Directional sensor network communication method considering hop count and distance constraint

Abstract

The invention discloses a directional sensor network communication method considering hop count and distance constraints, which comprises the following steps of: traversing each node serving as a main node to establish a network topology, and traversing the network layer by layer by adopting a breadth-first search algorithm and considering the distance and hop count constraints; and screening potential sub-nodes of the current node by using a sub-node selection strategy algorithm, wherein screening conditions comprise communication distances between the nodes, azimuth angles and sub-node number limitation. Through a sliding window mechanism, an angle range which contains the most child nodes and has the shortest communication distance is selected, and the antenna azimuth angle of the main node is calculated, so that the connection efficiency and the energy consumption are optimized. After a plurality of possible network topologies are generated, the optimal connectivity and energy efficiency of the network are realized by evaluating the connectivity rate, the hop count and the communication distance of the network. Compared with the prior art, on the premise that the connectivity requirement is met, the communication energy consumption is remarkably reduced, and the network efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of wireless communication, and in particular to a directional sensor network connection method considering hop count and distance constraints. Background Art

[0002] With the rapid development of the Internet of Things and wireless sensor networks, directional sensor networks, as an efficient communication model, have been widely used in environmental monitoring, smart agriculture, drone clusters, edge computing and other fields. In the traditional omnidirectional communication network model, nodes can broadcast signals evenly in any direction, but their energy consumption is high and signal interference is serious, which makes it difficult to meet the performance requirements in resource-constrained environments. In contrast, directional sensor networks limit the communication coverage range by directional antennas, which not only significantly reduces energy consumption, but also improves signal quality and reduces interference. However, in directional sensor networks, the communication range of nodes is limited by the antenna azimuth and coverage angle, resulting in sparse network topology and poor connectivity. In order to ensure the reliable connectivity of the network, it is usually necessary to consider multiple constraints at the same time, including communication distance restrictions between nodes, network hop restrictions, and node degree restrictions. Among them, communication distance restrictions are used to ensure the communication quality within the signal coverage range; hop restrictions are used to control path delay and network overhead; node degree restrictions are used to balance network load and energy consumption. How to build an efficient network topology while meeting the above constraints is a key issue in the study of directional sensor networks.

[0003] Existing network connectivity methods are mainly designed for omnidirectional sensor networks, mostly based on breadth-first search, depth-first search or minimum spanning tree methods. These methods have the following shortcomings in directional sensor networks:

[0004] (1) The algorithm fails to take into account the directional coverage characteristics of nodes, which results in the algorithm being unable to fully utilize the energy efficiency advantages of directional antennas;

[0005] (2) The constraints on communication distance and hop count are weak, which may easily lead to unnecessary energy waste and instability of network topology;

[0006] (3) Failure to optimize the node directionality and neighbor node distribution leads to inefficient network connection.

[0007] To solve the above problems, a network connectivity method is needed that takes into account the constraints of hop count, distance and directional coverage. By optimizing the connection relationship between nodes, the communication energy consumption can be minimized and the network performance can be improved while satisfying the network connectivity. Summary of the invention

[0008] The present invention discloses a directional sensor network connectivity method considering hop count and distance constraints, generating an efficient network topology satisfying multiple constraints, aiming to solve the problems of poor connectivity, high energy consumption and unstable topology in existing omnidirectional communication methods in directional sensor networks.

[0009] The technical solution of the present invention is: the present invention generates an efficient network topology that meets multiple constraints by constructing an optimization algorithm that combines breadth-first search and sub-node selection. The method uses the direction coverage angle, communication range and maximum number of hops of the node as the main parameters, dynamically adjusts the antenna azimuth angle of the node and the sub-node selection strategy, thereby optimizing the network's connectivity rate, total number of hops and communication distance. The specific steps are as follows:

[0010] S1: Breadth-first search network traversal;

[0011] Select a node from the network as the main node, and use the breadth-first search algorithm to traverse the entire network layer by layer. In each traversal process, determine whether to continue to expand the connection based on whether the number of hops of the current node reaches the preset maximum hop limit H. For all unvisited neighbor nodes of the current node, call the child node selection algorithm to filter out the child nodes that meet the constraints, and add these child nodes to the network connection, while recording the connection relationship and hop count.

[0012] S2: sub-node selection and antenna azimuth angle calculation;

[0013] The potential neighbor node set of the current node is screened through the child node selection algorithm. Specifically, it includes:

[0014] S2.1: Azimuth calculation: Calculate the azimuth relative to the current node based on the coordinates of the neighboring nodes, and sort these azimuths from small to large;

[0015] S2.2: Sliding window optimization: Take the antenna direction coverage angle Φ as the window width, traverse all possible angle ranges, and select the window with the largest number of nodes (if the node has a parent node, the window must include the parent node) as the optimal direction range;

[0016] S2.3: Azimuth adjustment: Calculate the maximum azimuth and minimum azimuth of the nodes in the selected window, take the average value as the antenna azimuth of the current node, and adjust the communication direction;

[0017] S2.4: Subnode number limit: If the number of subnodes in the selected window exceeds the subnode number limit K, the K closest subnodes are preferentially selected.

[0018] S3: Network topology generation and optimization;

[0019] S3.1: Network topology generation: traverse each node in the network, take it as the main node in turn, repeat the breadth-first search and child node selection operations, and generate multiple network topology structures that meet the hop count and distance constraints.

[0020] S3.2: Calculate the following indicators for each network topology:

[0021] 1) Connectivity rate: the ratio of successfully connected nodes to the total number of nodes;

[0022] 2) Total hop count: the sum of the hop counts from the master node to other nodes;

[0023] 3) Total communication distance: the sum of the communication distances of all connections in the network.

[0024] S3.3: Through the evaluation of the above indicators, the following optimization mechanism is adopted for the generated network topology: first, the network topology with the highest connectivity rate is selected; if the connectivity rates are the same, the topology with the least total hops is selected; if the total hops are the same, the topology with the shortest total communication distance is further selected, and finally the optimal network structure that meets multiple constraints is obtained.

[0025] Compared with the prior art, the present invention has the following significant advantages:

[0026] (1) The network connectivity method of the present invention significantly reduces communication energy consumption and improves network efficiency while meeting connectivity requirements;

[0027] (2) In the network connectivity method of the present invention, the antenna azimuth is dynamically adjusted through a sliding window optimization algorithm to achieve efficient use of the directional coverage range;

[0028] (3) The network connectivity method of the present invention comprehensively considers the number of hops, distance and direction coverage constraints to ensure the stability and adaptability of the network topology;

[0029] (4) The network connectivity method of the present invention is applicable to complex distributed network scenarios such as drone formations, intelligent traffic monitoring, and environmental monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Attached Figure 1 It is a flow chart of the present invention.

[0031] Attached Figure 2 It is a schematic diagram of subnode selection of the present invention. DETAILED DESCRIPTION

[0032] The terms used in the present invention are only for the purpose of illustrating the embodiments of the present invention and are not intended to limit the present invention. Figure 1-2 , some embodiments of the present invention are described in detail.

[0033] The directional sensor network connectivity method provided by the present invention combines the breadth-first search algorithm and the child node selection strategy to achieve efficient network topology generation that simultaneously considers the hop count limit, communication distance constraint, and directional coverage angle constraint. Figure 1 Describe the specific implementation steps of the present invention:

[0034] S1. Breadth-first search considering hop limit:

[0035] S1.1: Initialize the queue and the master node settings: randomly select a node as the master node s, initialize the queue Q, and put the master node and its initial hop count 0, represented as (s, 0), into the queue Q and mark it as visited.

[0036] S1.2: Check the number of hops corresponding to the node:

[0037] Take out the node i and its corresponding hop count h from the queue, define H as the maximum hop count, if h < H, go to the next step and expand the connection; otherwise, output the connection set C i :

[0038] S1.3: Use the neighbor node distance to filter child nodes; calculate the distance d from node i to all unvisited neighbor nodes ij , define R as the maximum distance between neighbor nodes, and select nodes that satisfy d ij ≤R distance requirement; So far, the set of child nodes that meet the distance and hop count requirements is obtained, and the next step is entered;

[0039] S1.4: According to S2, further filter the sub-node set that meets the conditions;

[0040] S1.5: Add each child node and its hop count h+1 to queue Q, record the connection relationship, and mark it as visited;

[0041] S1.6: Repeat S1.2-S1.5 until the queue is empty or the number of hops reaches the limit H; then output the connection set C i .

[0042] S2. Establish a subnode selection algorithm and calculate the antenna azimuth;

[0043] The potential neighbor node set of the current node is screened through the child node selection algorithm. Specifically, it includes:

[0044] S2.1: Azimuth calculation: Calculate the azimuth relative to the current node based on the coordinates of the neighboring nodes, and sort these azimuths from small to large;

[0045] Specifically: for the current node i, calculate the azimuth θ of all potential child nodes j ij; If node i has a parent node, calculate and record the azimuth angle θ of the parent node parent ; Sort all azimuths in ascending order.

[0046] S2.2: Sliding window optimization: as shown in the attached Figure 2 As shown, the sliding window method is used to traverse the azimuths of potential child nodes arranged in ascending order, and the window is [θ ij ,θ ij +Φ], Φ is the coverage angle of the antenna direction; the windows containing the most child nodes are counted. If a parent node exists, the child node windows containing the most parent nodes are counted;

[0047] S2.3: Azimuth adjustment: Determine the set of child nodes in the optimal window and calculate the maximum azimuth angle θ of the node max and the minimum azimuth angle θ min , and then calculate the antenna azimuth:

[0048] Where α i is the antenna azimuth.

[0049] S2.4: Sub-node number limit: If the number of nodes in the sliding window exceeds the node number limit K, only the nearest K sub-nodes are selected; and the filtered sub-node set is obtained.

[0050] S3: Network topology generation and optimization;

[0051] S3.1: Network topology generation: traverse each node in the network, take it as the main node in turn, repeat the breadth-first search in S1 and the child node selection operation in S2, and generate multiple network topology structures that meet the hop count and distance constraints.

[0052] S3.2: Calculate the following indicators for each network topology:

[0053] 1) Connectivity rate: the ratio of successfully connected nodes to the total number of nodes;

[0054] 2) Total hop count: the sum of the hop counts from the master node to other nodes;

[0055] 3) Total communication distance: the sum of the communication distances of all connections in the network.

[0056] S3.3: Compare all network topologies and select the final network structure according to the following optimization rules:

[0057] The selection rules are:

[0058] (1) First select the topology with the highest connectivity rate;

[0059] (2) If the connectivity rates are the same, the topology with the least total hops is selected;

[0060] (3) If the total number of hops is the same, the topology with the shortest communication distance is selected;

[0061] (4) Output the optimal network topology that satisfies the above rules.

[0062] The above is only an implementation mode of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A directional sensor network connectivity method considering hop count and distance constraints, characterized in that: Specifically include: S1: Breadth-first search considering hop limit; S2: Establish a subnode selection algorithm and calculate the antenna azimuth; S3: Traverse the entire network nodes, generate and optimize the network topology.

2. The directional sensor network connection method considering hop count and distance constraints according to claim 1 is characterized in that: In S1, consider the breadth-first search with hop limit: S1.1: Initialize the queue and the master node settings: randomly select a node as the master node s, with a hop count of 0, initialize the queue Q, and put (s,0) into the queue Q, marking it as visited. S1.2: Check the hop count corresponding to the node: Take out the node i and its corresponding hop count h from the queue, define H as the maximum hop count, if h<H, go to the next step and expand the connection; otherwise, output the connection set C i : S1.3: Use the neighbor node distance to filter child nodes: Calculate the distance d from node i to all unvisited neighbor nodes ij , define R as the maximum distance between neighbor nodes, and select nodes that satisfy d ij ≤R distance requirement; So far, the set of child nodes that meet the distance and hop count requirements is obtained, and the next step is entered; S1.4: further screen the sub-node set that meets the conditions according to the sub-node selection algorithm in S2; S1.5: Add each child node and hop count h+1 to queue Q, record the connection relationship, and mark it as visited; S1.6: Repeat S1.2-S1.5 until the queue is empty or the number of hops reaches the limit H; then output the connection set C i .

3. The directional sensor network connection method considering hop count and distance constraints according to claim 1 is characterized in that: In S2, the specific steps are: S2.1: Azimuth calculation: Calculate the azimuth relative to the current node based on the coordinates of the neighboring nodes, and sort these azimuths from small to large; S2.2: Sliding window optimization: Take the antenna direction coverage angle Φ as the window width, traverse all possible angle ranges, and select the window with the largest number of nodes (if the node has a parent node, the window must include the parent node) as the optimal direction range; S2.3: Azimuth adjustment: Calculate the maximum azimuth and minimum azimuth of the nodes in the selected window, take the average value as the antenna azimuth of the current node, and adjust the communication direction; S2.4: Subnode number limit: If the number of subnodes in the selected window exceeds the subnode number limit K, the K closest subnodes are preferentially selected.

4. The directional sensor network connection method considering hop count and distance constraints according to claim 1, characterized in that: In S3, the specific steps are: S3.1: Network topology generation: traverse each node in the network, take it as the main node in turn, repeat the breadth-first search and child node selection operations, and generate multiple network topology structures that meet the hop count and distance constraints; S3.2: Calculate the connectivity rate, total hop count and total communication distance of each network topology; S3.3: Compare all network topologies and select the final network structure according to the optimization rules.

5. The directional sensor network connection method considering hop count and distance constraints according to claim 5 is characterized in that: In S3.2: Connectivity rate: the ratio of successfully connected nodes to the total number of nodes; Total hops: the sum of the hops from the main node to other nodes; Total communication distance: the sum of the communication distances of all connections in the network.

6. The directional sensor network connection method considering hop count and distance constraints according to claim 5, characterized in that: In S3.3, the optimization rule is: first select the topology with the highest connectivity rate; if the connectivity rates are the same, then select the topology with the least total hops; if the total hops are the same, then select the topology with the shortest communication distance; output the optimal network topology that meets the above rules.

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