Wireless sensor network dynamic clustering method based on multi-factor balance

Abstract

The invention discloses a wireless sensor network dynamic clustering method based on multi-factor balance, and belongs to the technical field of wireless sensor network communication routing. The method comprises the following steps: dividing a wireless sensor network area; adding position identification information to the nodes; the identification is updated along with network operation, dynamic area clustering is achieved, and compared with random clustering in a fixed area, random clustering is improved; cluster head nodes are selected by comprehensively considering the positions of the nodes in the clusters and the energy condition of the nodes, and the communication energy consumption during communication in the clusters is reduced; a main cluster and an auxiliary cluster are respectively responsible for intra-cluster and inter-cluster communication, so that rapid node death caused by a large amount of energy consumption of cluster head nodes is relieved, and the survival time of the network is prolonged. The method is suitable for a large-scale wireless sensor network system, has the advantages of uniform clustering and reasonable cluster head selection, and can prolong the network survival time.

Description

Technical Field

[0001] The invention relates to the technical field of wireless sensor network communication routing, and in particular to a wireless sensor network dynamic clustering method based on multi-factor balance. Background Art

[0002] Wireless sensor network (WSN) is a self-organizing distributed network system composed of a large number of sensor nodes with wireless communication and computing capabilities. It has the advantages of low cost, easy deployment, and high reliability. In WSN, cluster routing algorithms form multiple cluster structures by dividing the wireless sensor network. Different from the flat routing algorithm, in which each node has the same function and equal status, the nodes in the cluster structure are divided into cluster head nodes and ordinary member nodes in the cluster. As the network continues to run, the energy consumption of sensor nodes and the uneven clustering will affect the survival effect of the network. Therefore, the rationality and efficiency of cluster head election and cluster area division have gradually become an important research topic in wireless sensor networks.

[0003] Clustering algorithm is an important part of wireless sensor network system research. Whether the network can be reasonably divided into clusters and the appropriate nodes in the clusters can be selected as cluster heads plays a critical role in the overall survival of the network and the network communication efficiency. In wireless sensor networks, traditional WSN clustering protocols such as LEACH protocol usually use random cycles to elect cluster heads, which is extremely blind. In addition, frequent information interaction also brings additional energy consumption. Summary of the invention

[0004] In view of this, the present invention proposes a dynamic clustering method for wireless sensor networks based on multi-factor balance. The present invention dynamically and evenly divides the clustering area of ​​the wireless sensor network, comprehensively considers the location and energy factors when electing cluster heads, and sets up multi-cluster head inter-cluster communication between the main and secondary cluster heads, which can optimize and ensure the balanced utilization of wireless sensors in terms of energy consumption.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] A dynamic clustering method for wireless sensor networks based on multi-factor balance, wherein the sensor nodes in the wireless sensor network are of the same type, randomly distributed in the network area, with known initial positions and initial maximum energies. ini , every two nodes can communicate with each other, the energy consumption of communication between nodes adopts the free space model, and the node dies after the energy is exhausted; the method includes the following steps:

[0007] Step 1, dividing the wireless sensor network area in a two-dimensional plane to form a plurality of rectangular cells of the same size arranged in rows and columns;

[0008] Step 2: Each sensor node sets its own location identifier Among them, i is the ID of the sensor node, the initial value of x is the row number of the cell where it is located, and the value of y is the column number of the cell where it is located;

[0009] Step 3, set the network operation round to r, and the initial value of r is 0;

[0010] Step 4: For each sensor node, when xmod3=0, it declares itself as a node in the central area cell, and the central area cell and the upper and lower adjacent cells together form the same cluster of this round of dynamic clustering;

[0011] Step 5: The nodes in the cells in the central area quantify their distance from the center point of the cell and their current remaining energy to obtain the position adjustment factor p i and the residual energy factor e i , according to p i and e i Calculate the cluster head election value T(i), and the node with the largest T(i) value is declared elected as the cluster head of this round;

[0012] Step 6, update round r=r+1, each sensor node updates x=x+r, y remains unchanged, start the next round, and repeat steps 4 to 6;

[0013] The node elected as the cluster head in each round declares itself as the deputy cluster head in the next round. The deputy cluster head is responsible for inter-cluster communication with the nearest adjacent cluster.

[0014] Furthermore, the specific method of step 5 is:

[0015] Step 501, obtaining the total number of nodes M in the cells of the central area;

[0016] Step 502: Calculate the distance adjustment coefficient α of node i in the cell of the central area i :

[0017] α i =(L max -d i ) / L max

[0018] Among them, d i is the distance from node i to the center of the cell where it is located, L max is the distance from the cell vertex to the cell center;

[0019] Step 503: Calculate the total distance adjustment coefficient α of the cells in the central area Total :

[0020]

[0021] Step 504: Calculate the average value of the distance adjustment coefficients in the cells in the central area

[0022]

[0023] Step 505: Calculate the position adjustment factor p of node i in the cell of the central area i :

[0024]

[0025] Step 506, obtaining the residual energy of node i in the cell of the central area is Calculate the total residual energy of each node in the central region cell:

[0026]

[0027] Step 507, calculate the average value of the remaining energy in the cells in the central area

[0028]

[0029] Step 507, calculate the remaining energy adjustment factor e i :

[0030]

[0031] Step 508, calculate the cluster head election value T(i):

[0032] T(i)=ωp i +se i

[0033] Among them, ω and s are weights, and ω+s=1;

[0034] Step 509: The node with the largest T(i) value is declared elected as the cluster head of this round.

[0035] The beneficial effects of the present invention are:

[0036] 1. The present invention adds location identification information to the nodes and updates the identification during network operation, thereby realizing dynamic regional clustering. Compared with random clustering in a fixed area, the method of the present invention is more balanced in terms of energy consumption.

[0037] 2. When selecting the cluster head, the present invention comprehensively considers the location of the node in the cluster and the node energy situation, which can reduce the communication energy consumption during intra-cluster communication.

[0038] 3. The present invention uses the main and sub clusters to be responsible for intra-cluster and inter-cluster communications respectively. Compared with a single cluster head that is responsible for both intra-cluster and inter-cluster communications, it can slow down the rapid death of nodes caused by the large amount of energy consumption of cluster head nodes, thereby extending the survival time of the network.

[0039] In summary, the present invention comprehensively considers the location and energy of the nodes to perform cluster head election, thereby improving the communication energy efficiency and the survival effect of the nodes. When the present invention divides the clusters, the distribution and scale of the clusters can be as uniform as possible, which can better ensure the balance of the overall energy consumption of the network and meet the network's needs for extending the survival time. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of the present invention.

[0041] Figure 2 This is a schematic diagram of the impact of cluster head position on energy consumption in the present invention.

[0042] Figure 3 It is a schematic diagram of dynamic clustering of the present invention. DETAILED DESCRIPTION

[0043] In order to clearly illustrate the technical features of the present solution, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0044] A dynamic clustering method for wireless sensor networks based on multi-factor balance is applied to wireless sensor network communication systems and is used for clustering and cluster head node selection in wireless sensor network areas. In wireless sensor network communication systems, sensor nodes are of the same type and randomly distributed in the network area. The initial position of each node is marked, and nodes can communicate with each other. The initial maximum energy of each sensor node is E ini ,The energy consumption of communication between nodes adopts the free space model, and the node dies after the energy is exhausted.

[0045] like Figure 1 As shown, the method comprises the following steps:

[0046] (1) The wireless sensor network area is divided into two-dimensional planes to form multiple adjacent rectangular cells of the same size, such as Figure 3 As shown;

[0047] (2) The wireless sensor node i is marked with the corresponding location according to the initial location area: i is the unique ID number corresponding to the node, such as Figure 3 As shown, the position of point a is marked as It is used to distinguish different nodes and identify the location of nodes. In the square cluster area, when the node closer to the center of the area becomes the cluster head, the overall energy consumption will be reduced. The x and y are used to determine whether the node is in the center of the cluster, such as Figure 3 As shown, point a is in the central cell in this round of the network, and the position of point a is declared as At the same time, the adjacent situation of the cell is determined;

[0048] The identification information of the node is used to determine the position of the node in the current round of the network. The actual position of the sensor node is fixed, but the virtual position changes through the change of the identification information, such as Figure 3 As shown, in the next round, in cluster A￠, point a will no longer be located in the central area cell, and point a￠ will become a node in the new central area cell;

[0049] (3) Set the network operation round r, and the initial value of r is 0;

[0050] (4) As the network runs, the node identification information in the network changes. According to the node identification information in the network, when xmod3=0, the node declares itself as a node in the central area cell, that is, the candidate cluster head node of this round. The central area cell and the adjacent cells form the same cluster of this round of dynamic clustering, that is, the three adjacent cells in the same column are divided into the same cluster;

[0051] like Figure 3 As shown, at this time x=x c , y=y c At the same time with x=x c +1, x=x c -1 and y=y c The nodes are in adjacent cells and belong to the same cluster in this round of the network.

[0052] (5) The nodes in the cells in the central area quantify their distance from the center point of the cell and their current remaining energy to obtain the position adjustment factor pi and the remaining energy factor ei. i and e i Calculate the cluster head election value T(i), and the node with the largest T(i) value is declared elected as the cluster head of this round;

[0053] For the cluster head candidate node marked as the central area, its distance from the center point of the cell is d, L max is the farthest distance from the center of the cell, and the relative position range of the node from the center of the cell is defined as [[L max ,L max (1+a)]], then the distance adjustment coefficient a=(L max -d) / Lmax The closer the cluster head candidate node is to the center point, the larger the value of the distance adjustment coefficient α is, and the corresponding total distance adjustment coefficient of all nodes in the central area cell is Mean adjustment coefficient of candidate cluster heads in the central cell Define the location adjustment factor for cluster head election The remaining energy of the node in the current network round r is The total remaining energy in the cells of the central region is Then the mean adjustment coefficient of the candidate cluster head in the central cell is Define the energy adjustment factor for cluster head election The final cluster head election formula is T(i) = ωp i +se i , where ω and s are weights, determined according to their importance, and ω+s=1.

[0054] (6) As the network runs, the update round r=r+1, and each node updates the node ID identification information in the corresponding round, x=x+r, and y remains unchanged; repeat steps 4 to 6; for the node elected as the cluster head in the central area cell in this round, it will no longer be in the central area cell in the next round of clustering. In the next round of clustering, it will declare itself as the deputy cluster head and be responsible for inter-cluster communication, that is, the cluster head in the central cell is the main cluster head, and the cluster heads in other cells in the cluster are deputy cluster heads to reduce the energy consumption of inter-cluster communication.

[0055] In the rth round of the network, the cells in the central area of ​​the cluster are still adjacent to the new central area in the r+1th round, such as Figure 3 As shown in the figure, point a is adjacent to the cell where a￠ is located in the next round of the network, and before the end of the rth round of the network, the cluster head selects the cluster head in the central area of ​​the next round based on the position and remaining energy information, and announces itself as the deputy cluster head in the r+1th round, responsible for inter-cluster communication.

[0056] The effect of this method is analyzed below:

[0057] like Figure 2 As shown in Figure 2, the expected value of intra-cluster communication energy consumption is calculated as follows:

[0058]

[0059] Where r is the distribution density of nodes in the cluster, r×Dr×r×Dβ is the probability that a node in the cluster is located in a certain area in the cluster, k is the size of the data packet sent, and E elec is the energy consumption of each bit of data sent or received by the node, ε fsis the amplification factor of the signal amplifier, r is the distance from the ordinary member node to the regional center, l is the distance from the cluster head to the regional center, β is the angle between the cluster head node and the ordinary member node, that is, the energy consumed when sending kbit data packets between the cluster head and the ordinary member node is:

[0060] E s (k,d)=kE elec +kd 2 ε fs

[0061] =kE elec +kε fs (r 2 -2lrcosβ+l 2 )

[0062] It can be seen from the calculation formula of the expected value of the intra-cluster communication energy consumption of the cluster head that when selecting the cluster head, selecting a cluster head close to the cluster center (i.e., the center point of the virtual cell) can bring lower intra-cluster communication energy consumption.

[0063] like Figure 3 As shown, the position of point a is marked as It is used to distinguish different nodes and identify the location of nodes. In the square cluster area, when the node closer to the center of the area becomes the cluster head, the overall energy consumption will be reduced. The x and y are used to determine whether the node is in the center of the cluster, such as Figure 3 As shown, point a is in the central cell in this round of the network, and the position of point a is declared as

[0064] In summary, the present invention realizes dynamic regional clustering by updating the location identifier of the node, which is improved compared with random clustering in a fixed area; the cluster head node is selected by comprehensively considering the location of the node in the cluster and the node energy situation, thereby reducing the communication energy consumption during intra-cluster communication; by using the main and secondary clusters to be responsible for intra-cluster and inter-cluster communication respectively, the rapid death of nodes caused by the large amount of energy consumption of the cluster head node is slowed down, and the survival time of the network is extended. The present invention is applicable to large-scale wireless sensor network systems, has the advantages of uniform clustering and reasonable cluster head selection, and can extend the network survival time.

Claims

1. A dynamic clustering method for wireless sensor networks based on multi-factor balance, wherein the sensor nodes in the wireless sensor network are of the same type, randomly distributed in the network area, with their initial positions known and their initial maximum energies E ini , every two nodes can communicate with each other, the energy consumption of communication between nodes adopts the free space model, and the node dies after the energy is exhausted; the characteristics are, The following steps are involved: Step 1, dividing the wireless sensor network area in a two-dimensional plane to form a plurality of rectangular cells of the same size arranged in rows and columns; Step 2: Each sensor node sets its own location identifier Among them, i is the ID of the sensor node, the initial value of x is the row number of the cell where it is located, and the value of y is the column number of the cell where it is located; Step 3, set the network operation round to r, and the initial value of r is 0; Step 4: For each sensor node, when xmod3=0, it declares itself as a node in the central area cell, and the central area cell and the upper and lower adjacent cells together form the same cluster of this round of dynamic clustering; Step 5: The nodes in the cells in the central area quantify their distance from the center point of the cell and their current remaining energy to obtain the position adjustment factor p i and the residual energy factor e i , according to p i and e i Calculate the cluster head election value T(i), and the node with the largest T(i) value is declared elected as the cluster head of this round; Step 6, update round r=r+1, each sensor node updates x=x+r, y remains unchanged, start the next round, and repeat steps 4 to 6; The node elected as the cluster head in each round declares itself as the deputy cluster head in the next round. The deputy cluster head is responsible for inter-cluster communication with the nearest adjacent cluster.

2. The method for dynamic clustering of wireless sensor networks based on multi-factor balance according to claim 1, characterized in that: The specific method of step 5 is: Step 501, obtaining the total number of nodes M in the cells of the central area; Step 502: Calculate the distance adjustment coefficient α of node i in the cell of the central area i : a i =(L max -d i ) / L max Among them, d i is the distance from node i to the center of the cell where it is located, L max is the distance from the cell vertex to the cell center; Step 503: Calculate the total distance adjustment coefficient α of the cells in the central area Total := Step 504: Calculate the average value of the distance adjustment coefficients in the cells in the central area Step 505: Calculate the position adjustment factor p of node i in the cell of the central area i : Step 506, obtaining the residual energy of node i in the cell of the central area is Calculate the total residual energy of each node in the central region cell: Step 507, calculate the average value of the remaining energy in the cells in the central area Step 507, calculate the remaining energy adjustment factor e i : Step 508, calculate the cluster head election value T(i): T(i)=ωp i +se i Among them, ω and s are weights, and ω+s=1; Step 509: The node with the largest T(i) value is declared elected as the cluster head of this round.

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