CN109673034B - Wireless sensor network clustering routing method based on longicorn stigma search - Google Patents
Wireless sensor network clustering routing method based on longicorn stigma search Download PDFInfo
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- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to a wireless sensor network clustering routing method based on longicorn stigma search, which comprises the following steps: establishing a wireless sensing network topology; selecting candidate cluster head nodes according to the positions of the sensor nodes, the residual energy of the sensor nodes and the ID of the sensor nodes; iterating the fitness function through a longicorn whisker search algorithm, and searching a candidate cluster head node with the minimum fitness function value as an optimal cluster head node; the selected optimal cluster head node broadcasts information of the selected optimal cluster head node, and waits for the addition of other common nodes; and selecting a relay node between the optimal cluster head node and the base station node according to the residual energy and the transmission energy consumption of the sensor node, and sending the fused data to the base station node by the optimal cluster head node through the relay node to finish communication. The invention fully considers the residual energy information and the position information of the sensor nodes, selects the proper cluster head node through the longicorn whisker search algorithm, can effectively balance the network energy consumption and prolong the network life cycle.
Description
Technical Field
The invention relates to a wireless sensor network communication technology, in particular to a wireless sensor network clustering routing method based on longicorn stigma search.
Background
Wireless Sensor Networks (WSNs) are distributed self-organized Wireless Networks composed of a large number of small-sized low-cost sensors capable of sensing the outside world, and due to the advantages of WSNs such as self-organization and low power consumption, WSNs are widely used in multiple fields such as military, smart grid, smart transportation, environmental monitoring, and medical health, but the energy limitation of Sensor nodes is one of the disadvantages of WSNs, and sensors in WSNs are generally installed in harsh environments where humans are not suitable for living, for example: high altitude, desert, forest, etc., in this kind of environment, the degree of difficulty of changing the sensor battery is very big, and the cost is very high. Therefore, the flexible and efficient use of the limited energy of the sensor nodes has become a problem that must be considered in the design of the WSN routing protocol.
The routing protocol of the wireless sensor network is mainly divided into a plane routing and a hierarchical routing. In the plane routing, all the sensor nodes have the same function and level, the sensor nodes cooperate to complete information sensing, acquisition, analysis processing and communication forwarding, the plane routing is simple and has good robustness, but the overhead of establishing and maintaining the routing is large, the expandability is poor, and the data transmission hop number is large, so the plane routing is only suitable for small-scale networks. The hierarchical routing is to divide the wireless sensor network into a plurality of clusters based on the clustering technology, and each cluster consists of a randomly selected cluster head node and a corresponding member sensor node. And the member sensor nodes in each cluster set send the sensing data to the cluster head node, and the cluster head node is responsible for receiving the data sent by all the sensor nodes in the cluster, performing data fusion and then forwarding the data to the base station node. Compared with the common plane routing, the hierarchical routing has the characteristics of good expandability, energy conservation and simple routing structure, and is suitable for large-scale wireless sensor networks. However, the cluster routing has a high requirement for selecting the cluster head node, and the cluster head node needs to additionally undertake the functions of data fusion and routing forwarding, which may result in the death of the cluster head node being too fast, so it is very important to design an efficient cluster head node selection algorithm to balance the energy consumption of the cluster head node.
Aiming at the problem of uneven energy consumption of sensor nodes, the existing wireless sensing routing protocol provides a solution, wherein the solutions are more typical of LEACH, PEGASIS and EAMMH. Specifically, the method comprises the following steps:
the low-power consumption self-adaptive cluster hierarchical protocol (LEACH) is a classic hierarchical energy self-adaptive routing protocol, breaks through the idea of fixing cluster head nodes in the prior clustering algorithm, improves the survival time of the whole network through random cluster head node election and data fusion technology, has better effect than a plane routing protocol, and is a classic clustering routing protocol. However, such a cluster routing protocol also has certain drawbacks: the election of the LEACH protocol cluster head node is random, so that the cluster head node is easily distributed unevenly, the sensor node with smaller residual energy still becomes the cluster head node, and single-hop communication is adopted between the cluster head node and the base station node, so that the energy consumption of the cluster head node far away from the base station node is too high;
the PEGASIS (Power-Efficient Heat in Sensor Information Systems) protocol adopts a chain structure to perform data fusion transmission, so that the overhead generated by the LEACH protocol in the process of cluster reconstruction is reduced, but the single chain structure of the protocol increases transmission delay and excessively depends on intermediate nodes;
the energy-aware multi-hop multi-path hierarchical routing protocol (EAMMH) is an improvement over the LEACH protocol. The EAMMH protocol organizes the sensor nodes into clusters and forms a multi-hop cluster network, establishes a plurality of paths from each sensor node to a cluster head node, and provides an energy perception heuristic function to select the optimal path, so that the energy consumption of the sensor nodes is effectively reduced, but the factors of the residual energy and the position of the sensor nodes are not considered in the selection of the cluster head node.
Disclosure of Invention
In order to solve the problems in the prior art, the invention aims to provide a wireless sensor network clustering routing method based on longicorn stigma search so as to balance the energy consumption of a network and prolong the life cycle of the network.
The invention relates to a wireless sensor network clustering routing method based on longicorn stigma search, which comprises the following steps:
step S1, after N sensor nodes and 1 base station node are deployed in a designated area, network presetting is carried out to establish wireless sensor network topology;
step S2, calculating and obtaining a candidate cluster head node weight of each sensor node according to a candidate cluster head node weight function based on the sensor node position coordinates and the sensor node residual energy of each sensor node, then arranging all the sensor nodes according to the candidate cluster head node weight from high to low, and selecting P% of sensor nodes which are sequentially arranged from the sensor node with the highest candidate cluster head node weight as the candidate cluster head node in all the sensor nodes;
step S3, acquiring a fitness function for selecting the best cluster head node, iterating the fitness function through a Tianniu must search algorithm, selecting the candidate cluster head node with the smallest fitness function value from all the candidate cluster head nodes selected in step S2 as the best cluster head node, and enabling the rest candidate cluster head nodes which are not selected as the best cluster head node to become common nodes;
step S4, the best cluster head node broadcasts the message of the best cluster head node in the communication range and waits for the joining of other common nodes; and
step S5, the best cluster head node receives the environmental perception information data sent by the common node in the cluster where the best cluster head node is located, performs data fusion, then selects a relay node from the common nodes in the cluster where the best cluster head node is located based on a relay node selection function, and finally transmits the fused environmental perception information data to the base station node through the relay node in a multi-hop manner.
Further, in the step S1,
the N sensor nodes and 1 base station node firstly broadcast and send data packets containing sensor node IDs and sensor node position coordinates;
each sensor node receives the data packet sent by the neighbor node to record the information of the neighbor node;
after the information recording of the neighbor nodes is completed, each sensor node sends an initialization message to the base station node, wherein the initialization message comprises: the ID of each sensor node, the position coordinates of the sensor nodes and the residual energy of the sensor nodes;
and after receiving all initialization messages, the base station node establishes a wireless sensor network topology.
Further, in step S2, the candidate cluster head node weight function f (i) of the sensor node with the sensor node ID i is:
wherein, E (n) i ) Residual energy for sensor node with sensor node ID i, E 0 For sensor node initial energy, MN i The number of neighbor nodes of the sensor node with the sensor node ID of i is 1 to N, MN is the standard number of the neighbor nodes in the ideal standard communication range of the sensor node, R is the radius of the standard communication range of the sensor node, and d (N) i ,CM j ) The method is characterized in that a sensor node with a sensor node ID of i and a neighbor node CM with a sensor node ID of j in a standard communication range are provided j The distance between the two, j, ranges from 1 to MN, d max Is the maximum distance of N sensor nodes from the base station node, d min Is the minimum distance of N sensor nodes from the base station node, d in The distance between a sensor node with a sensor node ID of i and a base station node, w1, w2, w3 and w4 are weighting coefficients, wherein w1 is a weight of a residual energy factor of the sensor node, w2 is a weight of a number factor of neighbor nodes of the sensor node, w3 is a weight of a distance factor between the sensor node and the neighbor nodes thereof, w4 is a weight of a distance factor between the base station node and the sensor node, and w1, w2, w3 and w4 satisfy the following conditions:
w1+w2+w3+w4＝1,w1,w2,w3,w4∈(0,1)。
further, in the step S2, P < 80.
Further, in step S3, the fitness function F is:
F＝β*f 1 +(1+β)*f 2
wherein f is 1 Is a sensor node location factor, f 2 Is a sensor node residual energy factor, and beta is a sensor node residual energy factorWeighting factors in the fitness function calculation process; i C k I is the number of sensor nodes in the standard communication range of the kth candidate cluster head node, d (n) i ,CH k ) Is a sensor node with a sensor node ID of i to the kth candidate cluster head node CH k Distance of (k) opt The number of the candidate cluster head nodes is; the value range of i is 1 to N; (do i range from 1 to N i ) Residual energy for sensor node with sensor node ID i, E (CH) k ) Is the kth candidate cluster head node CH k The remaining energy of (c).
Further, in step S4, the other common nodes preferentially select the cluster where the best cluster head node with the strongest signal strength is located according to the received signal strength of the message selecting the best cluster head node.
Further, in step S5, the relay node selection function S (j) of the normal node with the sensor node ID j is:
wherein, E (n) j ) Residual energy for a common node with sensor node ID j, E 0 For the initial energy of the sensor node, d (i, j) is the distance from the best cluster head node with the sensor node ID of i to the common node with the sensor node ID of j, d toBS (j) Distance from a common node with a sensor node ID of j to a base station node, d toBS (i) Distance alpha from the optimal cluster head node with the ID of the sensor node as i to the base station node s For the relay node energy factor, beta s Is a relay node distance factor, and alpha s 、β s Satisfies the following conditions:
α s +β s ＝1,α s and β s ∈(0,1)，
and aiming at each optimal cluster head node, calculating the value of a relay node selection function S (j) of each common node in the cluster where the optimal cluster head node is located, and selecting the common node with the minimum value as a relay node for communication between the optimal cluster head node and the base station node.
Further, in the step S5, the environmental awareness information data includes: and temperature, humidity and pressure data monitored by the sensor nodes.
Due to the adoption of the technical scheme, the invention fully considers the residual energy and the position information of the sensor nodes, selects a proper cluster head node set through a Tianniu searching algorithm, and solves the problems of unbalanced energy consumption of the cluster head nodes in the clustering routing of the wireless sensor network and faster energy consumption of the sensor nodes caused by poor relay node selection in the routing communication process from the cluster head nodes to the base station nodes.
Drawings
FIG. 1 is a flow chart of an embodiment of a method for clustering routing in a wireless sensor network based on longicorn whisker search of the present invention;
FIG. 2 is a schematic diagram comparing initial node death times using the present invention, the prior LEACH algorithm, and the EAMMH algorithm, respectively;
FIG. 3 is a schematic diagram comparing average residual energies of nodes using the present invention, the prior LEACH algorithm, and the EAMMH algorithm, respectively;
fig. 4 is a schematic diagram showing a comparison of information amounts from a common node to a cluster head node by using the present invention, the existing LEACH algorithm, and the EAMMH algorithm, respectively.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Here, the meaning of specific nouns appearing in the following description is first set forth:
the wireless sensor network includes: a base station node and a plurality of sensor nodes, wherein,
the base station node is used as a node except the sensor node in the wireless sensor network and is mainly responsible for collecting and summarizing data;
when the wireless sensor network is initialized, all sensor nodes are common nodes, namely, non-cluster-head nodes;
selecting a part of sensor nodes from the sensor nodes as candidate cluster head nodes through a specified algorithm;
selecting partial candidate cluster head nodes from the candidate cluster head nodes as optimal cluster head nodes by utilizing a longicorn search algorithm;
and the part of the sensor nodes which are used for forwarding data from the optimal cluster head node to the base station node in the sensor nodes which are not selected as the optimal cluster head node are used as relay nodes.
The invention relates to a wireless sensor network clustering routing method based on longicorn stigma search, which comprises the following steps:
step S1, after N sensor nodes and 1 base station node are deployed in the designated area, network presetting is carried out;
specifically, in step S1, first, N sensor nodes and 1 base station node broadcast and transmit data packets including sensor node IDs and sensor node position coordinates, and each sensor node receives data packets transmitted by its neighboring nodes to record information of the neighboring nodes; after the information recording of the neighbor nodes is completed, each sensor node sends an initialization message to the base station node by using the existing controlled flooding mechanism, wherein the initialization message comprises the following components: the ID of each sensor node, the position coordinates of the sensor nodes and the residual energy of the sensor nodes; after receiving all initialization messages, the base station node establishes a wireless sensor network topology;
step S2, firstly, based on the sensor node position coordinates of each sensor node and the sensor node residual energy, calculating to obtain the candidate cluster head node weight of each sensor node according to the candidate cluster head node weight function, then, arranging all the sensor nodes according to the candidate cluster head node weight from high to low, and selecting P% sensor nodes arranged in sequence from the sensor node with the highest candidate cluster head node weight as the candidate cluster head node, wherein the sensor node with the sensor node ID of i (hereinafter referred to as the sensor node n for short) is P < 80 i ) The candidate cluster head node weight function f (i) of (a) is:
wherein, E (n) i ) For the sensor node n i Residual energy of, E 0 For the sensor node initial energy (all sensor node initial energies are the same, i.e., E 0 A constant), MN i For the sensor node n i The value range of i is 1 to N, MN is the standard number of neighboring nodes in the ideal standard communication range of the sensor node (MN is a standard value, i.e., the standard number of uniform clusters under ideal conditions, and therefore can be regarded as a constant), R is the radius of the standard communication range of the sensor node, and d (N) i ,CM j ) For the sensor node n i Neighbor node CM with sensor node ID j in standard communication range j The distance between the two, j, ranges from 1 to MN, d max Is the maximum distance of N sensor nodes from the base station node, d min Minimum distances of N sensor nodes from a base station node, d in For the sensor node n i Distance from a base station node, w1, w2, w3 and w4 are weighting coefficients, wherein w1 is a weight of a sensor node residual energy factor, w2 is a weight of a sensor node neighbor node number factor, w3 is a weight of a sensor node and neighbor node distance factor, w4 is a weight of a base station node and sensor node distance factor, and w1, w2, w3 and w4 satisfy the following conditions:
w1+w2+w3+w4＝1,w1,w2,w3,w4∈(0,1)
it should be noted that the magnitude of each weighting coefficient can be flexibly adjusted within the value range according to the actual scene effect;
step S3, obtaining a fitness function for selecting an optimal cluster head node, and iterating the fitness function through a longicorn searching algorithm, so as to select a candidate cluster head node with the smallest fitness function value from all candidate cluster head nodes selected in step S2 as the optimal cluster head node, and turn the remaining candidate cluster head nodes not selecting the optimal cluster head node into common nodes, where the fitness function F is:
F＝β*f 1 +(1+β)*f 2
wherein f is 1 Is a sensor node location factor for ensuring that the average distance from a sensor node in a cluster to a candidate cluster head node is shortest, f 2 The residual energy factor of the sensor node is used for ensuring that the candidate cluster head node of the elected optimal cluster head node has higher residual energy, and beta is a weight factor of the residual energy factor of the sensor node in the fitness function calculation process;
specifically, f 1 Expressed as the maximum value of the average distance from all sensor nodes in the cluster to the candidate cluster head node, namely:
wherein, | C k I is the number of sensor nodes in the standard communication range of the kth candidate cluster head node, d (n) i ,CH k ) Is a sensor node n i To the kth candidate cluster head node CH k I ranges from 1 to N, k opt The number of the candidate cluster head nodes is;
f 2 expressed as the ratio of the sum of the remaining energy of all the sensor nodes in the wireless sensor network to the sum of the remaining energy of all the candidate cluster head nodes, namely:
wherein, E (n) i ) For the sensor node n i Residual energy of E (CH) k ) Is the kth candidate cluster head node CH k The residual energy of (d);
step S4, the best cluster head node (i.e. the last cluster head node) selected in step S3 broadcasts the message of the best cluster head node when selected in its communication range, and waits for the joining of other common nodes (i.e. sensor nodes other than the best cluster head node); specifically, other common nodes preferentially select and join the cluster where the best cluster head node with the strongest signal strength is located according to the received signal strength of the message selecting the best cluster head node;
step S5, the best cluster head node receives the environment perception information data (including the temperature, humidity, pressure and other data monitored by the sensor node) sent by the common nodes in the cluster where the best cluster head node is located, performs data fusion, then selects the relay node in the common nodes based on the relay node selection function, and finally the best cluster head node transmits the fused environment perception information data to the base station node through the relay node in a multi-hop manner, wherein the sensor node ID is the common node of j (hereinafter referred to as the sensor node n for short) j ) The relay node selection function s (j) of (a) is:
wherein, E (n) j ) For the sensor node n j Residual energy of E 0 For the initial energy of the sensor node, d (i, j) is the optimal cluster head node to the sensor node n with the ID of the sensor node being i j Distance of d toBS (j) For the sensor node n j Distance to base station node, d toBS (i) The distance from the optimal cluster head node with the ID of the sensor node as i to the base station node is selected, and when the optimal cluster head node to the relay node and the square sum of the distances from the relay node to the base station node are required to be satisfied, the square sum of the distances from the optimal cluster head node to the base station node is smaller than the square of the distance from the optimal cluster head node to the base station node, and alpha s For the relay node energy factor, beta s Is a relay node distance factor, and alpha s 、β s Satisfies the following conditions:
α s +β s ＝1,α s and β s ∈(0,1)
wherein alpha is s 、β s The value of (A) is flexibly selected according to the actual scene;
aiming at each optimal cluster head node, the value of a relay node selection function S (j) of a sensor node (namely a common node) of each non-optimal cluster head node in the cluster where the optimal cluster head node is located is calculated, the common node with the minimum value is selected as a relay node for communication between the optimal cluster head node and a base station node, and the like, so that a network routing transmission path is established, and then a stable data transmission stage can be entered.
The present invention will be described in detail with reference to specific examples.
As shown in fig. 1, in this embodiment, the wireless sensor network is composed of 1 base station node and N sensor nodes, the sensor nodes are randomly distributed in a monitoring area of M × M, and the network model assumes the following: the base station nodes are deployed outside the square monitoring area, the sensor nodes are randomly distributed inside the monitoring area, and after deployment is completed, the base station nodes and the sensor nodes do not move; all the sensor nodes are isomorphic, have the same function and have the same limited initial energy, and each sensor node has a unique ID; all the sensor nodes can be a sending node and a receiving node; all the sensor nodes can be used as cluster head nodes and common nodes and can communicate with the base station node; the communication power of the sensor nodes can be adjusted according to the transmission distance so as to reduce energy consumption; communication links among the sensor nodes are symmetrical, and the sensor nodes can estimate the distance among the sensor nodes by receiving a signal strength RSSI value; each sensor node can sense the residual energy and the position of the sensor node.
The network energy consumption model adopted in this embodiment is a first-order radio model, and the energy consumption for transmitting data of Lbits under the condition of ideal signal-to-noise ratio is composed of two parts: the energy consumption for sending the L bits length data and the energy consumption of the power amplifying circuit are as follows according to the difference of the transmission distance:
wherein E is Tx (l, d) is the energy consumed for transmitting the Lbits data at a transmission distance d (m), d is the transmission distance between the sending node and the receiving node, E elec Energy consumed for transmitting or receiving 1bit data; epsilon fd In a free space fading modelEnergy loss of the amplifier per unit distance of the unit bit data transmission, epsilon mp The energy loss of an amplifier is the unit distance of unit bit data transmission in a multipath fading model; d 0 Boundary threshold values for distinguishing different power amplifier model parameters; if the transmission distance d is less than or equal to d 0 Selecting a first class power amplifier model parameter epsilon fs If d > d 0 Selecting a second type power amplifier model parameter epsilon mp (ii) a Boundary threshold value
For the receiving node, the energy consumption of the receiving node for receiving the L bits data is E rx ＝L*E elec In addition, the energy consumed by the cluster head node for carrying out data fusion on the L bits length data is E DAx (l,d)＝E da L, wherein E da The energy consumed to fuse 1bit data.
Based on the above situation, the present invention mainly comprises: the method comprises five steps of network presetting, candidate cluster head node selection, cluster head node optimization, clustering establishment and route transmission.
Firstly, in the step of 'network presetting', each sensor node and a base station node firstly broadcast and send a data packet containing a node ID and a node position coordinate, and each sensor node respectively records neighbor node information; after the information recording of the neighbor nodes is completed, each sensor node sends an initialization message to the base station node by using an existing controlled flooding mechanism, the initialization message comprises the node ID of the sensor node, the position coordinates of the sensor node and the residual energy of the sensor node, and the base station node establishes a wireless sensor network topology after receiving the initialization message.
Secondly, in the step of selecting the candidate cluster head nodes, determining a set of the candidate cluster head nodes through a multi-target cluster head node selection weight function, wherein the function comprehensively considers the following four factors:
(1) node residual energy factor:
because the cluster head node needs to additionally bear the functions of cluster management and intra-cluster data fusion, the energy consumption is more, the algorithm selects the sensor node with larger residual energy as a candidate cluster head node, and the residual energy parameters are defined as follows:
Node energy ＝E c /E 0
wherein E is c For the current sensor node residual energy, E 0 Is the initial energy of the sensor node.
(2) Number of nodes in a cluster factor:
the number of the nodes in the cluster is considered by the number of neighbor nodes near the sensor node, in a WSN application scene, the sensor node with too few neighbor nodes is not suitable for serving as a cluster head node, the number of the neighbor nodes is defined as the number of other sensor nodes in a standard communication radius R of the sensor node, and a set N of the neighbor nodes of the sensor node with a node ID of i i Expressed as:
N i ＝{j|d(i,j)＜＜R,j∈N} i∈{1,2,…,N}
wherein, N is the number of the sensor nodes, d (i, j) is the distance between the sensor node with the node ID of i and the sensor node with the node ID of j, j belongs to [1, N ], and j is not equal to i, R is defined as the standard communication range of the sensor nodes, and the calculation process is as follows:
wherein, M is the length of side of wireless sensor network monitoring range, N is the total number of sensor node in the monitoring range, and p is the percentage that cluster head node quantity accounts for all sensor node quantity, and according to the minimum principle of whole network energy consumption, the more preferred cluster head node number of theory is:
wherein epsilon fs The energy loss of the amplifier per unit distance for a unit bit of data transmission in a free space fading model, epsilon mp For transmitting unit distance of unit bit data in multipath fading modelThe power consumption of the time amplifier;
the square of the average distance from all the sensor nodes to the base station node in the monitoring range;
therefore, the proportion p of the finally elected cluster head nodes opt (which is a theoretically preferred value of p above) is:
defining the number parameters of the nodes in the cluster as follows:
Node density ＝MN i /MN
wherein, MN i The number of neighbor nodes of the sensor node with the node ID of i belongs to [1, N ∈]MN is the standard number of neighbor nodes within the ideal standard communication range,
(3) intra-cluster node distance factor:
the distance factor of the nodes in the cluster is the distance from the sensor nodes (CMs) of non-cluster-head nodes in the cluster to the cluster-head node (CH), and the parameter can ensure the quality of the cluster and the quality of a link between the CH and the CMs. Defining the intra-cluster distance parameter as:
wherein, d (CH) i ,CM j ) For a sensor node with a node ID of i and a neighbor node CM in a standard communication range j The distance between, j, ranges from 1 to MN.
(4) Base station node spacing factor:
the distance between the sensor nodes and the base station nodes is considered by the base station node distance parameter, and the sensor nodes far away from the base station nodes are not suitable for being selected as cluster head nodes in theory. Defining the factors of the node spacing of the base station as follows:
wherein, d max Is the maximum distance of N sensor nodes from the base station node, d min Is the minimum distance of N sensor nodes from the base station node, d in And the distance between the sensor node with the node ID of i and the base station node is shown.
The 4 factors are weighted and combined to form a cluster selection head node weight function f, after the function is defined, the base station node calculates the f value of each sensor node, the f values are arranged in a descending order, a certain proportion (less than 80%) of the sensor nodes are selected from high to low to serve as candidate cluster head nodes, and the candidate cluster head nodes are represented as a list CH candidate Thereby completing the selection process of the candidate cluster head node.
Then, in the step of "cluster head node optimization", the base station node performs iterative optimization on the candidate cluster head nodes by using the longicorn search to obtain a better cluster head node set, namely an optimal cluster head node, wherein the cluster head node optimization algorithm based on the longicorn search has the following flow:
(1) creating a random vector of longicorn stigma orientations
Determining a spatial dimension k: since the wireless sensor network model in this embodiment is composed of 1 base station node and N wireless sensor nodes, and the sensor nodes are randomly distributed in the monitoring area of M × M, the search space dimension k ═ k opt Random vector
Comprises the following steps:
wherein, the rands () is a random function;
(2) setting a step factor δ: the step size factor is used for controlling the area searching capability of the longicorn whisker searching algorithm, and a proper step size factor can avoid trapping in a local optimal solution, and the linear attenuation step size factor is used in the embodiment to ensure the searching precision:
δ t+1 ＝δ t *eta t＝(0,1,2,…,n)
wherein: delta represents a step factor, t is iteration times, eta is a value close to 1 between [0 and 1], and the value of eta can be flexibly selected according to an actual application scene;
(3) determining a fitness function F: the fitness function F is used to select the optimal cluster head node distribution, and its expression is as follows:
Minimize:F＝β*f 1 +(1+β)*f 2
wherein f is 1 Is the maximum average distance, f, between a sensor node and its associated cluster head node 2 The method is characterized in that the ratio constant of the residual total energy of all sensor nodes in the network to the total energy of the cluster head nodes is a constant, beta is crucial to determining the action of each sub-optimization function, the value range of beta is not fixed, the change is flexible according to the actual application scene, and the goal of minimizing the fitness function is to reduce the intra-cluster distance between the cluster head nodes and the intra-cluster nodes thereof so as to optimize the energy efficiency of the sensor network;
(4) initialization of the longhorn position: initial parameter selection candidate cluster head node list CH candidate The k random cluster head nodes are used as an initial solution set of a longicorn whisker algorithm, namely the initial position of the longicorn centroid, and are stored in the CH best In (1).
(5) Calculating a fitness function: calculating the fitness function value at the initial position according to the fitness function F and storing the fitness function value in F best In (1).
(6) And updating the positions of the left and right longicorn beards. And updating the position coordinates of the left and right whiskers of the longicorn according to the following formula.
Wherein x is rt The position coordinates of the right longicorn stigma in the t iteration are obtained; x is a radical of a fluorine atom lt The position coordinates of the longicorn left hair in the t iteration are obtained; x is the number of t Representing the barycentric coordinates of the longicorn at the t-th iteration; d 0 Representing the distance between the left and right whiskers;
(7) calculating a new solution: according to the coordinate positions of the left and right whiskers in the longicorn whisker search algorithm, the fitness function values f (x) of the left and right whiskers are respectively obtained r ) And f (x) l ) Comparing the intensity of the two, calculating the fitness function value of the current longhorn beetle position according to the updated longhorn beetle position (shown in the following formula), and if the fitness function value at the moment is better than F best Then update F best And CH best ：
Wherein, delta t Represents the step factor at the t-th iteration; sign () is a sign function, f (x) l ) And f (x) r ) Respectively the fitness function values of the left and right whiskers;
(8) iteration: judging whether the iteration times reach the set maximum iteration times or whether the fitness function value of the current position reaches the set minimum threshold value, if so, performing the step (9), otherwise, continuing to start the iteration from the step (6) until the iteration stopping condition is met;
(9) generating an optimal cluster head node: when the BAS algorithm stops iterating, the CH at this time best Namely, the optimal cluster head node distribution of the round of cluster routing, and the cluster establishing stage after the optimal cluster head node distribution is applied.
Then, in the step of "establishing clusters", the optimal cluster head node broadcasts a message of the selected cluster head node in the communication range of the optimal cluster head node, and waits for the addition of other sensor nodes of the non-optimal cluster head node, and the sensor nodes of the non-optimal cluster head node preferentially select the cluster where the optimal cluster head node with the strongest signal strength is added according to the received signal strength of the message of the selected cluster head node.
Finally, in the "route transmission" step, the cluster head node (i.e., the previously determined optimal cluster head node) acts as an intra-cluster control center to coordinate data transmission, and the cluster head node (CH) receives data from the intra-cluster ordinary nodes, performs data fusion, and transmits the data to the base station node (BS). According to an energy transmission model, the transmission distance has great influence on transmission energy consumption, so that if the cluster head completes data fusion in the LEACH protocol, the cluster head directly communicates with a base station node, this may result in a large energy consumption of the cluster head node far away from the base station node, so that the relay node is introduced in this embodiment, and single-hop communication in a cluster is adopted, namely, a routing transmission mode of multi-hop communication between the cluster head node and the base station node through the relay node, wherein, the selection of the relay node comprehensively considers two factors of the distance between the sensor node and the base station node and the residual energy of the sensor node, aiming at each cluster head node, by calculating the value of the cost function of each non-cluster head sensor node in the cluster, the node with the minimum value is selected as the relay node for the communication between the cluster head node and the base station node, by analogy, a network routing transmission path is established, and then a stable data transmission stage is entered.
The wireless sensor network clustering routing method based on the longicorn stigma search is verified below, and a simulation experiment is compared with the existing clustering routing algorithm, so that the method can effectively balance the energy consumption of network nodes and prolong the life cycle of the network. Specifically, MATLAB software is used for simulation, firstly, parameter setting is carried out, and a simulation parameter table is as follows:
| parameter(s) | Numerical value |
| Sensing area | 100m*100m |
| Base station node (BS) location | (150m，50m) |
| Number of |
100 |
| Initial energy E 0 | 0.5J |
| Loss of transmission circuit E elec | 50nJ/bit |
| Coefficient of free space amplifier epsilon fs | 10pJ/bit/m2 |
| Multipath fading amplifier coefficient epsilon mp | 0.0013pJ/bit/m4 |
| Data fusion energy consumption E DA | 5nJ/bit/signal |
| Cluster head node proportion p | 5％ |
| Packet size | 4000bits |
The result of comparing the initial node death time with the existing LEACH algorithm and EAMMH algorithm is shown in FIG. 2. In fig. 2, the abscissa represents the network operation time, and the ordinate represents the survival number of the wireless sensor node. As can be seen from fig. 2, the present invention (LEACH-BAS) has a longer network life cycle than the other two algorithms, and the number of surviving nodes is also larger than the other two algorithms.
Fig. 3 shows the comparison result of the average residual energy of the nodes of the above 3 methods. In fig. 3, the abscissa represents the network operation time, and the ordinate represents the average remaining energy of the wireless sensor node. As can be seen from fig. 3, the average residual energy of the nodes in each round of the invention (LEACH-BAS) is higher than those of the other two algorithms, and the node energy exhaustion time is much longer than those of the other two algorithms, thereby verifying that the invention can effectively balance the node energy consumption and prolong the network life cycle.
Fig. 4 shows the comparison result of the information amount from the common node to the cluster head node of the above 3 algorithms. In fig. 4, the abscissa represents the network operation time, and the ordinate represents the information amount from the wireless sensor node to the cluster head node. It can be seen from fig. 4 that the information amount of the three algorithms before the first node dies is very close, but the information amount of the LEACH algorithm and the EAMMH algorithm is large in fluctuation, because the cluster head node selection mechanism of the LEACH algorithm and the EAMMH algorithm has randomness, the cluster head node amount in each round is unstable. After about 700 rounds, the amount of information of the invention (LEACH-BAS) is obviously larger than that of the other two algorithms, because the number of dead nodes of the other two algorithms is rapidly increased, so that the data amount from the node to the cluster head node is reduced. The information quantity from the common node to the cluster head node is larger than that of other two algorithms, so that the more detailed scene monitoring information is, the more stable the operation is, and the better effect is achieved in the actual application scene.
In conclusion, the method comprehensively considers the residual energy and the position information of the wireless sensor nodes, and selects the optimal cluster head through the longicorn searching algorithm, so that the energy consumption of the cluster head nodes is effectively balanced, and the life cycle of the network is prolonged. Meanwhile, in the invention, the data transmission from the cluster head node to the base station node adopts a multi-hop communication transmission mode, and the communication energy consumption and the node residual energy are comprehensively considered when the relay (forwarding) node is selected, so that the sensor node with lower residual energy is prevented from being used as the relay node, and the death speed of the sensor node is effectively delayed.
The above embodiments are merely preferred embodiments of the present invention, which are not intended to limit the scope of the present invention, and various changes may be made in the above embodiments of the present invention. All simple and equivalent changes and modifications made according to the claims and the content of the specification of the present application fall within the scope of the claims of the present patent application. The invention has not been described in detail in order to avoid obscuring the invention.
Claims (5)
1. A clustering routing method of a wireless sensor network based on longicorn stigma search is characterized by comprising the following steps:
step S1, after N sensor nodes and 1 base station node are deployed in a designated area, network presetting is carried out to establish wireless sensor network topology;
step S2, calculating and obtaining a candidate cluster head node weight of each sensor node according to a candidate cluster head node weight function based on the sensor node position coordinates and the sensor node residual energy of each sensor node, then arranging all the sensor nodes according to the candidate cluster head node weight from high to low, and selecting P% of sensor nodes which are sequentially arranged from the sensor node with the highest candidate cluster head node weight as the candidate cluster head node in all the sensor nodes; wherein, the candidate cluster head node weight function f (i) of the sensor node with the sensor node ID of i is:
wherein, E (n) i ) Residual energy for sensor node with sensor node ID i, E 0 For sensor node initial energy, MN i The number of neighbor nodes of the sensor node with the sensor node ID of i is 1 to N, and MN isThe standard number of neighbor nodes in the ideal standard communication range of the sensor node, R is the radius of the standard communication range of the sensor node, d (n) i ，CM j ) For the sensor node with the sensor node ID of i and the neighbor node CM with the sensor node ID of j in the standard communication range j The distance between the two, j, ranges from 1 to MN, d max Is the maximum distance of N sensor nodes from the base station node, d min Minimum distances of N sensor nodes from a base station node, d in The distance between a sensor node with a sensor node ID of i and a base station node, w1, w2, w3 and w4 are weighting coefficients, wherein w1 is a weight of a residual energy factor of the sensor node, w2 is a weight of a number factor of neighbor nodes of the sensor node, w3 is a weight of a distance factor between the sensor node and the neighbor nodes thereof, w4 is a weight of a distance factor between the base station node and the sensor node, and w1, w2, w3 and w4 satisfy the following conditions:
w1+w2+w3+w4＝1，w1，w2，w3，w4∈(0，1)；
step S3, acquiring a fitness function for selecting the best cluster head node, iterating the fitness function through a Tianniu must search algorithm, selecting the candidate cluster head node with the smallest fitness function value from all the candidate cluster head nodes selected in step S2 as the best cluster head node, and enabling the rest candidate cluster head nodes which are not selected as the best cluster head node to become common nodes; wherein the fitness function F is:
F＝β*f 1 +(1+β)*f 2 ，
wherein f is 1 Is a sensor node location factor, f 2 Is a residual energy factor of the sensor node, and beta is a fitness function of the residual energy factor of the sensor nodeWeighting factors in the number calculation process; i C k I is the number of sensor nodes in the standard communication range of the kth candidate cluster head node, d (n) i ，CH k ) Is a sensor node with a sensor node ID of i to the kth candidate cluster head node CH k I ranges from 1 to N, k opt As the number of candidate cluster head nodes, E (n) i ) The remaining energy of the sensor node with a sensor node ID of i, E (CH) k ) Is the kth candidate cluster head node CH k The remaining energy of (c);
the method for iterating the fitness function through the longicorn stigma search algorithm comprises the following steps:
(1) creating a random vector of longicorn stigma orientations
The following were used:
wherein rands () is a random function, k ═ k opt ；
(2) The step factor δ is set as follows:
δ t+1 ＝δ t *eta t＝(0，1，2，…，n)，
wherein, δ represents a step factor, t is the number of iterations, eta is a value close to 1 between [0,1 ];
(3) selecting a candidate cluster head node list CH candidate The k random cluster head nodes are used as an initial solution set of a longicorn whisker algorithm, namely the initial position of the longicorn centroid, and are stored in the CH best Performing the following steps;
(4) calculating the fitness function value at the initial position according to the fitness function F and storing the fitness function value in F best Performing the following steps;
(5) let t be 0, calculate the position coordinates of the longicorn stigma according to the following formula:
wherein x is rt The position coordinates of the longicorn stigma at the t iteration are obtained; x is the number of lt The position coordinates of the longicorn left hair in the t iteration are obtained; x is the number of t Representing the barycentric coordinates of the longicorn at the t-th iteration; d is a radical of 0 Representing the distance between the left and right whiskers;
(6) according to the position coordinates of the left and right longicorn whiskers, the fitness function values f (x) of the left and right longicorn whiskers are respectively obtained r ) And f (x) l ) And updating the longicorn position according to the following formula:
wherein, delta t Represents the step factor at the t-th iteration; sign () is a sign function, f (x) l ) And f (x) r ) Respectively the fitness function values of the left and right whiskers;
calculating the fitness function value of the current longicorn position, and if the fitness function value is better than F best Then update F best And CH best ；
(7) Judging whether the iteration time t reaches the set maximum iteration time or whether the fitness function value of the current position reaches the set minimum threshold value, if so, stopping the iteration, and the CH at the moment best Distributing the optimal cluster head nodes of the round of cluster routing; otherwise, let t be t +1, repeat steps (5) - (6);
step S4, the best cluster head node broadcasts the message of the best cluster head node in the communication range and waits for the joining of other common nodes; and
step S5, the best cluster head node receives the environmental perception information data sent by the common node in the cluster where the best cluster head node is located, performs data fusion, then selects a relay node from the common nodes in the cluster where the best cluster head node is located based on a relay node selection function, and finally transmits the fused environmental perception information data to the base station node through the relay node in a multi-hop manner; wherein, the relay node selection function s (j) of the ordinary node with the sensor node ID j is:
wherein, E (n) j ) Residual energy for a common node with sensor node ID j, E 0 For the initial energy of the sensor node, d (i, j) is the distance from the best cluster head node with the sensor node ID of i to the common node with the sensor node ID of j, d toBS (j) Distance from a common node with a sensor node ID of j to a base station node, d toBS (i) Distance alpha from the optimal cluster head node with the ID of the sensor node as i to the base station node s For the relay node energy factor, beta s Is a relay node distance factor, and alpha s 、β s Satisfies the following conditions:
α s +β s ＝1，α s and β s ∈(0，1)，
and aiming at each optimal cluster head node, calculating the value of a relay node selection function S (j) of each common node in the cluster where the optimal cluster head node is positioned, and selecting the common node with the minimum value as the relay node for communication between the optimal cluster head node and the base station node.
2. The method for clustered routing based on longicorn silk search in wireless sensor network of claim 1, wherein in the step S1,
the N sensor nodes and 1 base station node firstly broadcast and send data packets containing sensor node IDs and sensor node position coordinates;
each sensor node receives the data packet sent by the neighbor node to record the information of the neighbor node;
after the information recording of the neighbor nodes is completed, each sensor node sends an initialization message to the base station node, wherein the initialization message comprises: the ID of each sensor node, the position coordinates of the sensor nodes and the residual energy of the sensor nodes;
and after receiving all initialization messages, the base station node establishes a wireless sensor network topology.
3. The method for clustered routing based on longicorn silk search in wireless sensor network as claimed in claim 1, wherein in the step S2, P < 80.
4. The method for clustering routing in a wireless sensor network based on longicorn whisker search of claim 1, wherein in step S4, other common nodes preferentially select a cluster where the best cluster head node with the strongest signal strength is added according to the received signal strength of the message selecting the best cluster head node.
5. The method for clustered routing based on longicorn silk search in wireless sensor network of claim 1, wherein in the step S5, the environment awareness information data comprises: and temperature, humidity and pressure data monitored by the sensor nodes.
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