A Method for Detecting Boundary Nodes in Wireless Sensor Networks Based on Subset Partitioning

Abstract

The present invention designs a method for detecting boundary nodes in a wireless sensor network based on subset partitioning. This method aims to extend the lifespan of boundary nodes by switching the boundary. This method is divided into two stages. The first stage is to find the initial boundary nodes and expand the boundary nodes; the second stage is to partition the boundary nodes found in the first stage into subsets, that is, to further supplement the boundary nodes. The present invention adopts a method for detecting boundary nodes in a wireless sensor network based on subset partitioning, which can detect multiple groups of boundary nodes. In this way, the energy consumption of the nodes is reduced by the rotation of each group of boundary nodes, achieving the purpose of extending the lifespan of the boundary nodes.

Description

Technical Field

[0001] The present invention belongs to the field of node detection in wireless sensor networks. Background Art

[0002] Wireless Sensor Networks (WSN) are one of the current research hotspots. They have received a great deal of attention and are being applied to more and more applications, including traffic control, battlefield surveillance, healthcare, precision agriculture, and target tracking and monitoring, etc. In target tracking applications based on wireless sensor networks, a group of sensor nodes are randomly deployed in an area, and each sensor node can collect target information within its sensing range for target tracking. Since most sensor nodes are battery-powered and non-rechargeable, an important aspect of wireless sensor networks is to reduce energy consumption.

[0003] Target tracking and monitoring systems based on wireless sensor networks need to detect events such as objects entering and leaving the monitored area. According to the different roles they play, the nodes performing target area monitoring can be divided into internal nodes and boundary nodes. In surveillance applications, boundary nodes need to always remain active to perform the surveillance of the target area, while internal nodes can sleep to save energy. Therefore, compared with internal nodes, boundary nodes consume more energy, resulting in premature failure of boundary nodes and affecting the monitoring effect. And existing boundary node detection algorithms for wireless sensor networks rarely pay attention to the lifespan problem of boundary nodes. Summary of the Invention

[0004] Aiming at the problems of high energy consumption and short lifespan of boundary nodes in wireless sensor networks, the present invention proposes a method for detecting boundary nodes in wireless sensor networks based on subset partitioning, which aims to extend the lifespan of boundary nodes by switching boundaries. This method is divided into two stages. The first stage is to find initial boundary nodes and extended boundary nodes; the second stage is to perform subset partitioning on the boundary nodes found in the first stage, that is, to further supplement the boundary nodes.

[0005] The specific implementation process is as follows:

[0006] 1. Idea of the method for detecting boundary nodes in wireless sensor networks based on subset partitioning

[0007] First, run the convex hull algorithm on the nodes of the wireless sensor network to calculate the convex hull of the entire network area. For the convenience of description, the nodes located at each vertex of the convex hull are called initial boundary nodes. Then, as Figure 1As shown in the figure, multi-hop communication is used between two adjacent initial boundary nodes to find their shortest path, and the nodes located on the shortest path are called extended boundary nodes. The initial boundary nodes and extended boundary nodes are the boundary nodes found in the first stage of the algorithm. The boundary nodes found in the first stage of the algorithm are not perfect. As can be seen from Figure 1 , there are still some nodes outside the area connected by the initial boundary nodes and the extended boundary nodes. Therefore, we will further increase the number of boundary nodes during the process of dividing the boundary node subset in the second stage of the algorithm.

[0008] In the second stage of the algorithm, the initial boundary nodes and extended boundary nodes will dominate the generation process of a subset and are the generation centers of the subset. Assuming that the communication radius of the sensor nodes is R, the generation process of the node subset is as Figure 2 shown. Taking the initial boundary nodes and extended boundary nodes as the centers, draw a circle with a radius of R / 2. All the nodes falling inside this circle (including the nodes not selected as boundary nodes in the first stage, which we call supplementary boundary nodes here) are divided into the same subset. After obtaining the subset, color the nodes in the subset with different colors. Since we use a circle with a radius of R / 2 during the process of dividing the boundary node subset, the distance between the nodes of the same color in two adjacent subsets is less than 2R. Therefore, letting the nodes with the same color undertake the monitoring task can completely cover the boundary area of the network without detection blind spots. By dividing the boundary nodes into several subsets and coloring them, the nodes with the same color can form a group of boundary nodes. During a certain period of time, only one group of boundary nodes is allowed to undertake the task of monitoring the target, and the boundary nodes of the other groups can enter the sleep state, thereby prolonging the lifespan of the boundary nodes.

[0009] Since the number of groups of boundary nodes depends on the number of successfully colored colors, in order to detect more groups of boundary nodes, for the nodes located in the intersection part of the two circles, first compare the number of nodes inside the two circles. Secondly, the nodes in the intersection part are first colored in turn according to the circle with fewer nodes until the number of successfully colored nodes in the two circles is the same. Finally, if there are still remaining intersection nodes, if the number is even, the two circles divide them equally; if it is odd, leave one as a spare node to replace the node with lower remaining energy.

[0010] 2. Implementation steps of the method for detecting boundary nodes in wireless sensor networks based on subset division

[0011] (1) Initialize the network.

[0012] (2) Run the convex hull algorithm to calculate the convex hull of the entire network and detect the initial boundary nodes.

[0013] (3) Through multi-hop communication between the initial boundary nodes, find their shortest paths and detect the extended boundary nodes.

[0014] (4) With the initial boundary nodes and the extended boundary nodes as the leading part, perform subset partitioning.

[0015] (5) After obtaining the subsets, color the nodes within each subset with different colors. The nodes with the same color form a group of boundary nodes.

[0016] The beneficial effects of the present invention are as follows:

[0017] 1. Detection results

[0018] The method for detecting boundary nodes of a wireless sensor network based on subset partitioning can detect multiple groups of boundary nodes.

[0019] Figure 3 It is the detection result when the number of nodes is 500 and the communication radius is 120m. In the figure, "▲", "■" and "◆" respectively represent the first group, the second group and the third group of detected boundary nodes, and "○" represents internal nodes. It can be seen from the figure that the method for detecting boundary nodes of a wireless sensor network based on subset partitioning can detect multiple groups of boundary nodes. Therefore, during a certain period of time, only one group of boundary nodes is allowed to undertake the task of monitoring the target, and the boundary nodes of the other groups can enter the sleep state, which can extend the lifespan of the boundary nodes.

[0020] 2. Energy consumption

[0021] Energy consumption is an important indicator for measuring boundary node detection. Figure 4 It is a comparison chart of the energy consumption of boundary nodes between the detection method based on subset partitioning and the interpolation method and the DBN (Disjoint Boundary Nodes) method. It can be seen from the figure that the energy consumption of the interpolation method is the largest, because the interpolation method can only detect a single boundary node and cannot detect multiple groups of boundary nodes. Therefore, the boundary nodes must always be vigilant and the energy consumption is very high. Although the DBN algorithm can detect multiple groups of nodes, because the network reconstruction process of this algorithm consumes energy, the energy consumption is also relatively high. Compared with the other two algorithms, the algorithm we proposed can detect multiple groups of boundary nodes and at the same time cancel the network reconstruction process of the DBN algorithm. Therefore, the energy consumption of the detection algorithm based on subset partitioning we proposed is the lowest.

[0022] In summary, the present invention proposes a method for detecting boundary nodes based on subset partitioning. This method can detect multiple groups of boundary nodes. In this way, the energy consumption of the nodes is reduced by the rotation of each group of boundary nodes, achieving the purpose of extending the lifespan of the boundary nodes. In addition, compared with the DBN method, the method for detecting boundary nodes based on subset partitioning does not have the process of network reconstruction, further reducing the energy consumption of the boundary nodes. Description of the Drawings

[0023] Figure 1 It is a schematic diagram of the generation process of extended boundary nodes;

[0024] Figure 2 It is a schematic diagram of the generation process of the subset of boundary nodes;

[0025] Figure 3 It is a schematic diagram of the detection result when the number of nodes is 500 and the communication radius is 120m;

[0026] Figure 4 It is a comparison chart of the energy consumption of boundary nodes between the detection method based on subset partitioning and the interpolation method and the DBN method. Detailed Implementation Manner

[0027] The present invention will be further described in detail below in conjunction with the drawings and specific implementation examples.

[0028] The method for detecting boundary nodes of a wireless sensor network based on subset partitioning in the present invention is divided into two stages. The first stage is to find the initial boundary nodes and extended boundary nodes; the second stage is to perform subset partitioning on the boundary nodes found in the first stage, that is, to further supplement the boundary nodes.

[0029] The detection method of the present invention sequentially goes through the following steps:

[0030] Step (1) Initialize the network

[0031] In the initial stage of the network, each sensor node is assigned an ID number, and then each node sends a message to the nodes within its one-hop range. The message content includes its own ID number and coordinate value. Each node records the received message information.

[0032] (2) Detect the initial boundary nodes

[0033] Run the convex hull algorithm to calculate the convex hull of the entire network. The nodes located at the vertices of the convex hull are the initial boundary nodes.

[0034] (3) Detect the extended boundary nodes

[0035] Multi-hop communication is carried out between two adjacent initial boundary nodes to find their shortest path. The nodes located on the shortest path are the extended boundary nodes.

[0036] (4) Perform subset partitioning

[0037] The initial boundary nodes and the extended boundary nodes will dominate the generation process of a subset. Assume that the communication radius of the sensor nodes is R. Draw a circle with the initial boundary nodes and the extended boundary nodes as the centers and R / 2 as the radius. All the nodes falling inside this circle are divided into the same subset.

[0038] (5) Color the nodes within the subset

[0039] After obtaining the subsets, color the nodes within each subset with different colors. The nodes with the same color can form a group of boundary nodes. In this way, multiple groups of boundary nodes can be detected.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, the protection scope of the present invention is not limited thereto. Any modification or equivalent replacement that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention, without departing from the spirit and scope of the technical solutions of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A method for detecting boundary nodes in a wireless sensor network based on subset partitioning, characterized in that: By switching the boundary to achieve the purpose of extending the lifespan of boundary nodes; this method is divided into two stages: the first stage is to find the initial boundary nodes and extended boundary nodes; the second stage is to divide the boundary nodes found in the first stage into subsets, that is, to further supplement the boundary nodes. This method includes the following steps: Step 1: Initialize the network; Step 2: Run the convex hull algorithm to calculate the convex hull of the entire network, and define the nodes located at the vertices of the convex hull as the initial boundary nodes; Step 3: The initial boundary nodes communicate through multi-hop to find their shortest paths, and define the nodes located on the shortest paths as the extended boundary nodes; Step 4: With the initial boundary nodes and extended boundary nodes as the leading, conduct subset division; assume the communication radius of the sensor nodes is R; draw a circle with the initial boundary nodes and extended boundary nodes as the centers and R / 2 as the radius, and all the nodes falling inside the circle are divided into the same subset; Step 5: After obtaining the subsets, color the nodes within each subset with different colors; the nodes of the same color form a group of boundary nodes, and multiple groups of boundary nodes can be detected.

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