CN115278808A - SDWSN-oriented distributed efficient entropy energy-saving clustering routing method - Google Patents

SDWSN-oriented distributed efficient entropy energy-saving clustering routing method Download PDF

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CN115278808A
CN115278808A CN202210711924.7A CN202210711924A CN115278808A CN 115278808 A CN115278808 A CN 115278808A CN 202210711924 A CN202210711924 A CN 202210711924A CN 115278808 A CN115278808 A CN 115278808A
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cluster head
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马巧巧
麻佳辉
董黎刚
蒋献
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Zhejiang Gongshang University
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Abstract

The invention relates to an SDWSN-oriented distributed efficient entropy energy-saving clustering routing method. It comprises the following steps: 1-1) the network node selects a temporary cluster head according to a threshold condition formula, and node information is sent to a base station through the cluster head; 1-2) the base station selects cluster heads according to the firefly algorithm, updates the individual positions according to the position updating strategy of population diversity, and obtains the current global optimal solutionC best (ii) a 1-3) optimization of hybridization algorithm pairs using gravity search algorithm and biophysicsC best Further searching is carried out to obtain a global optimal solutionG best The node position with the maximum energy is obtained; 1-4) base station according toG best And calculating a cluster head switching table, loading flow table information and issuing the flow table information to the nodes, receiving and storing the information by each node, and selecting the node with the highest energy in each cluster as a cluster head. The invention can prolong the network life cycle, improve the energy utilization rate and improve the performance of the SDWSN.

Description

SDWSN-oriented distributed efficient entropy energy-saving clustering routing method
Technical Field
The invention relates to the technical field of software defined wireless sensors, in particular to an SDWSN-oriented distributed efficient entropy energy-saving clustering routing method.
Background
A Wireless Sensor Network (WSN) typically deploys one base station and a large number of sensor nodes. Due to the characteristics of low deployment cost and high benefit, the WSN is rapidly developed and widely popularized. However, the WSN has the problems of limited network resources, inflexible network management, local effectiveness of routing algorithms, and the like. In order to solve the above problems, a Software Defined Wireless Sensor Network (SDWSN) applies a software defined technology to the WSN, decoupling a data plane and a control plane. The SDWSN improves the flexibility of the network through centralized management and programmability, supports heterogeneous fast interconnection, flexible and efficient sensing and dynamic and reliable routing.
SDWSNs face significant challenges of saving energy, extending network lifetime, etc. Therefore, the WSN-oriented clustering routing method is used for SDWSN, mainly LEACH, DEEC, SEP, etc. The sensor nodes are divided into different clusters by the clustering routing, and each cluster is provided with a cluster head to collect cluster member information and transmit the cluster member information to the base station, so that the SDWSN has good expansibility and robustness. LEACH randomly selects cluster heads, so that network load is relatively balanced, but cluster head selection is frequently performed, and energy waste is caused; the DEEC considers the residual energy of the nodes when selecting the cluster heads, so that the network life cycle is prolonged, but delay is brought to data transmission. Samayveer et al propose a 3-level heterogeneous network model for WSN to describe the heterogeneity of the network and select cluster heads by weighting election probabilities and threshold functions. LEACH-O is based on GA and LEACH, improving energy efficiency and extending the life of WSN. The EADEEC improves data transmission, node lifetime and stability of the cluster based on the DEEC. Sixu et al, facing SDWSN, adopts Particle Swarm Optimization (PSO) to calculate the relative positions of the cluster head and the base station, and designs the moving path of the base station based on the ABC traversal path algorithm, thereby reducing the energy cost of the sensor nodes.
In recent years, many intelligent optimization algorithms have been used to improve the clustering phase of the clustering routing method, which approximately solves complex optimization problems by simulating the group behavior of animals. The intelligent optimization algorithm includes Genetic Algorithm (GA), firefly Algorithm (FA), artificial bee colony Algorithm (ABC), and the like. The GA convergence rate is high, but the method is not good for local search, the FA parameters are less than that of the GA, and the ABC has advantages in terms of global search and convergence rate. Among them, FA and ABC are easier to implement in the clustering routing method. The intelligent optimization algorithm has the problems of low solving precision, convergence to a local optimal solution, small search space range and the like. The IFCEER is proposed by Roxie et al, and FA is used in high-energy node clustering by the algorithm, so that energy consumed by multiple clustering is reduced, but the problems of low convergence speed, low solving precision and the like exist. The FFACM is proposed by the residual force et al, so that the algorithm is prevented from falling into a local optimal value, and the defect that the initial clustering center is set inaccurately exists. Limin and his team studied a Bayesian-based PSO algorithm that performs parameter setting based on a probability intensity function of a Bayesian principle, but faced with the problem of local optimization, and especially performed remarkably on the complex multiple infinitesimal problem. HFPSO blends FA with PSO and determines the start of the search process by checking the value of the current global best fitness. Pitchaimanickam et al propose HFAPSO, which uses HFPSO for optimal cluster head selection in LEACH-C, enhancing the global search capability of fireflies. The DEEC-FA applies the FA to a mixed-state logic model with energy parameters for detecting distributed denial of service (DDoS) attacks. Charan et al used FA and BBO for feature selection on software product lines. Shrivastava et al propose PSOGWO which mixes a Grey wolf optimization algorithm (GWO) with PSO, greatly reducing the computation time required to implement the algorithm.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the SDWSN-oriented distributed efficient entropy energy-saving clustering routing method, which can prolong the network life cycle and improve the energy utilization rate.
An SDWSN-oriented distributed efficient entropy energy-saving clustering routing method comprises the following steps: 1-1) the network node selects a temporary cluster head according to a threshold condition formula (1), and node information is sent to a base station through the cluster head;
Figure BDA0003708388650000021
wherein, N sensor nodes si(i =1,2.. N), C denotes a node group that does not become a cluster head, r is the current iteration round number, mod is the sign of the modulo operation, and H (Eni) is the relative energy entropy of the node;
Figure BDA0003708388650000022
where k is the expected cluster number ratio and Eni (r) is the sensor node siThe remaining energy in the r-wheel,
Figure BDA0003708388650000023
represents the average energy of the r-round network;
1-2) the base station selects cluster heads according to the firefly algorithm, updates the individual positions according to the position updating strategy of the population diversity shown in the formula (3), and obtains the current global optimal solution Cbest；
Figure BDA0003708388650000024
Wherein, ciAnd cjRepresenting the spatial positions of fireflies i and j, n is iteration number, beta is light intensity absorption coefficient, and gamma is0Is the maximum attraction of fireflies, rijIs the euclidean distance between the two nodes,
Figure BDA0003708388650000031
is the position of the brightest firefly individual in the current iteration number, rho is a diversity index, and alpha belongs to [0,1 ]]Indicating stepThe long factor, random parameter rand belongs to [0,1 ]]；
1-3) optimization of hybridization algorithm pair C using gravity search algorithm and BiogeographybestFurther searching is carried out to obtain a global optimal solution GbestThe node position with the maximum energy is obtained;
1-4) base station according to GbestAnd calculating a cluster head rotating table, loading flow table information and issuing the flow table information to the nodes, receiving and storing the information by each node, and selecting the node with the highest energy in each cluster as a cluster head.
In the step 1-1), the network node is positioned in a data layer, the network node evaluates the probability of becoming a cluster head according to a threshold condition formula (1), a random number is extracted between 0 and 1 to serve as a conditional probability, and if the probability is smaller than the conditional probability, the node selects the node as a temporary cluster head; if the distance between the node and the base station is larger than the distance between the node and the cluster head, the self position information, the residual energy information and the topology information data are directly transmitted to the base station, otherwise, the data are transmitted to the base station through the cluster head, and the cluster head only completes the functions of receiving, fusing and forwarding the data.
The firefly algorithm in the step 1-2) comprises the following steps:
2-1) initializing parameter population size N, initial step factor alpha and initial population diversity index beta0Maximum iteration times maxIterate;
2-2) constructing an initial population C according to relative energy entropy0Calculating and sequencing firefly brightness I according to the formula (4), wherein the individual position of the maximum fluorescence brightness is the current global optimal solution Cbest；
Figure BDA0003708388650000032
Wherein, I0Is the maximum fluorescence intensity of firefly;
Figure BDA0003708388650000033
wherein, cikAnd cjkCoordinates of fireflies i and j in the w dimension;
the attraction degree gamma of firefly is
Figure BDA0003708388650000034
2-3) updating the location of the firefly according to a location update strategy of population diversity, i.e., equation (3), where ρ is a diversity index β expressed according to equation (8)nThe weight of the change is expressed by equation (9), and the attraction between fireflies is updated according to equation (6) and C is updated at the same timebest；
Figure BDA0003708388650000035
Wherein | s | represents a population size,
Figure BDA0003708388650000036
representing the population mean center;
Figure BDA0003708388650000041
wherein, beta0For the initial population diversity index, σ is a linear decreasing function, which is calculated by the formula
Figure BDA0003708388650000042
2-4) if the iteration number n reaches maxIterate, stopping the algorithm and outputting the current CbestOtherwise, executing steps 2-2) -2-3) in a circulating mode.
The invention has the beneficial effects that:
aiming at the defects of the prior art, a distributed high-efficiency entropy energy-saving clustering routing method (DHEEC) facing to the SDWSN is provided, and a firefly algorithm and a mixed optimization algorithm of biological and geographic optimization designed based on the energy entropy are used for cluster head selection. The method can prolong the life cycle of the network and improve the energy utilization rate.
Drawings
Fig. 1 is a flowchart of a distributed efficient entropy energy-saving clustering routing method facing SDWSN.
FIG. 2 is a comparison of network node life cycles for IFCEER, DEEC-FA, and DHEEC, where (a) is a comparison of node death numbers and (b) is a comparison of node death times.
FIG. 3 is a comparison of the total number of packets received by the base stations of IFCEER, DEEC-FA and DHEEC.
Detailed Description
The invention is further elucidated with reference to the drawings and embodiments.
A distributed high-efficiency entropy energy-saving clustering routing method facing SDWSN is disclosed, the flow chart of the invention is shown in figure 1, a sensor node is positioned in a data layer, and self position information, residual energy information and topology information are sent to a base station through a cluster head. The base station is positioned at the control layer, calculates the cluster head switching table, loads the flow table information and issues the flow table information to the cluster head.
The method comprises the following steps: 1-1) the network node selects a temporary cluster head according to a threshold condition formula (1), and node information is sent to a base station through the cluster head;
Figure BDA0003708388650000043
wherein, N sensor nodes si(i =1,2.. N), C denotes a node group that does not become a cluster head, r is the current iteration round number, mod is the sign of the modulo operation, and H (Eni) is the relative energy entropy of the node;
Figure BDA0003708388650000044
where k is the expected cluster number ratio and Eni (r) is the sensor node siThe remaining energy in the r-wheel,
Figure BDA0003708388650000045
representing the average energy of the r-round network.
1-2) the base station selects cluster heads according to the firefly algorithm, updates the individual positions according to the position updating strategy of the population diversity shown in the formula (3), and obtains the current global optimal solution Cbest；
Figure BDA0003708388650000051
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003708388650000052
represents the spatial position of firefly i, and alpha belongs to [0,1 ]]Represents the step factor, and the random parameter rand belongs to [0,1 ]]，
Figure BDA0003708388650000053
And p is a diversity index, and represents the position of the brightest firefly individual in the current iteration number n rounds.
1-3) optimization of hybridization algorithm pair C using gravity search algorithm and BiogeographybestFurther searching is carried out to obtain a global optimal solution GbestThe position of the node with the maximum energy is obtained;
1-4) base station according to GbestAnd calculating a cluster head switching table, loading flow table information and issuing the flow table information to the nodes, receiving and storing the information by each node, and selecting the node with the highest energy in each cluster as a cluster head.
In the step 1-1): the network node is positioned in a data layer, the network node evaluates the probability of becoming a cluster head according to a threshold condition formula (1), a random number is extracted between 0 and 1 to serve as a conditional probability, and if the probability is smaller than the conditional probability, the node selects the node as a temporary cluster head. If the distance between the node and the base station is larger than the distance between the node and the cluster head, the self position information, the residual energy information and the topology information data are directly transmitted to the base station, otherwise, the data are transmitted to the base station through the cluster head, and the cluster head only completes the functions of receiving, fusing and forwarding the data.
In the step 1-2): the improved firefly algorithm comprises the following steps:
2-1) initializing parameter population size N, initial step factor alpha and initial population diversity index beta0Maximum iteration times maxIterate and the like;
2-2) constructing an initial population C according to relative energy entropy0Calculating and sequencing the brightness according to the formula (4), wherein the individual position of the maximum fluorescence brightness is the current global optimal solution Cbest；
Figure BDA0003708388650000054
Wherein, I0Is the maximum fluorescence brightness of firefly, beta is the light intensity absorption coefficient, rijIs Euclidean distance of
Figure BDA0003708388650000055
Wherein, ciAnd cjSpatial positions of fireflies i and j, cikAnd cjkCoordinates of fireflies i and j in the w-th dimension.
Figure BDA0003708388650000056
Wherein, gamma is0Is the maximum attraction of fireflies.
2-3) updating the location of firefly according to the location update strategy of population diversity, i.e., equation (3), where ρ is a diversity index β expressed according to equation (8)nThe weight of the change is expressed by equation (9), and the attraction between fireflies is updated according to equation (6) and C is updated at the same timebest；
Figure BDA0003708388650000061
Wherein | s | represents a population size,
Figure BDA0003708388650000062
representing the population mean center.
Figure BDA0003708388650000063
Wherein, beta0For the initial population diversity index, σ is a linear decreasing function, which is calculated by the formula
Figure BDA0003708388650000064
2-4) if the iteration number n reaches maxIterate, stopping the algorithm and outputting the current CbestOtherwise, executing steps 2-2) -2-3) in a circulating mode.
Examples
To facilitate the understanding and practice of the present invention by those of ordinary skill in the art, a specific embodiment of the method of the present invention will now be described. A core idea of a distributed high-efficiency entropy energy-saving clustering routing method facing SDWSN is as follows: and using the energy entropy and a designed firefly algorithm and a biogeographic optimization hybrid optimization algorithm for cluster head selection. The method can prolong the life cycle of the network and improve the energy utilization rate.
The software matlab2018a is used for establishing an SDWSN (software development station network) network model, and the network model has the following characteristics: 100 network nodes are randomly distributed in an area of 100m × 100m, and a base station is positioned in the center of the area; data information interaction is carried out between the base station and the cluster head according to an OpenFlow protocol; the nodes have the same software and hardware configuration, unique numbers and fixed positions; the links of the network are symmetrical; the calculation speed and the data transmission capacity of the base station are stronger than those of the sensor nodes. Part of the network simulation parameters are shown in table 1:
TABLE 1 network simulation parameters
Parameter(s) Parameter value
Network area 100m×100m
Number of sensor nodes 100
Base station location (50,50)
Transmission data packet 4000bit
E0 0.5J
Eelec 50nj/bit
εfs 10nJ/bit/m2
εmp 0.0013pJ/bit/m4
EDA 5nJ/bit/signal
The network node selects a temporary cluster head according to a threshold condition formula (1), and node information is sent to the base station through the cluster head;
Figure BDA0003708388650000071
wherein, N sensor nodes si(i =1,2.. N), C denotes a node group which does not become a cluster head, r is the current iteration round number, and H (Eni) is the relative energy entropy of the nodes;
Figure BDA0003708388650000072
where k is the expected cluster number ratio, where k =0.05, eni (r) is the sensor node siThe remaining energy in round r, E (r) represents the average energy of the round r network;
the base station selects cluster heads according to a firefly algorithm, partial simulation parameters are shown in a table 2 of a table 2, and the individual positions are updated according to a position updating strategy of population diversity to obtain a current global optimal solution Cbest；
TABLE 2 firefly Algorithm simulation parameters
Parameter(s) Parameter value
N
100
maxIterate 100
α 0.2
β0 1
Pair C using gravity search algorithm and biophysical optimizationbestA more refined search was performed and some of the simulation parameters are shown in table 3.
TABLE 3 gravitational search Algorithm and Biogeography optimized simulation parameters
Parameter(s) Parameter value
N 100
rmax 100
D 1
E 1
I 1
According to CbestConstruction of initial population C0Calculating the suitable index HSI, sequencing and updating the current global optimal solution Gbest(ii) a Calculating emigration immigration index mu of habitat according to the formula (7) and the formula (8)kAnd immigration index lambdakCarrying out migration operation, and carrying out mutation operation according to the formula (9);
Figure BDA0003708388650000073
Figure BDA0003708388650000081
wherein S iskIndicates the number of individuals in the current population, SmaxThe number of individuals of the maximum population is represented, I represents the maximum migration rate of immigration, and E represents the maximum migration rate of immigration.
Figure BDA0003708388650000082
Wherein M represents the maximum mutation rate, PiDenotes the probability of the species, PmaxDenotes the maximum species probability, here Pmax＝N。
Calculating the inertia mass of each population according to the formula (10), and updating the position of each individual according to the formula (11);
Figure BDA0003708388650000083
wherein, SIViDenotes the suitability index variable, MtRepresenting the inertial mass of the population, best (t) is the current optimal solution, and worst (t) is the current worst solution.
Figure BDA0003708388650000084
If the iteration number reaches 100, the algorithm stops and outputs the current global optimal solution Cbest。GbestNamely, the node with the largest energy in the current iteration times is selected as the cluster head.
The energy of the network nodes is reduced along with the increase of the iteration times of the algorithm, and is exhausted after a certain iteration times, so that the nodes die. The number of node deaths in a network represents the life cycle of the network. FIG. 2 shows the effect of the three algorithms IFCEER, DEEC-FA and DHEEC on the life cycle of a network. Wherein, part a in FIG. 2 shows the change of the node death number of the three algorithms including IFCEER, DEEC-FA and DHEEC when the maximum iteration number is 5000 rounds. It can be seen that the number of death rounds of all the nodes of the three algorithms of IFCEER, DEEC-FA and DHEEC is 3754, 2000 and 3568 rounds respectively. The first node of the network dies, the information collected by the base station is not comprehensive any more, the number of dead nodes is increased along with the operation of the algorithm, the residual energy of a small part of nodes living at the later stage is lower, and the communication capability is lost. Thus, in order to more fully measure the life cycle of the network, section b of FIG. 2 compares the first node death round (FND, first node ded), 10% node death round (TND, 10%) and the total node death round (LND, last node ded) of the three algorithms IFCEER, DEEC-FA and DHEEC. It can be seen that since the IFCEER only allows the high-energy node to participate in the election of the cluster head, the FND and the LND are very close, the FND of the DEEC-FA is much smaller than those of the other two algorithms, and the network performance is seriously degraded in the early stage of the algorithm. Considering the balance of the overall energy consumption of the network, the TND is defined as the life cycle of the network, and the TND of DHEEC is improved by about 13.89% compared with IFCEER and is improved by about 41.05% compared with DEEC-FA.
In the algorithm operation process, the surviving member nodes transmit the collected data to the cluster head, the cluster head is transmitted to the base station, and after all the nodes are completely transmitted, the base station counts the number of the data packets received in the current round. Therefore, the total number of data packets received by the base station is used for evaluating the energy utilization rate, and the more data packets are received by the base station, the more balanced the energy distribution is. Fig. 3 shows the variation of the total number of packets received by the base station after 5000 iterations of the three algorithms of IFCEER, DEEC-FA and DHEEC. It follows that DHEEC is about 31.58% higher in energy utilization than IFCEER and about 31.06% higher than DEEC-FA.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same, and those skilled in the art can make modifications or equivalent substitutions to the specific embodiments of the present invention with reference to the above embodiments, and any modifications or equivalent substitutions which do not depart from the spirit and scope of the present invention are within the scope of the claims of the present invention as filed in the application.

Claims (3)

1. An SDWSN-oriented distributed efficient entropy energy-saving clustering routing method is characterized by comprising the following steps:
1-1) the network node selects a temporary cluster head according to a threshold condition formula (1), and node information is sent to a base station through the cluster head;
Figure 858166DEST_PATH_IMAGE002
（1）
wherein, the first and the second end of the pipe are connected with each other,Nsensor node
Figure 244148DEST_PATH_IMAGE004
， CIndicating a group of nodes that are not to become a cluster head,rfor the current iteration round, mod is the sign of the modulo operation,H（Eni）is the relative energy entropy of the node;
Figure 23885DEST_PATH_IMAGE006
（2）
wherein, the first and the second end of the pipe are connected with each other,kin order to anticipate the proportion of the number of clusters,Eni（r）is a sensor nodes iIn thatrThe remaining energy of the wheel is used,
Figure 638668DEST_PATH_IMAGE008
representsrAverage energy of the wheel network;
1-2) the base station selects cluster heads according to the firefly algorithm, updates the individual positions according to the position updating strategy of the population diversity shown in the formula (3), and obtains the current global optimal solutionC best ；
Figure 921882DEST_PATH_IMAGE010
（3）
Wherein, the first and the second end of the pipe are connected with each other,c i andc j representative of firefliesiAndjthe position of the device in space is located,nin order to have a number of iteration rounds,βin order to be the light intensity absorption coefficient,γ 0 is the maximum attraction of the firefly,r ij is the euclidean distance between the two nodes,
Figure 213186DEST_PATH_IMAGE012
the position of the brightest individual firefly in the current iteration round,ρthe index of the diversity is the index of the diversity,α∈[0，1]representing step-size factors, random parametersrand∈[0，1]；
1-3) optimization of hybridization algorithm pairs using gravity search algorithm and BiogeographyC best Further searching is carried out to obtain a global optimal solutionG best The node position with the maximum energy is obtained;
1-4) base station according toG best And calculating a cluster head switching table, loading flow table information and issuing the flow table information to the nodes, receiving and storing the information by each node, and selecting the node with the highest energy in each cluster as a cluster head.
2. The method according to claim 1, wherein in step 1-1), the network node is located in the data layer, the network node evaluates the probability of becoming a cluster head according to the threshold condition formula (1), extracts a random number between 0 and 1 as a conditional probability, and if the random number is smaller than the conditional probability, the node selects itself as a temporary cluster head; if the distance between the node and the base station is larger than the distance between the node and the cluster head, the self position information, the residual energy information and the topology information data are directly transmitted to the base station, otherwise, the data are transmitted to the base station through the cluster head, and the cluster head only completes the functions of receiving, fusing and forwarding the data.
3. The method as claimed in claim 1, wherein the firefly algorithm of the step 1-2) comprises the steps of:
2-1) initialization parameter population sizeNInitial step size factorαInitial population diversity indexβ 0 Maximum number of iterationsmaxIterate；
2-2) constructing an initial population according to relative energy entropyC 0 Calculated according to equation (4) and sequencedBrightness of fireflyIThe individual position of the maximum fluorescence brightness is the current global optimal solutionC best ；
Figure 949061DEST_PATH_IMAGE014
（4）
Wherein, the first and the second end of the pipe are connected with each other,I 0 is the maximum fluorescence intensity of firefly;
Figure 866070DEST_PATH_IMAGE016
（5）
wherein the content of the first and second substances,c ik andc jk is fireflyiAndjin the first placewCoordinates of the dimension;
attraction of firefliesγIs composed of
Figure 3791DEST_PATH_IMAGE018
（6）
2-3) updating the positions of the fireflies according to a position updating strategy of population diversity, namely an equation (3),ρis a diversity index according to formula (8)β n The weight of the change is updated according to the formula (6) and the attraction between the fireflies is updated as shown in the formula (9)C best ；
Figure 997154DEST_PATH_IMAGE020
（7）
Wherein, the first and the second end of the pipe are connected with each other,
Figure 220325DEST_PATH_IMAGE022
the size of the population is represented by,
Figure 426179DEST_PATH_IMAGE024
representing the population mean center;
Figure 177927DEST_PATH_IMAGE026
（8）
wherein the content of the first and second substances,β 0 is an index of the diversity of the initial population,σis a linear decreasing function, and the calculation formula is
Figure 76613DEST_PATH_IMAGE028
（9）
2-4) number of iterationsnTo achievemaxIterateAlgorithm stops and outputs currentC best Otherwise, circularly executing the steps 2-2) -2-3).
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