CN109640359B - Communication load balancing method for wireless sensor network - Google Patents

Communication load balancing method for wireless sensor network Download PDF

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CN109640359B
CN109640359B CN201811608254.6A CN201811608254A CN109640359B CN 109640359 B CN109640359 B CN 109640359B CN 201811608254 A CN201811608254 A CN 201811608254A CN 109640359 B CN109640359 B CN 109640359B
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charging
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彭凯
刘强
彭麟雅
桂宾
王栋云
胡国亮
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Wuhan Fenghuo Kaizhuo Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • 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
    • H04L45/02—Topology update or discovery
    • 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
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/48—Routing tree calculation
    • 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

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

The invention provides a wireless sensor network communication load balancing method, which is characterized in that power consumption rates of nodes are calculated and classified, interval division is carried out through node charging periods, node intervals with different power consumption rates are obtained, a charging task scheduling table is obtained according to the charging periods, a tree is generated by using the scheduling table, a charging task path is obtained, cost value calculation is carried out according to the path, and the path with the minimum comprehensive cost value is the optimal path.

Description

Communication load balancing method for wireless sensor network
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a communication load balancing method of a wireless sensor network.
Background
Wireless sensor networks are used in many fields, for example: environmental monitoring, medical care, target tracking, military fields, and the like. The wireless sensor network is a communication network formed by distributed deployed sensors which are connected in a multi-hop mode. Each sensor node in the network can independently sense, collect and process information monitored within a certain range. As technology has developed, sensors today can already collect information such as sound, images, electromagnetism, humidity, temperature, pressure, etc.
Although the application prospect of the wireless sensor network is wide, the application of the wireless sensor network is limited to short-term scientific research and industrial monitoring because the energy consumption of the sensor is high. In recent years, with the rapid development of electronic technology, the cost of the sensor is lower and lower, and the energy cost has become the biggest bottleneck limiting the popularization of the wireless sensor network.
The sensor with the first exhausted power in the network determines the running time of the wireless sensor network, the power consumption of the sensor is mainly used for data transmission, and the time for the node to exhaust the power is determined by the transmission traffic. The nodes which are in the communication hotspot positions and have large information forwarding amount and high power consumption are called hotspot nodes. In general, information acquired by nodes is backed up and processed by a base station, so that the communication traffic of the nodes close to the base station is much higher than that of other nodes, the power consumption speed of the nodes is much higher than that of the nodes at the edge of the network, and the hot point nodes need to be charged frequently. In order to solve the above problem, we will design a communication route of the network, and hopefully reduce the communication traffic of the hot spot nodes, reduce the charging period thereof, balance the communication traffic of the whole network, and hopefully leave as little electric energy as possible when each node is charged, thereby maximizing the utilization of the capacity of the battery.
The advantages of the wireless sensor network mainly come from data communication among nodes, and the power consumption of the hot point nodes is relatively high due to the fact that the number of information exchange and communication processes is large. In recent years, with the maturity of technology, low-cost unmanned aerial vehicles are rapidly moving towards civilian use, and play a great role in many fields of national economy. If combine current unmanned aerial vehicle technique, then only need an unmanned aerial vehicle who is equipped with positioning system and carries bold battery and wireless charger, just can periodically charge in proper order along the route of setting for the sensor in the network to can ignore the influence of topography to airspeed, make the task of charging more automatic, it is more convenient and practice thrift the cost, thereby satisfy the demand of low-cost long-term energy supply. Therefore, a routing algorithm needs to be designed to generate a routing tree with balanced load, balance node communication routing conditions of the whole network, reduce power consumption rate of a hotspot node, reduce the power-on and power-off charging frequency of the unmanned aerial vehicle, and improve charging efficiency.
Disclosure of Invention
In view of this, the invention provides a wireless sensor network communication load balancing method which is long in charging period, low in communication load and more sufficient in power consumption.
The technical scheme of the invention is realized as follows: the invention provides a wireless sensor network communication load balancing method, which comprises the following steps:
s1, constructing initial classification of nodes, preliminarily classifying the nodes according to the power consumption rate of each node, dividing the nodes into different charging service period intervals to obtain a plurality of initial node sets, and establishing an initial charging task scheduling table according to the initial node sets;
s2, designing a routing algorithm to generate a routing tree with balanced load;
and S3, acquiring the access node of the unmanned aerial vehicle charging task according to the charging task scheduling table, and planning the access path of the unmanned aerial vehicle.
On the basis of the above technical solution, preferably, the step S1 further includes the following steps:
s11, calculating the power consumption rate of each node;
s12, classifying the nodes according to the power consumption rate;
s13, calculating the charging period of each node, preliminarily dividing the charging node subsets according to the charging period, and arranging the charging periods of all the nodes in an ascending order;
and S14, deriving a charging task schedule according to the classification result and the arrangement of all the nodes.
On the basis of the above technical solution, preferably, the routing algorithm of S2 further includes the following steps:
s21, traversing the whole sensor network to obtain the communication topological graph of all nodes;
s22, calculating whether the two nodes can communicate with each other, and setting the weight between the nodes;
s23, setting three node sets: the set S is used for storing nodes added into the spanning tree, the set K is used for storing nodes not added into the spanning tree, the nodes are adjacent to the nodes in the set S, and the set T is used for storing the rest nodes;
and S24, continuously selecting nodes from the K by the algorithm, adding the nodes into the spanning tree, and updating the three sets at any time.
On the basis of the above technical solution, preferably, the method further includes step S25, setting the base station as a root node v0, writing the root node into the set S, and updating the sets K and T.
Further preferably, the method further includes step S26, assigning the node i in the set K to join the spanning tree, examining all candidate joining points in the set K, each node maintaining a cost of itself, and calculating the candidate node i that minimizes the comprehensive cost of the set K.
On the basis of the above technical solution, preferably, the method further includes step S27, traversing all candidate nodes in the set K, assigning the candidate nodes to candidate parent nodes capable of communicating with the candidate parent nodes in the set S, respectively calculating the comprehensive cost of the whole path from the root node to the candidate nodes after assignment, recording the comprehensive cost, then selecting the candidate node with the minimum comprehensive cost from all cost results, adding the candidate node into the set S, and updating the three sets.
On the basis of the above technical solution, preferably, the method further includes step S28, and step S27 is repeated until S includes all node positions, and finally the multi-hop communication path of the wireless sensor network node is determined.
Compared with the prior art, the method for balancing the communication load of the wireless sensor network has the following beneficial effects that:
(1) the method comprises the steps of calculating and classifying power consumption rates of nodes, dividing intervals according to node charging cycles to obtain node intervals with different power consumption rates, obtaining a charging task scheduling table according to the charging cycles, generating a tree by using the scheduling table to obtain charging task paths, calculating the cost value according to the paths, and taking the path with the minimum comprehensive cost value as the optimal path;
(2) according to the invention, a routing algorithm is designed according to the characteristic that the nodes divide the charging period, so that the charging period of the hot point nodes is reduced, the communication load of the whole network is balanced, the communication traffic of the hot point nodes is reduced, and the charging period of the hot point nodes is reduced;
(3) according to the invention, the charging period intervals of the nodes are divided according to the power consumption rate, and a subclass is periodically selected from the nodes of each charging period to be charged in each charging process, so that the balance of charging tasks is ensured;
(4) the invention also introduces the unmanned aerial vehicle into the charging problem of the wireless sensor network, so that the charging task is more automatic, more convenient and cost-saving.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a method for balancing communication load of a wireless sensor network according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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 obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
In the following embodiment, it is assumed that the sensor nodes are randomly placed in a well-defined working area, all the nodes start to work when fully charged at the same time, and the data acquisition rates of the nodes and the nodes are the same.
As shown in fig. 1, a method for balancing communication load of a wireless sensor network according to the present invention includes:
s1, constructing initial classification of nodes, preliminarily classifying the nodes according to the power consumption rate of each node, dividing the nodes into different charging service cycle intervals to obtain a plurality of initial node sets, and establishing an initial charging task scheduling table according to the initial node sets;
s2, designing a routing algorithm to generate a routing tree with balanced load;
and S3, acquiring the access node of the unmanned aerial vehicle charging task according to the charging task scheduling table, and planning the access path of the unmanned aerial vehicle.
In a specific embodiment, the S1 further includes the following steps:
s11, calculating the power consumption rate of each node;
s12, classifying the nodes according to the power consumption rate;
s13, calculating the charging period of each node, preliminarily dividing the charging node subsets according to the charging period, and arranging the charging periods of all the nodes in an ascending order;
and S14, deriving a charging task schedule according to the classification result and the arrangement of all the nodes.
In the above embodiment, the classification order m of the node is first determined
Figure BDA0001923053250000061
Wherein r ismaxAnd rminRespectively representing a maximum power consumption rate and a minimum power consumption rate of the node, and alpha is an integer parameter greater than 1. [ x ] of]Returning a first integer greater than x;
then calculating the charging period T of each nodei
Figure BDA0001923053250000062
Wherein the minimum charging period
Figure BDA0001923053250000063
Maximum charge cycle
Figure BDA0001923053250000064
Wherein B represents the battery capacity of the node
And preliminarily dividing the charging node subsets according to the charging period, arranging the charging periods of all the nodes in an ascending order, and dividing the charging periods into m intervals. The intervals are then represented as follows:
(Tmin，αTmin]，(αTmin，α2Tmin]，...，(αm-1Tmin，Tmax]
the intervals are marked C from left to right1，C2，...，Ci，...，CmFinally, all the nodes are divided into m intervals, and the length of each interval is increased in an alpha exponential mode;
deriving a charging task schedule of periodic work according to the classification result, assuming that all nodes are divided into m intervals, CiThe charging period of the nodes of the interval is denoted piThen, C isiInterval is in
Figure BDA0001923053250000065
The time slot is charged, wherein
Figure BDA0001923053250000066
Where L is the length of the entire charging cycle, which is the least common multiple of the charging periods of all subsets.
In a specific embodiment, the S2 routing algorithm further includes the following steps:
s21, traversing the whole sensor network to obtain the communication topological graph of all nodes;
s22, calculating whether the two nodes can communicate with each other, and setting the weight between the nodes;
s23, setting three node sets: the set S is used for storing nodes added into the spanning tree, the set K is used for storing nodes not added into the spanning tree, the nodes are adjacent to the nodes in the set S, and the set T is used for storing the rest nodes;
and S24, continuously selecting nodes from the K by the algorithm, adding the nodes into the spanning tree, and updating the three sets at any time.
In the above embodiment, whether two nodes can communicate with each other is calculated, and the weight between the nodes is set, and the weight between two nodes that can communicate is the same and is set to 1, and the weight between two nodes that cannot communicate is infinite.
In the specific embodiment, the method further comprises the step of S25, and setting the base station as a root node v0The root node is written into the set S, and the sets K and T are updated.
In a specific embodiment, the method further comprises the step of S26, assigning a node i in the set K to join the spanning tree, examining all candidate joining points in the set K, maintaining one own cost for each node, and calculating the candidate node i which enables the comprehensive cost of the set K to be minimum.
In the above embodiment, the assigned node i in the set K is added to the spanning tree, the comprehensive cost value after the node i is added is calculated, the candidate node with the minimum comprehensive cost is selected, the node j is assigned from the set S as the parent node of the node i, the number of child nodes of the node j and the parent node thereof is increased, and meanwhile, the change of the cost value is caused, each node maintains one own cost WiThe composite cost W represents the sum of the root node to node path costs.
In the specific implementation, the method further includes S27, traversing all candidate nodes in the set K, assigning the candidate nodes to candidate parent nodes capable of communicating with the candidate parent nodes in the set S, respectively calculating the comprehensive cost of the whole path from the root node to the candidate nodes after the assignment, recording the comprehensive cost, then selecting the candidate node with the minimum comprehensive cost from all cost results, adding the candidate node into the set S, and updating the three sets.
In the above embodiment, the candidate node i with the minimum comprehensive cost obtained in S26 is assigned to the candidate parent node capable of communicating with the candidate parent node, the comprehensive cost of the entire path from the root node after assignment is calculated and recorded, then the candidate node with the minimum comprehensive cost is selected from all the results and added to the set S, and the node costs in the three sets and the set S are updated.
In a specific embodiment, the method further comprises the step of S28, repeating the step S27 until the step S includes all the node positions, and finally determining the multi-hop communication path of the wireless sensor network nodes.
In the above embodiment, the communication situation of each node is known, and the relative power consumption rate of each node can be calculated accordingly.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.

Claims (1)

1. A communication load balancing method of a wireless sensor network is characterized by comprising the following steps:
s1, constructing initial classification of nodes, preliminarily classifying the nodes according to the power consumption rate of each node, dividing the nodes into different charging service cycle intervals to obtain a plurality of initial node sets, and establishing an initial charging task scheduling table according to the initial node sets, wherein the method for establishing the initial charging task scheduling table specifically comprises the following steps:
s11, calculating the power consumption rate of each node;
s12, classifying the nodes according to the power consumption rate;
s13, calculating the charging period of each node, preliminarily dividing the charging node subsets according to the charging period, and arranging the charging periods of all the nodes in an ascending order;
s14, deriving a charging task schedule according to the classification result and the arrangement of all the nodes;
s2, designing a routing algorithm, and generating a routing tree with balanced load, which specifically includes:
s21, traversing the whole sensor network to obtain the communication topological graph of all nodes;
s22, calculating whether the two nodes can communicate with each other, and setting the weight between the nodes;
s23, setting three node sets: the set S is used for storing the nodes added into the spanning tree; the set K is used for storing nodes which are not added into the spanning tree, and the nodes are adjacent to the nodes in the set S; the set T is used for storing the rest nodes;
s24, continuously selecting nodes from the K by the algorithm, adding the nodes into the spanning tree, and updating the three sets at any time;
s25, setting the base station as a root node v0Writing the root node into the set S, and updating the sets K and T;
s26, assigning a node i in a set K to join a spanning tree, investigating all candidate joining points in the set K, calculating whether two nodes can communicate with each other, setting a weight between the nodes, setting the same weight between the two nodes which can communicate as 1, setting the weight between the two nodes which cannot communicate to be infinite, maintaining one own cost for each node, and calculating a candidate node i which enables the comprehensive cost of the set K to be minimum;
s27, traversing all candidate nodes in the set K, assigning the candidate nodes to candidate parent nodes which can communicate with the candidate parent nodes in the set S, respectively calculating the comprehensive cost of the whole path from the root node to the candidate nodes after assignment, recording the comprehensive cost, then selecting the candidate node with the minimum comprehensive cost from all cost results, adding the candidate node into the set S, and updating the three sets;
s28, repeating the step S27 until the set S contains all the node positions, and finally determining the multi-hop communication path of the wireless sensor network nodes;
and S3, acquiring the access node of the unmanned aerial vehicle charging task according to the charging task scheduling table, and planning the access path of the unmanned aerial vehicle.
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