CN107801227B - Routing scheduling method for hierarchical analysis of wireless sensor network - Google Patents

Routing scheduling method for hierarchical analysis of wireless sensor network Download PDF

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CN107801227B
CN107801227B CN201710891080.8A CN201710891080A CN107801227B CN 107801227 B CN107801227 B CN 107801227B CN 201710891080 A CN201710891080 A CN 201710891080A CN 107801227 B CN107801227 B CN 107801227B
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CN107801227A (en
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马忠建
常玉超
唐洪莹
李宝清
刘建坡
丁园园
袁晓兵
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Shanghai Institute of Microsystem and Information Technology of CAS
University of Chinese Academy of Sciences
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/124—Shortest path evaluation using a combination of metrics
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to a routing scheduling method facing hierarchical analysis of a wireless sensor network, which utilizes a hierarchical analysis method to establish a decision matrix, wherein the decision matrix takes residual energy of neighbor nodes, transmission energy required to be consumed, distance to a base station and node degree as key factors. And taking element values of the eigenvectors corresponding to the maximum eigenvalues of the decision matrix as weights of the three factors, calculating the performance weight of each neighbor node, and performing hierarchical ordering and consistency check. And selecting the node with the largest performance weight value as the relay node of the next hop from the performance weight values of all the neighbor nodes. The invention can effectively improve the energy consumption of the nodes and finally prolong the network life of the wireless sensor network.

Description

Routing scheduling method for hierarchical analysis of wireless sensor network
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a routing scheduling method for hierarchical analysis of a wireless sensor network.
Background
With the gradual maturity of 4G technology and the arrival of the 5G era, the issue of wireless sensor networks as one of the important components of future cellular networks, especially the issue of improving the lifetime of wireless sensor networks, has become the hot spot and the focus of wireless sensor network research. The wireless sensor network is a new network in which nodes transmit and collect data to a base station in a mode of dynamically and automatically searching for an optimal path, and is widely applied to various fields such as military, industrial control, agricultural production and the like. The limited energy supply and processing power characteristics of the nodes make conventional routing algorithms not directly applicable to wireless sensor networks. Therefore, how to find an effective route between the source node and the base station becomes a difficult point of research. The traditional routing algorithm of the wireless sensor network is based on the shortest-path idea, and can cause some nodes of the network to die due to energy exhaustion, so that the network is divided into a plurality of isolated sub-networks, and the connectivity and stability of the network are seriously affected. Therefore, the research on the routing algorithm of the node energy consumption has important significance.
According to the route establishment method, the wireless sensor network routing protocols can be divided into an active routing protocol and a reactive routing protocol. Existing routing protocols are classified into proactive routing protocols and reactive routing protocols. The proactive routing protocol is also called a proactive routing protocol or a table-driven routing protocol, and in the protocol, a strategy of periodically broadcasting a routing request packet is adopted to deal with the change of a topological structure and maintain the latest routing. Proactive routing protocols, whether or not there is a communication need, each node periodically broadcasts routing request packets, maintaining up-to-date routes to all nodes in the network in real time. Common proactive routing protocols include the DSDV protocol and the OLSR protocol, among others. Reactive routing protocols are also known as on-demand routing protocols, by which is meant that route discovery is only performed when a node needs to communicate, and no routing information needs to be maintained if communication is not required. When a source node needs to send data, whether an available route exists in a local routing table is checked, if yes, the data is directly sent, if not, a routing request packet is broadcasted, and after the available route is found, a data packet is sent. And, the node only needs to store the routing information to the desired destination node. Therefore, the reactive routing protocol can be well adapted to a wireless network environment in which resources such as energy, bandwidth and storage are limited and nodes move frequently. Compared with the active routing protocol, the reactive routing protocol reduces the routing overhead and the storage overhead, saves energy resources and is more suitable for the wireless environment with frequently changed topology. Therefore, for a scenario that resources such as energy, bandwidth, storage, and the like of a node are generally limited and a network topology changes rapidly, a reactive routing protocol performs better than a proactive routing protocol. Common reactive routing protocols include DSR and AODV.
Disclosure of Invention
The invention aims to provide a routing scheduling method oriented to hierarchical analysis of a wireless sensor network, which can effectively improve the energy consumption of nodes and prolong the network life of the wireless sensor network.
The technical scheme adopted by the invention for solving the technical problems is as follows: the routing scheduling method for hierarchical analysis of the wireless sensor network comprises the following steps:
(1) judging whether the base station is accessible: judging whether the base station is in the reliable communication range of the node or not according to the relative positions of the source node and the base station; if so, the source node directly communicates with the base station, and the current route establishing process is finished; otherwise, turning to the step (2), selecting a node with better performance from the neighbor nodes as a next hop;
(2) establishing a decision matrix: counting data of four key factors of residual energy of neighbor nodes, transmission energy to be consumed, distance to a base station and node degree, adjusting a statistical data value, removing a data dimension, and constructing a judgment matrix after making a ratio of every two processed data; solving the maximum eigenvalue and the corresponding eigenvector of the decision matrix, and checking whether the decision matrix is a consistency matrix according to the eigenvalue; if not, readjusting the statistical data; otherwise, continuing to step (3);
(3) solving the neighbor node with the maximum performance weight: taking the four elements of the feature vector in the step (2) as weight values of the four key factors respectively, calculating the performance weight of each neighbor node, and then obtaining the neighbor node with the maximum performance weight, wherein the node is the optimal next hop node; judging whether the current node can directly and reliably communicate with the base station; if not, turning to the step (1), and continuously selecting the relay node of the next hop on the basis of the current node; otherwise, the current route establishment procedure is finished.
The decision matrix in the step (2) is as follows:
Figure GDA0002829179770000021
wherein the content of the first and second substances,
Figure GDA0002829179770000022
Figure GDA0002829179770000023
and G'iAnd respectively representing the residual energy of the neighbor nodes, the transmission energy required to be consumed, the distance to the base station and the node degree after the data dimension is removed.
In the step (2), whether the judgment matrix is a consistency matrix is checked through a consistency judgment index, wherein the consistency judgment index is as follows:
Figure GDA0002829179770000031
RI is the distribution value, λmaxIs a consistency matrix AiThe maximum eigenvalue of (a) and p are the number of key factors; and when the consistency ratio CR is less than 0.1, indicating that the judgment matrix meets the consistency requirement.
The step (2) and the step (3) further comprise the following steps: and obtaining a weight vector of a group of elements to a certain element in the upper layer according to the judgment matrix, finally obtaining the sequencing weight of each scheme in the lowest layer to the target, and selecting a consistency judgment implementation scheme.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: the invention comprehensively considers the residual energy of the neighbor nodes, the transmission energy to be consumed, the distance from the neighbor nodes to the base station and the degree of the neighbor nodes, quantifies the four factors by a hierarchical analysis method, constructs a decision matrix with consistency characteristics, and finally selects the neighbor nodes with better performance as the relay nodes of the next hop. The invention effectively improves the node energy consumption in the network and prolongs the service life of the network.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a hierarchical analysis model;
fig. 3 is a schematic diagram of a detection zone with a random distribution of 100 sensor nodes.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to a node hierarchical routing scheduling method facing a wireless self-organizing network, as shown in figure 1, comprising the following steps: (1) judging whether the source node and the base station can directly and reliably communicate; (2) applying a hierarchical analysis model to four key factors of a neighbor node, constructing a decision matrix and calculating the performance weight of each neighbor node; (3) and selecting the node with the maximum performance weight as the relay node of the next hop, and determining whether the current route establishment process is finished according to whether the relay node and the base station can directly and reliably communicate.
The hierarchical analysis model is divided into three layers as shown in fig. 2: the neighbor node performance weight is the target layer of the model; the next layer is a factor layer of the model and comprises four key factors, namely residual energy of the neighbor nodes, transmission energy required to be consumed, the distance between the neighbor nodes and the base station and the degree of the neighbor nodes; the bottom layer is n neighbor nodes, wherein the node with the largest performance weight value can become the relay node of the next hop.
As shown in FIG. 3, a wireless sensor network detection system composed of 100 network nodes is constructed below, the network nodes are uniformly deployed in a square area with the side length of 100m, and a node v is used for detecting the wireless sensor networkiFor example, simulation calculations were performed by MATLAB software to further illustrate the present invention.
The method comprises the following steps: let node viAnd Base Station (BS) node v0A distance of di，d0And a reference value representing the threshold distance, wherein the link relationship between the two is judged according to the following steps:
Figure GDA0002829179770000041
in this example, di0＝57,d0＝40，
Figure GDA0002829179770000042
Is the minimum energy that the node can transmit and receive information,
Figure GDA0002829179770000043
and
Figure GDA0002829179770000044
respectively represent nodes viAnd vjThe remaining energy of (c). Although node viAnd Base Station (BS) node v0All satisfy the requirements, but di0＞d0Thus m isi0Therefore, a neighbor node with better performance needs to be searched as a relay node.
Step two: at node viG in the reliable communication range ofiEach node constructs a neighbor node set
Figure GDA0002829179770000045
The residual energy of the neighbor node, the transmission energy required to be consumed, the distance from the neighbor node to the base station and the degree of the neighbor node, and the data adjustment and dimension removal operations of the four key factors are as follows:
Figure GDA0002829179770000046
in the formula (I), the compound is shown in the specification,
Figure GDA0002829179770000051
is node viOf neighbor node v'jThe remaining amount of energy of (a) is,
Figure GDA0002829179770000052
is node viOf neighbor node v'jThe transmission energy to be consumed, d (j,0) is node viNeighbor node v'jTo base station v0Distance of (G)jIs node viOf neighbor node v'jDegree of (c).
Then the decision matrix is
Figure GDA0002829179770000053
Wherein the content of the first and second substances,
Figure GDA0002829179770000054
and Gi' represents the residual energy of the neighbor nodes, the transmission energy to be consumed, the distance to the base station and the node degree after the data dimension is removed respectively.
AiThe maximum eigenvalue and corresponding eigenvector of (A) is Wi＝[w1 w2 w3 w4]. The consistency determination index is:
Figure GDA0002829179770000055
RI is the distribution value, λmaxIs a consistency matrix AiThe maximum characteristic value p of (2) represents the number of key factors of the method, and the value is 4.
In the formula, the RI distribution values are referred to as the following table, and n in the table represents the number value of the key factor.
n 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, matrix AiThe consistency property is satisfied, no adjustment is needed, otherwise, the judgment matrix A is adjustediThe value of (c). Compute node viThe calculation method of the performance weight of each neighbor node is as follows:
Figure GDA0002829179770000056
step three: according to the node v in step twoiThe node with the maximum performance weight is solved according to the performance weights of all the neighbor nodes:
Figure GDA0002829179770000057
checking base stations v0Whether or not at a node
Figure GDA0002829179770000058
Within a reliable communication range. If so, directly with the base station v0Communication; otherwise, continuing to step one until reliable communication with the base station is achieved.
It is worth mentioning that the step two and the step three further include a step of selecting a scheme, specifically: and obtaining a weight vector of a group of elements to a certain element in the upper layer according to the decision matrix, and finally obtaining the sequencing weight of each scheme in the lowest layer to the target, thereby carrying out scheme selection.
The total ranking weight is to combine the weights under the singleton criterion from top to bottom.
Figure GDA0002829179770000061
The key factors are 4 in total, and the total hierarchical ranking weights of the key factors are respectively [ a ]1 a2 a3 a4]. The neighbor node comprises n [ B ]1,…,Bn]The hierarchical single-rank weights of them with respect to the key factors are respectively [ 2 ], [b1j,…,bnj]. Now, the weight of each factor in the neighbor node with respect to the total target is solved, namely, the total hierarchical ranking weight [ b ] of each factor in the neighbor node is solved1,…,bn]The calculation is carried out in the manner indicated above, i.e.
Figure GDA0002829179770000062
The consistency check is also carried out on the total hierarchical ordering, and the check is still carried out layer by layer from a high layer to a low layer like the total hierarchical ordering. This is because although each level has been checked for consistency by the level single ordering, each pair of comparison decision matrices has satisfactory consistency. However, when the analysis is comprehensively conducted, the inconsistency of each layer may be accumulated, which causes the inconsistency of the final analysis result.
If the consistency check is performed on the paired comparison judgment matrixes of the factors related to the key factors in the neighbor nodes in the single-row sequence, the obtained single-row sequence consistency index is CI (j), (j ═ 1, …, m), the corresponding average random consistency index is RI (j), (CI (j), RI (j) is obtained when the single-row sequence is arranged in a hierarchical order, and then the total sequencing random consistency proportion of the neighbor nodes is as follows:
Figure GDA0002829179770000071
when CR < 0.10, the overall ranking result is considered to have more satisfactory consistency and the analysis result is accepted.
In order to highlight the improvement of the invention on the communication performance of the wireless sensor network, the network life cycle of the network is taken as a performance index to be compared with the classical LEACH and HEED routing algorithm. The MATLAB simulation calculation shows that the network life span of the invention is improved by 25% compared with the LEACH routing algorithm and 20% compared with the HEED routing algorithm. Therefore, the routing scheduling algorithm of the wireless sensor network provided by the invention has the advantages that the service life of the network is obviously prolonged; especially for a large-scale wireless self-organizing network, the performance improvement effect is more obvious.
Most of the traditional wireless sensor network routing algorithms only consider a single energy factor or a distance factor and cannot reflect the comprehensive characteristics of nodes in the wireless sensor network as much as possible, so that nodes with good performance cannot be selected as much as possible when next-hop relay nodes are selected, and partial nodes in the network die prematurely due to energy exhaustion, thereby destroying the connectivity of the network. Compared with the traditional wireless sensor network algorithm, the routing algorithm provided by the invention comprehensively considers the residual energy of the neighbor nodes, the transmission energy required to be consumed, the distance from the neighbor nodes to the base station and the degree of the neighbor nodes, quantifies the four factors by a hierarchical analysis method, constructs a decision matrix with consistency characteristics, and finally selects the neighbor nodes with better performance as the relay nodes of the next hop. The invention effectively improves the node energy consumption in the network and prolongs the service life of the network.

Claims (3)

1. A routing scheduling method for hierarchical analysis of a wireless sensor network is characterized by comprising the following steps:
(1) judging whether the base station is accessible: judging whether the base station is in the reliable communication range of the node or not according to the relative positions of the source node and the base station; if so, the source node directly communicates with the base station, and the current route establishing process is finished; otherwise, turning to the step (2), selecting a node with better performance from the neighbor nodes as a next hop;
(2) establishing a decision matrix: the method comprises the following steps of counting data of four key factors of residual energy of neighbor nodes, transmission energy to be consumed, distance to a base station and node degree, adjusting a statistical data value, removing a data dimension, and constructing a judgment matrix after the processed data are subjected to ratio values in pairs, wherein the judgment matrix is as follows:
Figure FDA0002829179760000011
wherein the content of the first and second substances,
Figure FDA0002829179760000012
and Gi' separately after removing data dimensionThe residual energy of the neighbor nodes, the transmission energy to be consumed, the distance to the base station and the node degree; solving the maximum eigenvalue and the corresponding eigenvector of the decision matrix, and checking whether the decision matrix is a consistency matrix according to the eigenvalue; if not, readjusting the statistical data; otherwise, continuing to step (3);
(3) solving the neighbor node with the maximum performance weight: taking the four elements of the feature vector in the step (2) as weight values of the four key factors respectively, calculating the performance weight of each neighbor node, and then obtaining the neighbor node with the maximum performance weight, wherein the node is the optimal next hop node; judging whether the current node can directly and reliably communicate with the base station; if not, turning to the step (1), and continuously selecting the relay node of the next hop on the basis of the current node; otherwise, the current route establishment procedure is finished.
2. The routing scheduling method for hierarchical analysis of wireless sensor network according to claim 1, wherein in the step (2), whether the decision matrix is a consistency matrix is checked through a consistency decision index, wherein the consistency decision index is:
Figure FDA0002829179760000013
RI is the distribution value, λmaxIs a consistency matrix AiThe maximum eigenvalue of (a) and p are the number of key factors; and when the consistency ratio CR is less than 0.1, indicating that the judgment matrix meets the consistency requirement.
3. The routing scheduling method for wireless sensor network hierarchical analysis according to claim 1, further comprising, between step (2) and step (3): and obtaining a weight vector of a group of elements to a certain element in the upper layer according to the judgment matrix, finally obtaining the sequencing weight of each scheme in the lowest layer to the target, and selecting a consistency judgment implementation scheme.
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