CN117377021B - Low-energy-consumption safe routing method based on dynamic trust perception and load balancing - Google Patents
Low-energy-consumption safe routing method based on dynamic trust perception and load balancing Download PDFInfo
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Abstract
The invention discloses a low-energy-consumption safe routing method based on dynamic trust perception and load balancing. The neural network is adopted to predict the dynamic trust degree of the network nodes, so that the speed and accuracy of malicious node detection are improved. The indexes such as the average dynamic trust degree of the cluster head nodes, the load of the cluster head nodes, the network energy consumption, the network life cycle and the like are comprehensively considered, and an accurate route evaluation model is established by using an analytic hierarchy process. The searching method of the low-energy-consumption safe route and the selection, crossing and mutation methods of the chromosome are designed in detail based on the genetic algorithm, so that an optimal cluster head node set and an optimal route path of each node are established rapidly. The routing protocol can obviously improve the detection speed and the detection success rate of malicious nodes, reduce the energy consumption and the load of the network, and effectively improve the safety and the energy conservation of the network.
Description
Technical Field
The invention relates to the technical field of wireless network routing security, in particular to a low-energy-consumption secure routing method based on dynamic trust perception and load balancing.
Background
A wireless sensor network (WSNs, wireless sensor networks) is a wireless network composed of a large number of sensor nodes deployed in a detection area, and is mainly used for collecting, analyzing and transmitting data information of a monitoring area to a base station. Wireless sensor nodes are susceptible to multiple factors such as power, bandwidth, and energy, and particularly routing attacks from malicious nodes when WSNs are randomly deployed in complex environments. Therefore, the wireless sensor network routing protocol needs to consider factors such as reliability, node energy consumption, load balancing and the like.
Prior art related to the invention
A safe clustering routing protocol [ J ] based on trust and energy consumption balance, beijing university post telegraph, 2019,42 (03): 29-36], adopts a fuzzy comprehensive judgment model and introduces various judgment factors to calculate direct trust, filters and weight distributes recommended trust according to deviation degree, and considers historical trust when calculating comprehensive trust. Meanwhile, a cluster head punishment coefficient is designed, so that the identification and isolation speeds of malicious cluster heads are increased. And the monitoring area is divided to carry out non-uniform clustering, so that the energy cavity phenomenon is relieved. And taking the node trust value, the residual energy and the data transmission distance as routing factors. And a clustering function and a forwarding function are designed, so that the probability of a malicious node participating in data transmission is reduced. The number and the energy consumption balance of the routed data packets are improved, and the safety and the reliability of the wireless sensor network are improved.
Shortcomings of the prior art
The WSNs network node trust value calculation directly uses the current information and the historical information, and cannot reflect the change trend of the lending node trust value; the evaluation model of the route is simple, only the trust value, the residual energy and the transmission distance of the node are considered, and the residual survival time of the node cannot be reflected well by adopting a method of manually giving weight; the cluster head selection and the node clustering of the route do not consider the optimization selection, so that the safety and the low-energy consumption characteristic of the route are affected to a certain extent.
Prior art II related to the invention
The wireless sensor network security routing protocol based on trust perception [ Korean optimal, hu Huangshui, yao Meiqin ] the wireless sensor network security routing protocol based on trust perception [ J ]. Computer engineering, 2021,47 (09): 145-152], calculates the comprehensive trust value of the neighbor node according to the new direct trust value, indirect trust value, volatile factor and residual energy to evaluate the security index of the node and rapidly identify and exclude malicious nodes which initiate black hole attack, selective forwarding attack, hello flooding attack and slot hole attack. Aiming at the worm hole attack which is difficult to find, the routing node calculates an optimal path according to the link quality, the transmission distance and the hop count of a plurality of links to ensure the safety and the energy conservation of the selected route.
Disadvantages of the second prior art
The WSNs routing model only considers the node trust value and the residual energy, the change trend of the node trust value and the change speed of the residual energy cannot be reflected, and a mode of manually giving weight is adopted when a plurality of parameters are combined into an evaluation model, so that the route establishment and maintenance process cannot optimize the whole route in time according to the node trust value and the residual survival time, and the safety and energy conservation of the WSNs network route are affected.
Disclosure of Invention
Aiming at the problem of high requirements on the safety and energy conservation of a wireless sensor network, the invention provides a low-energy-consumption safe routing method based on dynamic trust perception and load balancing. The neural network is adopted to predict the dynamic trust degree of the network nodes, so that the speed and accuracy of malicious node detection are improved. The indexes such as the average dynamic trust degree of the cluster head nodes, the load of the cluster head nodes, the network energy consumption, the network life cycle and the like are comprehensively considered, and an accurate route evaluation model is established by using an analytic hierarchy process. The searching method of the low-energy-consumption safe route and the crossing and mutation method of the chromosome are designed in detail based on the genetic algorithm, so that an optimal cluster head node set and an optimal route path of each node are established rapidly. The routing protocol can obviously improve the detection speed and the detection success rate of malicious nodes, reduce the energy consumption and the load of a network and effectively prolong the life cycle of the network.
In order to achieve the above object, the present invention adopts the following technical scheme:
a low-energy-consumption secure routing method based on dynamic trust perception and load balancing comprises the following steps:
a Chebyshev neural network is employed in WSNs networks to predict dynamic trust of nodes.
The number of non-cooperations of the node is attenuated using the abnormal attenuation factor mu. By attenuating the number of incoordination times, the influence of external factors on the trust degree can be reduced, thereby improving the accuracy of trust evaluation.
The average dynamic trust of all cluster head nodes is used to measure the trust of the cluster head nodes in the network.
And calculating the load and the network energy consumption of each cluster head node to obtain the load and the energy consumption of the whole network.
And calculating the residual running time of all cluster head nodes to obtain the running time of the whole network.
And taking the average dynamic trust degree, load, network energy consumption and residual running time of the cluster head nodes as evaluation indexes. The cluster head node with the best performance is selected.
And constructing a routing performance evaluation model based on an analytic hierarchy process, wherein the analytic hierarchy process evaluates and analyzes the weights of all evaluation indexes, and establishes a performance index system of a routing path. And selecting a routing path with better performance through a routing performance evaluation model.
Constructing a fitness function and a path searching method of a route based on a genetic algorithm: genetic algorithms are used to construct fitness functions and path search methods for routes. The genetic algorithm selects the optimal routing path by optimizing the objective function of the route. Through crossing, mutation and selection operation of genes, a route path with better performance is searched.
Further, the dynamic trust of the node is predicted by adopting a Chebyshev neural network, and the formula is as follows:
in the method, in the process of the invention,dynamic trust prediction value representing node i
N: the size of the hidden layer is represented, representing the dimension in which the characteristics of the node are transformed in the hidden layer.
ω n : the weight of the nth neuron in the hidden layer is represented for weighted summation of the hidden layer outputs.
R n : representing the Chebyshev polynomial and representing the function of converting the input vector x. The nth hidden layer neuron will be transformed using the nth Chebyshev polynomial to obtain a hidden layerAnd (3) outputting the layer.
The integrated trust level of node i is represented as an element in the input vector x. It represents historical trust information of the node for predicting dynamic trust of the node.
Further, the average dynamic trust is obtained by:
in the method, in the process of the invention,representing the average dynamic trust of the cluster head nodes.
Representing summing all selected cluster head nodes.
Representing the dynamic trust level of the mth selected cluster head node.
M opt : indicating the number of selected cluster head nodes.
I: representing the total number of nodes in the entire network, i.e. the network scale.
Further, the load and energy consumption of the whole network are obtained by the following formula:
in the method, in the process of the invention,representing the load of the mth cluster head node, namely the total data traffic received by the cluster head nodeAnd, a method for producing the same.
R: representing the frequency at which each sensor node transmits data.
Load: representing the data traffic generated by a single sensor node.
M opt The maximum Load in each cluster head node is denoted as Load CH-max
Wherein E is total The energy consumption of the whole network is represented, namely the sum of the energy consumption of all cluster head nodes plus the sum of the energy consumption of all non-cluster head nodes.
Representing the energy consumption of the mth cluster head node.
Representing the power consumption of the nth non-cluster head node.
N live : representing the number of non-cluster head nodes.
Further, the run time of the entire network is derived from the following formula:
wherein E is res (h m ): representing a cluster head node h m Is a residual energy of (a);
representing the energy consumption of the mth cluster head node.
Minimum value of remaining run time of all cluster head nodesExpressed as:
further, a routing performance evaluation model based on an analytic hierarchy process is constructed, and the specific steps are as follows:
1) Building a comparison table: and comparing the relative weights among the indexes of each layer according to a nine-level scale method, and constructing a comparison table.
2) Basic judgment matrix: and forming a basic judgment matrix according to the comparison table.
3) Calculating characteristic roots and consistency indexes: calculating the maximum characteristic root lambda of the judgment matrix max And calculates a consistency index CI.
4) Calculating a consistency ratio: introducing an average random consistency index RI, calculating a consistency ratio CR, and judging consistency of the judgment matrix.
5) Calculating a weight vector: according to aω=λ max And calculating the weight vector to obtain an unnormalized weight vector omega.
6) Normalized weight vector: and normalizing the weight vector omega to obtain a normalized weight vector.
7) Route performance evaluation model: and calculating a routing performance evaluation model Eva according to the standardized weight vector.
Further, the fitness function and path searching method for constructing the route based on the genetic algorithm comprises the following specific steps:
step 1: chromosome coding and initial population selection;
1.1 Using real number encoding, each node in the network is assigned a unique positive integer ranging from 1 to K, where K is the number of nodes in the network.
1.2 For cluster head node selection, only if the node's remaining energy and integrated trust level are greater than the average network remainingAnd the election of the cluster head nodes can be participated only when the rest energy and the comprehensive trust degree are achieved. Randomly selecting [ K ] according to the calculated cluster head node number live ×p opt +0.5]And a cluster head node.
1.3 For the selected cluster head nodes, the cluster head nodes are ordered according to the distance from the base station and marked in sequence.
1.4 Determining a routing path from the cluster head node to the base station in the order of the labels. If the base station is within the direct communication range of the cluster head node, the cluster head node communicates directly with the base station. If not, selecting the nearest cluster head node as the upper layer forwarding cluster head node according to a path selection formula, wherein the formula is as follows:
in the method, in the process of the invention,representing a cluster head node h i From h i To base station h 0 A set of all cluster head nodes forwarded via other cluster head nodes;
representing data from cluster head node h i Reach base station h after forwarding via other cluster head nodes 0 Is a signal transmission distance of (2);
indicating that data arrives at base station h after being forwarded via any cluster head node 0 Is provided.
1.5 For a common node, selecting a cluster head node which is in a communication range and has the nearest route to a base station as the cluster head node thereof according to the following formula;
in the method, in the process of the invention,representing node g p And node h i A positional distance therebetween;
representing a data slave node g p Reach base station h after forwarding via cluster head node 0 Is a signal transmission distance of (2);
representing that the representation data arrives at the base station h after being forwarded via any cluster head node 0 Which reflects the signal transmission distance of data to the base station after being forwarded through an arbitrary path in the network.
Step 2: calculating an fitness function;
2.1 Building an fitness function based on the performance evaluation model Eva.
2.2 Considering whether the node can enter the route, multiplying a punishment coefficient for the cluster head node and the common node which can not enter the route, and avoiding the node which can not enter the route.
2.3 At the end of the iteration, the chromosome with the highest fitness value is the optimal path.
Step 3: gene manipulation;
3.1 A) selecting operation: individuals are selected using the roulette method, and the probability of each individual being selected is calculated based on fitness.
3.2 Cross operation): random crossing is carried out in the chromosome, the cluster head nodes are unchanged, and the common nodes randomly select the cluster head nodes again according to the following formula to form new genes.
In the method, in the process of the invention,representing node g p Upper layer forwarding cluster head node set of (1), i.e. from g) p To base station h 0 A set of all cluster head nodes forwarded via other cluster head nodes.
Representing node g p Is provided for a new upper layer forwarding cluster head node set. In the new set, only all nodes g are satisfied p And node h i The position distance between the two nodes is less than or equal to the maximum allowable distance node h i Is included.
Representing node g p And node h i The position distance between them. It reflects node g p And node h i Physical distance between them.
dopt max Maximum allowable position distance. When node g p And node h i The distance between the two positions is less than or equal to dopt max The physical distance between them is shown to meet the maximum allowable distance requirement. Only node h satisfying this condition i Will be included inIn the collection.
3.3 Mutation operation: and randomly selecting the number of the cluster head nodes and the common nodes, reselecting the cluster head nodes and the cluster head node paths according to the principle of closest distance, and selecting new cluster head nodes by the common nodes.
3.4 Retaining elite policy): at the end of each iteration, the optimal solution of the current population is compared with the solution in the memory device, and the optimal solution is selected as the solution in the memory device.
Step 4: route maintenance
4.1 When the trust value of the cluster head node is lower than the average trust degree of the survival nodes in the network, or the residual energy of the cluster head node is lower than 70% of the residual energy of the survival nodes in the network, reconstructing the network route.
4.2 When a common node is determined to be a malicious node or energy is exhausted, it is isolated from the network.
Compared with the prior art, the invention has the advantages that:
1. the malicious node detection speed is improved: through a dynamic trust perception mechanism, the protocol can evaluate the credibility of the nodes in real time and rapidly detect malicious nodes. Compared with the traditional secure routing protocol, the protocol can identify and exclude malicious nodes more quickly, and the security of the network is protected.
2. The malicious node detection success rate is improved: through a trust perception mechanism, the protocol can accurately judge whether the node is a malicious node according to the historical behavior and trust evaluation result of the node. The accurate node evaluation can improve the success rate of malicious node detection and effectively prevent the malicious node from attacking and damaging the network.
3. Network energy consumption and load are reduced: through a load balancing mechanism, the protocol can reasonably distribute the load of the nodes, and avoid the energy consumption caused by overload of part of the nodes. Meanwhile, unnecessary data transmission in the network is reduced by eliminating malicious nodes, and the energy consumption and load of the network are further reduced.
4. Prolonging the life cycle of the network: by reducing energy consumption and load, the protocol can effectively extend the life cycle of the network. The node energy is effectively utilized and the load is uniformly distributed, so that the consumption speed of the node energy is reduced, the running time of the network is prolonged, and the reliability and stability of the network are improved.
Drawings
FIG. 1 is a model diagram of a WSNs network routing hierarchy mechanism in accordance with an embodiment of the present invention;
FIG. 2 is a model diagram of an embodiment of the present invention implementing routing based on genetic algorithms.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings and by way of examples in order to make the objects, technical solutions and advantages of the invention more apparent.
The invention provides a low-energy-consumption safe routing method based on dynamic trust perception and load balancing, which comprises the following steps:
the low-energy-consumption safe route of the WSNs is sensitive to the parameters such as the trust degree, the load, the energy consumption, the survival time and the like of the nodes, and in order to solve the route with more parameters and better simultaneously, the construction of a multi-objective optimization model and an evaluation method of the WSNs route are key.
WSNs network model
Suppose that WSNs are transmitted by a base station h 0 Is composed of K sensor nodes and distributed in a square monitoring area with a side length of L, wherein CH= { h 1 ,h 2 ,…,h M And g= { G) is a cluster head node set 1 ,g 2 ,…,g N And is a common set of nodes, then there is k=m+n,for node g p And node h i Distance between them. The network has the following characteristics:
(1) the positions of the sensor nodes and the base station are not changed after being determined, and all the sensor nodes have an ID;
(2) the relative position of the node in the network directly adopts the position estimation data of the positioning method;
(3) the initial energy of all sensor nodes is the same, base station h 0 The energy is unlimited;
(4) the sensor node is provided with an energy control mechanism, and the transmitted energy can be adjusted according to the transmission distance.
The network adopts dynamic clustering thought, is arranged in a square monitoring area with side length L, and randomly distributes K live Active nodes, optimal cluster head node percentage p opt The method comprises the following steps:
wherein ε fs Epsilon as the magnification of the free space model amp Amplifying multiple of multipath transmission model, d toBS Is the distance of the node from the base station.
Optimal cluster head node quantity M opt For the number K of active nodes live Multiplying by the optimal cluster head node percentage p opt Post rounding the resulting integer:
the energy consumption of a node to send q bytes of data to a location with a distance d is calculated as:
wherein E is elec Power consumption for transmitting or receiving 1-bit data.
The energy consumption of a node to receive q bytes of data is:
E Rx (q)＝E Rx-elec (q)＝qE elec (5)
the energy consumed by the node fusion q-bit data is as follows:
E DA (q)＝E fu ×q (6)
wherein E is fu The energy consumption for fusing 1-bit data.
Aiming at the complexity and the variability of factors such as network environment, attack behaviors and the like, in order to quickly reduce the trust degree of malicious nodes, the Chebyshev neural network is adopted to carry out longitudinal analysis and dynamic prediction on the trust degree of the nodes, and the accuracy of trust degree evaluation and the detection speed of the malicious nodes are improved.
Direct confidence levelThe method can be obtained through the statistical expectation of Beta distribution, and simultaneously, the influence of external factors on node communication behaviors is considered, and the original model is improved by introducing an abnormal attenuation factor mu. Direct trust of node j on node i>The calculation formula of (2) is as follows:
wherein alpha is ji 、β ji Respectively representing the number of successful forwarding of the data packet from the node i and the number of failure of the node j, wherein mu represents the probability that the abnormal behavior of the node is a malicious attack behavior, and Num instausion Represents the number of non-cooperation times generated by node due to attack behavior, num detection Indicating the number of times the nodes are not co-operated.
The abnormal attenuation factor mu is used for attenuating the number of times of non-cooperation of the node j detected by the node j, so that the influence of external factors on the trust degree can be reduced, and the accuracy of trust evaluation is improved.
Assuming that the node i has J neighbor nodes, the comprehensive trust degree of the node iThe method comprises the following steps:
the trust level of WSNs network nodes is predictable. The Chebyshev neural network is a neural network based on a Chebyshev orthogonal polynomial, has excellent approximation performance, ensures that the neural network is fast in weight solving at the nodes of the polynomial due to the orthogonality of the basis function, and can better meet the demand of WSNs network node trust degree prediction. And dynamically predicting the comprehensive trust degree of the sensor network nodes by adopting the Chebyshev neural network.
Chebyshev neural network input layer x= (x) 1 ,x 2 ,…,x n ) T . Hidden layer H k ＝T k (x k )，R k (x) =cos (karccosx), a first class Chebyshev polynomial. Output layer y= (y) 1 ,y 2 ,…,y n ) T ，And (5) carrying out weight correction by adopting a gradient descent method during network training.
Predicting the comprehensive trust degree of wireless sensor network nodes according to the comprehensive trust degree at the current momentAs the input of the Chebyshev neural network, the output of the network is the dynamic predictive value of the node comprehensive trust degree +.>
In order to find a routing path with high reliability, all cluster head nodes are required to have high reliability, and the average dynamic reliability of all cluster head nodes is adopted for measurement. The number of cluster head nodes in the network is M opt Average dynamic trust of cluster head nodeThe method comprises the following steps:
the cluster head node not only has the cluster member node in the affiliated nodes of the cluster head node, but also canThere can be a cluster head node of the next layer. Assuming that the transmitted data comes from R nodes in total, cluster head node h m Load of (2) hm The method comprises the following steps:
where Load is the data traffic generated by a single sensor node.
M opt The maximum Load in each cluster head node is denoted as Load CH-max
Energy consumption per second per cluster head node in a sensor networkAnd energy consumption per second of the normal node +.>Expressed as:
in the method, in the process of the invention,is a cluster head node h m Upper cluster head node (or base station),>is a cluster head node h m Distance to its upper cluster head node or base station, < > or->Is a common node g n Cluster head node->For node g n Distance to its cluster head node.
Obtaining energy consumption E of the whole network total Is that
Wherein N is live Is the number of surviving normal nodes in the network.
The minimum running time of all cluster head nodes can intuitively reflect the life cycle of the whole network, because once the cluster head nodes die, network coverage holes and discontinuity of network forwarding paths can be caused. Thus, the run time of the network has a direct relationship with the death time of the first cluster head node. Estimating cluster head node h by using current residual energy and energy consumption level of cluster head node m Is not longer than the remaining run time of (1)The calculation formula is as follows:
wherein E is res (h m ) Representing a cluster head node h m Is a function of the remaining energy of the engine.
Minimum value of remaining run time of all cluster head nodesExpressed as:
(II) routing performance evaluation model based on analytic hierarchy process
The problem of optimizing the safe route of the wireless sensor network is a multi-objective optimization problem, and the average trust degree of cluster head nodes needs to be maximizedCluster head node minimum run time +.>And minimizing cluster head node maximum Load CH-max Network energy consumption E total 。
The evaluation model Eva of the routing performance is:
wherein E is 0max Maximum power consumption for a single sensor.
The multi-objective optimization problem has a contradictory relation among objectives, so that the multi-objective optimization problem is complex. Analytic hierarchy process is employed to accurately estimate the weight of each factor, as shown in figure 1.
Layer-by-layer determination of weights ω using analytic hierarchy process 1 、ω 2 、ω 3 、ω 4 First, the relative weights of the indexes of each layer are compared in pairs according to a nine-level scale method to obtain a comparison table shown in tables 1 and 2 and a basic judgment matrix A shown in a formula (20).
Table 1 relative importance scale
Table 2 comparison of routing factors
The maximum characteristic root of the matrix is calculated as lambda max ＝4.0434
The consistency index CI is:
in order to measure the consistency of the judgment matrix, an average random consistency index RI is introduced, and RI values of 1-8-order matrices are shown in a table 3.
TABLE 3 average random uniformity index RI values
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| RI | 0 | 0 | 0.52 | 0.89 | 1.12 | 1.26 | 1.32 | 1.41 |
When the order of the judgment matrix is smaller than 3, the judgment matrix is always consistent; when the order of the judgment matrix is 3 or more, the consistency ratio CR of the judgment matrix is expressed by the formula (22):
as shown in table 3, when the consistency index ci= (4.0434-4) (4-1) =0.0145 of the judgment matrix is calculated, and the consistency index ri=0.89 of the fourth-order matrix is found, the consistency ratio cr= 0.01450.89 =0.0163 < 0.1 indicates that the judgment matrix a satisfies the consistency test.
According to aω=λ max And (3) calculating and normalizing the weight vector omega to obtain the weight vector omega, wherein the weight vector omega is as follows: omega= [0.522,0.2,0.2,0.078 ]] T 。
The route performance evaluation model Eva is:
secure low-energy-consumption routing method based on GA algorithm
As shown in fig. 2, a genetic algorithm is adopted to find an optimal route path in the WSNs network, and a proper coding scheme, selection, crossing and mutation operator are designed in detail according to the requirements of route safety, energy consumption and load.
Step 1: chromosome coding and initial population selection
Using real number coding, the network consists of K nodes, each of which is assigned a unique positive integer between 1 and K. The network route adopts a structure of multi-hops among clusters and single-hops in clusters.
(1) Cluster head node selection and cluster head node path search
The cluster head node path selection is carried out according to the following steps:
(1) in order to improve the route security, the survival time of the nodes and accelerate algorithm convergence, the cluster head node election is only participated when the self residual energy and the comprehensive trust degree of the nodes are larger than the average residual energy and the average comprehensive trust degree of the network. Randomly selecting M according to the number of cluster head nodes calculated in the step (2) opt ＝[K live ×p opt +0.5]And a cluster head node. When M is present opt When each node is selected as a cluster head node, the gene of the chromosome consists of respective codes, and the length of the chromosome is M opt 。
(2) For M opt The cluster head nodes are marked as the following according to the distance sequence from the base station I.e. the distance of the cluster head node to the base station +.>
(3) According toSequentially determining routing paths from the cluster head node to the base station. If base station h 0 In the direct communication range of the cluster head node, the cluster head node directly communicates with the base station. If from cluster head node CH i Starts to be out of direct communication range of the base station (i.e +.>) Cluster head node CH i According to formula (24), i.e. CH i On CH 1 、CH 2 、……CH i-1 The cluster head node within the communication distance range and closest to the base station is selected as its upper layer forwarding cluster head node.
In the method, in the process of the invention,for data from cluster head node h i Reach base station h after forwarding via other cluster head nodes 0 Signal transmission distance,/, of>For data to reach base station h after being forwarded from any cluster head node 0 Is provided.
(2) Common node clustering selection
To speed algorithm convergence and achieve an efficient solution, each sensor ordinary node selects as its own cluster head node the cluster head node that is specifically closest within its communication range and routed to the base station according to equation (25).
In the method, in the process of the invention,for node g p And node h i The distance between the two positions>For the data slave node g p Reach base station h after forwarding via cluster head node 0 Signal transmission distance,/, of>For slave node g p Reach base station h after forwarding via arbitrary cluster head node 0 Is provided.
Step 2: calculating fitness function
And establishing a fitness function f based on the Eva route evaluation model, and multiplying a penalty coefficient if the node incapable of entering the route appears, so as to avoid the node incapable of entering the route, and simultaneously consider the importance difference of the cluster head node and the common node in the wireless sensor network with the cluster structure.
f＝Eva·5 -s ·2 -t (26)
In the formula, s is the number of cluster head nodes which cannot access the network, and t is the number of common nodes which cannot access the network.
The chromosome with the highest fitness value is the optimal path at the end of iteration, and the optimal route with safety, reliability, low energy consumption and balanced load can be constructed through the function.
Step 3: gene manipulation
The genetic manipulation mainly includes a selection operation, a crossover operation, a mutation operation and an elite retention operation.
(1) Selecting an operator: the traditional roulette method is adopted, the fitness of each individual is calculated firstly, and then the proportion of the fitness in the group fitness value sum is calculated, so that the probability that the individual is selected in the selection process is indicated.
(2) Crossover operator: to enhance population diversity, random crossover is performed inside one chromosome. All cluster head nodes of each chromosome are unchanged, and each common node randomly selects own cluster head node again according to a formula (27) in the cluster head nodes within the direct communication range of the common node, so that a new gene is formed.
(3) Mutation operator: chromosome re-randomly selects own M when mutated opt Root of cluster head nodeThe cluster head node paths are formed according to (24) on the basis of the principle of closest distance. The common node selects own cluster head nodes according to (25), and each cluster head node forms own genes. New chromosomes are thereby formed by reselection of cluster head nodes and normal nodes.
(4) Retaining elite strategy: in order to ensure that the calculation result when the algorithm is terminated is the best solution (also called the optimal solution) which is once achieved in the whole search, a memory device is introduced to memorize the best solution in the whole iterative process. At each end of a new iteration, the best solution of the current population is compared with the solution of the memory device, i.e. if the best solution of the current population is better than the solution in the memory device, the solution in the memory device is replaced with the best solution in the current population, otherwise the solution in the memory device is kept unchanged. After the whole optimization process is finished, comparing the solution in the memory device with the best solution in the optimization result, and selecting and obtaining the best solution.
GA algorithm iteration termination conditions: (1) the iteration times reach the maximum genetic evolution algebra; (2) the optimal value searched by the algorithm is kept unchanged for a plurality of successive generations.
Step 4: route maintenance
(1) When the trust value of the cluster head node is lower than the average trust degree of the survival nodes in the network, or the residual energy of the cluster head node is lower than 70% of the average residual energy of the survival nodes in the network, executing the step 1, and starting to reconstruct the network route; (2) when a common node is determined to be a malicious node or energy is exhausted, it is isolated from the network.
The above-described method according to the present invention may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, RAM, floppy disk, hard disk, or magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium and to be stored in a local recording medium downloaded through a network, so that the method described herein may be stored on such software process on a recording medium using a general purpose computer, special purpose processor, or programmable or special purpose hardware such as an ASIC or FPGA. It is appreciated that a computer, processor, microprocessor controller, or programmable hardware includes a memory component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by the computer, processor, or hardware, implements the dynamic trust awareness and load balancing based low-power secure routing methods described herein. Further, when the general-purpose computer accesses code for implementing the processes shown herein, execution of the code converts the general-purpose computer into a special-purpose computer for executing the processes shown herein.
Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the practice of the invention and that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (6)
1. A low-energy-consumption secure routing method based on dynamic trust perception and load balancing comprises the following steps:
the dynamic trust of the node is predicted by adopting a Chebyshev neural network in the WSNs network, and the formula is as follows:
wherein T is i dyn : dynamic trust prediction value representing node i
N: representing the size of the hidden layer, representing the dimension in which the characteristics of the node are transformed in the hidden layer;
ω n : the weight of the nth neuron in the hidden layer is represented and is used for carrying out weighted summation on the hidden layer output;
R n : representing a Chebyshev polynomial representing a function that converts the input vector x; the nth hidden layer neuron will be transformed using the nth Chebyshev polynomial to obtainAn output of the hidden layer;
T i com : representing the comprehensive trust degree of the node i as one element in the input vector x; the method is used for representing historical trust information of the node and predicting dynamic trust of the node;
attenuating the number of non-cooperations of the nodes by using an abnormal attenuation factor mu;
the average dynamic trust degree of all cluster head nodes is used for measuring the trust degree of the cluster head nodes in the network;
calculating the load and the network energy consumption of each cluster head node to obtain the load and the energy consumption of the whole network;
calculating the residual operation time of all cluster head nodes to obtain the operation time of the whole network;
taking the average dynamic trust degree, load, network energy consumption and residual running time of the cluster head nodes as evaluation indexes; selecting a cluster head node with the best performance;
constructing a routing performance evaluation model based on an analytic hierarchy process, wherein the analytic hierarchy process evaluates and analyzes the weight of each evaluation index, and establishes a performance index system of a routing path;
constructing a fitness function and a path searching method of a route based on a genetic algorithm: constructing a fitness function and a path searching method of the route by using a genetic algorithm; the genetic algorithm optimizes the objective function of the route to select the optimal route path through the operations of gene selection, mutation, crossing and elite preservation;
the fitness function is as follows:
building a construction fitness function f based on Eva route evaluation model, wherein f=Eva.5 -s ·2 -t ，
In the formula, s is the number of cluster head nodes which cannot access the network, and t is the number of common nodes which cannot access the network;
the Eva route evaluation model formula is:
in the method, in the process of the invention,representing the average dynamic trust of cluster head nodes; load: representing data traffic generated by a single sensor node; load CH-max Represents M opt Maximum load in each cluster head node, M opt Representing the number of selected cluster head nodes; e (E) total Representing the energy consumption of the whole network; d, d max Representing a maximum allowable position distance; k (K) live Representing the number of active nodes; e (E) 0max Representing initial energy of a cluster head node; t (T) min Representing a minimum acceptable confidence threshold; e (E) Tx Representing the energy consumption of transmitting one bit; k represents the number of cluster head nodes; m represents an index of a cluster head node.
2. The low-power secure routing method of claim 1, wherein: the average dynamic trust is obtained by the following formula:
in the method, in the process of the invention,representing the average dynamic trust of cluster head nodes;
representing summing all selected cluster head nodes;
representing the dynamic trust degree of the mth selected cluster head node;
M opt : representing the number of selected cluster head nodes;
i: representing the total number of nodes in the entire network, i.e. the network scale.
3. The low-power secure routing method of claim 1, wherein: the load and energy consumption of the whole network are obtained by the following formula:
in the method, in the process of the invention,representing the load of the mth cluster head node, namely the sum of data traffic received by the cluster head node;
r: representing the frequency at which each sensor node transmits data;
load: representing data traffic generated by a single sensor node;
M opt the maximum Load in each cluster head node is denoted as Load CH-max
Wherein E is total Representing the energy consumption of the whole network, namely the sum of the energy consumption of all cluster head nodes plus the sum of the energy consumption of all non-cluster head nodes;
representing the energy consumption of the mth cluster head node;
representing the energy consumption of the nth non-cluster head node;
N live : representing the number of non-cluster head nodes.
4. The low-power secure routing method of claim 1, wherein: the running time of the whole network is obtained by the following formula:
wherein E is res (h m ): representing a cluster head node h m Residual energy of (2)
Representing the energy consumption of the mth cluster head node;
minimum value of remaining run time of all cluster head nodesExpressed as:
5. the low-power secure routing method of claim 1, wherein: constructing a routing performance evaluation model based on an analytic hierarchy process, which comprises the following specific steps:
1) Building a comparison table: according to a nine-level scale method, comparing the relative weights among indexes of all layers in pairs to construct a comparison table;
2) Basic judgment matrix: forming a basic judgment matrix according to the comparison table;
3) Calculating characteristic roots and consistency indexes: calculating the maximum characteristic root lambda of the judgment matrix max And calculates a consistency index CI;
4) Calculating a consistency ratio: introducing an average random consistency index RI, calculating a consistency ratio CR, and judging consistency of a judgment matrix;
5) Calculating a weight vector: according to aω=λ max Calculating a weight vector to obtain an unnormalized weight vector omega;
6) Normalized weight vector: normalizing the weight vector omega to obtain a normalized weight vector;
7) Route performance evaluation model: and calculating a routing performance evaluation model Eva according to the standardized weight vector.
6. The low-power secure routing method of claim 1, wherein: the fitness function and path searching method for constructing the route based on the genetic algorithm comprises the following specific steps:
step 1: chromosome coding and initial population selection;
1.1 Using real number coding, assigning each node in the network a unique positive integer ranging from 1 to K, where K is the number of nodes in the network;
1.2 For the selection of the cluster head nodes, the cluster head nodes can be selected only when the residual energy and the comprehensive trust degree of the nodes are larger than the average residual energy and the comprehensive trust degree of the network; randomly selecting [ K ] according to the calculated cluster head node number live ×p opt +0.5]Each cluster head node K live For the number of active nodes, p opt The percentage of the nodes is the optimal cluster head;
1.3 For the selected cluster head nodes, sorting according to the distance from the base station from the near to the far, and marking in sequence;
1.4 Determining a routing path from the cluster head node to the base station according to the marked sequence; if the base station is in the direct communication range of the cluster head node, the cluster head node directly communicates with the base station; if not, selecting the nearest cluster head node as the upper layer forwarding cluster head node according to a path selection formula, wherein the formula is as follows:
in the method, in the process of the invention,representing a cluster head node h i From h i To base station h 0 A set of all cluster head nodes forwarded via other cluster head nodes;
representing data from cluster head node h i Reach base station h after forwarding via other cluster head nodes 0 Is a signal transmission distance of (2);
indicating that data arrives at base station h after being forwarded via any cluster head node 0 Is a signal transmission distance of (2);
1.5 For a common node, selecting a cluster head node which is in a communication range and has the nearest route to a base station as the cluster head node thereof according to the following formula;
in the method, in the process of the invention,representing node g p And node h i A positional distance therebetween;
represented as data slave node g p Reach base station h after forwarding via cluster head node 0 Is a signal transmission distance of (2);
indicating that data arrives at base station h after being forwarded via any cluster head node 0 The signal transmission distance of the base station reflects the signal transmission distance of the data which arrives at the base station after being forwarded by any path in the network;
step 2: calculating an fitness function;
2.1 Establishing an fitness function based on the performance evaluation model Eva;
2.2 Considering whether the node can enter the route, multiplying a punishment coefficient for the cluster head node and the common node which can not enter the route, and avoiding the occurrence of the node which can not enter the route;
2.3 At the end of the iteration, the chromosome with the highest fitness value is the optimal path;
step 3: gene manipulation;
3.1 A) selecting operation: selecting individuals by using a roulette method, and calculating the probability of each individual being selected according to the fitness;
3.2 Cross operation): randomly crossing in the chromosome, wherein the cluster head nodes are unchanged, and the common node randomly selects the cluster head nodes again according to the following formula to form a new gene;
in the method, in the process of the invention,representing node g p Upper layer forwarding cluster head node set of (1), i.e. from g) p To base station h 0 A set of all cluster head nodes forwarded via other cluster head nodes;
representing node g p A new upper layer forwarding cluster head node set; in the new set, only all nodes g are satisfied p And node h i Between (a) and (b)Node h with position distances less than or equal to the maximum allowable distance i Will be included;
representing node g p And node h i A positional distance therebetween; it reflects node g p And node h i Physical distance between;
d max : representing a maximum allowable position distance; when node g p And node h i The position distance between the two is less than or equal to d max The physical distance between them meets the requirement of the maximum allowable distance; only node h satisfying this condition i Will be included inIn the collection;
3.3 Mutation operation: randomly selecting the number of the cluster head nodes and the common nodes, reselecting the cluster head nodes and the cluster head node paths according to the principle of closest distance, and selecting new cluster head nodes by the common nodes;
3.4 Retaining elite policy): at the end of each iteration, comparing the optimal solution of the current population with the solution in the memory device, and selecting the optimal solution as the solution in the memory device;
step 4: route maintenance
4.1 When the trust value of the cluster head node is lower than the average trust degree of the survival nodes in the network, or the residual energy of the cluster head node is lower than 70% of the residual energy of the survival nodes in the network, reconstructing the network route;
4.2 When a common node is determined to be a malicious node or energy is exhausted, it is isolated from the network.
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