CN112203299A - Wireless sensor network clustering safe routing method based on improved GA - Google Patents

Wireless sensor network clustering safe routing method based on improved GA Download PDF

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CN112203299A
CN112203299A CN202010659586.8A CN202010659586A CN112203299A CN 112203299 A CN112203299 A CN 112203299A CN 202010659586 A CN202010659586 A CN 202010659586A CN 112203299 A CN112203299 A CN 112203299A
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cluster head
trust value
chromosome
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CN112203299B (en
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王出航
刘晓理
赵宏伟
韩优佳
胡黄水
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Changchun Normal University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/06—Testing, supervising or monitoring using simulated traffic
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • 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
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • 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 wireless sensor network security Routing method, in particular to a wireless sensor network Clustering security Routing method SCRGA (a trust-aware Secure Clustering Routing protocol for wireless sensor network using an enhanced Genetic Algorithm) based on improved GA. The method uses the same chromosome for coding cluster head election and route search, constructs a fitness function based on the purposes of maximum cluster head comprehensive trust value, minimum whole network energy consumption and load balance, defines constraint conditions during genetic operation to avoid unreasonable individuals, improves convergence speed, improves energy efficiency and load balance, and effectively prolongs the life cycle on the basis of ensuring network safety.

Description

Wireless sensor network clustering safe routing method based on improved GA
Technical Field
The invention relates to a wireless sensor network security Routing method, in particular to a wireless sensor network Clustering security Routing method SCRGA (a trust-aware Secure Clustering Routing protocol for wireless sensor network using an enhanced Genetic Algorithm) based on improved GA. The method uses the same chromosome for coding cluster head election and route search, constructs a fitness function based on the purposes of maximum cluster head comprehensive trust value, minimum whole network energy consumption and load balance, defines constraint conditions during genetic operation to avoid unreasonable individuals, improves convergence speed, improves energy efficiency and load balance, and effectively prolongs the life cycle on the basis of ensuring network safety.
Background
With the rapid development of the internet of things and intelligent manufacturing places, the wireless sensor network is widely applied to the fields of military affairs, environmental monitoring, space exploration and the like. In order to effectively extend the life cycle of the wireless sensor network, a lot of research proves that the method is very effective by organizing nodes through clustering routes and transmitting data to a base station. However, due to the large scale and dynamic nature of wireless sensor networks, routes are vulnerable to attacks from malicious nodes both outside and inside the network. Therefore, the design of the secure clustering routing protocol becomes a research hotspot of the wireless sensor network.
Encryption and identity authentication are effective methods for improving the security of clustering routing, but such algorithms are complex in calculation and high in energy consumption, and can only resist external attacks generally. And the safe clustering routing based on trust perception can identify malicious nodes, resist internal attack and improve the network safety. Different from the non-clustering safe routing algorithm, all nodes participate in routing calculation, and the clustering safe routing only selects the nodes with high trust degree in the network to participate in the optimal path search, so that the network energy efficiency is improved while the safety is ensured. However, the existing routing security algorithm selects a cluster head through local decision and selects the next hop of the cluster head, and the optimal cluster head and path cannot be found. In addition, the existing algorithm usually does not consider the node load in the trust value calculation, cluster head election and routing, so that the network load is unbalanced, and the network life cycle is reduced. The genetic algorithm has good global searching capability and is often used for finding the optimal cluster head or route. However, the basic genetic algorithm has the defects of low convergence rate, easy falling into local optimization and the like.
Disclosure of Invention
Aiming at the problems of insufficient overall security and unbalanced energy consumption and load caused by local decision of the existing trust perception wireless sensor network security routing protocol, the wireless sensor network trust perception security routing protocol SCRGA based on the improved genetic algorithm is provided. The invention is divided into two parts, namely trust evaluation and improved genetic algorithm way finding. Trust evaluation is the evaluation of the security of neighbors based on the behavior of their neighbors. And the node calculates a new comprehensive trust value according to the direct trust value, the indirect trust value, the volatilization factor and the residual energy. And evaluating the security of the node by using the comprehensive trust value, wherein the higher the comprehensive trust value is, the more secure the node is. The improved genetic algorithm routing is to ensure that the nodes can find safe and reliable routes, and meanwhile, the energy consumption of the routes is low. The method comprises the steps of constructing a corresponding fitness function by taking the maximum comprehensive trust value, the minimum network energy consumption and the load balance as targets, coding cluster head selection and routing search by using a single chromosome, and forming an optimized next generation through improved genetic operation, so that an optimal cluster head set and an optimal routing path of each cluster head are found.
The trust evaluation comprises calculating a direct trust value, calculating an indirect trust value and calculating a comprehensive trust value. As with the traditional secure routing algorithm, the comprehensive trust value of the node is the basis for participating in cluster head election and path search, the node with the large comprehensive trust value has higher security, and the node is more likely to be selected as a cluster head and a relay node. The node calculates the direct trust value of the node to each neighbor node according to the number of the data packets received and sent by the neighbor node, and simultaneously defines a volatility factor for ensuring the accuracy of the direct trust value, so as to quickly reduce the trust value of a malicious node captured from a common node with a high trust value. The indirect trust value of the node i to the node j is comprehensively calculated by the direct trust values provided by the public trusted neighbor nodes of the node i, so as to avoid the malicious evaluation of a certain node. And the comprehensive trust value of the node is obtained by integrating the calculated direct trust value and the indirect trust value and is used as the security evaluation standard of the final node.
The improved genetic algorithm is different from the traditional genetic algorithm in route searching, but cluster head election and path search are carried out simultaneously, in order to improve convergence speed, improvement is carried out on aspects of chromosome coding, selection, crossing and mutation operations and termination conditions, particularly a fitness function based on comprehensive trust value, energy consumption and load is constructed, so that the selected cluster heads and paths have high comprehensive trust, minimum energy consumption and balanced load, and the life cycle of the network is effectively prolonged.
Drawings
FIG. 1 is a schematic diagram of cluster head selection and route search chromosomes according to the present invention;
FIG. 2 is a schematic diagram of a two-point crossover of chromosomes according to the present invention;
FIG. 3 is a schematic flow chart of the improved genetic algorithm of the present invention
Fig. 4 is a schematic diagram of network packet loss ratios under different malicious nodes according to the present invention;
fig. 5 is a schematic diagram of the network residual energy of the present invention.
Detailed Description
The invention is further described in detail with reference to the accompanying drawings, and the secure routing method SCRGA based on the improved GA trust clustering of the wireless sensor network of the invention is divided into two parts, namely trust evaluation and improved genetic algorithm routing. Trust evaluation is the evaluation of the security of neighbors based on the behavior of their neighbors. And the node calculates a new comprehensive trust value according to the direct trust value, the indirect trust value, the volatilization factor and the residual energy. And evaluating the security of the node by using the comprehensive trust value, wherein the higher the comprehensive trust value is, the more secure the node is. The improved genetic algorithm route searching aims to ensure that the nodes can search safe and reliable routes, and meanwhile, the energy consumption of the routes is low. The method comprises the steps of constructing a corresponding fitness function by taking the maximum comprehensive trust value, the minimum network energy consumption and the load balance as targets, coding cluster head selection and routing search by using a single chromosome, and forming an optimized next generation through improved genetic operation, so that an optimal cluster head set and an optimal routing path of each cluster head are found.
The trust evaluation comprises calculating a direct trust value, calculating an indirect trust value and calculating a comprehensive trust value. The direct trust value is calculated based on the number of the data packets received and sent by the neighbor node, and the direct trust value of the node i to the neighbor node j can be expressed as follows:
Figure RE-GDA0002694362970000031
where the former represents the historical trust value and the latter represents the current trust value. γ and 1- γ (0 < γ < 1) are weights for the historical confidence value and the current confidence value, respectively, whose values are sized according to the particular WSN, where γ is made 0.5 for fairness. RtAnd StThe ratio of the number of transmitted and received data packets to the total number of data packets, respectively, can be expressed as:
Figure RE-GDA0002694362970000032
Figure RE-GDA0002694362970000033
furthermore, a volatility factor is defined, which aims to quickly reduce the trust value of a normal node captured as a malicious node from a high trust value. The volatilization factors are expressed as follows:
Figure RE-GDA0002694362970000034
Figure RE-GDA0002694362970000035
where T is the current time of the network and τ is the time threshold. c. C1And c2Are constants used for adjusting the change speed of the trust value; furthermore, mod (T, τ) is introduced) So as to ensure that the historical trust value is not too small and the volatilization factor is periodically attenuated within a certain range.
The indirect trust values are computed from direct trust values provided by their common neighbor nodes. The common neighbor node set can use Bh＝[B1，B2，...，Bm]It means that m is the number of common nodes. The indirect trust value of node i to node j is expressed as:
Figure RE-GDA0002694362970000041
wherein
Figure RE-GDA0002694362970000043
Is the direct trust value of node i to node u,
Figure RE-GDA0002694362970000044
is the direct trust value of node u to node j, and node u is the trusted public neighbor node of node i and node j. Further, a set of trusted public neighbors is defined as NTh＝[NT1，NT2，...，NTQ]Q is less than or equal to m, if the trust value of the node i to the node u is less than the threshold THNOWThen node u is deleted from the common neighbor node. Text THNOWThe value of (d) is set to 0.35.
The calculation of the integrated trust value is integrated by equations (1) and (6):
Figure RE-GDA0002694362970000042
wherein p is the number of neighbor nodes of the node i.
The improved genetic algorithm path searching specifically comprises the steps of constructing a fitness function and genetic operation. The fitness function is constructed for evaluating the individual quality, and the following variables are defined firstly:
n: the number of network nodes is such that,
·H＝{h1，h2，...，hk}: set of cluster heads, M ═ M1，m2，...，mq}: a set of members. Then there is k + q ═ n and for computational convenience, the base station is denoted as hk+1。
·dij: distance between nodes i and j, and dmaxRepresenting the maximum value thereof.
·
Figure RE-GDA0002694362970000053
Member miA candidate cluster head set of
Figure RE-GDA0002694362970000054
Is miThe cluster head of (1).
·
Figure RE-GDA0002694362970000055
Cluster head hiIs selected, and
Figure RE-GDA0002694362970000056
is hiNext hop cluster head. In addition to this, the present invention is,
Figure RE-GDA0002694362970000057
indicates cluster head hiAs the number of relays.
Ld: load of node
·Einitial: the initial energy of the node is such that,
Figure RE-GDA0002694362970000058
representing the remaining energy of node i.
Node i sends l bits of data to node j, which consumes energy as:
Figure RE-GDA0002694362970000051
wherein EelecRepresenting the energy consumed by transmitting or receiving 1-bit data, ∈fsAnd εmpAmplifier under model of representing free space and multipath attenuation respectivelyCoefficient, dijIs the distance between nodes i and j,
Figure RE-GDA0002694362970000052
further, the energy consumed to receive the l-bit data is:
ERij＝l*Eelec (9)
the energy consumed to fuse l bits of data is:
EDA＝l*EpDb (10)
wherein EpDbIs the power consumption for fusing 1-bit data. From equations (8), (9) and (10), the energy consumed by each cluster head node and member node can be calculated as:
Figure RE-GDA0002694362970000061
Figure RE-GDA0002694362970000062
the energy consumption of the entire network can then be found as:
Figure RE-GDA0002694362970000063
one of the objectives of SCRGA is to minimize EtotalIt can be seen from equation (13) that it simultaneously uniformly distributes clusters because it takes into account both inter-cluster and intra-cluster energy minima. The loads of member nodes in the clustered network are all the same, so the network load balancing only considers the cluster head, and is expressed as:
Figure RE-GDA0002694362970000064
wherein
Figure RE-GDA0002694362970000065
Respectively represent cluster heads hiLoad of and leveling of all cluster headsAre all loaded. Another goal of the network is to minimize LBCHsTo balance the network load.
In addition, in order to accelerate algorithm convergence, the node is considered to participate in cluster head election only when the self residual energy and the comprehensive trust value of the node are larger than the network average residual energy and the network average comprehensive trust value. That is, for an arbitrary hiSatisfies the following conditions:
Figure RE-GDA0002694362970000066
in addition, in order to find a routing path with high reliability, the average comprehensive trust value of the whole cluster head is considered and is expressed as:
Figure RE-GDA0002694362970000071
the third objective of the network is to secure the cluster heads and routes, and therefore, construct the fitness function as follows:
Figure RE-GDA0002694362970000072
the function is used for finding the optimal cluster head and the optimal path which are safe, reliable, high in energy efficiency and balanced in load.
The genetic operation adopts real number coding to represent chromosomes, and cluster head selection and route searching are represented by one chromosome, so that the algorithm efficiency is improved while the chromosome length is not increased, namely, the front part of the chromosome represents route searching, and the rear part represents cluster head selection, as shown in figure 1. As can be seen from the figure, k (k ═ 6) cluster heads 9, 23,48,69,81,95 are arbitrarily selected from 100 nodes according to equation (15), and thus a candidate cluster head set of each member node and a next hop candidate cluster head set of each cluster head can be obtained according to variable definitions. The individual genes in the chromosome were calculated according to the following formula:
Figure RE-GDA0002694362970000073
respectively obtaining the cluster head of each member node, wherein the cluster head of the member 1 is 9, the cluster head of the member 2 is 48, …, the cluster head of the member 100 is 48 and the like. Similarly, the routing path from each cluster head to the base station, {98123 BS }, {23BS }, {4895BS }, {694895101}, {8123BS }, {95BS }, respectively, can be obtained. As the genes of the chromosome are constrained, the reasonability of the chromosome is ensured and the convergence of the algorithm is improved. Thus, the initial population can be obtained.
Then, for each chromosome in the initial population, fitness values are calculated according to equation (17) and arranged from small to large. Adopting an elite selection strategy to select 15 percent of elite individuals to be directly inherited to the next generation of population, and performing other cross operations. Since the chromosome is composed of two parts, the child chromosome is generated by two-point crossing, namely, one crossing point is randomly determined in the route searching and cluster head selecting parts of the parent chromosome, and the genes between the crossing points are exchanged, as shown in fig. 2. It can be seen from the figure that the generated child chromosomes are also reasonable chromosomes, and then the fitness values of the two generated child chromosomes are respectively calculated according to the formula (17) and compared with the corresponding fitness value of the parent chromosome, and the two generated child chromosomes are used for mutation operation with small fitness. Then, mutation operation is carried out on each chromosome by adopting the site mutation, namely, a certain position gene is randomly selected and modified into a new gene, and the new gene is generated by adopting the formula (18) so as to ensure the reasonability of the new chromosome. The generated new chromosome is calculated by adopting the formula (17) and compared with the father chromosome, and the value is small and is passed to the next generation. Thus, all chromosomes remaining after mutation operations combine with elite chromosomes to form a new generation of population. If the number of iterations reaches a preset value or the following condition is met, the algorithm terminates.
Figure RE-GDA0002694362970000081
Wherein omega is the size of the population, epsilon is an arbitrarily small positive number for indicating the individual difference of the population, and 10 is taken-5. Once the algorithm terminates, in the populationAnd the individual with the minimum fitness value is the optimal solution, and the optimal solution is added into the initial population of the next round to accelerate the convergence of the algorithm. The flow is shown in fig. 3.
To verify the performance of SCRGA, it was subjected to simulation testing in the MATLAB environment and comparative analysis with TC-SRA and SRBNT. 100 nodes in the network are randomly distributed in a target area of 100m × 100m, a base station is located in the center (50m ), and meanwhile, 10 malicious nodes are distributed in the network, and specific simulation parameter settings are shown in table 1.
TABLE 1 simulation parameters
Figure RE-GDA0002694362970000082
Fig. 4 shows packet loss ratios of the 3 methods under different numbers of malicious nodes. With the increase of the number of malicious nodes, the packet loss rates of the 3 methods all show a rising trend. As can be seen from fig. 4, the packet loss rate of the SCRGA is lower than that of the TC-SRA, which is averagely reduced by 15.98%, mainly because the SCRGA considers the influence of the historical trust value, and introduces the volatility factor to reduce the effect of the historical trust value on the comprehensive trust value of the node, thereby improving the speed of detecting the malicious node. Compared with the SRBNT, the packet loss rate of the SCRGA is reduced by 19.44% on average, because the time factor of the SRBNT is a predetermined fixed value, and the time factor may not be suitable for the trust evaluation function of the network as the network operates. Therefore, the safety performance of SCRGA is better than that of TC-SRA and SRBNT.
For a wireless sensor network with limited resources, the residual energy of the network is one of important indexes for evaluating the excellent performance of the network. Fig. 5 shows the network energy versus network energy for the 3 algorithms. As the network operates, the remaining energy of the network gradually decreases. The slope of the curves for SRBNT and TC-SRA is steeper, indicating that the network consumes more energy. The reason why the residual energy of the SCRGA network is more than that of the SRBNT and the TC-SRA is that when the SCRGA designs the fitness function of the genetic algorithm, the comprehensive trust value of the node is considered, and two indexes of load borne by the CH and network energy consumption are also considered, so that the load borne by each CH is reasonably planned, and the energy efficiency of the network is improved.
The invention discloses a secure routing method SCRGA of wireless sensor network trust clustering based on improved GA, which aims at maximizing average comprehensive trust value, minimizing network energy consumption and balancing network load, and adopts a genetic algorithm to search a global optimal solution to obtain a whole network optimal cluster head set and an optimal routing path. Simulation results show that the SCRGA is superior to TC-SRA and SRBNT in the aspects of energy consumption, packet loss rate and network delay, and has good defense effect on black hole attack, selective forwarding attack, Hello flooding attack and slot hole attack.

Claims (1)

1. A wireless sensor network clustering safe routing method SCRGA based on improved GA is characterized by comprising two parts of trust evaluation and improved genetic algorithm routing; the trust evaluation is to evaluate the security of the neighbor according to the behavior of the neighbor node of each node; the node calculates a new comprehensive trust value according to the direct trust value, the indirect trust value, the volatilization factor and the residual energy; evaluating the security of the node by using the comprehensive trust value, wherein the higher the comprehensive trust value is, the safer the node is; the improved genetic algorithm route searching aims to ensure that the nodes can search safe and reliable routes, and meanwhile, the energy consumption of the routes is low; constructing a corresponding fitness function by taking the maximum comprehensive trust value, the minimum network energy consumption and the load balance as targets, coding cluster head selection and routing search by using a single chromosome, and forming an optimized next generation through improved genetic operation so as to find an optimal cluster head set and an optimal routing path of each cluster head;
the trust evaluation comprises calculating a direct trust value, calculating an indirect trust value and calculating a comprehensive trust value; the direct trust value is calculated based on the number of the data packets received and sent by the neighbor node, and the direct trust value of the node i to the neighbor node j can be expressed as follows:
Figure FDA0002576568620000011
where the former represents historical trust valuesThe latter representing the current trust value; γ and 1- γ (0 < γ < 1) are weights for the historical confidence value and the current confidence value, respectively, whose values are sized according to the particular WSN, where for fairness herein γ is made 0.5; rtAnd StThe ratio of the number of transmitted and received data packets to the total number of data packets, respectively, can be expressed as:
Figure FDA0002576568620000012
Figure FDA0002576568620000013
in addition, a volatilization factor is defined, and the purpose is to quickly reduce the trust value of a malicious node captured from a common node with a high trust value; the volatilization factors are expressed as follows:
Figure FDA0002576568620000014
Figure FDA0002576568620000015
where T is the current time of the network and τ is a time threshold; c. C1And c2Are constants used for adjusting the change speed of the trust value; in addition, mod (T, τ) is introduced to ensure that historical confidence values are not too small and that volatility factors decay periodically over a range;
the indirect trust value is calculated by the direct trust value provided by the public neighbor nodes; the common neighbor node set can use Bh＝[B1，B2，...，Bm]Representing that m is the number of common nodes; the indirect trust value of node i to node j is expressed as:
Figure FDA0002576568620000016
wherein
Figure FDA0002576568620000021
Is the direct trust value of node i to node u,
Figure FDA0002576568620000022
is the direct trust value of node u to node j, node u is the trusted public neighbor node of node i and node j; further, a set of trusted public neighbors is defined as NTh＝[NT1，NT2，...，NTQ]Q is less than or equal to m, if the trust value of the node i to the node u is less than the threshold THNOWIf yes, deleting the node u from the public neighbor node; text THNOWIs set to a value of 0.35;
the calculation of the integrated trust value is integrated by equations (1) and (6):
Figure FDA0002576568620000023
wherein p is the number of neighbor nodes of the node i.
The improved genetic algorithm path-finding model specifically comprises a constructed fitness function and genetic operation; constructing a fitness function in order to evaluate the individual quality, the following variables are defined:
n: the number of network nodes is such that,
·H＝{h1，h2，...，hk}: set of cluster heads, M ═ M1，m2，...，mq}: a set of members; then there is
k + q ═ n, and for computational convenience, the base station is denoted as hk+1；
·dij: distance between nodes i and j, and dmaxRepresents the maximum value thereof;
·
Figure FDA0002576568620000024
member miA candidate cluster head set of
Figure FDA0002576568620000025
Is miThe cluster head of (a);
·
Figure FDA0002576568620000026
cluster head hiIs selected, and
Figure FDA0002576568620000027
is hiNext hop cluster head; in addition to this, the present invention is,
Figure FDA0002576568620000028
indicates cluster head hiAs the number of relays;
ld: load of node
·Einitiai: the initial energy of the node is such that,
Figure FDA0002576568620000029
representing the remaining energy of node i;
node i sends l bits of data to node j, which consumes energy as:
Figure FDA00025765686200000210
wherein EelecRepresenting the energy consumed by transmitting or receiving 1-bit data, ∈fsAnd εmpRepresenting the amplifier coefficients in free space and multipath fading models, respectively, dijIs the distance between nodes i and j,
Figure FDA0002576568620000031
further, the energy consumed to receive the l-bit data is:
ERij＝l*Eelec (9)
the energy consumed to fuse l bits of data is:
EDA＝l*EpDb (10)
wherein EpDbEnergy consumption for fusing 1-bit data; from equations (8), (9) and (10), the energy consumed by each cluster head node and member node can be calculated as:
Figure FDA0002576568620000032
Figure FDA0002576568620000033
the energy consumption of the entire network can then be found as:
Figure FDA0002576568620000034
one of the objectives of SCRGA is to minimize EtotaiIt can be seen from equation (13) that it simultaneously uniformly distributes clusters because it takes into account both inter-cluster and intra-cluster energy minima; the loads of member nodes in the clustered network are all the same, so the network load balancing only considers the cluster head, and is expressed as:
Figure FDA0002576568620000035
wherein
Figure FDA0002576568620000036
Respectively represent cluster heads hiAnd the average load of all cluster heads; another goal of the network is to minimize LBCHsTo balance the network load;
in addition, in order to accelerate algorithm convergence, the nodes are considered to participate in the cluster head only when the self residual energy and the comprehensive trust value of the nodes are larger than the network average residual energy and the network average comprehensive trust valueElecting; that is, for an arbitrary hiSatisfies the following conditions:
Figure FDA0002576568620000037
in addition, in order to find a routing path with high reliability, the average comprehensive trust value of the whole cluster head is considered and is expressed as:
Figure FDA0002576568620000038
the third objective of the network is to secure the cluster heads and routes, and therefore, construct the fitness function as follows:
Figure FDA0002576568620000041
finding out the optimal cluster head and the optimal path which are safe, reliable, high in energy efficiency and balanced in load through the function;
the genetic operation adopts real number coding to represent chromosomes, and cluster head selection and route search are represented by one chromosome, so that the algorithm efficiency is improved without increasing the chromosome length, namely, the front part of the chromosome represents route search, and the rear part represents cluster head selection; assuming that the network has n nodes (n equals to 100), k (k equals to 6) cluster heads 9, 23,48,69,81,95 are arbitrarily selected from the 100 nodes according to equation (15), so that a candidate cluster head set of each member node and a next hop candidate cluster head set of each cluster head can be obtained according to variable definition; the individual genes in the chromosome were calculated according to the following formula:
Figure FDA0002576568620000042
respectively obtaining the cluster head of each member node, wherein the cluster head of the member 1 is 9, the cluster head of the member 2 is 48, …, the cluster head of the member 100 is 48 and the like; similarly, the routing path from each cluster head to the base station can be obtained, 9 → 81 → 23 → BS, 48 → 95 → BS, 69 → 48 → 95 → 101, 81 → 23 → BS, 95 → BS; because the genes of the chromosome are constrained, the rationality of the chromosome is ensured, and the algorithm convergence is improved; thus obtaining an initial population;
then, respectively calculating the fitness value of each chromosome in the initial population according to a formula (17), and arranging the fitness values from small to large; selecting 15% of elite individuals by adopting an elite selection strategy to be directly inherited to a next generation population, and performing other cross operations; because the chromosome is composed of two parts, two-point crossing is adopted to generate a child chromosome, namely, a crossing point is randomly determined at the routing search and cluster head election parts of a father chromosome respectively, genes between the crossing points are exchanged, the generated child chromosome is also a reasonable chromosome, then the fitness values of the two generated child chromosomes are respectively calculated according to the formula (17) and are compared with the corresponding father chromosome fitness values, and the high fitness is used for mutation operation; then carrying out mutation operation on each chromosome by adopting the site mutation, namely randomly selecting a certain position gene to modify the position gene into a new gene, and generating the new gene by adopting a formula (18) so as to ensure the rationality of the new chromosome; calculating the fitness value of the generated new chromosome by adopting a formula (17), comparing the fitness value with the parent chromosome, and transmitting the large value to the next generation; thus, all chromosomes reserved after mutation operation are combined with elite chromosomes to form a new generation of population; if the iteration times reach a preset value or the following conditions are met, the algorithm is terminated;
Figure FDA0002576568620000043
wherein omega is the size of the population, epsilon is an arbitrarily small positive number for indicating the individual difference of the population, and 10 is taken-5(ii) a Once the algorithm is terminated, the individual with the minimum fitness value in the population is the optimal solution, and the optimal solution is added into the initial population of the next round to accelerate the convergence of the algorithm.
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