CN103813406B - Layering chain tree route method based on region division - Google Patents
Layering chain tree route method based on region division Download PDFInfo
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Abstract
The present invention relates to a kind of layering chain tree route method based on region division, including：Step 1, the wireless device sense network that passes is divided into multiple regions；Step 2, make each independent cluster in the region, and the node in the cluster is formed into the first link by genetic algorithm in corresponding each region according to PEGASIS agreements；Step 3, according to energy maximization principle, cluster head is chosen in each cluster；Step 4, the communication link between the cluster head and Sink node is formed into the second link by genetic algorithm according to PEGASIS agreements；Step 5, the layering chain tree second link being transformed into centered on Sink node；Step 6, make node data along the layering chain tree and Sink node is passed to by data fusion, after scheduled call duration time, redirect and execute step 1.The present invention uses energy consumption caused by long haul communication caused by single-hop mode between effectively reducing LEACH algorithm interior joints.
Description
Technical Field
The invention relates to the technical field of wireless routing, in particular to a hierarchical chain tree routing method based on region division.
Background
The wireless sensor network is a special wireless communication, which is formed by a plurality of nodes through a wireless self-organizing network, and because the power supply energy, the computing capability and the communication capability of the sensor nodes are very limited, a good routing protocol must be provided to prolong the survival time and the network performance of the network as much as possible.
The LEACH protocol is a hierarchical routing algorithm. The common nodes select the cluster heads as the switching to communicate in order to avoid overlong distance, so that the energy consumption of the common nodes is effectively reduced, the stability time of the network is greatly prolonged, and the survival time of the sensor network is prolonged. However, the LEACH protocol has the following disadvantages, specifically:
the method for electing the cluster heads of LEACH adopts a random election mode, the cluster heads are not uniformly distributed, the communication distance between the nodes in partial areas and the cluster heads is large, and the communication energy consumption of partial nodes is increased.
The cluster head directly communicates with the base station in a single-hop mode, and when the network scale is large, the communication range is wide no matter how far the distance between the cluster head and the base station is, so that the cluster head consumes excessive energy and the nodes die prematurely.
The communication between the nodes in the cluster and the cluster heads is also realized in a single-hop mode, and when the network scale is large, the energy burden of the cluster heads is increased, and meanwhile, the communication energy consumption of the nodes is also increased.
Disclosure of Invention
The invention aims to provide a hierarchical chain tree routing method based on region division, which aims to solve the problem of high energy consumption of long-distance communication caused by a single-hop mode adopted among nodes in an LEACH algorithm in the prior art.
In order to solve the above technical problem, as an aspect of the present invention, there is provided a hierarchical chain tree routing method based on region partitioning, including: step 1, dividing a wireless sensor network into a plurality of areas; step 2, enabling each region to be clustered independently, and enabling nodes in the clusters to form first links in each corresponding region through a genetic algorithm according to a PEGASIS protocol; step 3, selecting a cluster head in each cluster according to an energy maximization principle; step 4, forming a second link by the communication link between the cluster head and the Sink node through a genetic algorithm according to a PEGASIS protocol; step 5, reconstructing the second link into a hierarchical chain tree with the Sink node as the center; and 6, transmitting the node data to the Sink node along the hierarchical chain tree through data fusion, and after a preset communication time, skipping to execute the step 1.
Further, the wireless sensor network in step 1 is square, and the square wireless sensor network is divided into 16 regions.
Further, the dividing of the square wireless sensor network into 16 areas includes: dividing the square into eight regions along its diagonal and median lines; drawing a concentric inner square in the square; the inner square divides the eight regions into 16 regions.
Further, the step 1 further comprises: and changing the load balance factor of the cluster head by adjusting the side length of the internal square.
Further, the load balancing factor LBF is calculated according to the following formula:
wherein head _ num is the number of cluster heads, xiThe number of nodes contained in the ith cluster is shown, and u is the average number of nodes contained in the cluster in the current round.
Further, in step 2, a new first link is reconstructed only when any one of the nodes within the first link dies.
Further, the fitness function fit of the genetic algorithm in the step 2 or 5 is:
fit＝(1-(len-minlen)/(maxlen-minlen+0.001))2
where len represents the total length of the links formed according to the current sequence, minlen represents the length of the link with the shortest length among all links of the generation group, and maxlen represents the length of the link with the longest length among all links of the generation group.
Further, in the step 5, on the second link, energy consumption of two communication modes, namely, between the cluster head and between the cluster head and the Sink node, is sequentially analyzed according to the sequence of the second link, and if the energy consumption of communication between the cluster head and a superior cluster head is less than the energy consumption of communication between the cluster head and the Sink node, the cluster head maintains an original communication route; otherwise, the cluster head directly communicates with the Sink, thereby reducing energy consumption.
The invention selects the node with the maximum energy in each area to select the cluster head, thereby avoiding the energy imbalance of the whole network caused by randomly selecting the cluster head in the LEACH protocol. Furthermore, energy consumption caused by long-distance communication due to the fact that a single-hop mode is adopted among nodes in an LEACH algorithm is effectively reduced through a multi-hop communication link mode of the nodes in the cluster, the cluster heads and the Sink.
Drawings
FIG. 1 is a network area division diagram;
FIG. 2 is a topology diagram of clusters in a chain;
FIG. 3 is a cluster head chaining topology;
FIG. 4 is a topology diagram after cluster head chaining modification;
FIG. 5 is a comparison of the life cycle of a network;
FIG. 6 is a network node remaining energy;
fig. 7 is a comparison of cluster head numbers.
Detailed Description
The following detailed description of embodiments of the invention, but the invention can be practiced in many different ways, as defined and covered by the claims.
Based on the description in the background art, in order to improve the above-mentioned shortcomings of the LEACH algorithm, the invention provides a routing method of a hierarchical chain tree based on region division.
The wireless sensor network is divided into a plurality of areas, an optimal first link is formed in each area through a genetic algorithm, a cluster head is selected on the link according to the principle of maximum residual energy, and a second link is formed between the cluster head and a Sink node (base station). And then, reconstructing the second link under the premise of considering communication energy consumption to construct the whole hierarchical chain tree.
Referring to fig. 1 to 4, the present invention provides a hierarchical chain tree routing method based on region partitioning, including:
step 1, please refer to fig. 1, the wireless sensor network is divided into a plurality of areas.
And 2, referring to fig. 2, clustering each region individually, and forming a first link in each corresponding region by using a genetic algorithm through nodes in the cluster according to the pegsis protocol, wherein in fig. 2, ○ represents a normal node, + represents a high-level node, ﹡ represents a cluster head, and a region center coordinate origin represents Sink.
Step 3, referring to fig. 2, selecting a cluster head in each cluster according to an energy maximization principle. Specifically, the remaining energy of the node is recorded, and a cluster head is selected on each intra-cluster link according to the principle that the remaining energy is the largest.
And step 4, referring to fig. 3, forming a second link by the communication link between the cluster head and the Sink node through a genetic algorithm according to the pegsis protocol. For example, the same genetic algorithm as in step 2 may be employed.
And 5, referring to fig. 4, reconstructing the second link into a hierarchical chain tree with the Sink node as the center. In particular, the principle of retrofitting is to reduce energy consumption. Preferably, the hierarchical chain tree is a tree-cluster multi-hop communication link.
And 6, transmitting the node data to the Sink node along the hierarchical chain tree through data fusion, and jumping to execute the step 1 after a preset communication time so as to enter a new round. Particularly, the node data is transmitted to Sink through a cluster head and data fusion along a chain tree according to the formed hierarchical chain tree route, and after a period of time of communication, the network enters a new round.
The invention selects the node with the maximum energy in each area to select the cluster head, thereby avoiding the energy imbalance of the whole network caused by randomly selecting the cluster head in the LEACH protocol. Furthermore, energy consumption caused by long-distance communication due to the fact that a single-hop mode is adopted among nodes in an LEACH algorithm is effectively reduced through a multi-hop communication link mode of the nodes in the cluster, the cluster heads and the Sink.
On the basis of region division, each region is clustered independently, nodes in each region form a first link by adopting a genetic algorithm according to the PEGASIS chaining idea, and a node with the largest energy is selected to be selected as a cluster head, so that energy imbalance of the whole network caused by random selection of the cluster heads in an LEACH protocol is avoided. Further, after the election of the cluster head is finished, the communication link between the cluster head and the Sink is also made to form a second link through a genetic algorithm according to the PEGASIS thought, and the second link is reformed on the premise of considering reduction of communication energy consumption, so that a tree-cluster type multi-hop communication link taking the Sink as the center is formed. The layered chaining mode reduces energy consumption generated by long-distance communication caused by a single-hop mode adopted between nodes in an LEACH algorithm.
Preferably, the wireless sensor network in step 1 is square, and the square wireless sensor network is divided into 16 areas. For example, the size of the square is 200m by 200 m.
Preferably, the dividing of the square wireless sensor network into 16 areas includes: dividing the square into eight regions along the diagonal and median lines thereof (i.e., two orthogonal lines passing through the geometric center of the square and respectively corresponding to the adjacent two sides of the square); drawing a concentric inner square in the square; the inner square divides the eight regions into 16 regions.
Preferably, the step 1 further comprises: and changing the load balance factor of the cluster head by adjusting the side length of the internal square.
Preferably, the load balancing factor LBF is calculated according to the following formula:
wherein,
head _ num is the number of cluster heads, xiThe number of nodes contained in the ith cluster is shown, and u is the average number of nodes contained in the cluster in the current round.
Preferably, in said step 2, a new first link is reconstructed only if any one of the nodes within said first link dies.
Preferably, the fitness function fit of the genetic algorithm in step 2 or 5 is:
fit＝(1-(len-minlen)/(maxlen-minlen+0.001))2
where len represents the total length of the links formed according to the current sequence, minlen represents the length of the link with the shortest length among all links of the generation group, and maxlen represents the length of the link with the longest length among all links of the generation group.
Preferably, in the step 5, on the second link, energy consumption of two communication modes, namely, between the cluster head and between the cluster head and the Sink node, is sequentially analyzed according to the sequence of the second link, and if energy consumption of communication between the cluster head and a superior cluster head is less than energy consumption of communication between the cluster head and the Sink node, the cluster head maintains an original communication route; otherwise, the cluster head directly communicates with the Sink, thereby reducing energy consumption.
In one embodiment, the second link is adapted as follows:
(1) the cluster heads on the link directly communicate with the Sink, and the energy consumption E of the whole network1Comprises the following steps:
E1＝Eεlεc+εfs·d1 2+EDA
(2) the cluster head is communicated with the cluster head on the link, and the energy consumption E of the whole network2Comprises the following steps:
E2＝Eelec+εfs·d2 2+EDA+Eelec+EDA
(3) when E is1≤E2Cluster head selects to communicate directly with sink to change link, while when E1＞E2The cluster head still keeps the original communication route and communicates with the superior cluster head.
In the above formulas: eelecIs the energy consumed to receive or transmit a bit; eDAThe energy consumed per bit of data processed is determined by the hardware of the sensor, which is generally only EelecOne tenth of the total; epsilonfxIs a quantity that depends on the transmission amplifier model used by the sensor. d1The distance between a random cluster head on the link and Sink directly, d2Is the distance between a random cluster head on the link and the superior cluster head on the link.
By MATLAB simulation analysis, please refer to fig. 5 to 7, the invention is obviously improved in the aspects of prolonging the network stability period and energy consumption balance compared with the LEACH protocol, and overcomes the defects of cluster head election and node single-hop mechanism in the LEACH routing protocol under the large-scale wireless sensor network energy heterogeneous environment.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A hierarchical chain tree routing method based on region division is characterized by comprising the following steps:
step 1, dividing a wireless sensor network into a plurality of areas;
step 2, enabling each region to be clustered independently, and enabling nodes in the clusters to form first links in each corresponding region through a genetic algorithm according to a PEGASIS protocol;
step 3, selecting a cluster head in each cluster according to an energy maximization principle;
step 4, forming a second link by the communication link between the cluster head and the Sink node through a genetic algorithm according to a PEGASIS protocol;
step 5, reconstructing the second link into a hierarchical chain tree with the Sink node as the center; on the second link, analyzing energy consumption of two communication modes, namely between the cluster head and between the cluster head and the Sink node in sequence according to the sequence of the second link, wherein if the energy consumption of the communication between the cluster head and the superior cluster head is less than the energy consumption of the communication between the cluster head and the Sink node directly, the cluster head maintains the original communication route, otherwise, the cluster head and the Sink node directly communicate, thereby reducing the energy consumption;
and 6, transmitting the node data to the Sink node along the hierarchical chain tree through data fusion, and after a preset communication time, skipping to execute the step 1.
2. The method according to claim 1, wherein the wireless sensor network in step 1 is a square, and the square wireless sensor network is divided into 16 areas;
the division of the square wireless sensor network into 16 of the regions comprises: dividing the square into eight regions along its diagonal and median lines; drawing a concentric inner square in the square; the inner square divides the eight regions into 16 regions.
3. The method of claim 2, wherein step 1 further comprises: and changing the load balance factor of the cluster head by adjusting the side length of the internal square.
4. The method of claim 3, wherein the load balancing factor LBF is calculated according to the following equation:
wherein,
head _ num is the number of cluster heads, xiThe number of nodes contained in the ith cluster is shown, and u is the average number of nodes contained in the cluster in the current round.
5. A method according to claim 1, wherein in step 2, a new first link is only reconfigured if any node within the first link dies.
6. The method according to claim 1, characterized in that the fitness function fit of the genetic algorithm in step 2 or 5 is:
fit＝(1-(len-minlen)/(maxlen-minlen+0.001))2
where len represents the total length of the links formed according to the current sequence, minlen represents the length of the link with the shortest length among all links of the generation group, and maxlen represents the length of the link with the longest length among all links of the generation group.
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Inventor after: Yu Guicai Inventor after: Xiang Mantian Inventor after: Zhou Xiaoming Inventor after: Liao Sha Inventor after: Long Chengzhi Inventor before: Xiang Mantian Inventor before: Zhou Xiaoming Inventor before: Liao Sha Inventor before: Long Chengzhi |
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