CN107801227A - A kind of routing scheduling method towards wireless sensor network stratification analysis - Google Patents

Abstract

The present invention relates to a kind of routing scheduling method towards wireless sensor network stratification analysis, establish trip current using hierarchical parsing approach, trip current using the dump energy of neighbor node, the transmission energy for needing to consume, to base station distance and node degree as key factor.Using the element value of trip current eigenvalue of maximum character pair vector as the weight of three factors, the performance weights of each neighbor node are calculated, and carry out hierarchical ranking and consistency check.In the performance weights of all neighbor nodes, that node of performance maximum weight will be chosen as the via node of next-hop.The present invention can effectively improve node energy consumption, and finally improve the network life of wireless sensor network.

Description

A Routing Scheduling Method Oriented to Hierarchical Analysis of Wireless Sensor Networks

technical field

The invention relates to the technical field of wireless sensor networks, in particular to a routing scheduling method for layered analysis of wireless sensor networks.

Background technique

With the maturity of 4G technology and the arrival of 5G era, wireless sensor network is one of the important components of future cellular network, especially the problem of improving the life of wireless sensor network has become a hot spot and focus of wireless sensor network research. Wireless sensor network is a network in which nodes can dynamically and automatically find the optimal path to transmit and collect new information to the base station. It is widely used in military, industrial control, agricultural production and many other fields. Due to the limited energy supply and processing capability of nodes, traditional routing algorithms cannot be directly applied to wireless sensor networks. Therefore, how to find an effective route between the source node and the base station has become a difficult point of research. The traditional wireless sensor network routing algorithm is based on the shortest path idea, which will cause some nodes in the network to die due to energy exhaustion, and then cause the network to be divided into multiple isolated sub-networks, seriously affecting the connectivity and stability of the network. Therefore, the research on the routing algorithm of node energy consumption is of great significance.

According to the routing establishment method, wireless sensor network routing protocols can be divided into active routing protocols and reactive routing protocols. Existing routing protocols are divided into active routing protocols and reactive routing protocols. Proactive routing protocols are also known as a priori routing protocols or table-driven routing protocols. In this protocol, the strategy of periodically broadcasting routing request packets is adopted to cope with changes in the topology and maintain the latest routing. Regardless of whether there is a communication requirement in the active routing protocol, each node periodically broadcasts routing request packets, and maintains the latest routes to all nodes in the network in real time. Commonly used proactive routing protocols include DSDV and OLSR. Reactive routing protocols are also called on-demand routing protocols. The so-called "on-demand" means that routing discovery is performed only when nodes need to communicate. If no communication is required, routing information does not need to be maintained. When the source node needs to send data, first check whether there is an available route in the local routing table, if it exists, it will send it directly, if it does not exist, it will broadcast a route request packet, and then send the data packet after finding an available route. Also, nodes only need to store routing information to desired destination nodes. Therefore, the reactive routing protocol can well adapt to the wireless network environment where resources such as energy, bandwidth and storage are limited, and nodes move frequently. Compared with active routing protocols, reactive routing protocols reduce routing overhead and storage overhead, save energy resources, and are more adaptable to wireless environments with frequent topological changes. Therefore, for scenarios where the energy, bandwidth, storage and other resources of nodes are usually limited, and the network topology changes rapidly, reactive routing protocols perform better than proactive routing protocols. Commonly used reactive routing protocols include protocols such as DSR and AODV.

Contents of the invention

The technical problem to be solved by the present invention is to provide a routing scheduling method oriented to the hierarchical analysis of the wireless sensor network, which can effectively improve the energy consumption of nodes and improve the network life of the wireless sensor network.

The technical solution adopted by the present invention to solve the technical problems is: provide a routing scheduling method for hierarchical analysis of wireless sensor networks, comprising the following steps:

(1) Judging whether the base station is reachable: According to the relative position of the source node and the base station, judge whether the base station is within the reliable communication range of the node; if so, the source node directly communicates with the base station, and the current route establishment process ends; otherwise, turn to Step (2), select a node with better performance from the neighbor nodes as the next hop;

(2) Establish a decision matrix: count the data of three key factors, namely, the remaining energy of neighboring nodes, the transmission energy to be consumed, the distance to the base station, and the node degree, then adjust the statistical data value and remove the data dimension, and divide the processed data into two Construct the decision matrix after comparing the two; find the maximum eigenvalue and corresponding eigenvector of the decision matrix, and check whether the decision matrix is a consistency matrix according to the eigenvalue; if not, readjust the statistical data; otherwise, continue to step (3 );

(3) Find the neighbor node with the maximum performance weight: use the three elements of the feature vector in step (2) as the weight values of the three key factors respectively, calculate the performance weight of each neighbor node, and then obtain the The neighbor node of the performance weight, which is the optimal next-hop node; judge whether the current node can directly communicate reliably with the base station; if not, go to step (1), and continue to select the next node based on the current node One-hop relay node; otherwise, the current route establishment process ends.

In the step (2), the decision matrix is: in, and G _i 'represent the remaining energy of neighboring nodes after removing the data dimension, the transmission energy to be consumed, the distance to the base station and the node degree.

In the step (2), check whether the judgment matrix is a consistency matrix by the consistency judgment index, wherein the consistency judgment index is: RI is the distribution value, λ _max is the maximum eigenvalue of the consistency matrix A _i , and p is the number of key factors; when the consistency ratio value CR<0.1, it means that the decision matrix meets the consistency requirements.

Between said step (2) and step (3), it also includes: according to the decision matrix, the weight vector of a group of elements to an element in the upper layer is obtained, and finally the ranking weights of each scheme for the target in the lowest layer are obtained, and Consistency judgment implements the steps of scheme selection.

Beneficial effect

Due to the adoption of the above-mentioned technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention comprehensively considers the remaining energy of neighbor nodes, the transmission energy to be consumed, the distance from neighbor nodes to the base station and the For the degree of nodes, these four factors are quantified by hierarchical analysis method, and a decision matrix with consistency characteristics is constructed, and finally the neighbor node with better performance is selected as the relay node of the next hop. The invention effectively improves the energy consumption of nodes in the network and increases the life span of the network.

Description of drawings

Fig. 1 is a flow chart of the present invention;

Figure 2 is a hierarchical analysis model diagram;

Fig. 3 is a schematic diagram of a detection area with 100 sensor nodes randomly distributed.

Detailed ways

Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.

Embodiments of the present invention relate to a node hierarchical routing scheduling method for wireless ad hoc networks, as shown in Figure 1, comprising the following steps: (1) judging whether the source node and the base station can communicate directly and reliably; The hierarchical analysis model is applied to the four key factors of neighbor nodes, constructs a decision matrix and calculates the performance weight of each neighbor node; (3) selects the node with the largest performance weight as the relay node of the next hop, according to Whether the relay node and the base station can directly and reliably communicate determines whether the current route establishment process ends.

Among them, the hierarchical analysis model is shown in Figure 2, which is divided into three layers: the neighbor node performance weight is the target layer of the model; the next layer is the factor layer of the model, including the remaining energy of the neighbor nodes, the transmission energy that needs to be consumed, The distance from the neighbor node to the base station and the degree of the neighbor node are four key factors; the bottom layer is n neighbor nodes, and the node with the largest performance weight will become the relay node of the next hop.

As shown in Figure 3, a wireless sensor network detection system consisting of 100 network nodes is constructed below. The network nodes are evenly deployed in a square area with a side length of 100m. Taking node v _i as an example, the simulation calculation is carried out through MATLAB software. To further illustrate the present invention.

Step 1: Set the distance between node v _i and base station (BS) node v ₀ as d _i , and d ₀ represents the reference value of the threshold distance, then the link relationship between the two is judged as follows:

In this embodiment, d _i0 =57, d ₀ =40, is the minimum energy that satisfies the node sending and receiving information, and Denote the remaining energy of nodes v _i and v _j respectively. Although the energy of node v _i and base station (BS) node v ₀ both meet the requirements, but d _i0 >d ₀ , so m _i0 =0, so it is necessary to find a neighbor node with better performance as a relay node.

Step 2: G _i nodes within the reliable communication range of node v _i construct a neighbor node set V _i ={v ₁ ', v' ₂ ,...,v' _j ,...,v' _Gi }.

The remaining energy of the neighbor node, the transmission energy that needs to be consumed, the distance from the neighbor node to the base station, and the degree of the neighbor node, the data adjustment and dimension removal operations of the four key factors are as follows:

In the formula, is the remaining energy of the neighbor node v' _j of node v _i , is the transmission energy that the neighbor node v' _j of node v _i needs to consume, d(j,0) is the distance from the neighbor node v' _j of node v _i to the base station v ₀ , G _j is the neighbor node v' _j of node v _i degree.

Then the decision matrix is

in, and G _i 'represent the remaining energy of neighboring nodes after removing the data dimension, the transmission energy to be consumed, the distance to the base station and the node degree.

The largest eigenvalue and corresponding eigenvector of A _i is W _i =[w ₁ w ₂ w ₃ w ₄ ]. The consistency judgment index is:

RI is the distribution value, λ _max is the maximum eigenvalue of the consistency matrix A _i , p represents the number of key factors of this method, and the value is 4.

In the formula, the reference of RI distribution value is in the following table, and n in the table represents the quantity value of key factors.

no 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45

When the consistency ratio value CR<0.10, the matrix A _i satisfies the consistency property and no further adjustment is needed, otherwise the value of the decision matrix A _i needs to be adjusted. Calculate the performance weight of each neighbor node of node v _i , the calculation method is as follows:

Step 3: According to the performance _weights of all neighbor nodes of node vi in step 2, find the node with the largest performance weight:

Check if base station v ₀ is at node within the reliable communication range. If so, communicate directly with the base station v ₀ ; otherwise, go to step 1 until reliable communication with the base station is possible.

It is worth mentioning that step 2 and step 3 also include the step of selecting the scheme, specifically: according to the decision matrix, the weight vector of a group of elements to an element in the upper layer is obtained, and finally the weight vector of each element in the lowest layer is obtained. The ranking weight of the scheme for the target, so as to select the scheme.

The total sorting weight is to synthesize the weights under the single criterion from top to bottom.

There are four key factors, and their total ranking weights are [a ₁ a ₂ a ₃ a ₄ ]. Neighbor nodes include n [B ₁ ,…,B _n ], and their hierarchical single ranking weights on key factors are [b _1j ,…,b _nj ]. Now find the weight of each factor in the neighbor node with respect to the total goal, that is, find the hierarchical total ranking weight [b ₁ ,…,b _n ] of each factor in the neighbor node, and the calculation is carried out as shown in the above table, that is The consistency check is also required for the total sorting of the levels, and the test is still carried out layer by layer from high level to low level like the total level sorting. This is because although each level has passed the consistency test of single-level sorting, each pairwise comparison judgment matrix has a relatively satisfactory consistency. However, when comprehensively inspected, inconsistencies at all levels may still accumulate, resulting in serious inconsistencies in the final analysis results.

Assuming that the pairwise comparison judgment matrix of the factors related to the key factors in the neighbor nodes is tested for consistency in the single sorting, the consistency index of the single sorting is obtained as CI(j), (j=1,...,m), and the corresponding The average random consistency index is RI(j) (CI(j), RI(j) has been obtained in single-level sorting), then the random consistency ratio of the total sorting of neighbor nodes is:

When CR<0.10, it is considered that the hierarchical total ranking results have a relatively satisfactory consistency and the analysis results are accepted.

In order to highlight the improvement of the present invention to improve the communication performance of the wireless sensor network, the network life cycle of the network of the present invention is chosen as the performance index to compare with the classic LEACH and HEED routing algorithms. Through MATLAB simulation calculation, it is found that the network lifetime of the present invention is 25% higher than that of the LEACH routing algorithm and 20% higher than that of the HEED routing algorithm. It can be seen that the wireless sensor network routing scheduling algorithm of the present invention significantly improves the network life; especially for large-scale wireless self-organizing networks, its performance improvement effect is more obvious.

Most of the traditional wireless sensor network routing scheduling algorithms only consider a single energy factor or distance factor, and cannot reflect the overall characteristics of the nodes in the wireless sensor network as much as possible. Select nodes with good performance, so that some nodes in the network will "die" prematurely due to energy exhaustion, destroying the connectivity of the network. Compared with the traditional wireless sensor network algorithm, the routing algorithm proposed by the present invention comprehensively considers the remaining energy of the neighbor node, the transmission energy that needs to be consumed, the distance from the neighbor node to the base station and the degree of the neighbor node, and these four factors are passed through the hierarchy Quantification is carried out with the method of chemical analysis, a decision matrix with consistency characteristics is constructed, and finally the neighbor node with better performance is selected as the relay node of the next hop. The invention effectively improves the energy consumption of nodes in the network and increases the life span of the network.

Claims (4)

1. A routing scheduling method for hierarchical analysis of wireless sensor networks, characterized in that, comprising the following steps: (1) Judging whether the base station is reachable: According to the relative position of the source node and the base station, judge whether the base station is within the reliable communication range of the node; if so, the source node directly communicates with the base station, and the current route establishment process ends; otherwise, turn to Step (2), select a node with better performance from the neighbor nodes as the next hop; (2) Establish a decision matrix: count the data of three key factors, namely, the remaining energy of neighboring nodes, the transmission energy to be consumed, the distance to the base station, and the node degree, then adjust the statistical data value and remove the data dimension, and divide the processed data into two Construct the decision matrix after comparing the two; find the maximum eigenvalue and corresponding eigenvector of the decision matrix, and check whether the decision matrix is a consistency matrix according to the eigenvalue; if not, readjust the statistical data; otherwise, continue to step (3 ); (3) Find the neighbor node with the maximum performance weight: use the three elements of the feature vector in step (2) as the weight values of the three key factors respectively, calculate the performance weight of each neighbor node, and then obtain the The neighbor node of the performance weight, which is the optimal next-hop node; judge whether the current node can directly communicate reliably with the base station; if not, go to step (1), and continue to select the next node based on the current node One-hop relay node; otherwise, the current route establishment process ends. 2. the route scheduling method facing wireless sensor network hierarchical analysis according to claim 1, is characterized in that, in the described step (2), decision matrix is: in, and G _i 'represent the remaining energy of neighboring nodes after removing the data dimension, the transmission energy to be consumed, the distance to the base station and the node degree. 3. the route scheduling method facing wireless sensor network hierarchical analysis according to claim 1, it is characterized in that, in the described step (2), check whether the judgment matrix is a consistency matrix by the consistency judgment index, wherein the consistency judgment The indicators are: RI is the distribution value, λ _max is the maximum eigenvalue of the consistency matrix A _i , and p is the number of key factors; when the consistency ratio value CR<0.1, it means that the decision matrix meets the consistency requirements. 4. the route dispatching method facing wireless sensor network hierarchical analysis according to claim 1, is characterized in that, also comprises between described step (2) and step (3): according to decision matrix, obtain a group of element pairwise The weight vector of an element in the upper layer finally obtains the ranking weights of each plan in the lowest layer for the target, and conducts a consistency judgment to realize the steps of plan selection.