CN108566658B - Clustering algorithm for balancing energy consumption in wireless sensor network - Google Patents

Clustering algorithm for balancing energy consumption in wireless sensor network Download PDF

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CN108566658B
CN108566658B CN201810174172.9A CN201810174172A CN108566658B CN 108566658 B CN108566658 B CN 108566658B CN 201810174172 A CN201810174172 A CN 201810174172A CN 108566658 B CN108566658 B CN 108566658B
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CN108566658A (en
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姚彦鑫
何枭宇
郭杰
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Beijing Information Science and Technology University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/082—Load balancing or load distribution among bearers or channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention relates to a clustering algorithm for energy consumption balance in a wireless sensor network, which is characterized in that: in the wireless sensor network, a clustering algorithm with balanced energy consumption is designed because nodes with larger energy are well utilized to serve as cluster heads and cross region division is allowed. The invention has the advantages that: instead of finding a dominant node, the dominant node is responsible for wrapping nodes in all communication areas, thereby reducing cluster head burden, being beneficial to avoiding excessive consumption of cluster heads in a hot area and relieving the hot area problem. Compared with the DCEM algorithm, the method has the advantages of total energy consumption of the network and the number of the surviving nodes, and the network life cycle can be better prolonged.

Description

Clustering algorithm for balancing energy consumption in wireless sensor network
Technical Field
The invention relates to a clustering algorithm for energy consumption balance in a wireless sensor network, which can be used for carrying out node clustering for energy consumption balance in the wireless sensor network so as to achieve the aim of prolonging the life cycle of the network.
Background
The wireless sensor network consists of a large number of cheap micro sensor nodes deployed in a monitoring area, and a self-organized network system is formed in a wireless communication mode, and aims to sense, collect and process information of a sensed object in the monitoring area in a mutual cooperation mode and send the information to an observer. Routing protocols of wireless sensor networks can be divided into a planar routing protocol and a cluster routing protocol according to different topological structures. The plane routing protocol has poor expansibility, small network scale and limited application. And the aspects of expansibility, fault tolerance, energy conservation and the like of the clustering routing protocol are excellent, so that the clustering routing protocol is widely applied.
In the wireless sensor network, the cluster head election mechanism of the clustering algorithm mainly comprises the following mechanisms. The cluster head election of LEACH (Low-Energy Adaptive Clustering Hierarchy) and TEEN (threshold sensitive Energy sensor Network protocol) algorithms is that a node randomly generates a random number of 0-1, the random number can become a cluster head as long as the random number is greater than a threshold value, and the threshold value is calculated through cluster head election probability and the number of currently operated rounds of the Network. The method has the disadvantages that cluster head election is completely random, the selected cluster head is not guaranteed to be most appropriate, energy and position information of nodes are not considered, and the number of the cluster heads is not calculated according to an actual theoretical basis. The cluster head selection of LEACH-C (Low-Energy Adaptive Clustering Hierarchy Centralized) algorithm is not random, but is completely controlled by the base station. The base station calculates an energy mean value in the network by collecting global node position and energy information, performs clustering by using a simulated annealing algorithm, and selects a cluster head for each cluster. The method has the defects that the method is only suitable for a small-scale system, and large-scale network flooding can cause much energy consumption and even network blockage; the HEED (hybrid energy efficient Distributed clustering) algorithm determines a cluster head through multiple iterative calculations according to the residual energy of a node, the proximity of the node to a neighbor node and the node density. The method has the defect that the energy consumption is large when the cluster heads are selected in a loop iteration mode.
In the aspect of a clustering mode, according to an heed (hybrid Energy Efficient Distributed clustering) algorithm, a node determines which cluster to add according to average reachable Energy of an amrp (average Minimum availability) carried in a received cluster head message, which solves the problem that a node is finally added after being covered by multiple cluster heads. The nodes which do not receive the cluster head message identify themselves as the isolated nodes and declare themselves as the cluster heads. The method has the disadvantages that isolated nodes can be generated, the distance factor from a neighbor node to a cluster head node is not considered, large clusters are more in the network, and some nodes in the network are easy to die too fast, so that the stability of the network is influenced.
In terms of inter-cluster routing, a teen (threshold sensitive Energy Efficient sensor Network protocol) algorithm determines communication between nodes through two thresholds, namely a hard threshold and a soft threshold. The upper node broadcasts two thresholds of hard and soft to the lower node, is used for determining the condition that the node sends the data to the upper node, namely when the data sensed by the sensor node exceeds the hard threshold for the first time, the node sends the data to the upper node, and the monitoring value is stored; and then only when the sensing data exceeds the hard threshold and the difference between the sensing data and the monitoring value is larger than or equal to the soft threshold, the node uploads the data to the previous-level node and stores a new monitoring value. The disadvantages are as follows: if the sensing data does not reach the threshold value, the nodes cannot communicate with each other, and a user cannot acquire any data from the network and cannot judge whether the nodes are failed or the threshold value is set wrongly.
The above algorithms are uniform clustering algorithms, the network is divided into a plurality of clusters with similar sizes, and the number of members in the clusters is approximately the same due to the uniform distribution of the nodes. Within each cluster, the sum of the energy consumptions of all nodes is approximately equal. However, when the cluster head communicates with the base station, the distance between each cluster head and the base station is different, which causes a problem of uneven energy consumption. For a single-hop network between clusters, nodes far away from the base station will consume more energy due to the use of larger data transmission power. For an inter-cluster multi-hop network, a cluster head node near a base station may consume a large amount of energy by participating in multiple data forwarding. When multi-hop forwarding is carried out among clusters, cluster head nodes near a base station undertake more forwarding tasks, so that more energy is consumed, and the problem of uneven network energy consumption is caused, and researchers refer to the problem of a 'hot zone'.
In order to solve the problem of 'hot zone', a non-uniform clustering mode is developed. Typical non-uniform clustering algorithms include an EEUC algorithm, a UCFIA algorithm, a UDEB algorithm and the like.
The EEUC algorithm is an energy efficient non-uniform clustering algorithm. In each clustering period of the algorithm, the node firstly selects a candidate cluster head according to the probability T, and then elects the cluster head in the candidate cluster head according to the residual energy. Particularly, in the stage that the candidate cluster heads compete for the cluster heads, other nodes in the network are all in a dormant state until the election is finished. In order to construct a non-uniform cluster, the EEUC algorithm provides a clustering competition radius related to the position of a node in a network, and the closer the node is to a Sink node, the smaller the clustering radius is. In the data transmission stage, the EEUC algorithm specifies that if the distance from a cluster head to a Sink node is smaller than a preset threshold value, the cluster head adopts a single-hop routing model to communicate with the Sink node; otherwise, a multi-hop routing model is adopted to communicate with the Sink node. When a cluster head selects a next hop route, a cluster head with the residual energy and the forwarding cost meeting the requirements is selected as the next hop route from a neighbor cluster head. The EEUC multi-hop routing algorithm is a more classical algorithm adopting a non-uniform clustering idea, divides a network into clusters with non-uniform sizes through clustering competition radius, considers the residual energy and forwarding cost of the next-hop routing when routing, and balances the energy consumption of the network. However, the selection of the parameter for controlling the value range of the competitive radius of the EEUC algorithm is difficult, and the performance of the EEUC algorithm is restricted.
The UCFIA algorithm is a distributed self-organizing clustering algorithm. When a cluster head is elected, the UCFIA algorithm takes the residual energy of the nodes, the distance from the nodes to a base station, the density and other local information as reference indexes of candidate cluster head competition cluster heads and cluster competition radius, and a non-uniform cluster is constructed by utilizing a fuzzy theory model. In the data transmission stage, a multi-hop route is selected by utilizing an adaptive max-min Ant Colony Optimization (ACO). The UCFIA algorithm utilizes fuzzy theory and ACO to construct the WSNs with high energy utilization efficiency, the clustering difficulty is simplified, but the selection of a plurality of parameters in the UCFIA algorithm, such as maximum local density, maximum competition radius and the like, is not optimal, and the parameters can be further optimized to improve the network performance.
UDEB is a dynamic routing non-uniform clustering algorithm. The algorithm takes a Sink node as a center, a network is divided into concentric circles with uniform intervals from inside to outside, and the interval between the circles is delta. Through the analysis of the energy consumption of the single-hop routing model and the multi-hop routing model, the algorithm specifies that when delta is less than or equal to d0When the rings are spaced at the network critical distance d0When the ring is used, the nodes among the rings can directly carry out multi-hop communication, and the nodes in the rings carry out single-hop communication so as to reduce unnecessary energy consumption. The UDEB algorithm carries out theoretical calculation on the energy consumption of the whole network, determines the optimal number of cluster heads in each ring, calculates the competition radius of the cluster heads according to the optimal number of the cluster heads, and ensures that each area in the network can select the cluster heads. The UDEB algorithm comprehensively considers energy cost and residual energy when selecting the route, gives the route selection probability in a weighting mode, and simplifies the complexity of the algorithm. The UDEB algorithm well solves the problem of energy holes by constructing a ring-divided non-uniform clustering network, but a cluster head node adopts two weight parameters when selecting a route, and the two parameters are not subjected to quantitative analysis and research, so that the improvement of the life cycle of the WSNs is restricted.
The DCEM (Delay-Constrained Energy Multi-hop) algorithm is an Energy-efficient clustering Multi-hop routing algorithm based on time constraints proposed in 2016. Compared with classical algorithms such as LEACH, HEED and the like, the method has advantages in the aspects of energy consumption, the number of surviving nodes and the like. However, the clustering method has the following problems: as long as the dominant node is within the communication distance, the dominant node is taken as a cluster head, so that the dominant node in a certain area occupies the whole area, the dominant node can be rapidly exhausted, the cluster head needs to be frequently replaced, and if the updating time is slightly slower than the exhaustion time, the cluster information is lost. The selection of the multi-hop route among the clusters is based on minimizing energy consumption, which can lead to uneven energy consumption of nodes in the network.
Disclosure of Invention
The technical problem solved by the invention is as follows: the nodes with larger energy are well utilized, the regions are allowed to be divided in a crossed mode, instead of finding a dominant node to serve as a cluster head, and the dominant node is responsible for covering all communication regions, so that the cluster head burden is reduced, the cluster head excessive consumption in a hot zone is avoided, the hot zone problem is relieved, and the network life cycle is effectively prolonged.
The technical solution of the invention is characterized by comprising the following steps:
1) and selecting a proper cluster head according to a cluster head selection method.
2) And clustering the nodes in the network according to the clustering method.
3) And forming an inter-cluster cooperative transmission route according to an inter-cluster multi-hop routing algorithm.
4) And (3) judging whether the hot area problem exists, namely setting a gate threshold value of transmission data quantity for the nodes in the hot area, and when the transmission data quantity of the nodes exceeds the gate threshold value, judging that the hot area problem exists and performing the step 5), or else, performing the step 6).
5) The hot zone problem is solved, namely the cluster node with the largest energy is used as a candidate node to transmit data exceeding the threshold value of the gate.
6) According to the multi-hop route between clusters, the data transmission between the nodes is carried out
7) And calculating the energy consumption of the nodes in the cluster and the cluster head nodes, and updating the residual energy of the nodes in the network.
8) And counting the total energy consumption of the network and the number of the surviving nodes in the network, and adding 1 to the number of rounds.
9) And judging whether a survival node exists or not, if so, performing the step 10), and if not, ending the simulation.
10) And calculating the number of cluster head nodes with energy failure in the network.
11) If the number of the cluster heads with energy failure is less than or equal to 3, performing the step 12), otherwise, performing the step 1).
12) And (4) selecting and switching cluster heads, namely selecting the cluster nodes with the maximum energy in the cluster to replace the original cluster heads for the clusters with the energy failure of the cluster heads, and continuously finishing the work of the original cluster heads. Followed by step 3).
The principle of the invention is as follows: in a wireless sensor network, when a clustering algorithm is designed, a node with large energy is well utilized to serve as a cluster head, and a dynamic search radius r is setchAnd a cluster intersection area dividing method and an f value formula playing a role in measurement are also designed so as to achieve the purposes of balancing energy consumption and prolonging the life cycle of the network.
Compared with the prior art, the invention has the advantages that: compared with the inter-cluster routing algorithm based on the minimum energy consumption of the DCEM algorithm, the method has more advantages in energy consumption balance and can better prolong the life cycle of the network.
Drawings
FIG. 1 is a flow chart of the algorithm of the present invention.
FIG. 2 is example 1
FIG. 3 is example 2
FIG. 4 is example 3
FIG. 5 shows angles a and L1/L0Curve of the relationship (D)
FIG. 6 is example 4
FIG. 7 is example 5
FIG. 8 is a network node distribution diagram
FIG. 9 is a graph of total energy consumption of a network
FIG. 10 is a network surviving node number graph
Detailed Description
1: the cluster head selection method comprises the following steps: when the network is initialized, the sink node collects and stores the ID information, the position information and the residual energy information of the nodes of the whole network, and thenOnly the remaining energy of the node is updated. The sink node sorts all nodes and energy, takes the first 5% of points as cluster heads and takes the radius rchAnd searching nearby nodes to form a cluster. If the whole area can not realize full coverage, taking the nodes with the energy of the first 5% as cluster heads for the rest non-clustered nodes, and taking the radius rchAnd searching nearby nodes to form a cluster. And circulating the steps until the full communicable coverage of the whole area is reached. And after the node sleeps for a fixed time, receiving a network clustering table broadcasted by the sink node.
Because of the search radius rchRelated to the remaining energy of the node and the distance from the sin k node. The greater the energy, the greater the distance to the sin k node, rchThe larger the principle, the search radius r is setchThe formula of (1) is:
Figure BSA0000160008770000061
α1+β1＝1 (2)
wherein: di_sin kIs the distance from any cluster head node i to the sin k node, dmaxThe distance from the cluster head node selected in this cluster head selection cycle to sin k is farthest, EiIs the remaining energy of cluster head node i, EM，iDenotes the maximum battery capacity, d0For transmission threshold, alpha, in a radio channel model1And beta1Are parameters.
2: the clustering method comprises the following steps: the distance from a cluster head node, the residual energy of the cluster head node and the distance from the cluster head node to a sin k node should be considered when clustering the nodes located in the cluster intersection area. Because the cluster head node close to the sin k node needs to forward data from other clusters and has heavy burden, the cluster head node exhausts self energy early and fails to enter a dormant state, network segmentation is caused, and the network survival time is reduced. Researchers call this problem a "hot zone" problem.
To avoid this problem, as shown in example 1 of fig. 2, if cluster head node i1And i2Distance from sin k node is not more than rchOf cluster head node j from sin k nodeA distance greater than rchThen cluster head node i1And i2And the nodes in the areas respectively intersected with the cluster head nodes j are classified as cluster nodes of the cluster head nodes j. In cluster head node i1And i2And f values are respectively calculated for the nodes in the intersected areas, and the nodes in the intersected areas become cluster nodes of cluster head nodes with large f values.
If the distances from the cluster head nodes to the sin k node are all larger than rch(as shown in example 2 of fig. 3), the nodes in the intersection area respectively calculate f values, and the nodes in the intersection area become cluster head nodes with large f values. The calculation formula of the f value is as follows:
Figure BSA0000160008770000071
in the formula (f)m_jAdaptation value, E, of node m to cluster head node j representing intersection areajRepresents the remaining energy of cluster head node j, dm_jRepresenting the distance from node m to cluster head node j of the intersection area. Alpha is alpha2And beta2The weight value can be adjusted according to needs.
3: the inequality relationship that is favorable for cooperation is: when in use
Figure BSA0000160008770000072
The cooperation is advantageous. Wherein d is1Distance of a node to a cooperative node, d2Distance of cooperative node to sin k node, d3Is the distance from node to sin k node.
Derivation: an ideal omnidirectional antenna, the free space loss is
Figure BSA0000160008770000073
In the formula PtIs the signal power of the transmitting antenna, PrIs the signal power of the receiving antenna, λ is the carrier wavelength, d is the propagation distance between the antennas, c is the speed of light (3 x 10)8m/s)。
The position relationship of the nodes is shown in example 3 of fig. 4, and if the data amount collected by the cluster heads a and B is l bits and the total data amount is 2l bits, then:
Figure BSA0000160008770000081
Figure BSA0000160008770000082
Figure BSA0000160008770000083
L0＝LAC+LBC (8)
Figure BSA0000160008770000084
Figure BSA0000160008770000085
L1＝LAB+LBC (11)
Figure BSA0000160008770000086
from equation (12), let L1/L0< 1, then
Figure BSA0000160008770000087
The cooperation is advantageous. In particular: when d is1＝d2From the angles a and L in FIG. 51/L0The relationship between (A) and (B) can be known as
Figure BSA0000160008770000088
When L is1/L 01. So as to make the angle
Figure BSA0000160008770000089
When the cooperation is favorable, the conclusion is proved again when
Figure BSA00001600087700000810
The cooperation is advantageous.
The position relationship of the nodes is as shown in example 4 of fig. 6, the total data volume is 2l bits, the energy of the cluster head a is larger than that of the cluster head B, and the intersecting regions are divided; the amount of data collected by cluster head a is xl, and the amount of data collected by cluster head B is (2-x) l, then:
Figure BSA0000160008770000091
Figure BSA0000160008770000092
Figure BSA0000160008770000093
Figure BSA0000160008770000094
Figure BSA0000160008770000095
let L1/L0< 1, then
Figure BSA0000160008770000096
The cooperation is advantageous.
4: inter-cluster multi-hop routing algorithm:
the inter-cluster routing adopts a single-hop or multi-hop data transmission mode, and needs to be judged according to the distance between a cluster head node and a sin k node. Distance value dlinAs the critical distance, the distance from the cluster head node i to the cluster head node j is di_jThe distance from the cluster head node j to the sin k node is dj_sin kThe distance from the cluster head node i to the sin k node is di_sin k. When d isi_sin k≤dlinAnd then, a 'single-hop' mode is adopted, namely the cluster head node i directly sends the information to the sin k node. When d isi_sin k＞dlinWhen, if inequality relationship is satisfied
Figure BSA0000160008770000097
The cooperation is advantageous, so a 'multi-hop' approach is adopted.
The pseudo-code for a multi-hop route between clusters is as follows:
Figure BSA0000160008770000098
Figure BSA0000160008770000101
and sorting all cluster head nodes in a descending order according to the distance from the node sin k, so as to search a next hop node for the cluster head node farthest from the node sin k until an inter-cluster route from the node sin k to the node sin k is formed. And then searching a next hop node for the node with the farthest distance remained outside the hot area until forming an inter-cluster route from the node to the sin k node. And repeating the loop until all cluster head nodes have definite routes.
When the next hop node is searched for by the cluster head node i in the 5 th behavior of the algorithm, the requirement is met
Figure BSA0000160008770000102
The node j of the condition is a next hop alternative node, wherein di_jDistance from cluster head node i to cluster head node j, dj_sin kDistance from cluster head node j to sin k node, di_sin kThe distance from the cluster head node i to the sin k node. And (3) calculating an F judgment value for each alternative node by using a formula (18), sequencing in a descending order, and selecting the node j corresponding to the maximum F judgment value as the determined next hop node.
And selecting a proper node from the candidate next hop node j, wherein the current real-time residual energy needs to be considered, the node with large energy needs not to be considered, and the forwarding times of other cluster head nodes need to be considered. If the larger the energy is, the less the number of times of the help forwarding is, the candidate node is the next hop node, and then the judgment value is:
Figure BSA0000160008770000103
ER(j)＝lEelecNumCH(j) (19)
EF(j)＝lEfuseNumCH(j) (20)
wherein, F (j) is a judgment value for judging whether the candidate next hop node j is suitable, each candidate node calculates a value, the values are sorted in a descending order, the node with the largest judgment value is selected as the next hop node, E (j) is the residual energy of the cluster head node j, ER(j) The energy consumed by collecting all intra-cluster node data for cluster head node j is shown in formula (19), EF(j) The energy consumed by performing data fusion on the collected intra-cluster node data for the cluster head node j is shown in formula (20), and the data length is lbit and NumCH(i) Number of nodes in a cluster belonging to a cluster head node i, NjIn order to record the forwarding times, lambda, of the cluster head node j helping other cluster head nodes to forward1As a parameter, a constant Eelec＝50nJ/bit，Efuse＝5nJ/bit。
For example, the following steps are carried out: the inter-cluster multi-hop routing algorithm also considers and solves the cooperative competition relationship among the nodes. As shown in example 5 of fig. 7, nodes 2, 3, 4 may be candidate next hop nodes for node 1 and node 5, and if node 1 and node 5 both select node 2 as a cooperative node according to formula (18), would a contention relationship occur? According to the multi-hop routing algorithm among clusters, the following steps are known: the next hop cooperative node is first found for the node 1 farthest from sin k until the inter-cluster route from the node to the sin k node is formed. And then searching the next hop cooperative node for the node 5 until forming an inter-cluster route from the node to the sin k node. Because of the ordering, consideration of the competition relationship is involved.
And equation (18) takes into account N when finding the next hop cooperative node according to equation (18)j(it is to record the number of times that the cluster head node j helps other cluster head nodes to forward), the larger the energy is, the fewer the number of times that the backup node helps to forward is the next hop node. Because the number of assisted forwarding is considered, the contention relationship is also considered.
The possibility that node 2 is a cooperative node of both node 1 and node 5 is only: after node 2 has helped node 1, it is reasonable that node 2 acts as a cooperative node for node 5, again in accordance with equation (18) to be preferred over nodes 3 and 4.
5: energy consumption calculation
Because the nodes in the cluster only need to send the acquired data to the cluster head node, the energy consumption of each node in the cluster is as follows:
Figure BSA0000160008770000111
wherein d isjThe distance from a node j in a cluster to a cluster head node thereof, the data length of lbit and a constant Eelec＝50nJ/bit，εfs＝10pJ/(bit·m2)。
And the cluster head nodes need to fuse the data of all the nodes in the cluster, so that the comprehensive data is transmitted. The energy consumption of each cluster head node is as follows:
ECH(i)＝ER(i)+EF(i)+ETx(i) (22)
ER(i)＝lEelecNumCH(i) (23)
EF(i)＝lEfuseNumCH(i) (24)
Figure BSA0000160008770000121
wherein E isR(i) Collecting the energy consumed by all intra-cluster node data for cluster head node i, EF(i) Energy, Num, consumed by data fusion of the collected intra-cluster node data for the cluster head node iCH(i) The number of in-cluster nodes belonging to a cluster head node i, ETx(i) As a cluster head nodei energy consumption to transmit lbit data to other cluster head nodes or sin k nodes. EelecLoss energy for transmitting and receiving unit bit information; if the transmission distance is less than the threshold d0The power amplifier loss adopts a free space model; if the transmission distance is greater than the threshold value d0Then a multipath fading model is used. EpsilonfsAnd εampThe energy required by the transmission amplifier per unit bit of information transmitted in the two models, d0And d is the distance between two nodes. d0The smaller the probability of adopting the multipath fading model is, the larger the energy consumption is, and the time for the network to live and maintain the normal routing performance is shortened.
The specific implementation steps of the clustering algorithm for energy consumption balance in the wireless sensor network are described as follows:
1) and selecting a proper cluster head according to a cluster head selection method.
2) And clustering the nodes in the network according to the clustering method.
3) And forming an inter-cluster cooperative transmission route according to an inter-cluster multi-hop routing algorithm.
4) And (3) judging whether the hot area problem exists, namely setting a gate threshold value of transmission data quantity for the nodes in the hot area, and when the transmission data quantity of the nodes exceeds the gate threshold value, judging that the hot area problem exists and performing the step 5), or else, performing the step 6).
5) The hot zone problem is solved, namely the cluster node with the largest energy is used as a candidate node to transmit data exceeding the threshold value of the gate.
6) According to the multi-hop route between clusters, the data transmission between the nodes is carried out
7) And calculating the energy consumption of the nodes in the cluster and the cluster head nodes, and updating the residual energy of the nodes in the network.
8) And counting the total energy consumption of the network and the number of the surviving nodes in the network, and adding 1 to the number of rounds.
9) And judging whether a survival node exists or not, if so, performing the step 10), and if not, ending the simulation.
10) And calculating the number of cluster head nodes with energy failure in the network.
11) If the number of the cluster heads with energy failure is less than or equal to 3, performing the step 12), otherwise, performing the step 1).
12) And (4) selecting and switching cluster heads, namely selecting the cluster nodes with the maximum energy in the cluster to replace the original cluster heads for the clusters with the energy failure of the cluster heads, and continuously finishing the work of the original cluster heads. Followed by step 3).
The simulation environment is as follows: the 100 nodes are randomly and evenly distributed in the 100 x 100 region, and the sin k node is located at (100, 50), as shown in fig. 8. The energy of 100 nodes is [10 muJ, 120 muJ ]]And (4) randomly distributing. The length of the data packet is 1bit, Efuse＝5nJ/bit，d0＝87m，Eelec＝50nJ/bit；εfs＝10pJ/(bit·m2)；εamp＝0.0013pJ/(bit·m4)，EM，i120 muJ, inter-cluster multihop critical distance dlin60m, parameter α1＝β1＝α2＝β2＝0.5，λ1＝1。
In the simulation process, the total energy consumption of the network is an important index for evaluating the clustering algorithm of the wireless sensor network. The method is abbreviated as EBCR, and the total energy consumption of the network and the DCEM algorithm is shown in figure 9, so that the total energy consumption of network nodes is lower than that of the DCEM in each round of operation of the EBCR, the life cycle of the network is obviously longer than that of the DCEM, and the life cycle of the network is effectively prolonged. The number of surviving nodes of DCEM and EBCR is shown in FIG. 10, and the comparison table of the number of death rounds of nodes is shown in the following table. It can be seen that the EBCR, due to the optimization in the clustering mode and the inter-cluster routing, makes the network node load balanced, does not generate a node failure too early, and the network life cycle is significantly prolonged.
Node death round number meter
Figure BSA0000160008770000141

Claims (3)

1. A clustering algorithm for energy consumption balance in a wireless sensor network is characterized by comprising the following steps:
1) selecting a proper cluster head according to a cluster head selection method;
2) clustering nodes in the network according to a clustering method;
3) forming inter-cluster cooperative transmission routes according to an inter-cluster multi-hop routing algorithm;
4) judging whether a hot area problem exists, namely setting a gate threshold value of transmission data quantity for nodes in a hot area, and when the transmission data quantity of the nodes exceeds the gate threshold value, judging that the hot area problem exists, and performing the step 5), or else, performing the step 6);
5) solving the problem of a hot area, namely enabling the cluster node with the largest energy to serve as a candidate node and transmitting data exceeding a threshold part;
6) carrying out data transmission among nodes according to the inter-cluster multi-hop route;
7) calculating the energy consumption of the nodes in the cluster and the cluster head nodes, and updating the residual energy of the nodes in the network;
8) counting total energy consumption of the network and the number of surviving nodes in the network, and adding 1 to the number of turns;
9) judging whether a survival node exists, if so, performing the step 10), and if not, ending the simulation;
10) calculating the number of cluster head nodes with energy failure in the network;
11) if the number of the cluster heads with energy failure is less than or equal to 3, performing step 12), otherwise, performing step 1);
12) and (3) selecting and switching cluster heads, namely selecting the cluster nodes with the maximum energy in the cluster to replace the original cluster heads for the clusters with the energy failure of the cluster heads, and performing the step 3) after the work of the original cluster heads is continuously completed.
2. The clustering algorithm for energy consumption balancing in the wireless sensor network according to claim 1, wherein: the cluster head selecting method in the step 1) comprises the steps of dynamically searching a radius calculation formula; wherein the radius r is dynamically searchedchThe formula of (1) is:
Figure FSB0000191359020000011
in the formula: di_sin kIs the distance from any cluster head node i to the sin k node, dmaxFor the distance of the selected cluster head node from the sin k node in the cluster head selection cycle, EiBeing the remaining energy of the cluster head node, EM，iTo represent the maximum battery capacity, d0For transmission threshold, alpha, in a radio channel model1And beta1Are parameters.
3. The clustering algorithm for energy consumption balancing in the wireless sensor network according to claim 1, wherein: the clustering method and the calculation formula of the judgment value f in the step 2); the clustering method comprises the following steps: the distance from a cluster head node, the residual energy of the cluster head node and the distance from the cluster head node to a sin k node are considered when clustering is carried out on the nodes positioned in the cluster intersection area; if cluster head node i1And i2Distance from sin k node is not more than rchThe distance between the cluster head node j and the sin k node is larger than rchThen cluster head node i1And i2The nodes in the areas respectively intersected with the cluster head nodes j are classified as cluster nodes of the cluster head nodes j; in cluster head node i1And i2F values are respectively calculated for the nodes in the intersected areas, and the nodes in the intersected areas become cluster nodes of cluster head nodes with large f values; if the distances from the cluster head nodes to the sin k node are all larger than rchRespectively calculating f values by the nodes in the intersection area, wherein the nodes in the intersection area become cluster nodes of cluster head nodes with large f values;
the calculation formula of the f value is as follows:
Figure FSB0000191359020000021
in the formula (f)m_jAdaptation value, E, of node m to cluster head node j representing intersection areajRepresents the remaining energy of cluster head node j, dm_jDistance, alpha, from node m to cluster head node j representing intersection area2And beta2The weight value can be adjusted according to needs.
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