximum merging on the degree and then calculates the score;

[0116] The maximum value merging process is to merge the degrees of the nodes that are one-hop neighbors of u. The two nodes are regarded as one node, and the degree value is the maximum value of the degrees of the two one-hop neighbors.

[0117] DGR combined = MAX (DGR of nodes that are one-hop neighbors)

[0118] Assume that the set of neighbor nodes after the maximum value merging process is M, and calculate the score of node u according to the following formula:

[0119]

[0120] Where:

[0121] SCORE u represents the score of node u.

[0122] Furthermore, the traffic count value is increased by 1 each time data is sent, received, and forwarded.

[0123] Step 3: The server traverses all nodes at set time intervals, accesses nodes in a depth-first manner, collects key performance scores and traffic count values, and constructs score sets and traffic sets.

[0124] Preferably, step 3 specifically includes:

[0125] Step 3.1: The server sends a traversal statistics frame, which includes three parts: statistical value, key performance score set, and traffic count set. The statistical value is the set number of target key nodes, and the key performance score set and traffic count set are initially empty.

[0126] Step 3.2: The server visits each node in turn. After receiving the traversal statistics frame, each node determines whether the number of nodes recorded in the key performance score set and the traffic count set is less than the statistical value. If the key performance score set is not full, the data pair consisting of the current node identifier and the key performance score is added to the key performance score set; if the traffic count set is not full, the data pair consisting of the current node identifier and the traffic count value is added to the traffic count set.

[0127] Step 3.3: After the statistical frame has completed all node visits, it will fall back to the server step by step along the original traversal path. When the intermediate nodes along the way receive the fallback frame, they will update the key performance score set and traffic count set copies stored locally at this node for initial reference in the next round of traversal.

[0128] Specifically, the server first sends a traversal statistics frame at regular intervals. The traversal method can be depth-first traversal or breadth-first traversal, and various other traversal methods can be used. The only requirement is to add forward and backward information mechanisms during the traversal process. Forward refers to the process of visiting a child node after visiting its parent node, and backward refers to the process of feeding back information to the child node's parent node after visiting a child node. The initial traversal statistics frame sent by the server contains the statistical value K, the traffic set L (initially empty), and the score set P (initially empty).

[0129] When a child node u is accessed in the forward direction of the traversal and receives a traversal statistics frame, it compares and counts the number of tuples in the aggregate flow set L and the aggregate score set P. If the number of tuples in the flow set L is less than the statistical value K, the ID and flow data of node u are combined into a tuple and added to the aggregate flow set L. If the number of tuples in the score set P is less than the statistical value K, the ID and score data of node u are combined into a tuple and added to the aggregate score set P.

[0130] Step 4: Update the score set and traffic set based on the key performance scores and traffic count values uploaded by the nodes. When the set is full, replace the node data with the smallest key performance score value in the set. When there are multiple nodes with the same score, replace the node data with the smallest traffic count value.

[0131] Preferably, step 4 specifically includes:

[0132] Step 4.1: Compare the current node's key performance score with the key performance scores of each data pair in the key performance score set. If the current key performance score is greater, replace the data pair with the smallest key performance score in the key performance score set.

[0133] Step 4.2: If there are multiple data pairs with the same key performance score, continue to compare the corresponding flow count values and replace the data pair with the smaller flow count value with the data pair with the larger flow count value;

[0134] Step 4.3: Execute the same logic on the traffic count set. If the current traffic count value is greater than the traffic count value of a data pair in the set, replace it with the data pair consisting of the current node identifier and traffic count value.

[0135] Specifically, when a child node u accessed in the forward direction of the traversal receives a traversal statistics frame, it compares and counts the number of tuples in the collective traffic set L and the collective score set P. If the number of tuples in the traffic set L is ≥ the statistical value K, the traffic of each tuple in the traffic set L is compared with the traffic of node u. If the traffic of node u is greater than the traffic value of a tuple in the traffic set L, the tuple consisting of node u's ID and traffic is used to replace the original tuple in the traffic set L. If the number of tuples in the score set P is ≥ the statistical value K, the score of each tuple in the score set P is compared with the score of node u. If the score of node u is greater than the score of a tuple in the score set P, the tuple consisting of node u's ID and score is used to replace the original tuple in the score set P.

[0136] Step 5: When the maximum flow count value in the flow set exceeds the set threshold ratio, the server broadcasts a data contraction frame.

[0137] Preferably, step 5 specifically includes:

[0138] Step 5.1: After completing a node traversal statistics, the server extracts the traffic count values of all nodes in the current traffic set and calculates the maximum value among them;

[0139] Step 5.2: If the maximum traffic count value exceeds the set ratio threshold of the maximum allowable value of stored data, the server broadcasts a data shrinkage frame to all nodes in a traversal manner, triggering each node to perform traffic compression processing.

[0140] Specifically, after the server completes a round of traversal and constructs the traffic count set L, it extracts the traffic count values of all nodes from the set L and calculates the maximum traffic value. If this maximum value exceeds the set compression threshold ratio (that is, it is greater than the product of the node's maximum acceptable traffic count value and a preset scaling factor θ), the server determines that a high-frequency communication node has appeared in the current network, posing a congestion risk.

[0141] Step 6: The node shrinks the frame according to the received data, compresses the traffic count value, and uploads the compressed count result in the next traversal.

[0142] Preferably but not limitatively, step 5 specifically includes:

[0143] After the current node receives the data contraction frame broadcast by the server, it performs a square root operation on the current traffic count value and replaces the original traffic count value of the current node with the operation result; during the next traversal access process of the server, the compressed traffic count value is uploaded.

[0144] Specifically, when the server finds that a traversal statistics is completed, it will check the tuples in the collective traffic set L. If the maximum traffic value in the tuple exceeds a certain proportion of the maximum allowable value of the stored data (for example, 80%), the server sends a data contraction frame in a traversal manner, and the node that receives the data contraction frame takes the square root of the traffic data.

[0145] Step 7: After the traversal is completed, the server marks the key nodes according to the score set and the traffic set to complete the distributed detection of the key nodes.

[0146] Preferably but not limitatively, step 7 specifically includes:

[0147] Step 7.1: After the traversal statistics frame is returned to the server, the server reads the node identifiers in the score set and the flow set respectively, confirms all the nodes included in the set; and marks all the nodes in the score set and the flow set as key nodes;

[0148] Step 7.2: After the detection process is completed, a new round of node information construction and traversal process is restarted to form a key node distributed detection mechanism with cyclic execution.

[0149] Specifically, when a traversal statistics is completed, the traversal statistics frame will be returned to the server. The server counts the tuples in the set traffic set L and the set score set P, and then marks the corresponding nodes of the ID in the set traffic set L and the score set P as key nodes. The administrator can query the specific situation of the key nodes on the server.

[0150] Example 3 of the present invention provides a distributed detection system for key nodes in a wireless multi-hop network, which runs the distributed detection method for key nodes in a wireless multi-hop network as described in Example 1 or 2, including a server and a distributed single-node statistical processing logic module;

[0151] The distributed single-node statistical processing logic module broadcasts neighbor collection frames and neighborhood collection frames. Nodes connected by one hop reply to the frame data. This distributed calculation calculates the degree of each node. Based on the degree value of each node and the degree values of all nodes connected by one hop, the key performance score of each node is calculated. The traffic count value of each node is also counted.

[0152] The server is used to traverse each node, iteratively compare the key performance score and traffic count value of each node, and select the nodes with the top key performance score and traffic count value as key nodes.

[0153] Compared with the existing technology, the present invention constructs adjacency information through local communication between nodes, and combines the joint evaluation method of structural characteristics and traffic information to improve the representativeness and accuracy of key node identification; adopts the server traversal collection mechanism to construct key performance score sets and traffic sets, and dynamically updates the set content based on replacement rules, so that the detection results have continuity and adaptability; combines real-time statistics and threshold judgment of data traffic to trigger data contraction control operations, and timely alleviates the data transmission pressure of high-frequency nodes during network operation, avoiding connection failures caused by local congestion; finally, through periodic traversal and judgment, a distributed key node detection process that can be executed cyclically is constructed, which does not rely on centralized control, and realizes low-overhead, high-stability dynamic monitoring capabilities of key nodes, which is suitable for wireless multi-hop network scenarios with frequently changing structures.

[0154] This invention can quickly and accurately identify key nodes across an entire wireless multi-hop network. Its identification method combines local structural scoring with traffic data for a more comprehensive evaluation. Because structural scoring utilizes a distributed approach, it avoids the need to acquire the entire wireless multi-hop network's topology. The overall operation is efficient and concise, with wide applicability. Only a single network-wide traversal and statistical analysis is required at regular intervals, significantly reducing the communication overhead associated with detecting key nodes across the entire network.

[0155] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0156] 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 above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A distributed detection method for key nodes in a wireless multi-hop network, characterized in that: Includes statistical processing logic for individual nodes and the entire network, and detects steps for key nodes; The statistical processing logic for a single node includes: each node broadcasts neighbor collection frames and neighborhood collection frames, and the nodes connected by one hop reply frame data. This is used to calculate the degree of each node in a distributed manner. Based on the degree value of each node and the degree values of all nodes connected by one hop, the key performance score of the single node is calculated; and the traffic count value of each node is counted. The statistical processing logic of the entire network and the detection of key nodes include: the server traverses each node, iteratively compares the key performance score and traffic count value of each node, and selects the nodes with the top key performance score and traffic count value as key nodes.

2. The distributed detection method for key nodes in a wireless multi-hop network according to claim 1, characterized in that: Each node broadcasts neighbor collection frames and neighborhood collection frames to the outside, and the nodes connected by one hop reply frame data. The distributed calculation of the degree value of each node includes: Each node in the wireless multi-hop network regularly broadcasts neighbor collection frames. The node that receives the neighbor collection frame replies with the sending node ID and the receiving node ID. The neighbor collection frame replies are counted to form a set containing neighbor node IDs. Broadcast the neighborhood collection frame externally, count the neighborhood collection frame replies, and form a neighborhood node ID set that includes the sending node ID, the receiving node ID, and its neighbor node IDs; Each node deduplicates the collected neighborhood node IDs and calculates the degree of each node.

3. The distributed detection method for key nodes in a wireless multi-hop network according to claim 1, characterized in that: The calculation of the key performance score of a single node based on the degree value of each node and the degree values of all nodes connected to it in one hop includes: After each node calculates its own degree value, it broadcasts a degree collection frame and counts the degree collection frame replies from neighboring nodes. First perform maximum value merging on the degree value; After merging the maximum values, the degree values of all nodes in the neighbor node set are subtracted by 1 and then summed up. The sum is added by 1 and used as the denominator. The square of the node's degree value is used as the denominator. The ratio of the two is the key performance score of the single node.

4. The distributed detection method for key nodes in a wireless multi-hop network according to claim 1, characterized in that: The counting of traffic count values of each node includes: Whenever data is sent, received, transmitted or forwarded on a node, the traffic count value of the node is increased by 1; Whenever a node receives a data contraction frame, it performs square root operation on the traffic count value of the node and updates and replaces the previous traffic count value.

5. The distributed detection method for key nodes in a wireless multi-hop network according to claim 1, characterized in that: The server traverses all nodes at set time intervals, accesses nodes in a depth-first manner, collects key performance scores and traffic counts, and constructs score sets and traffic sets, including: The server sends a traversal statistics frame, which includes three parts: statistical value, key performance score set and traffic count set. The statistical value is the set number of target key nodes, and the key performance score set and traffic count set are initially empty. The server visits each node in turn. After receiving the traversal statistics frame, each node determines whether the number of nodes recorded in the key performance score set and the traffic count set is less than the statistical value. If the key performance score set is not full, the data pair consisting of the current node identifier and the key performance score is added to the key performance score set; if the traffic count set is not full, the data pair consisting of the current node identifier and the traffic count value is added to the traffic count set. After the traversal statistics frame completes the visit of all nodes, it falls back to the server step by step along the original traversal path. When the intermediate nodes along the way pass through the statistics frame, they update the key performance score set and traffic count set copies stored locally at the node when receiving the fallback frame, which will be used as the initial reference in the next round of traversal.

6. The distributed detection method for key nodes in a wireless multi-hop network according to claim 5, characterized in that: The method of updating the score set and traffic set according to the key performance score and traffic count value uploaded by the node, replacing the node data with the smallest key performance score value in the set when the set is full, and replacing the node data with the smallest traffic count value when there are multiple nodes with the same score, includes: Compare the key performance score of the current node with the key performance scores of each data pair in the key performance score set. If the current key performance score is greater, replace the data pair with the smallest key performance score in the key performance score set. If there are multiple data pairs with the same key performance score, continue to compare the corresponding flow count values and replace the data pair with the smaller flow count value with the data pair with the larger flow count value; The same logic is executed on the traffic count set. If the current traffic count value is greater than the traffic count value of a data pair in the set, the data pair consisting of the current node identifier and the traffic count value is replaced.

7. The distributed detection method for key nodes in a wireless multi-hop network according to claim 1, characterized in that: When the maximum flow count value in the flow set exceeds the set threshold ratio, the server broadcasts a data contraction frame, including: After completing a node traversal statistics, the server extracts the traffic count values of all nodes in the current traffic set and calculates the maximum value among them; If the maximum traffic count value exceeds the set ratio threshold of the maximum allowable value of stored data, the server broadcasts the data contraction frame to all nodes in a traversal manner, triggering each node to perform traffic compression processing.

8. The distributed detection method for key nodes in a wireless multi-hop network according to claim 1, characterized in that: The node performs compression processing on the traffic count value, including: After the current node receives the data contraction frame broadcast by the server, it performs a square root operation on the current traffic count value and replaces the original traffic count value of the current node with the operation result; during the next traversal access process of the server, the compressed traffic count value is uploaded.

9. The distributed detection method for key nodes in a wireless multi-hop network according to claim 1, characterized in that: After the traversal is completed, the server marks the key nodes according to the score set and the traffic set, and completes the distributed detection of the key nodes, including: After the traversal statistics frame is returned to the server, the server reads the node identifiers in the score set and the traffic set respectively, confirms all the nodes included in the set; and marks all the nodes in the score set and the traffic set as key nodes; After the detection process is completed, a new round of node information construction and traversal process is restarted to form a key node distributed detection mechanism with cyclic execution.

10. A distributed detection system for key nodes in a wireless multi-hop network, running the distributed detection method for key nodes in a wireless multi-hop network according to any one of claims 1 to 9, characterized in that: Includes server and distributed single-node statistical processing logic modules; The distributed single-node statistical processing logic module broadcasts neighbor collection frames and neighborhood collection frames. Nodes connected by one hop reply to the frame data. This distributed calculation calculates the degree of each node. Based on the degree value of each node and the degree values of all nodes connected by one hop, the key performance score of each node is calculated. And count the traffic count value of each node; The server is used to traverse each node, iteratively compare the key performance score and traffic count value of each node, and select the nodes with the top key performance score and traffic count value as key nodes.

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