CN112312511A - Improved LEACH method for balancing energy consumption of wireless sensor network based on tree - Google Patents
Improved LEACH method for balancing energy consumption of wireless sensor network based on tree Download PDFInfo
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- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
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- H—ELECTRICITY
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- H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract
The invention provides an improved LEACH method for balancing energy consumption of a tree-based wireless sensor network, which comprises the steps of firstly determining cluster head sensor nodes and working nodes in an area, and then constructing a routing tree based on a tree algorithm to realize the balance of the energy consumption of the wireless sensor network; when the cluster head is selected, the distance from the sensor node to the base station and the current residual energy of the sensor node are considered, so that the sensor node which is closer to the base station and has large residual energy preferentially becomes the cluster head; secondly, after all sensor nodes in the wireless sensor network are added with corresponding cluster heads to form clusters, area division is carried out on each cluster and a working node is selected, and the sensor nodes with the residual energy larger than the average residual energy of the clusters are preferentially selected as the working nodes, so that huge energy consumption caused by long-distance transmission is avoided, the energy consumption of the sensor nodes is balanced, and the life cycle of the wireless sensor network is prolonged.
Description
Technical Field
The invention relates to the technical field of wireless sensor network communication, in particular to an improved LEACH method for balancing energy consumption of a tree-based wireless sensor network.
Background
With the rapid development of Wireless communication, the Sensor technology and the Sensor manufacturing process level are greatly improved, various micro sensors are manufactured and applied to real life, wherein a Wireless Sensor Network (WSN) is composed of thousands of micro sensors, and the Wireless Sensor network composed of the sensors is widely applied to various fields such as environment monitoring, industrial control, military battlefield and the like.
Although the wireless sensor network has many advantages in practical application, there are some problems to be solved: the number of sensors in the WSN is very large, and the deployment and energy replenishment of the sensors are not easy, so the sensors are not moved or replaced after deployment; because the energy carried by each sensor node is limited, under the condition that energy supplement cannot be obtained, once the energy of the node is exhausted, the node loses the monitoring function, and the monitoring performance of the whole WSN is influenced; how to reduce the energy consumption of node data transmission and prolong the life cycle of WSN under the premise of limited energy is one of the important problems to be solved in the wireless communication technology.
The effective method for prolonging the life cycle of the WSN is a routing algorithm with reasonable design.
In 2000, Heinzelman et al at MIT proposed a low power adaptive clustering routing protocol (LEACH routing protocol), which was the earliest clustering-based routing protocol; the protocol firstly proposes the ideas of execution process rotation and network node clustering, the cluster is reconstructed in each rotation of the operation process, and corresponding data transmission is executed after the cluster construction is finished; therefore, in each round, the LEACH algorithm can be divided into two phases, namely a cluster construction phase and a data transmission phase, which are sequentially executed in each round; in the construction stage of the cluster, firstly, a cluster head node and a non-cluster head node are determined, the cluster head node is determined through a cluster head selection threshold, the node meeting the cluster head selection threshold becomes a cluster head node, and other nodes become non-cluster head nodes; after the cluster head nodes and the non-cluster head nodes are determined, the non-cluster head nodes select corresponding cluster heads to join and form clusters according to a certain rule; in the data transmission stage, the non-cluster head node in each cluster is responsible for sending the collected data to the corresponding cluster head node, the cluster head node is responsible for receiving the data sent by the non-cluster head node in the cluster and fusing the data, the cluster head node sends the data to the base station after the fusion, and finally the base station transmits the data to the monitoring center for corresponding operation; after each cluster building and data transmission is executed, the network enters the next round to re-execute the cluster building and data transmission until all nodes in the network consume the energy.
The LEACH algorithm is the most classical clustering routing algorithm and has very important guiding significance for researching the clustering routing algorithm; the LEACH algorithm, while it can extend the lifetime of the network, still has some problems:
firstly, the LEACH uses a random selection method when selecting the cluster head, so that the cluster head is unreasonable to select, and the death of some nodes can be accelerated;
secondly, all nodes of each round of the LEACH algorithm participate in monitoring, so that node energy is wasted;
finally, when transmitting data, the cluster head directly transmits the data to the base station in a single-hop manner, which may cause a cluster head far away from the base station to waste a large amount of energy.
All the above problems will cause the energy consumption of the nodes in the network to be unbalanced, which causes unnecessary energy waste and affects the life cycle of the network.
Disclosure of Invention
The invention aims to provide an improved LEACH method for balancing energy consumption of a tree-based wireless sensor network, which can solve the problems of unreasonable selection of cluster head nodes, node energy waste and unbalanced energy consumption of the conventional LEACH algorithm.
In order to achieve the purpose, the invention adopts the following technical scheme:
the improved LEACH method for balancing the energy consumption of the tree-based wireless sensor network comprises the following steps:
step 1: setting parameters, specifically:
setting a monitoring area of the wireless sensor network as an M multiplied by M square area, wherein a base station is positioned in the center of the area and is fixed in position; m sensors are randomly deployed in the wireless sensor network, form a sensor set and are recorded as S ═ S1,s2,…,si} (i ∈ m); position no longer changes after sensor deployment, each sensor siThe initial energy of (a) is equal to Eo; the maximum running round number of the routing protocol based on the LEACH algorithm is rmax；
Step 2: calculating a cluster head selection factor, specifically:
calculating a distance set D from each sensor node to a base stationsbAnd residual energy set ErWherein D issb＝{dsb(1),dsb(2),…,dsb(i)}，Er＝{Er(1),Er(2),....Er(i) }; according to the distance set D from each sensor node to the base stationsbCalculating the maximum distance d from the sensor node to the base stationsbmaxAnd minimum distance d from sensor node to base stationsbmin(ii) a Set the distances D from the sensor nodes to the base stationsbNormalization is a set W of distance factors from the sensor node to the base stationsb，Wsb＝{wsb(1),wsb(2),…,wsb(i)}(ii) a Gathering the residual energy E of the sensor nodes through the initial energy Eo of the sensor nodesrNormalized to a set of remaining energy factors Er，Ef＝{Ef(1),Ef(2),…,Ef(i)}；
And step 3: constructing a cluster head selection threshold, specifically:
according to the distance factor set W from the sensor node to the base station obtained in the step 2sbSet of rest energy factors EfAnd the maximum distance d from the sensor node to the base stationsbmaxAnd minimum distance d from sensor node to base stationsbminCombined with optimal cluster head selection probability poptAnd initial energy E of sensor nodeoCalculating to obtain a cluster head selection threshold T (i);
and 4, step 4: selecting a cluster head, specifically:
according to the cluster head selection threshold value T (i) obtained in the step 3, cluster head selection is carried out on each sensor node, namely each sensor node siGenerating a random number T between 0 and 1rand(i) And a random number T is addedrand(i) Comparing with its corresponding threshold value T (i), if Trand(i) If the number of the sensor nodes is less than or equal to T (i), i belongs to m, selecting the corresponding sensor nodes as cluster head nodes, and if not, selecting the corresponding sensor nodes as non-cluster head nodes; recording a set formed by cluster head nodes as C, and recording a set formed by non-cluster head nodes as N;
and 5: formation of clusters, in particular:
the non-cluster head node calculates the distance d from the non-cluster head node to each cluster head nodesc(i) Obtaining the distance d between the cluster head nearest to the cluster headscminComparison of dscminDistance d from itself to the base stationsb(i) If d is large or smallscmin＜dsb(i) If not, the non-cluster head node directly communicates with the base station without adding the cluster;
step 6: the cluster region division specifically includes:
selecting the working nodes in the clusters according to the clusters formed in the step 5; utensil for cleaning buttockFirstly, dividing the cluster into areas, setting the farthest distance between a cluster head node and a non-cluster head node in the cluster as r, then the coverage area of the cluster is a circle taking the cluster head node in the cluster as the center of the circle and r as the radius, and defining the area within r/2 of the distance from the cluster head node as a short-distance area Z1The region within r/2-r distance from the cluster head node is a long-distance region Z2Then dividing the whole cluster into 8 parts by two lines which equally divide the cluster region;
and 7: selecting a working node according to the partition area of the cluster, specifically:
firstly, calculating the average residual energy E of all sensor nodes in the clusteravgThen, in each divided area, the residual energy E of each sensor node in the area is comparedr(i) Average residual energy E of all sensor nodes in clusteravgSize of (E), if Er(i)≥EavgIf not, the corresponding sensor node enters a dormant state; if the sensor nodes meeting the conditions do not exist in the region, selecting the sensor node with the maximum residual energy as a working node;
and 8: the working nodes in each area in the cluster send monitoring data to the cluster head nodes of the cluster, and the cluster head nodes receive the data transmitted by the working nodes and perform fusion processing on the data;
and step 9: constructing a cluster head transmission routing tree, specifically:
when the cluster head node sends data to the base station, the data can be forwarded through other cluster head nodes in the wireless sensor network and finally reaches the base station, namely a routing tree taking the base station as a root node is formed;
the route tree construction process is as follows:
step 9.1: sorting cluster head nodes in the wireless sensor network from near to far according to the distance between the cluster head nodes and a base station, and forming a cluster head set C' which is sorted from near to far1,c2,…,cj}；
Step 9.2: directly connecting a cluster head node closest to a base station to the base station, and forming a first branch by taking the base station as a father node of the cluster head node;
step 9.3: sequentially selecting father nodes for next cluster head nodes according to the sequence of the distances between the cluster head nodes and the base station from near to far, and connecting the father nodes to a tree, wherein except the cluster head node closest to the base station, the selectable father nodes of other cluster head nodes in the wireless sensor network belong to a set H of other cluster head nodes closer to the base station than the father nodes; specifically, a cluster head node next to the cluster head node calculates a path weight W (i, j) for each selectable father node in a set H according to a next hop energy factor EF (i, j) and a next hop path energy consumption factor REF (i, j), wherein i is the cluster head node next to the currently selected father node, j is the selectable father node, and j belongs to H;
step 9.4: the next nearest cluster head node of the current selected father node selects an optional father node with the maximum path weight W (i, j) as the father node of the current selected father node, and calculates the extra energy consumption E caused by the path after the selected father node is connected to the optional father nodepThen, calculating the energy consumption E caused by directly sending data to the base station by the next cluster head node of the currently selected father nodebIf E isp＜EbIf not, the cluster head node next to the selected father node is directly connected to the base station;
step 9.5: repeating the step 9.3 and the step 9.4 until all cluster head nodes in the wireless sensor network are connected to the routing tree, namely the construction of the routing tree is completed;
step 10: each cluster head node in the wireless sensor network sends data to a father node of the wireless sensor network, and finally transmits the data to a base station through a routing tree;
step 11: repeating the steps 2 to 10 until the preset running wheel number r is reachedmaxOr full node energy depletion.
Each sensor node s in step 2iDistance d to base stationsb(i) The following method is adopted for calculation:
wherein, the position coordinate of the base station is (x)b,yb) Sensor node siThe coordinates of (x), (i), y (i);
step 2 the sensor node siNormalized distance factor omega to base stationsb(i) The calculation method comprises the following steps:
wherein, ω issb(i) As sensor node siA distance factor after the distance normalization with the base station;
step 2 the sensor node siNormalized residual energy factor Ef(i) The calculation method comprises the following steps:
wherein E isf(i) As sensor node siNormalized residual energy factor, Er(i) As sensor node siAnd the current residual energy m represents the number of sensor nodes in the wireless sensor network.
The method for calculating the cluster head selection threshold t (i) in step 3 is as follows:
where α is a weighting coefficient of the threshold value, pbReference probability, omega, for a sensor node to become a cluster headsb(i) As sensor node siNormalized distance factor to base station, Ef(i) As sensor node siNormalized residual energy factor, poptThe optimal percentage of cluster head nodes in all nodes of the wireless sensor network is represented, r is the current round number, and mod is the remainder operation.
Step 5, the non-cluster head node calculates the distance d from the non-cluster head node to each cluster head nodesc(i) The method comprises the following steps:
wherein the position coordinate of the cluster head node is (x)c,yc) The position coordinates of the non-cluster-head nodes are (x), (i), y (i)), and the non-cluster-head nodes are other sensor nodes in the wireless sensor network.
In step 9, the calculation method of the next hop energy factor EF (i, j) and the next hop path energy factor REF (i, j) includes:
wherein E ispar(i, j) is the current remaining energy of the selectable parent node of the next closest cluster head node of the currently selected parent node;
wherein the content of the first and second substances,
Erece(i,j)＝lEelec；
wherein E istrans(i, j) represents the energy consumed by the next closest cluster head node currently selecting a parent node to send data to its parent node, Erece(i, j) represents the energy consumed by the parent node to receive the transmitted data, Etrans(j) Representing the total energy consumed by all nodes on the path of the parent node by transmitting data, Erece(j) Representing the total energy consumed by all nodes on the path of the father node by receiving data, k being the optional father node of the cluster head node next to the father node currently selectedNumber, di,jThe distance between the next closest cluster head node of the currently selected parent node and the optional parent node.
The method for calculating the path weight W (i, j) in step 9 is:
W(i,j)＝μEF(i,j)+λ[1-REF(i,j)]；
where μ and λ are weighting coefficients for the path weights.
The invention has the beneficial effects that:
by the technical scheme, the invention provides the improved LEACH method for balancing the energy consumption of the wireless sensor network based on the tree aiming at the problems of unbalanced network energy consumption and the like of the conventional LEACH algorithm;
firstly, when selecting a cluster head, the invention considers the distance between a sensor node and a base station and the current residual energy of the sensor node, and leads the sensor node which is closer to the base station and has large residual energy to preferentially become the cluster head;
secondly, after all sensor nodes in the wireless sensor network are added with corresponding cluster heads to form clusters, performing region division on each cluster and selecting working nodes, and preferentially selecting the sensor nodes with residual energy larger than the average residual energy of the clusters as the working nodes;
finally, in the data transmission process, the invention avoids huge energy consumption caused by long-distance transmission by constructing the routing tree as a data transmission path, thereby balancing the energy consumption of the sensor nodes and prolonging the life cycle of the wireless sensor network.
Drawings
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 embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and obviously, the described embodiments are some, but not all embodiments of the present invention. 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.
As shown in fig. 1: the invention discloses an improved LEACH method for balancing energy consumption of a tree-based wireless sensor network, which comprises the following steps of:
step 1: setting parameters, specifically:
setting a monitoring area of the wireless sensor network as an M multiplied by M square area, wherein a base station is positioned in the center of the area and is fixed in position; m sensors are randomly deployed in the wireless sensor network, form a sensor set and are recorded as S ═ S1,s2,…,si} (i ∈ m); position no longer changes after sensor deployment, each sensor siThe initial energy of (a) is equal to Eo; the maximum running round number of the routing protocol based on the LEACH algorithm is rmax；
Step 2: calculating a cluster head selection factor, specifically:
calculating a distance set D from each sensor node to a base stationsbAnd residual energy set ErWherein D issb＝{dsb(1),dsb(2),…,dsb(i)}，Er＝{Er(1),Er(2),....Er(i) }; according to the distance set D from each sensor node to the base stationsbCalculating the maximum distance d from the sensor node to the base stationsbmaxAnd minimum distance d from sensor node to base stationsbmin(ii) a Set the distances D from the sensor nodes to the base stationsbNormalization is a set W of distance factors from the sensor node to the base stationsb，Wsb＝{wsb(1),wsb(2),…,wsb(i) }; gathering the residual energy E of the sensor nodes through the initial energy Eo of the sensor nodesrNormalized to a set of remaining energy factors Er，Ef＝{Ef(1),Ef(2),…,Ef(i)}；
Wherein, each sensor node s in step 2iDistance d to base stationsb(i) The following method is adopted for calculation:
wherein, the position coordinate of the base station is (x)b,yb) Sensor node siThe coordinates of (x), (i), y (i);
step 2 the sensor node siNormalized distance factor omega to base stationsb(i) The calculation method comprises the following steps:
wherein, ω issb(i) As sensor node siA distance factor after the distance normalization with the base station;
step 2 the sensor node siNormalized residual energy factor Ef(i) The calculation method comprises the following steps:
wherein E isf(i) As sensor node siNormalized residual energy factor, Er(i) As sensor node siThe current residual energy m represents the number of sensor nodes in the wireless sensor network;
and step 3: constructing a cluster head selection threshold, specifically:
according to the distance factor set W from the sensor node to the base station obtained in the step 2sbSet of rest energy factors EfAnd the maximum distance d from the sensor node to the base stationsbmaxAnd minimum distance d from sensor node to base stationsbminKnot ofCombining optimal cluster head selection probabilities poptAnd initial energy E of sensor nodeoCalculating to obtain a cluster head selection threshold T (i);
the method for calculating the cluster head selection threshold t (i) in step 3 includes:
where α is a weighting coefficient of the threshold value, pbReference probability, omega, for a sensor node to become a cluster headsb(i) As sensor node siNormalized distance factor to base station, Ef(i) As sensor node siNormalized residual energy factor, poptThe optimal percentage of cluster head nodes in all nodes of the wireless sensor network is set, r is the current round number, and mod is a remainder operation;
and 4, step 4: selecting a cluster head, specifically:
according to the cluster head selection threshold value T (i) obtained in the step 3, cluster head selection is carried out on each sensor node, namely each sensor node siGenerating a random number T between 0 and 1rand(i) And a random number T is addedrand(i) Comparing with its corresponding threshold value T (i), if Trand(i) If the number of the sensor nodes is less than or equal to T (i), i belongs to m, selecting the corresponding sensor nodes as cluster head nodes, and if not, selecting the corresponding sensor nodes as non-cluster head nodes; recording a set formed by cluster head nodes as C, and recording a set formed by non-cluster head nodes as N;
and 5: formation of clusters, in particular:
the non-cluster head node calculates the distance d from the non-cluster head node to each cluster head nodesc(i) Obtaining the distance d between the cluster head nearest to the cluster headscminComparison of dscminDistance d from itself to the base stationsb(i) If d is large or smallscmin＜dsb(i) If not, the non-cluster head node directly communicates with the base station without adding the cluster;
wherein the steps5, the non-cluster head node calculates the distance d from the non-cluster head node to each cluster head nodesc(i) The method comprises the following steps:
wherein the position coordinate of the cluster head node is (x)c,yc) The position coordinates of the non-cluster head nodes are (x), (i), y (i)), and the non-cluster head nodes are other sensor nodes in the wireless sensor network;
step 6: the cluster region division specifically includes:
selecting the working nodes in the clusters according to the clusters formed in the step 5; specifically, firstly, the cluster is divided into areas, the farthest distance between a cluster head node and a non-cluster head node in the cluster is set as r, the coverage area of the cluster is a circle with the cluster head node in the cluster as the center of the circle and r as the radius, and an area within r/2 of the distance from the cluster head node is defined as a short-distance area Z1The region within r/2-r distance from the cluster head node is a long-distance region Z2Then dividing the whole cluster into 8 parts by two lines which equally divide the cluster region; for example, the two bisectors may be two diameters perpendicular to each other within a circle;
and 7: selecting a working node according to the partition area of the cluster, specifically:
firstly, calculating the average residual energy E of all sensor nodes in the clusteravgThen, in each divided area, the residual energy E of each sensor node in the area is comparedr(i) Average residual energy E of all sensor nodes in clusteravgSize of (E), if Er(i)≥EavgIf not, the corresponding sensor node enters a dormant state; if the sensor nodes meeting the conditions do not exist in the region, selecting the sensor node with the maximum residual energy as a working node;
and 8: the working nodes in each area in the cluster send monitoring data to the cluster head nodes of the cluster, and the cluster head nodes receive the data transmitted by the working nodes and perform fusion processing on the data;
and step 9: constructing a cluster head transmission routing tree, specifically:
when the cluster head node sends data to the base station, the data can be forwarded through other cluster head nodes in the wireless sensor network and finally reaches the base station, namely a routing tree taking the base station as a root node is formed;
the route tree construction process is as follows:
step 9.1: sorting cluster head nodes in the wireless sensor network from near to far according to the distance between the cluster head nodes and a base station, and forming a cluster head set C' which is sorted from near to far1,c2,…,cj}；
Step 9.2: directly connecting a cluster head node closest to a base station to the base station, and forming a first branch by taking the base station as a father node of the cluster head node;
step 9.3: sequentially selecting father nodes for next cluster head nodes according to the sequence of the distances between the cluster head nodes and the base station from near to far, and connecting the father nodes to a tree, wherein except the cluster head node closest to the base station, the selectable father nodes of other cluster head nodes in the wireless sensor network belong to a set H of other cluster head nodes closer to the base station than the father nodes; specifically, a cluster head node next to the cluster head node calculates a path weight W (i, j) for each selectable father node in a set H according to a next hop energy factor EF (i, j) and a next hop path energy consumption factor REF (i, j), wherein i is the cluster head node next to the currently selected father node, j is the selectable father node, and j belongs to H;
step 9.4: the next nearest cluster head node of the current selected father node selects an optional father node with the maximum path weight W (i, j) as the father node of the current selected father node, and calculates the extra energy consumption E caused by the path after the selected father node is connected to the optional father nodepThen, calculating the energy consumption E caused by directly sending data to the base station by the next cluster head node of the currently selected father nodebIf E isp＜EbIf not, the cluster head node next to the selected father node is directly connected to the base station;
step 9.5: repeating the step 9.3 and the step 9.4 until all cluster head nodes in the wireless sensor network are connected to the routing tree, namely the construction of the routing tree is completed;
in step 9.4, the calculation method of the next-hop energy factor EF (i, j) and the next-hop path energy factor REF (i, j) includes:
wherein E ispar(i, j) is the current remaining energy of the selectable parent node of the next closest cluster head node of the currently selected parent node;
wherein the content of the first and second substances,
Erece(i,j)＝lEelec；
wherein E istrans(i, j) represents the energy consumed by the next closest cluster head node currently selecting a parent node to send data to its parent node, Erece(i, j) represents the energy consumed by the parent node to receive the transmitted data, Etrans(j) Representing the total energy consumed by all nodes on the path of the parent node by transmitting data, Erece(j) Representing the total energy consumed by all nodes on the path of the father node by receiving data, k is the number of selectable father nodes of the cluster head node next to the father node currently selected, di,jThe distance between the next closest cluster head node of the currently selected parent node and the optional parent node.
The method for calculating the path weight W (i, j) in step 9.4 is:
W(i,j)＝μEF(i,j)+λ[1-REF(i,j)]；
wherein μ and λ are weighting coefficients of the path weights;
step 10: each cluster head node in the wireless sensor network sends data to a father node of the wireless sensor network, and finally transmits the data to a base station through a routing tree;
step 11: repeating the steps 2 to 10 until the preset running wheel number r is reachedmaxOr full node energy depletion.
It needs to be further explained that:
in the invention, the energy consumption of any two sensor nodes in the wireless sensor network subjected to big data can be calculated by the following method:
any sensor node has a distance dTRThe total energy E consumed by the sending of the l bit data by the sensor receiving nodeTxThe expression of (a) is:
total energy E consumed by receiving l bit data by sensor receiving nodeRxThe expression of (a) is:
ERx(l)＝ERx-elec(l)＝lEelec；
wherein E isRx-elecEnergy consumed for the transmitting circuit, ETx-ampFor power consumption of the power amplifying circuit of the transmitting end, EelecThe energy consumption of each 1bit of data processed is averaged for the sending end; dTRThe data transmission distance between the receiving end and the transmitting end is obtained; epsilonfsEnergy consumption of transmitting 1bit data to unit area by a transmitting power amplifier under a free space attenuation channel model, epsilonmpEnergy consumption for transmitting 1bit data to unit area by transmitting power amplifier under multi-path attenuation channel model, d0Is the effective communication distance.
By the technical scheme, the invention provides the improved LEACH method for balancing the energy consumption of the wireless sensor network based on the tree aiming at the problems of unbalanced network energy consumption and the like of the conventional LEACH algorithm;
firstly, when selecting a cluster head, the invention considers the distance between a sensor node and a base station and the current residual energy of the sensor node, and leads the sensor node which is closer to the base station and has large residual energy to preferentially become the cluster head;
secondly, after all sensor nodes in the wireless sensor network are added with corresponding cluster heads to form clusters, performing region division on each cluster and selecting working nodes, and preferentially selecting the sensor nodes with residual energy larger than the average residual energy of the clusters as the working nodes;
finally, in the data transmission process, the invention avoids huge energy consumption caused by long-distance transmission by constructing the routing tree as a data transmission path, thereby balancing the energy consumption of the sensor nodes and prolonging the life cycle of the wireless sensor network.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. An improved LEACH method for balancing energy consumption of a tree-based wireless sensor network is characterized in that: the method comprises the following steps:
step 1: setting parameters, specifically:
setting a monitoring area of the wireless sensor network as an M multiplied by M square area, wherein a base station is positioned in the center of the area and is fixed in position; m sensors are randomly deployed in the wireless sensor network, form a sensor set and are recorded as S ═ S1,s2,…,si} (i ∈ m); after deployment of the sensors, the position is no longer changed, and each sensor siHas the same initial energy as Eo(ii) a The maximum running round number of the routing protocol based on the LEACH algorithm is rmax；
Step 2: calculating a cluster head selection factor, specifically:
calculating a distance set D from each sensor node to a base stationsbAnd residual energy set ErWherein D issb＝{dsb(1),dsb(2),…,dsb(i)}，Er＝{Er(1),Er(2),....Er(i) }; according to the distance set D from each sensor node to the base stationsbCalculating the maximum distance d from the sensor node to the base stationsbmaxAnd minimum distance d from sensor node to base stationsbmin(ii) a Set the distances D from the sensor nodes to the base stationsbNormalization into a set W of distance factors from the sensor node to the base stationsb，Wsb＝{wsb(1),wsb(2),…,wsb(i) }; initial energy E through sensor nodeoAggregating the residual energy of the sensor nodes ErNormalized to a set of remaining energy factors Er，Ef＝{Ef(1),Ef(2),…,Ef(i)}；
And step 3: constructing a cluster head selection threshold, specifically:
according to the distance factor set W from the sensor node to the base station obtained in the step 2sbSet of rest energy factors EfAnd the maximum distance d from the sensor node to the base stationsbmaxAnd minimum distance d from sensor node to base stationsbminCombined with optimal cluster head selection probability poptAnd initial energy E of sensor nodeoCalculating to obtain a cluster head selection threshold T (i);
and 4, step 4: selecting a cluster head, specifically:
according to the cluster head selection threshold value T (i) obtained in the step 3, cluster head selection is carried out on each sensor node, namely each sensor node siGenerating a random number T between 0 and 1rand(i) And a random number T is addedrand(i) Comparing with its corresponding threshold value T (i), if Trand(i) If the number of the sensor nodes is less than or equal to T (i), i belongs to m, selecting the corresponding sensor nodes as cluster head nodes, and if not, selecting the corresponding sensor nodes as non-cluster head nodes; recording a set formed by cluster head nodes as C, and recording a set formed by non-cluster head nodes as N;
and 5: formation of clusters, in particular:
the non-cluster head node calculates the distance d from the non-cluster head node to each cluster head nodesc(i) Obtaining the distance d between the cluster head nearest to the cluster headscminComparison of dscminDistance d from itself to the base stationsb(i) If d is large or smallscmin＜dsb(i) If not, the non-cluster head node directly communicates with the base station without adding the cluster;
step 6: the cluster region division specifically includes:
selecting the working nodes in the clusters according to the clusters formed in the step 5; specifically, firstly, the cluster is divided into areas, the farthest distance between a cluster head node and a non-cluster head node in the cluster is set as r, the coverage area of the cluster is a circle with the cluster head node in the cluster as the center of the circle and r as the radius, and an area within r/2 of the cluster head node is defined as a short-distance area Z1The region within r/2-r distance from the cluster head node is a long-distance region Z2Then dividing the whole cluster into 8 parts by two lines which equally divide the cluster region;
and 7: selecting a working node according to the partition area of the cluster, specifically:
firstly, calculating the average residual energy E of all sensor nodes in the clusteravgThen, in each divided area, the residual energy E of each sensor node in the area is comparedr(i) Average residual energy E of all sensor nodes in clusteravgSize of (E), if Er(i)≥EavgIf not, the corresponding sensor node enters a dormant state; if the sensor nodes meeting the conditions do not exist in the region, selecting the sensor node with the maximum residual energy as a working node;
and 8: the working nodes in each area in the cluster send monitoring data to the cluster head nodes of the cluster, and the cluster head nodes receive the data transmitted by the working nodes and perform fusion processing on the data;
and step 9: constructing a cluster head transmission routing tree, specifically:
when the cluster head node sends data to the base station, the data can be forwarded through other cluster head nodes in the wireless sensor network and finally reaches the base station, and a routing tree taking the base station as a root node is formed;
the route tree construction process is as follows:
step 9.1: sorting cluster head nodes in the wireless sensor network from near to far according to the distance between the cluster head nodes and a base station, and forming a cluster head set C' which is sorted from near to far1,c2,…,cj}；
Step 9.2: directly connecting a cluster head node closest to a base station to the base station, and forming a first branch by taking the base station as a father node of the cluster head node;
step 9.3: sequentially selecting father nodes for next cluster head nodes according to the sequence of the distances between the cluster head nodes and the base station from near to far, and connecting the father nodes to a tree, wherein except the cluster head node closest to the base station, the selectable father nodes of other cluster head nodes in the wireless sensor network belong to a set H of other cluster head nodes closer to the base station than the father node; specifically, a cluster head node next to the cluster head node calculates a path weight W (i, j) for each optional father node in a set H according to a next hop energy factor EF (i, j) and a next hop path energy consumption factor REF (i, j), wherein i is the cluster head node next to the currently selected father node, j is the optional father node, and j belongs to H;
step 9.4: the next nearest cluster head node of the current selected father node selects an optional father node with the maximum path weight W (i, j) as the father node of the current selected father node, and calculates the extra energy consumption E caused by the path after the selected father node is connected to the optional father nodepThen, calculating the energy consumption E caused by directly sending data to the base station by the next cluster head node of the currently selected father nodebIf E isp＜EbIf not, the cluster head node next to the selected father node is directly connected to the base station;
step 9.5: repeating the step 9.3 and the step 9.4 until all cluster head nodes in the wireless sensor network are connected to the routing tree, namely the construction of the routing tree is completed;
step 10: each cluster head node in the wireless sensor network sends data to a father node of the wireless sensor network, and finally transmits the data to a base station through a routing tree;
step 11: repeating the steps 2 to 10 until the preset running wheel number r is reachedmaxOr full node energy depletion.
2. The improved LEACH method for energy consumption balancing for tree-based wireless sensor networks of claim 1, wherein: each sensor node s in step 2iDistance d to base stationsb(i) The following method is adopted for calculation:
wherein, the position coordinate of the base station is (x)b,yb) Sensor node siThe coordinates of (x), (i), y (i);
step 2 the sensor node siNormalized distance factor omega to base stationsb(i) The calculation method comprises the following steps:
wherein, ω issb(i) As sensor node siThe distance factor after the distance normalization with the base station;
step 2 the sensor node siNormalized residual energy factor Ef(i) The calculation method comprises the following steps:
wherein E isf(i) As sensor node siNormalized residual energy factor, Er(i) As sensor node siCurrent residual energyAnd m represents the number of sensor nodes in the wireless sensor network.
3. The improved LEACH method for energy consumption balancing for tree-based wireless sensor networks of claim 1, wherein: the method for calculating the cluster head selection threshold t (i) in step 3 is as follows:
where α is a weighting coefficient of the threshold value, pbReference probability, omega, for a sensor node to become a cluster headsb(i) As sensor node siNormalized distance factor to base station, Ef(i) As sensor node siNormalized residual energy factor, poptThe optimal percentage of cluster head nodes in all nodes of the wireless sensor network is represented, r is the current round number, and mod is the remainder operation.
4. The improved LEACH method for energy consumption balancing for tree-based wireless sensor networks of claim 1, wherein: step 5, the non-cluster head node calculates the distance d from the non-cluster head node to each cluster head nodesc(i) The method comprises the following steps:
wherein the position coordinate of the cluster head node is (x)c,yc) The position coordinates of the non-cluster head nodes are (x), (i), y (i)), and the non-cluster head nodes are other sensor nodes in the wireless sensor network.
5. The improved LEACH method for energy consumption balancing for tree-based wireless sensor networks of claim 1, wherein: in step 9, the calculation method of the next hop energy factor EF (i, j) and the next hop path energy factor REF (i, j) includes:
wherein E ispar(i, j) is the current remaining energy of the selectable parent node of the next closest cluster head node of the currently selected parent node;
wherein the content of the first and second substances,
Erece(i,j)＝lEelec；
wherein E istrans(i, j) represents the energy consumed by the next closest cluster head node currently selecting a parent node to send data to its parent node, Erece(i, j) represents the energy consumed by the parent node to receive the transmitted data, Etrans(j) Representing the total energy consumed by all nodes on the path of the parent node by transmitting data, Erece(j) Representing the total energy consumed by all nodes on the path of the father node by receiving data, k is the number of selectable father nodes of the cluster head node next to the father node currently selected, di,jThe distance between the next closest cluster head node of the currently selected parent node and the optional parent node.
6. The improved LEACH method for energy consumption balancing for tree-based wireless sensor networks of claim 1, wherein: the method for calculating the path weight W (i, j) in step 9 is:
W(i,j)＝μEF(i,j)+λ[1-REF(i,j)]；
where μ and λ are weighting coefficients for the path weights.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112996076A (en) * | 2021-02-05 | 2021-06-18 | 东北大学 | Mobile charging and data collection method in wireless sensor network |
| CN113395660A (en) * | 2021-06-18 | 2021-09-14 | 河南大学 | WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on tree |
| CN114449609A (en) * | 2022-02-22 | 2022-05-06 | 安徽农业大学 | LEACH clustering routing method based on energy consumption balance |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104539542A (en) * | 2014-12-03 | 2015-04-22 | 南京邮电大学 | Low-energy-consumption routing tree pruning method based on mobile Sink data collection |
| US20160337441A1 (en) * | 2013-09-27 | 2016-11-17 | Transvoyant Llc | Computer-implemented systems and methods of analyzing data in an ad-hoc network for predictive decision-making |
| CN109451557A (en) * | 2018-12-24 | 2019-03-08 | 广东理致技术有限公司 | A kind of wireless sensor network dynamic clustering method for routing and device |
| US20190098573A1 (en) * | 2018-05-31 | 2019-03-28 | Peyman Neamatollahi | Method for dynamically scheduling clustering operation |
| CN109587753A (en) * | 2018-11-26 | 2019-04-05 | 河南大学 | The cluster head selection algorithm of improvement LEACH agreement based on targets threshold constraint |
-
2020
- 2020-09-04 CN CN202010926383.0A patent/CN112312511B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160337441A1 (en) * | 2013-09-27 | 2016-11-17 | Transvoyant Llc | Computer-implemented systems and methods of analyzing data in an ad-hoc network for predictive decision-making |
| CN104539542A (en) * | 2014-12-03 | 2015-04-22 | 南京邮电大学 | Low-energy-consumption routing tree pruning method based on mobile Sink data collection |
| US20190098573A1 (en) * | 2018-05-31 | 2019-03-28 | Peyman Neamatollahi | Method for dynamically scheduling clustering operation |
| CN109587753A (en) * | 2018-11-26 | 2019-04-05 | 河南大学 | The cluster head selection algorithm of improvement LEACH agreement based on targets threshold constraint |
| CN109451557A (en) * | 2018-12-24 | 2019-03-08 | 广东理致技术有限公司 | A kind of wireless sensor network dynamic clustering method for routing and device |
Non-Patent Citations (6)
| Title |
|---|
| WANG JUN: ""An energy-efficient clustering routing algorithm for WSN-assisted IoT"", 《IEEE》 * |
| 付春玲: ""基于量测自适应辨识的多传感器数据融合算法"", 《传感器与微系统》 * |
| 周智勇: ""基于蚁群算法和能耗均衡的改进LEACH协议"", 《通信技术》 * |
| 杨瑞: ""基于能耗均衡的无线传感网移动Sink数据收集技术研究"", 《信息科技辑》 * |
| 闵林: ""一种能量有效的无线传感器网络路由算法"", 《微电子学与计算机》 * |
| 陈炳才: ""一种基于LEACH协议改进的簇间多跳路由协议"", 《传感技术学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112996076A (en) * | 2021-02-05 | 2021-06-18 | 东北大学 | Mobile charging and data collection method in wireless sensor network |
| CN112996076B (en) * | 2021-02-05 | 2023-03-10 | 东北大学 | Mobile charging and data collection method in wireless sensor network |
| CN113395660A (en) * | 2021-06-18 | 2021-09-14 | 河南大学 | WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on tree |
| CN113395660B (en) * | 2021-06-18 | 2022-05-13 | 河南大学 | WSNs mobile convergence node self-adaptive position updating energy consumption optimization method based on tree |
| CN114449609A (en) * | 2022-02-22 | 2022-05-06 | 安徽农业大学 | LEACH clustering routing method based on energy consumption balance |
| CN114449609B (en) * | 2022-02-22 | 2022-11-29 | 安徽农业大学 | LEACH clustering routing method based on energy consumption balance |
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