JP7362088B1 - Distributed, entropy-efficient, energy-saving cluster-based routing method for SDWSN - Google Patents

Abstract

The present invention provides a distributed, entropy-enhanced, and energy-saving cluster-based routing method for SDWSN. [Solution] In this method, a network node selects a temporary cluster head using a threshold conditional expression, transmits node information from the cluster head to a base station, and the base station selects a cluster head using a firefly algorithm to create a population. Update the position of the individual based on the diversity position update policy, obtain the current global optimal solution Cbest, further explore using an algorithm that combines gravity search and biogeography-based optimization, and The base station calculates the cluster head rotation table based on the global optimal solution, loads it into the flow table information and distributes it to the nodes, and each node It receives and stores information and selects the node with the highest energy from each cluster as the cluster head. [Selection diagram] Figure 1

Description

The present invention relates to the technical field of software-defined wireless sensors, and in particular to a distributed, entropy-efficient, and energy-saving cluster-based routing method for SDWSNs.

A wireless sensor network (WSN) typically includes one base station and many sensor nodes. Due to the characteristics of low deployment cost and high profit, WSN has achieved rapid development and wide spread. However, WSNs have problems such as limited network resources, lack of flexibility in network management, and local effectiveness of routing algorithms. To solve the above problem, a software defined wireless sensor network (SDWSN) uses software defined technology in WSN to separate data plane and control plane. SDWSN improves network flexibility through centralized management and programmability, and supports heterogeneous high-speed interconnections, flexible and efficient sensing, and dynamic and reliable routing.

SDWSN faces major challenges such as energy conservation and network lifetime extension. To this end, cluster-based routing methods for WSNs have come to be used in SDWSNs, mainly LEACH, DEEC, SEP, etc. In cluster-based routing, sensor nodes are divided into different clusters, and each cluster has one cluster head that collects information of cluster members and transmits it to the base station, and this method makes SDWSN have good scalability and robustness. have. In LEACH, cluster heads are randomly selected, so the network load is relatively balanced, but frequently selecting cluster heads results in wasted energy. DEEC considers the residual energy of a node when selecting a cluster head, which extends the life cycle of the network, but causes a delay in data transmission. Samayveer et al. proposed a three-level heterogeneous network model for use in WSNs to account for network heterogeneity, which selects cluster heads by weighted winning probabilities and threshold functions. LEACH-O is based on GA and LEACH to increase energy efficiency and extend the lifetime of WSN. EADEEC is based on DEEC with improvements in cluster data transmission, node lifetime and stability. For SDWSN, Sixu et al. adopted particle swarm optimization (PSO) to calculate the relative position of the cluster head and base station, and designed the movement path of the base station using an ABC-based route search algorithm. This reduces the energy cost of sensor nodes.

In recent years, many smart optimization algorithms have been utilized to improve the clustering process of cluster-based routing methods, simulating the collective behavior of animals to approximately solve complex optimization problems. Smart optimization algorithms include genetic algorithms (GA), firefly algorithms (FA), artificial bee colony algorithms (ABC), and the like. Although GA has a fast convergence speed, it is not suitable for local search, FA has fewer parameters than GA, and ABC has an advantage in global search and convergence speed. Of these, FA and ABC are easier to implement using the cluster-based routing method. Smart optimization algorithms have problems such as low solution accuracy, convergence to a locally optimal solution, and small search space range. Luo Jian et al. have proposed IFCEER, which reduces the energy consumed for multiple clustering by using FA to cluster high-energy nodes, but the convergence speed is slow and the accuracy of solution is low. There are problems such as low Yu Shutake et al. have proposed FFACM, which avoids the algorithm from ending up with a locally optimal solution, but it has the drawback that the initial cluster center setting is inaccurate. Limin and his team are researching a Bayesian-based PSO algorithm, which sets parameters based on the probability density function of Bayes' theorem, but where the problem of local optima is unavoidable and complex. This is especially noticeable in multiple minimization problems. The HFPSO determines the start of the search process by checking the current global best fitness value in combination with the FA and PSO. Pitchaimanickam et al. have proposed HFAPSO, and this algorithm improves the global search ability of fireflies by using HFAPSO to select the optimal cluster head for LEACH-C. DEEC-FA is used to detect distributed denial of service (DDoS) attacks by applying FA to a combined state logic model with energy parameters. Charan et al. use FA and BBO to select features for a software product line. Shrivastava et al. have proposed PSOGWO, which significantly reduces the calculation time required to realize the algorithm by combining a gray wolf optimization algorithm (GWO, gray wolf optimizer) and PSO.

Overcoming the shortcomings of the prior art, the present invention provides a distributed, entropy-efficient, and energy-saving cluster-based routing method for SDWSNs, which can extend the network life cycle and increase the energy utilization rate.

A distributed, entropy-enhanced, and energy-saving cluster-based routing method for SDWSN,
1-1) The network node selects a temporary cluster head according to the threshold conditional expression (1), and transmits node information to the base station by the cluster head,

where N sensor nodes s _i (i=1,2...N), C represents a group of nodes that did not become cluster heads, r is the current iteration round number, and mod is is the modulo operator, H(En _i ) is the relative energy entropy of the node,

where k is the expected proportion of the number of clusters, En _i (r) is the residual energy of sensor node s _i in r rounds,
representing the average energy of the network for r rounds;

1-2) The base station selects a cluster head using the firefly algorithm, updates the location of the individual based on the population diversity location update policy shown in equation (3), and determines the current global optimal solution C _best The step of obtaining

where c _i , c _j represent the spatial positions of fireflies i, j, n is the number of repetition rounds, β is the light absorption coefficient, γ ₀ is the maximum attraction degree of fireflies, r _ij is the Euclidean distance;

1-3) Further search for C _best using an algorithm that combines gravity search algorithm and biogeography-based optimization to obtain global optimal solution G _best , that is, find the node with the maximum energy. a step that is a position;

1-4) The base station calculates the cluster head rotation table based on G _best , loads it into the flow table information and distributes it to the nodes, each node receives and stores the information, and selects the highest energy from each cluster. selecting the node as the cluster head.

In the step 1-1), the network node is located in the data layer, and the network node evaluates the probability of becoming a cluster head according to the threshold conditional expression (1), and sets one conditional probability between 0 and 1. If the random number is smaller than the conditional probability, the node selects itself as the temporary cluster head, and if the distance between the node and the base station is greater than the distance between the node and the cluster head, the node selects itself as the temporary cluster head. , residual energy information and topology information data directly to the base station, otherwise data is transmitted to the base station by the cluster head, and the cluster head only performs the functions of receiving, merging and forwarding data.

The step of using the firefly algorithm in step 1-2) is as follows.
2-1) Initialize the parameters population size N, initial step size factor α, initial population diversity index β ⁰ , and maximum number of iterations maxIterate,
2-2) Build an initial population C ₀ based on relative energy entropy, calculate by equation (4) and rearrange the firefly brightness I, and the position of the individual with the maximum fluorescence brightness is determined based on the current global The optimal solution C _best is

where I ₀ is the maximum fluorescence brightness of the firefly,

In the formula, c _ik and c _jk are the coordinates of fireflies i and j in the w-th dimension,
The firefly attraction degree γ is

and

2-3) Population diversity position update policy, that is, update the firefly position according to equation (3),
As shown in Equation (9), the degree of attraction between fireflies is updated by Equation (6), and at the same time C _best is updated,

and

2-4) When the number of iterations n reaches maxIterate, stop the algorithm and output the current C _best ; otherwise, repeat steps 2-2) to 2-3).

The beneficial effects of the present invention are as follows.
To address the shortcomings of the conventional technology, we proposed a distributed high-efficiency entropy energy-saving cluster-based routing algorithm (DHEEC) for SDWSNs, and improved energy efficiency. P and firefly algorithm An optimization algorithm designed by combining biogeography-based optimization is used for cluster head selection. The method can extend the life cycle of the network and increase the energy utilization rate.

FIG. 1 is a flowchart of a distributed, entropy-efficient, and energy-saving cluster-based routing method for SDWSN. FIG. 2 is a comparison of the life cycles of network nodes in IFCEER, DEEC-FA, and DHEEC, of which (a) is a comparison of the number of node deaths, and (b) is a comparison of node death timing. FIG. 3 is a comparison of the total number of data packets received by a base station in IFCEER, DEEC-FA, and DHEEC.

The present invention will be further explained below using drawings and examples.

This is a distributed, entropy-efficient, energy-saving cluster-based routing method for SDWSN.As shown in FIG. Energy information and topology information are sent to the base station by the cluster head. The base station is located at the control layer, calculates the cluster head rotation table, loads the flow table information and distributes it to the cluster heads.

The method includes the following steps.
1-1) The network node selects a temporary cluster head using the threshold conditional expression (1), transmits node information to the base station by the cluster head,

where N sensor nodes s _i (i=1,2...N), C represents a group of nodes that did not become cluster heads, r is the current iteration round number, and mod is is the modulo operator, H(En _i ) is the relative energy entropy of the node,

where k is the expected proportion of the number of clusters, En _i (r) is the residual energy of sensor node s _i in r rounds,

1-2) The base station selects a cluster head using the firefly algorithm, updates the location of the individual based on the population diversity location update policy shown in equation (3), and determines the current global optimal solution C _best obtained,

During the ceremony,

1-3) Further search for C _best using an algorithm that combines gravity search algorithm and biogeography-based optimization to obtain global optimal solution G _best , that is, find the node with the maximum energy. location,

1-4) The base station calculates the cluster head rotation table based on G _best , loads it into the flow table information and distributes it to the nodes, each node receives and stores the information, and selects the highest energy from each cluster. select the node as the cluster head.

In the step 1-1), the network node is located in the data layer, and the network node evaluates the probability of becoming a cluster head according to the threshold conditional expression (1), and sets one conditional probability between 0 and 1. A random number is extracted, and if it is smaller than the conditional probability, the node selects itself as a temporary cluster head. When the distance between the node and the base station is larger than the distance between the node and the cluster head, it transmits its own location information, residual energy information, and topology information data directly to the base station; otherwise, the cluster head transmits the data to the base station. The cluster head only performs the functions of receiving, merging and forwarding data.

The steps of using the improved firefly algorithm in step 1-2) are as follows.

2-1) Initialize parameters such as population size N, initial step size factor α, initial population diversity index β ⁰ , maximum number of iterations maxIterate, etc.
2-2) Build an initial population C ₀ based on the relative energy entropy, calculate the brightness according to equation (4) and rearrange it, and the position of the individual with the maximum fluorescence brightness is the current global optimal solution C It is _{the best} ,

where I ₀ is the maximum fluorescence brightness of the firefly, β is the light absorption coefficient, r _ij is the Euclidean distance,

where c _i , c _j represent the spatial positions of fireflies i, j, and c _ik , c _jk are the coordinates of fireflies i, j in the wth dimension.

where γ ₀ is the maximum attraction of fireflies.

2-3) Population diversity position update policy, that is, update the firefly position according to equation (3),
As shown in Equation (9), the degree of attraction between fireflies is updated by Equation (6), and at the same time C _best is updated,

The calculation formula is

and

2-4) When the number of iterations n reaches maxIterate, stop the algorithm and output the current C _best ; otherwise, repeat steps 2-2) to 2-3).

(Example)
To help those skilled in the art understand and practice the invention, specific examples of the methods described in the invention are presented. The concept of a distributed, entropy-enhancing, and energy-saving cluster-based routing method for SDWSN is to select cluster heads using an optimization algorithm designed by combining energy entropy, firefly algorithm, and biogeography-based optimization. It is to use. The method can extend the life cycle of the network and increase the energy utilization rate.

An SDWSN network model was established using the software matlab2018a, and the network model has the following characteristics. 100 network nodes are randomly distributed in an area of 100m x 100m, the base station is located at the center of the area, the base station and cluster head exchange data information based on the OpenFlow protocol, and the nodes' hardware and software The configuration is the same, each has a unique number, the location is fixed, the network links are symmetrical, and the calculation speed and data transmission capacity of the base station exceeds that of the sensor node. Some of the network simulation parameters are shown in Table 1.

Table 1
Table 1: Network simulation parameters

The network node selects a temporary cluster head according to the threshold conditional expression (1), transmits node information to the base station by the cluster head,

where N sensor nodes s _i (i=1, 2...N), C represents a group of nodes that did not become cluster heads, r is the current iteration round number, and H(En _i ) is the relative energy entropy of the node,

where k is the expected proportion of the number of clusters, where k=0.05, En _i (r) is the residual energy of sensor node s _i in r rounds, and E( r) represents the average energy of the network for r rounds,
The base station selects the cluster head by the firefly algorithm, some of the simulation parameters are as shown in Table 2, and updates the location of the individual based on the population diversity location update policy to update the current global Obtain the optimal solution C _best .

Table 2
Table 2: Simulation parameters of firefly algorithm
A gravity search algorithm and biogeography-based optimization were used to search more precisely for C _best , and some of the simulation parameters are as shown in Table 3.

Table 3
Table 3: Simulation parameters of gravity search algorithm and biogeography-based optimization

Build an initial population C ₀ based on C _best , calculate the fitness index HSI, sort it, and update the current global optimal solution G _best ;

Calculate the migration resident index μ _k and migration resident index λ _k of the habitat using equations (7) and (8), perform a movement operation, perform a mutation operation using equation (9),

In the formula, S _k represents the number of individuals in the current population, S _max represents the number of individuals in the maximum population, I represents the maximum resident migration rate, and E represents the maximum resident migration rate.

where M represents the maximum mutation rate, P _i represents the species probability, and P _max represents the maximum species probability, where P _max =N.
Calculate the inertial mass of each population using equation (10), update the position of the individual using equation (11),

where SIV _i represents the fitness index variable, M _t represents the inertial mass of the population, best(t) is the current optimal solution, and worst(t) is the current worst solution. be.

When the number of iterations reaches 100, the algorithm is stopped and the current global optimal solution C _best is output. G _best , ie, the node with the maximum energy at the current number of iterations, is selected as the cluster head.

The energy of a network node decreases as the number of iterations of the algorithm increases and is exhausted after a given number of iterations leading to node death. The number of dead nodes in a network represents the life cycle of the network. FIG. 2 shows the impact of three algorithms, IFCEER, DEEC-FA and DHEEC, on the network life cycle. Part (a) of FIG. 2 shows the change in the number of dead nodes for three algorithms, IFCEER, DEEC-FA, and DHEEC, when the maximum number of iterations is 5000 rounds. It is shown that the number of rounds of all node death for three algorithms, IFCEER, DEEC-FA, and DHEEC, is 3754, 2000, and 3568 rounds, respectively. When the first node death of the network occurs, the information collected by the base station is no longer complete, and as we run the algorithm, the number of dead nodes increases, and the few nodes that survive in the later stages have low residual energy. They have lost their ability to communicate. Therefore, in order to more comprehensively evaluate the network life cycle, the number of rounds of first node death (FND) of the three algorithms IFCEER, DEEC-FA and DHEEC is shown in Fig. 2(b). , the number of rounds of 10% node death (TND, 10% node died) and the number of rounds of all node death (LND, last node died) are compared. In IFCEER, only high-energy nodes are candidates for cluster head selection, so the FND and LND are very close, and the FND of DEEC-FA is much smaller than the other two algorithms, which means that the network performance degrades at the beginning of the algorithm. It turned out to be big. In order to balance the energy consumption of the entire network, TND is defined as the life cycle of the network, and the TND of DHEEC is approximately 13.89% higher than that of IFCEER and approximately 41.05% higher than that of DEEC-FA.

In the process of executing the algorithm, the surviving member nodes transmit the collected data to the cluster head, and the cluster head also transmits it to the base station, and when the transmission of all nodes is completed, the base station transfers the data received in this round. Total the number of packets. Therefore, the energy utilization rate is evaluated based on the total number of data packets received by the base station, and the more data packets the base station receives, the more balanced the energy distribution is. FIG. 3 shows the change in the total number of data packets received by the base station after the three algorithms IFCEER, DEEC-FA and DHEEC are iterated for 5000 rounds. This concludes that DHEEC has improved energy utilization by about 31.58% over IFCEER and about 31.06% over DEEC-FA.

Finally, it should be stated that the above embodiments are only for explaining the technical solutions of the present invention in a non-limiting manner, and those skilled in the art can still understand the specific embodiments of the present invention with reference to the above embodiments. Modifications or equivalent substitutions may be made, and any such modifications or equivalent substitutions that do not depart from the spirit and scope of the invention are included within the scope of the claims of the invention.

Claims (3)

1-1) The network node selects a temporary cluster head according to the threshold conditional expression (1), and transmits node information to the base station by the cluster head,
where N sensor nodes s _i (i=1,2...N), C represents a group of nodes that did not become cluster heads, r is the current iteration round number, and mod is is the modulo operator, H(En _i ) is the relative energy entropy of the node,
where k is the expected proportion of the number of clusters, En _i (r) is the residual energy of sensor node s _i in r rounds,
1-2) The base station selects a cluster head using the firefly algorithm, updates the location of the individual based on the population diversity location update policy shown in equation (3), and determines the current global optimal solution C _best The step of obtaining
In the formula, c _i and c _j represent the spatial positions of fireflies i and j,
n is the number of iteration rounds,
β is the light absorption coefficient,
γ ₀ is the maximum attraction degree of fireflies,
r _ij is the Euclidean distance;
1-3) Further search for C _best using an algorithm that combines gravity search algorithm and biogeography-based optimization to obtain global optimal solution G _best , that is, find the node with the maximum energy. a step that is a position;
1-4) The base station calculates the cluster head rotation table based on G _best , loads it into the flow table information and distributes it to the nodes, each node receives and stores the information, and selects the highest energy from each cluster. A distributed, entropy-enhanced, and energy-saving cluster-based routing method for SDWSN, comprising the step of selecting a node as a cluster head. 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 conditional expression (1), extracts one random number between 0 and 1 as a conditional probability,
If the conditional probability is less than the conditional probability, the node selects itself as the temporary cluster head;
When the distance between the node and the base station is greater than the distance between the node and the cluster head, directly transmitting its own location information, residual energy information, and topology information data to the base station;
The method according to claim 1, characterized in that the data is otherwise transmitted to the base station by the cluster head, the cluster head only performing the functions of receiving, merging and forwarding the data. In step 1-2), as a step of using the firefly algorithm,
2-1) Initialize the parameters population size N, initial step size factor α, initial population diversity index β ⁰ , and maximum number of iterations maxIterate,
2-2) Build an initial population C ₀ based on relative energy entropy, calculate by equation (4) and rearrange the firefly brightness I, and the position of the individual with the maximum fluorescence brightness is determined based on the current global The optimal solution C _best is
where I ₀ is the maximum fluorescence brightness of the firefly,
In the formula, c _ik and c _jk are the coordinates of fireflies i and j in the w-th dimension,
The firefly attraction degree γ is
and
2-3) Population diversity location update policy,
That is, update the firefly position using equation (3),
ρ is a weight that changes based on the diversity index β ⁿ shown in equation (8),
As shown in Equation (9), the degree of attraction between fireflies is updated by Equation (6), and at the same time C _best is updated,
In the formula, |s| represents the population size,
In the formula, β ⁰ is the initial population diversity index, σ is a linear decreasing function, and the calculation formula is
and
2-4) When the number of iterations n reaches maxIterate, the algorithm is stopped and the current C _best is output, otherwise steps 2-2) to 2-3) are repeatedly executed. The method according to claim 1.