CN114363985A - Method for constructing binary tree based on node weight and updating method of binary tree - Google Patents
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Abstract
A method for constructing a binary tree based on node weight and an updating method of the binary tree belong to the field of wireless multi-hop networks. The method solves the defects that the cache resource and the electric quantity resource of the parent node are easily exhausted, the message submission rate is low, and the overall performance of the network is influenced in the conventional wireless multi-hop network routing method which adopts a binary tree constructed in a random mode. According to the current state of each node, calculating the weight values of all nodes in the network; determining the identity type of the node in the binary tree according to the ranking of the weight value of each node in the network; connecting each parent node with the corresponding left child node and each parent node with the corresponding right child node to form an initial binary tree; then setting the weight value of each edge in the initial binary tree; and finally, distributing an initial bitmap to each node in the initial binary tree, and updating bitmap information of the parent nodes, thereby completing the construction of the binary tree. The invention is mainly applied to the field of wireless multi-hop networks.
Description
Technical Field
The invention belongs to the field of wireless multi-hop networks, and particularly relates to a method for constructing a binary tree based on node weights and an updating method of the binary tree in a wireless multi-hop network.
Background
With the rapid development of wireless communication technology and the rapid popularization of intelligent terminal devices, the requirements of people on wireless networks are also continuously improved. The wireless multi-hop network becomes a bridge connecting people and things, and between people and things by the characteristics of distributed operation, three-dimensional networking, strong expansibility and the like, and is widely applied to various fields of daily life. Since the birth of wireless multi-hop networks, routing protocols have been receiving high attention from researchers at home and abroad as one of key technologies for networking. In a wireless multi-hop network, nodes do not typically rely on a pre-deployed infrastructure in communicating messages, but rather let the nodes act as routers to transmit messages to destination nodes. However, due to some features unique to the wireless multi-hop network itself, for example: the mobility of the nodes, the dynamic change of the network topology, the limited resource of the nodes, the limited bandwidth and the like bring great challenges to the design of the routing method. Therefore, designing an efficient routing method for a wireless multi-hop network to improve the network performance is one of the key problems to be solved urgently in the research process of the wireless multi-hop network.
At present, researchers at home and abroad have proposed a large number of routing methods for wireless multi-hop networks, wherein Derakhshanfar et al propose an Opportal routing in wireless networks using bitmap-based weighted tree method, which randomly constructs a binary tree by using nodes in the network, and allocates edge weight values of 0 and 1 to an edge connecting a parent node and a left child node of the binary tree and an edge connecting the parent node and a right child node of the binary tree, respectively. Each node in the binary tree is assigned an initial bitmap and the bitmap information for each parent node is updated by collecting the corresponding bitmap information from the child nodes and the edge weight values of the edges associated with the child nodes. When a message is sent to a certain node in the binary tree, the message is stored in the nodes of the binary tree in the form of a bitmap, and the bitmap of the message is multiplied by the corresponding bits of the bitmap of the node where the bitmap is located. And if the multiplication results of the corresponding bits are all 0, the destination node is not in the current node subdirectory. At this point, the message is sent to the parent node that is higher and can reach the destination node. And if the multiplication results of the corresponding bits are not all 0, the destination node selects the next relay node in the current node subdirectory according to the result obtained by multiplying the corresponding bits of the bitmap.
The method can effectively guarantee the delivery rate and the time delay of the network, but when the method constructs the binary tree, the nodes with less cache resources or electric quantity resources are easily used as parent nodes by adopting a random mode to construct the binary tree. In the process of forwarding the message, the parent node forwards the message more times than the child nodes, so that the cache resource and the power resource of the parent node are easily exhausted, thereby affecting the overall performance of the network. In addition, in the process of constructing the binary tree, the times of forwarding the message by the nodes are not used as the basis for constructing the binary tree, so that the nodes with low message forwarding capability are easily used as root nodes or parent nodes, thereby reducing the message submission rate and increasing the time delay. Therefore, a solution to the above drawbacks is needed.
Disclosure of Invention
The invention aims to solve the defects that cache resources and electric quantity resources of parent nodes are easily exhausted, the message delivery rate is low and the overall performance of a network is influenced in the conventional wireless multi-hop network routing method which adopts a binary tree constructed in a random mode; therefore, the invention provides a method for constructing a binary tree based on node weight and an updating method of the binary tree.
A method for constructing a binary tree based on node weights comprises the following steps:
s1, calculating the weight values of all nodes in the network according to the current state of each node; the current state of the node comprises the times of forwarding the message by the node, the residual cache of the node and the residual electric quantity of the node; the network is a wireless multi-hop network;
s2, determining the identity type of each node in the binary tree according to the ranking of the weight value of each node in the network; the node identity types comprise parent nodes and child nodes, and the child nodes comprise left child nodes and right child nodes;
s3, connecting each parent node with the corresponding left child node and connecting each parent node with the corresponding right child node to form an initial binary tree;
s4, setting the weight value of each edge in the initial binary tree;
s5, an initial bitmap is distributed to each node in the initial binary tree, and the bitmap of the parent node corresponding to each child node is updated by using the information in the initial bitmap of each child node and the edge weight value of the edge between the child node and the parent node corresponding to the child node, so that the binary tree is constructed.
The invention has the following beneficial effects:
(1) the binary tree can be periodically constructed in the wireless multi-hop network, so that the binary tree which is beneficial to improving the network performance can be constructed according to the resource condition of the network and the attribute of the node, and the situation that the node with excessive resource consumption or low forwarding capability always executes the message forwarding task is avoided, so that the overall performance of the network is influenced.
(2) In the process of constructing the binary tree, attributes such as cache and electric quantity of the nodes are comprehensively considered, so that the nodes with more cache resources or electric quantity resources serve as parent nodes, and the phenomenon that the cache resources and the electric quantity resources of the parent nodes are exhausted after multiple times of message forwarding is avoided, and the overall network performance is influenced.
(3) In the process of constructing the binary tree, the message forwarding times are used as the basis for constructing the binary tree, so that the nodes with higher forwarding times are used as the parent nodes, the message submission rate can be effectively improved, and the time delay can be reduced.
Drawings
FIG. 1 is a flowchart of a method for constructing a binary tree based on node weights according to one embodiment;
FIG. 2 is a schematic diagram of each node in a wireless multi-hop network route when the number of nodes is 8;
FIG. 3 is a structural diagram of an initial binary tree formed when the number of nodes is 8;
FIG. 4 is a graph of edge weight assignments for each edge of the binary tree for a node number of 8;
FIG. 5 is a diagram of an initial binary tree structure after an initial bitmap is allocated when the number of nodes is 8;
fig. 6 is a schematic diagram of a binary tree structure after updating bitmap information of a parent node when the number of nodes is 8.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The first embodiment is as follows: the following describes this embodiment with reference to fig. 1, fig. 4, and fig. 5, and the method for constructing a binary tree based on node weights in this embodiment includes the following steps:
s1, calculating the weight values of all nodes in the network according to the current state of each node; the current state of the node comprises the times of forwarding the message by the node, the residual cache of the node and the residual electric quantity of the node; the network is a wireless multi-hop network;
s2, determining the identity type of each node in the binary tree according to the ranking of the weight value of each node in the network; the node identity types comprise parent nodes and child nodes, and the child nodes comprise left child nodes and right child nodes;
s3, connecting each parent node with the corresponding left child node and connecting each parent node with the corresponding right child node to form an initial binary tree;
s4, setting the weight value of each edge in the initial binary tree; see in particular fig. 4;
s5, allocating an initial bitmap to each node in the initial binary tree, and updating the bitmap of the parent node corresponding to the child node by using the information in the initial bitmap of each child node and the edge weight value of the edge between the child node and the parent node corresponding to the child node, thereby completing the construction of the binary tree, see fig. 5.
In this embodiment, the process of allocating an initial bitmap to each node in the initial binary tree and updating the bitmaps of the parent nodes is implemented by using a node bitmap allocation method in the prior art.
In the process of constructing the binary tree, node weights in a network are calculated according to the number of times of forwarding messages by the nodes, the residual caches of the nodes and the residual electric quantity of the nodes as bases, and the identity types of the nodes in the binary tree are determined according to the ranking of weighted values, so that an accurate binary tree is constructed.
Further, step S1, according to the current state of each node, the implementation manner of calculating the weight values of all nodes in the network is as follows:
for any node (i) in the network, calculating the weight value S (i) of the node (i) is realized by adopting a formula I,
(i) ═ α × delitemes (i) + β × buffsize (i) + γ × battpower (i) (formula one);
wherein the content of the first and second substances,
(i) the number of times the message is forwarded for node i; i is the serial number of the node;
buffsize (i) is the remaining cache size for node i;
battpower (i) is the residual capacity of the node i;
α, β, γ are all adjustment coefficients, and α + β + γ is 1.
Further, referring to fig. 2 and fig. 3 specifically, in step S2, the implementation manner of determining the identity type of each node in the binary tree according to the rank of the weight value of each node in the network is as follows: s21, sorting the weight values of all nodes in the network from small to large;
s22, sequentially enabling the nodes to be used as parent nodes according to the sequence of the node serial numbers from large to small, wherein the selection mode of each parent node for the left child node and the right child node is the same, and therefore the identity types of all the nodes are determined;
the selection mode of each parent node for the left child node and the right child node is as follows:
when node (n) is the parent node, selecting node (n-1) and node (n-2) as the left child node and right child node of parent node (n), respectively; n is the number of the node.
In specific application, taking 8 nodes as an example, specifically referring to fig. 3, sequentially making each node as a parent node according to a sequence of node sequence numbers from large to small, so as to complete a specific process of determining identity types of all nodes:
firstly, taking the node (n) with the largest sequence number as a root node of a binary tree, and taking the node as a parent node of the binary tree;
secondly, when the node (n) is used as a parent node, selecting the node (n-1) and the node (n-2) as a left child node and a right child node of the parent node (n), respectively;
when the node (n-1) is used as a parent node, selecting the node (n-3) and the node (n-4) as a left child node and a right child node of the parent node (n-1), respectively;
when the node (n-2) is used as a parent node, selecting the node (n-5) and the node (n-6) as a left child node and a right child node of the parent node (n-2), respectively;
and repeating the steps in the above manner until all the node identity type confirmation is completed.
In the preferred embodiment, the node bitmap updating method is a method for updating a node bitmap in the prior art.
Further, in step S1, the number of times the node forwards the message is the attribute of the node itself. The number of times each node forwards a message is constant at a time and varies over time.
Further, referring to fig. 4 specifically, in step S4, the implementation manner of setting the edge weight value of each edge in the initial binary tree is as follows:
and setting the edge weight value of the edge between each parent node and the corresponding left child node of the initial binary tree to be 0, and setting the edge weight value of the edge between each parent node and the corresponding right child node of the initial binary tree to be 1.
Furthermore, the bitmap of each node comprises a plurality of positions, the positions are sequentially ordered from left to right, and each position is associated with a node with the same position sequence number;
the number of positions in each node is the same as the number of nodes in the network;
each location includes two memory cells, wherein,
the left storage unit is called as a counter and is used for storing the step number of the node where the current left storage unit is located reaching the node with the same position serial number as the current left storage unit;
the right storage unit is called a footprint memory and is used for storing the edge weight value when the node where the current right storage unit is located reaches the edge between nodes with the same position sequence number as the current right storage unit.
Further, the information in the initial bitmap corresponding to each node is as follows:
storing a number 0 in a counter at the position same as the serial number of the current node;
storing a number 1 in a footprint memory at the same position as the serial number of the current node;
the counter and footprint memory for the remaining positions in the current node both store the number 0.
The second embodiment is as follows: the updating method of the binary tree according to this embodiment is obtained by using the method for constructing the binary tree based on the node weights according to the first embodiment, and the specific process of the updating method is as follows:
when the life duration of the constructed binary tree in the network is T, detecting the current states of all nodes in the network, constructing all nodes in the network by using a method for constructing the binary tree based on the weight of the nodes, and obtaining a new binary tree, thereby completing the updating of the binary tree.
In specific application, the number of nodes in the wireless multi-hop network is constant, the resource state of each node changes along with the time, and the weight of each node in the network is rearranged by updating the binary tree, so that the node with better resource plays a more important role in the binary tree, and the binary tree in the network is continuously updated.
The binary tree can be periodically constructed in the wireless multi-hop network, so that the binary tree which is beneficial to improving the network performance can be constructed according to the resource condition of the network and the attribute of the node, and the situation that the node with excessive resource consumption or low forwarding capability always executes the message forwarding task is avoided, so that the overall performance of the network is influenced.
In specific application, the specific process of constructing the binary tree is as follows: taking fig. 2 as an example, suppose that there are 8 nodes in the network, and the weighted values of the 8 nodes are sorted from small to large, and are node (1), node (2),.. and node (8) in turn. The sorted sequence number is also the sorted sequence number of the node number. Node (8) is selected as the first parent node of the binary tree (the root node of the tree). Selecting a node (7) and a node (6) from the rest nodes as a left child node and a right child node of a node (8), respectively; selecting a node (5) and a node (4) as a left child node and a right child node of a node (7), respectively; selecting the node (3) and the node (2) as a left child node and a right child node of the node (6), respectively; node (1) is selected as the left child node of node (5) and the initial binary tree structure is shown in fig. 3.
An edge weight value of 0 and an edge weight value of 1 are assigned to the edge of the initial binary tree where the parent node is connected to the left child node and the edge of the initial binary tree where the parent node is connected to the right child node, respectively, as shown in fig. 4.
Each node in the initial binary tree is assigned an initial bitmap as shown in fig. 5. The number of positions in the initial bitmap of each node is the same as the number of nodes, and the bitmap is used to indicate paths to a certain node. The bitmap consists of the locations where the numbers are to be stored, the number of locations depending on the number of nodes.
For example, when there are 8 nodes in the network, then the bitmap will also contain 8 positions. Each of these locations is associated with a particular node and represents a node in the network. For example, in the initial bitmap, the first position from left to right is associated with node (1) number 1. Each location consists of 2 memory cells, the left memory cell is called a counter, and the number of steps to reach the node associated with the location number 1 is recorded. The storage unit on the right is called footprint memory, and records the edge weight value of the path to the node associated with the position number 1, which needs to pass through the corresponding edge. Since each location is associated with a node in the network, in an initial state, each node places a number 0 in the counter of its own associated location and a number 1 in the footprint memory of its own associated location, the counters and footprint memories of the locations associated with the remaining nodes each placing a 0.
The bitmap information for each parent node is updated by collecting the corresponding bitmap information from the child nodes and the edge weights of the edges associated with the child nodes. The updated bitmap is shown in fig. 6.
The binary tree constructed by the invention is used for message transmission, which is the prior art in the field;
if a message is sent to a certain node in the binary tree, the message is stored in the node of the binary tree in the form of bitmap. Assuming node (1) has a message to send to node (2), the message may be represented in bitmap form as 0011000000000000. And multiplying the bitmap 0011000000000000 of the message by the corresponding bit of the bitmap of the node (1), wherein if the multiplication results of the corresponding bits are all 0, the destination node (2) is not in the current node (1) subdirectory. In this case, the message is sent to the parent node of the higher layer until the destination node can be reached through the parent node. When a message is sent to the node (8), the bitmap 0011000000000000 of the message is multiplied by the corresponding bits of the bitmap of the node (8), the result is 0021000000000000, and the multiplication results of the corresponding bits are not all 0, which indicates that the destination node (2) needs 2 steps in the subdirectory of the node (8) and can reach the destination node through the edge with the edge weight of 1.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (8)
1. The method for constructing the binary tree based on the node weight is characterized by comprising the following steps:
s1, calculating the weight values of all nodes in the network according to the current state of each node; the current state of the node comprises the times of forwarding the message by the node, the residual cache of the node and the residual electric quantity of the node; the network is a wireless multi-hop network;
s2, determining the identity type of the node in the binary tree according to the ranking of the weight value of each node in the network; the node identity types comprise parent nodes and child nodes, and the child nodes comprise left child nodes and right child nodes;
s3, connecting each parent node with the corresponding left child node and connecting each parent node with the corresponding right child node to form an initial binary tree;
s4, setting the weight value of each edge in the initial binary tree;
s5, an initial bitmap is distributed to each node in the initial binary tree, and the bitmap of the parent node corresponding to each child node is updated by using the information in the initial bitmap of each child node and the edge weight value of the edge between the child node and the parent node corresponding to the child node, so that the binary tree is constructed.
2. The method for constructing a binary tree based on node weights according to claim 1, wherein step S1 is implemented by calculating the weight values of all nodes in the network according to the current state of each node:
for any node (i) in the network, calculating the weight value S (i) of the node (i) is realized by adopting a formula I,
(i) ═ α × delitemes (i) + β × buffsize (i) + γ × battpower (i) (formula one);
wherein the content of the first and second substances,
(i) the number of times the message is forwarded for node i; i is the serial number of the node;
buffsize (i) is the remaining cache size for node i;
battpower (i) is the residual capacity of the node i;
α, β, γ are all adjustment coefficients, and α + β + γ is 1.
3. The method for constructing a binary tree based on node weights according to claim 1, wherein step S2 is implemented by determining the identity type of each node in the binary tree according to the ranking of the weight value of each node in the network:
s21, sorting the weight values of all nodes in the network from small to large;
s22, sequentially enabling the nodes to be used as parent nodes according to the sequence of the node serial numbers from large to small, wherein the selection mode of each parent node for the left child node and the right child node is the same, and therefore the identity types of all the nodes are determined;
the selection mode of each parent node for the left child node and the right child node is as follows:
when node (n) is the parent node, selecting node (n-1) and node (n-2) as the left child node and right child node of parent node (n), respectively; n is the number of the node.
4. The method for constructing a binary tree based on node weights as claimed in claim 1, wherein in step S1, the number of times the node forwards the message is the node' S own property.
5. The method according to claim 1, wherein the step S4 of setting the edge weight value of each edge in the initial binary tree is implemented by:
and setting the edge weight value of the edge between each parent node and the corresponding left child node of the initial binary tree to be 0, and setting the edge weight value of the edge between each parent node and the corresponding right child node of the initial binary tree to be 1.
6. The method according to claim 1, wherein the bitmap of each node includes a plurality of positions, and the positions are sequentially ordered from left to right, and each position is associated with a node with the same position sequence number;
the number of positions in each node is the same as the number of nodes in the network;
each location includes two memory cells, wherein,
the left storage unit is called as a counter and is used for storing the step number of the node where the current left storage unit is located reaching the node with the same position serial number as the current left storage unit;
the right storage unit is called a footprint memory and is used for storing the edge weight value when the node where the current right storage unit is located reaches the edge between nodes with the same position sequence number as the current right storage unit.
7. The method for constructing a binary tree based on node weights as claimed in claim 6, wherein the information in the initial bitmap corresponding to each node is:
storing a number 0 in a counter at the position same as the serial number of the current node;
storing a number 1 in a footprint memory at the same position as the serial number of the current node;
the counter and footprint memory for the remaining positions in the current node both store the number 0.
8. A binary tree updating method, wherein the binary tree is obtained by the method for constructing a binary tree based on node weights as claimed in claim 1, and the updating method comprises the following specific steps:
when the life duration of the constructed binary tree in the network is T, detecting the current states of all nodes in the network, constructing all nodes in the network by using a method for constructing the binary tree based on the weight of the nodes, and obtaining a new binary tree, thereby completing the updating of the binary tree.
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