CN103747498A - Direction angle-based wireless sensor network routing void optimization method - Google Patents

Abstract

The invention proposes a method for optimizing a routing hole in a wireless sensor network based on a direction angle, which belongs to the technical field of wireless sensor networks (WSNs). In this method, the distance between any two nodes is less than the communication radius R to judge the routing hole node; the hole node is the center to construct the direction neighbor node set based on the direction angle hole node; the next hop cost function of the neighbor node is calculated; according to the next hop The cost function divides the priority level of the next hop node of the routing hole; uses random selection to select a node from the high-priority node set as the next hop of the hole node; finally streamlines and optimizes the path near the hole to reduce the number of nodes on the path, and obtains Routing Hole Optimization Method for Wireless Sensor Networks Based on Directional Angle. The invention can not only deal with the hole routing problem, but also meet the QoS requirement of WSNs; it is simple and feasible, and has remarkable effect on solving the hole problem.

Description

Routing Hole Optimization Method for Wireless Sensor Networks Based on Directional Angle

technical field

The invention relates to a method for optimizing a routing hole in a wireless sensor network based on a direction angle, and belongs to the technical field of wireless sensor networks (WSNs).

Background technique

The calculation, storage and communication capabilities of wireless sensor network (WSNs) nodes are limited, and the routing protocols of traditional fixed networks and mobile ad-hoc networks cannot be effectively applied to WSNs. It is of great significance to study the routing protocols of WSNs. The classification methods are divided into many categories, among which geographic location routing is widely used. In geographic location routing, nodes obtain their own location information through GPS or positioning algorithms. When data needs to be forwarded, nodes use the local network information they have. Using the distance greedy forwarding method, the neighbor node whose position is closer to the target node is selected as the next hop forwarding node, and the data is transmitted along a shorter path. Since it uses the distance greedy forwarding method to forward data, the problem of greedy forwarding failure will inevitably occur, and the node that fails to forward becomes a hole node. The definition of a routing hole node: when using the distance greedy algorithm to establish a path to the target node, the node The neighbor node whose position is closer to the target node will be selected as the next hop forwarding node, but when there is no node closer to the target node among the neighbor nodes, this node is a hollow node. Therefore, the resolution mechanism of the hole problem marks the validity of the routing protocol.

An efficient routing hole processing mechanism is crucial for geographic location routing protocols. When designing a routing hole processing mechanism, the number of sensor nodes that handle holes should be as small as possible. It is best that the hole nodes themselves can complete the hole detection. processing; the additional energy overhead brought by the routing hole processing mechanism should be as small as possible to improve energy utilization; use a small amount of local network information to complete the processing of the hole without affecting the scalability of the routing protocol; as close as possible to the shortest path.

Contents of the invention

Aiming at the problem of routing holes in WSNs, the purpose of the present invention is to provide a method for optimizing routing holes in wireless sensor networks based on direction angles.

The technical scheme of the present invention is,

A method for optimizing a routing hole in a wireless sensor network based on a direction angle, the steps of which are as follows:

Step 1: Determine the routing hole node;

Firstly, calculate the distance between every two nodes, and usually use the communication radius to determine the neighbor nodes of each node, that is, the distance between two nodes is less than a certain value, that is, they are neighbor nodes to each other, and each node has its own set of neighbor nodes ;Calculate the distance between each node and the target node. The target node is generally known. In the process of establishing the path, when selecting the next hop node, if there is no node closer to the target node than the current node in the neighbor nodes, This node is the hole node;

Step 2: Construct a set of directional neighbor nodes centered on the hollow node;

Take the hole node as the center, and rebuild the directional neighbor node set {Node _i } of the hole node with the plus or minus 120 degrees of the connection between the hole node and the target node. The number of elements in the set is n, and the n nodes in the set are used for later calculation Alternative nodes of the cost function; when the angle between the connection between the neighbor node and the hole node, and the connection between the hole node and the target node is less than 120 degrees, the neighbor nodes of the hole node determined according to the communication radius R in step 1 belong to the direction neighbor node set{Node _i }; i.e.,

时，N_i+1∈{Node_i}；when

, N _i+1 ∈{Node _i };

为邻居节点与空洞节点连线、空洞节点与目标节点连线的夹角；Among them, N _i is the hole node, N _i+1 is the neighbor node of the hole node N _i determined by the communication radius R in step 1,

is the angle between the connection line between the neighbor node and the hole node, and the connection line between the hole node and the target node;

Step 3: Calculate the next-hop cost function of the neighbor node in the direction;

A cost function is computed for each node in the set, which is:

${\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; N</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; ,</span>$

Among them, L(N _i ,D) represents the distance from the empty node to the destination node, L(N _i+1 ,D) represents the distance from the node in the set {Node _i } to the destination node, and the value of the cost function is always greater than 1. Sort the calculated C _i from small to large, build a set {C _i }, and calculate the median value of {C _i }:

C _mid = mid{C _i },

Use C _mid as a standard for subsequent judgment of priority;

Step 4: According to the next hop cost function, the priority level of the next hop node of the routing hole is divided;

According to the C _mid calculated in the previous step, divide the priority selection level of the neighbor nodes in the direction of the hole node, and divide the priority level into 2 levels. If the value of C _i of the node is less than C _mid , the node belongs to the high priority {level _i = 1} ; Otherwise, the node belongs to low priority {level _i = 0};

Step 5: Randomly select and determine the next-hop node from the high-priority node set;

Randomly select the next hop node of the hole node according to the priority level {level _i }, randomly select the next hop node of the hole node from the high priority node set {level _i = 1} established in step 4;

Step 6: Path simplification and optimization reduces the number of nodes on the path;

In the process of establishing a path, all nodes on the path are numbered starting from 1, the source node is numbered 1, the next hop node of the source node is numbered 2, and the next next hop node is numbered 3. By analogy, the node number that is not on the path is 0. After the path is successfully established, it will be streamlined and optimized. The optimization principle is to select the node with the largest number among the neighbor nodes from the source node as the next hop node directly, and then from the next hop node according to The same principle is used to streamline and optimize backwards until the destination node is established, and the final path is established. Since the streamlined nodes are no longer on the final path, the numbers are reset to 0;

In this way, a routing hole optimization method for wireless sensor networks based on direction and angle is obtained, which solves the problem of holes and meets the QoS requirements of wireless sensor networks.

Step 1 of the present invention calculates the distance between nodes to determine neighbor nodes. The distance between each node and the target node is calculated, and the empty nodes are judged. Each node only needs to maintain a small amount of topological information. The step 2 takes the hole node as the center, introduces direction and angle restrictions, and reconstructs the set {Node _i } of direction neighbor nodes. The step 3 defines the cost function C _i for calculating the directional neighbor nodes of the hollow node, sorts the set {C _i } and calculates the median value C _mid as the next division threshold. Each cost function in the step 4 {Node _i } is divided into two priorities, 0 and 1, by comparing with C _mid , where 0 is a low priority and 1 is a high priority. In step 5, a node is randomly selected from the high-priority nodes as the next-hop node of the hole node. The step 6 reduces the number of path nodes, optimizes the path, and finally obtains an optimized solution to the problem of routing holes in WSNs—a method for optimizing routing holes in wireless sensor networks based on direction angles.

The beneficial effects of the invention are: the invention is simple and feasible, can effectively solve the problem of hole routing, and also satisfy the QoS requirement of WSNs.

Description of drawings

Fig. 1 is a flowchart of the present invention.

FIG. 2 is a schematic diagram of the principle of introducing a cost function when selecting the next hop of a hole node in the present invention.

FIG. 3 is a schematic diagram of a principle of path pruning and optimization.

FIG. 4 is a topological diagram of a path selected when the present invention encounters a hole node.

Fig. 5 is the final topology diagram of the path after streamlining and optimization.

Fig. 6 is a schematic diagram of the comparison of the number of nodes after the hole problem is dealt with by the present invention and the TPGF method.

Fig. 7 is a schematic diagram showing the comparison of the energy utilization rate between the present invention and the TPGF method after dealing with the void problem.

Detailed ways

The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

Figure 1 is a flow chart of the present invention. As shown in Figure 1, a kind of wireless sensor network route hole optimization method based on direction angle, the method step is, comprises the following steps:

Step 1: To identify routing hole nodes, first use the communication radius to determine the neighbor nodes of each node. We take the communication radius R=60, unit: meter (m), then the distance between any two nodes is less than 60m, that is, they are neighbor nodes, and then calculate the distance between each node and the target node, the target node is generally known, In the process of establishing the path, if there is no node closer to the target node in the neighbor node when selecting the next hop node, the node is a hollow node;

时，N_i+1∈{Node_i}，其中，N_i为空洞节点，N_i+1为步骤1中通信半径R所确定的空洞节点N_i的邻居节点，

为邻居节点与空洞节点连线、空洞节点与目标节点连线的夹角；这个集合中的元素个数n小于等于步骤1中按通信半径计算出的空洞节点的邻居节点个数。在这些节点中选取空洞节点的下一跳节点。Step 2: Take the hole node as the center, rebuild the directional neighbor node set of the hole node with the plus or minus 120 degrees of the connection between the hole node and the target node, when

, N _i+1 ∈{Node _i }, where N _i is a hole node, N _i+1 is the neighbor node of the hole node N _i determined by the communication radius R in step 1,

is the angle between the connection between the neighbor node and the hole node, and the connection between the hole node and the target node; the number n of elements in this set is less than or equal to the number of neighbor nodes of the hole node calculated according to the communication radius in step 1. Select the next hop node of the hollow node among these nodes.

Step 3: Introduce a cost function Figure 2 is a schematic diagram of the principle of introducing a cost function when selecting the next hop of a hole node in the present invention. As shown in Figure 2, Ni in the figure is a hole node, and the next hop must be selected within the angle of plus or minus 120 degrees, then the calculation The cost function values of N _i+1 a, N _i+1 b, N _i+1 c and N _i+1 d, after calculating the cost function of the set {Node _i }, then calculate the intermediate value C _mid , which is used as Threshold for subsequent prioritization.

Step 4: Use C _mid as the threshold to divide the priority of nodes in {Node _i } into 2 levels, {level _i = 1} and {level _i = 0}. After calculation, N _i+1 a in Figure 2, N _i+1 b belongs to high priority {level _i =1}, while N _i+1 c and N _i+1 d belong to low priority {level _i =0}.

Step 5: Randomly select a node in {Node _i } as the next hop of the hole node. Then randomly select high-priority nodes N _i+1 a and N _i+1 b.

Step 6: Reduce the number of path nodes and optimize the path;

As shown in Figure 3, Figure 3 is a schematic diagram of the principle of path simplification and optimization. A path is established from source node S to destination node D. The path is S→a→b→c. The number of S is 1, a is 2, and b is 3 , c is 4. a, b, and c are all neighbor nodes of S, so directly select c with the largest number as the next hop of S, and then simplify and optimize from c to the destination node according to the same principle.

[Example]

Using MATLAB as the simulation tool, the simulation parameters are set as follows:

The topological range is 600×400, the destination node is set at (0,0), the source node is set at the farthest distance from the target node, the initial energy of the nodes is set to 1 Joule, and the nodes do not have mobility.

As shown in Fig. 4, Fig. 4 is a path topology diagram selected when the present invention encounters a hollow node (a topology diagram for establishing a path when the number of nodes is n=150). Then according to the present invention, a kind of wireless sensor network routing hole optimization method based on direction angle, the method steps are:

1) Determine the empty node, use the greedy algorithm to select the next hop node to establish a path until the node N _i ; the distance from node N _i to the destination node is 316.51m, which are all smaller than the distances of node N _i 's neighbor nodes A, B, C, D, E The distance of the destination node, its value is 346.25m, 370.8m, 370.59m, 347.92m, 381.98 respectively, the node N _i becomes the routing hole node;

2) Construct a set of directional neighbor nodes. According to the fact that the angle between the hollow node N _i and the destination node is less than 120 degrees, the calculated directional neighbor node set of the hollow node N _i includes nodes A, B, C, and D;

3) Calculate the cost function set of the neighbor nodes in the direction, and sort them from small to large. The cost function values C _i calculated by A, B, C, and D are 1.09, 1.17, 1.172, and 1.1 respectively, and the constructed set {C _i } is:

{1.06, 1.1, 1.17, 1.172}; calculate the median value C _mid as 1.123;

4) According to the calculated threshold C _mid , divide A, B, C, and D into two priorities. Since the C _i values of A and D are smaller than C _mid , A, D∈{level=1}; B, D∈{level=1}; C ∈ {level=0}.

5) Randomly select the next hop node of the hole node between the two points A and D, and select point D as the next hop node of the hole node.

6) Afterwards, the final path determined according to the principle of path optimization and simplification is shown in Figure 5. (Figure 5 is the final topology of the path after streamlining and optimization). It can be clearly seen from FIG. 5 that the path calculated according to the method of the present invention bypasses the hole node N _i .

The path not only solves the hole problem, but also meets the QoS requirements of wireless sensor networks.

Fig. 6 is a schematic diagram of the comparison of the number of nodes after the hole problem is dealt with by the present invention and the TPGF method. Shown in Fig. 6 is the graph that the final path node of the present invention and TPGF calculation path node change along with the number of nodes, concrete data is as shown in table 1:

Table 1

Number of nodes 150 160 170 180 190 200 TPGF twenty one 19 twenty one 20 twenty one 20 The method of the invention 19 18 19 19 20 19

The number of nodes on the path is proportional to the path delay, and the statistical principle is shown in the following formula:

D _end-to-end = D _{transmission delay} + D _{other factors} ,

Wherein, D _{transmission delay} mainly refers to the delay of sending data between nodes, and _{other factors of} D mainly represent MAC layer delay and other delays, and the unified statistics are carried out by 20ms, then the inventive method reduces respectively 9.5 compared with TPGF delay %, 5.3%, 4.5%, 5%, 4.8%, 5%.

Fig. 7 is a schematic diagram showing the comparison of the energy utilization rate between the present invention and the TPGF method after dealing with the void problem. There are many ways to count energy, and now it terminates when the first node dies, and divides the remaining energy by the total energy, that is, the energy utilization rate. In the energy model, the energy consumed by transmitting and receiving circuits to process 1 bit of data is 50J ^-9 , and in the free space model, the energy consumed by transmitting and receiving circuits transmitting 1 bit of data to a unit area is 100J ^-12 , and the data packet length is 1600 bits. The final energy utilization rate is shown in Table 2:

Table 2

Number of nodes 150 160 170 180 190 200 TPGF 0.2832 0.25 0.2414 0.2364 0.2387 0.2723 The method of the invention 0.3069 0.2725 0.2488 0.262 0.2492 0.2893

To sum up, the present invention not only satisfies the requirements of fewer path nodes after hole processing, less energy consumption, and high utilization rate, but also is simple and easy to implement.

The invention can not only deal with the hole routing problem, but also meet the QoS requirement of WSNs; it is simple and feasible, and has remarkable effect on solving the hole problem.

The above is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present invention can easily think of changes or Replacement should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the protection scope of the claims.

Claims (2)

1. the cavity of the radio sensing network route based on an orientation angle optimization method, is characterized in that, the method comprises the following steps:

Step 1: service range greedy algorithm is set up the path that arrives destination node, judges route cavity node;

Set communication radius R, any two internodal distances are less than communication radius R, two nodes neighbor node each other, and each node has the neighbor node set of self; Calculate again the distance of all nodal distance destination node D; Set up in the process of path, if while there is not the nearer node of compared with this node distance objective node D while choosing next-hop node in neighbor node, this node is empty node;

Step 2: build the set of direction neighbor node centered by empty node;

Centered by empty node, with positive and negative 120 degree of empty node and destination node line, rebuild the direction neighbor node set { Node of empty node _i, wherein, when the angle of neighbor node and empty node line, empty node and destination node line is less than 120 while spending, in step 1, according to the neighbor node of the determined empty node of communication radius R, belong to direction neighbor node set { Node _i, that is,

When

time, N _i+1∈ { Node _i,

Wherein, N _i+1for the determined empty node N of communication radius R in step 1 _ineighbor node, for the angle of neighbor node and empty node line, empty node and destination node line;

Set { Node _iin element number n be less than or equal to the neighbor node number of the empty node calculating by communication radius R in step 1;

Step 3: the down hop cost function C of calculated direction neighbor node _i;

Described cost function

l (N _i, D) and represent that empty node arrives the distance of destination node, L (N _i+1, D) and represent the set { Node that again establishes in step 2 _iin direction neighbor node to the distance of destination node;

By set of computations { Node _icost function C _i, and to C _iafter ascending sequence, build set { C _i, then calculate median C _mid, as the threshold value of subsequent divided priority;

Step 4: according to down hop cost function C _idivide route next jump node priority level;

With C _midfor threshold value is by { Node _iin the priority of node be divided into 2 ranks: { level _i=1} and { level _i=0}, if cost function value C _ibe less than median C _mid, node belongs to high priority { level so _i=1}; Otherwise node belongs to low priority { level _i=0};

Step 5: adopt random selection mode from priority node set { level _iin=1}, determine next-hop node;

According to priority level { level _ichoose at random the next-hop node of empty node, the priority node set { level establishing from step 4 _iin=1}, choose at random the next-hop node of empty node;

Step 6: optimization is simplified in path, reduces node number on path;

In the process of setting up path, all nodes on path are carried out to the numbering by 1 beginning, source node be numbered 1, the next-hop node of source node is numbered 2, back to back next-hop node is numbered 3, by that analogy, the node serial number on path is not 0, simplify optimization setting up after path success, optimization principles is to start to select to number maximum node directly as next-hop node neighbor node from source node, from next-hop node, according to same principle, simplify backward optimization afterwards, until destination node, establish final path, the node of being simplified is not due to on final path, numbering sets to 0 again,

Like this, obtain a path that has solved empty problem and met wireless sensor network QoS demand, obtained the radio sensing network route cavity optimization method based on orientation angle.

2. a kind of radio sensing network route cavity optimization method based on orientation angle according to claim 1, is characterized in that described communication radius R=60, unit: rice.

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