CN111385853B - Directional diffusion routing method based on improved ant colony algorithm in wireless sensor network - Google Patents

Directional diffusion routing method based on improved ant colony algorithm in wireless sensor network Download PDF

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CN111385853B
CN111385853B CN202010153008.7A CN202010153008A CN111385853B CN 111385853 B CN111385853 B CN 111385853B CN 202010153008 A CN202010153008 A CN 202010153008A CN 111385853 B CN111385853 B CN 111385853B
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袁泉
方正
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Chongqing Information Technology Designing Co ltd
Chongqing University of Post and Telecommunications
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    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
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    • H—ELECTRICITY
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    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04W40/00—Communication routing or communication path finding
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention provides a directional diffusion routing method based on an improved ant colony algorithm in a wireless sensor network. In the method, an improved ant colony algorithm is mainly introduced for the directional diffusion protocol. According to the invention, the node energy factor and the global average energy factor are introduced into the ant colony algorithm, so that the network energy consumption factor is always considered in the process of optimizing the path. In the gradient establishing stage, a gradient path is established by taking pheromone concentration generated by the improved ant colony algorithm as a gradient field. In the path enhancement stage, according to the iteration effect of the ant colony algorithm, the optimal paths generated in each iteration are counted and ranked, and the paths with the top three ranks are enhanced to be used as selectable routing paths. Compared with the original protocol, the improved model is more suitable for the scene with huge network nodes, the life cycle of the network is improved, and the selection of the path is more reasonable.

Description

Directional diffusion routing method based on improved ant colony algorithm in wireless sensor network
Technical Field
The invention belongs to the field of directional diffusion routing algorithms in a wireless sensor network, and particularly relates to a directional diffusion routing method based on an improved ant colony algorithm in the wireless sensor network.
Background
With the advent of the 5G era, the development of the internet of things industry is forced to be promoted, and as an important part of the internet of things, a routing protocol of the wireless sensor network is an important ring in the design of a WSN network, and the routing protocol mainly guides data packets to be forwarded from a source node to a destination node through the network. Under different scenes, WSN routing protocols are also very different. The invention provides improvement aiming at the problems of search strategy of an optimized path and neglect of network energy consumption in a directional diffusion protocol, wherein the directional diffusion protocol is the most basic and common plane routing protocol in a wireless sensor network and is a data-centered query routing protocol. Therefore, in the path searching process, the detection data, the message and the like are sent to the nodes of the whole network in a flooding way, and the path optimizing process can be obtained only by traversing all the nodes. Meanwhile, the node energy consumption is not considered because the node energy consumption is only suitable for networks with smaller scale. After the ant colony algorithm is improved, the optimal path with relatively balanced path distance and energy can be obtained through algorithm simulation, and the method can be applied to scenes with large network scale. And obtaining a directional diffusion model based on the improved ant colony algorithm. In the model, a node energy factor and a global average energy factor are introduced into a gradient establishing stage and a path enhancing stage of a directional diffusion protocol, so that a network energy consumption factor is always considered in the process of optimizing a path. Compared with the original protocol, the improved model is more suitable for the scene with huge network nodes, the life cycle of the network is improved, and the selection of the path is more reasonable.
Disclosure of Invention
The present invention is directed to solving the problems of the prior art. A directional diffusion routing method based on an improved ant colony algorithm in a wireless sensor network is provided. The technical scheme of the invention is as follows:
a directional diffusion routing method based on an improved ant colony algorithm in a wireless sensor network comprises the following steps:
the method comprises the following steps: an interest flooding step; the sink node broadcasts interest to the wireless sensor network through full-network flooding periodically, and the target node determines the position of a source node after receiving the corresponding interest message;
step two: establishing a gradient; starting from a source node, using a sink node as a destination node, operating an improved ant colony algorithm, wherein the improved ant colony algorithm is mainly characterized in that a node energy factor and a global average energy factor are added into the ant colony algorithm, the method is embodied in an updating method of a heuristic function with an improved transition probability function and an pheromone updating function, initializing iteration times and ant number, initializing initial energy of each node, searching a next node according to the improved transition probability, and the improvement of the transition probability mainly lies in the improvement of the heuristic function, wherein the node energy factor is added, and the energy factor is sent and received; meanwhile, pheromones are left on the paths, pheromone concentration gradient fields are iterated finally through the continuous exploration of all ants, and nodes with the maximum gradient are sequentially found in the gradient establishing stage to form gradient paths;
step three: path enhancement; and according to the pheromone concentration gradient field in the second step, calculating and counting the optimal first 3 paths under N iterations, enhancing and routing, wherein the enhancing method in the path enhancing process still comprises the step of reversely sending an interest enhancing message by a sink node and reversely establishing the path in the direction of the maximum pheromone concentration.
Further, the interest flooding step in the step one still adopts the flooding process in the original directed diffusion protocol, and does not change.
Further, the initialization iteration number and the ant number of the second step and the initialization initial energy of each node are specifically as follows: in the wireless sensor network, assuming that n nodes exist, the nodes are initialized first, and initial energy E is given to the nodes1，E2，E3，...，EnInitializing the node pheromone tau with the same energy of n nodes at the initial time1，τ2，τ3...，τnThe number of initialization iterations N is 0, and the number of ants is m.
Further, the step two of calculating the pheromone concentration gradient field comprises the following steps:
(1) the improved ant colony algorithm transition probability is shown in the formulas (15) and (16):
Figure GDA0002482702510000021
Figure GDA0002482702510000031
in the formula (15), the reaction mixture is,
Figure GDA0002482702510000032
represents the transition probability of the ant k from the node i to the node j at the time t, tauij(t) represents the amount of information remaining on the path (i, j) at time t, ηijFor the heuristic function, α is an information heuristic factor, β is an expectation heuristic factor, s denotes a next hop node, τis(t) pheromone from node i to node s in the current search, etaisThe heuristic function from the node i to the node s is shown, and allowedk shows that ant k allows selection in the next stepA set of nodes of (c);
in formula (16), EiAnd EjResidual energy values of nodes i, j, respectively, ETxIs the power consumption of the sensor node transmitting data, ERxThe power consumption of the sensor node for receiving data is;
the energy consumption of the sensor node for sending kbit data is as follows:
Figure GDA0002482702510000033
the sensor node receives kbit data and consumes energy:
ERx(k)＝kEelec (4)
in the formula (17), d0D represents the Euclidean distance between two nodes, epsilonfsAnd εmpFor power consumption of amplifiers, EelecEnergy consumption is unit bit data;
and each ant selects the next hop node according to the transition probability, modifies the tabu table and records the retained pheromone after walking one node, and finishes one iteration when m ants finish walking the path to reach the sink node, and updates the pheromone.
(2) In the pheromone updating stage, the pheromone is updated by selecting a path and energy in a combined manner, and the path with a shorter path and the path with a higher average energy are defined as an optimal path, so that the concentration of the pheromone on the path is highlighted compared with other paths, and the updating method is shown in the formulas (19), (20) and (21):
τij(t+n)＝(1-ρ)τij(t)+Δτij best (5)
Figure GDA0002482702510000041
Figure GDA0002482702510000042
in the formula (19), ρ is a pheromone volatilization factor,
Figure GDA0002482702510000043
indicating the amount of pheromone, omega, that needs to be added on the defined optimal path0And rho is more than 0 and less than 1, which represents that the pheromone from i to j at the moment t + n is equal to the sum of the part remained after the pheromone is volatilized at the moment t and the pheromone increment on the optimal path under the global condition; pathbestA criterion representing a defined best path.
Figure GDA0002482702510000044
Representing the inverse of the average residual energy of all nodes on a path;
the definition of the optimal path also adds the path average residual energy factor, E in equation (21)avgRepresents the path average residual energy factor with a value equal to the average of the energies of all nodes on the path, L represents a path distance of all possible paths, ω0Is a weight used to control the importance between path length and average energy when defining an optimal path;
step 2.2: establishment of a gradient field
According to the ant colony algorithm improved in the step 2.1, after the operation is finished, a gradient field determined by pheromone concentration is generated, in the gradient establishing process, a source node sequentially finds a next hop node along the direction with the maximum pheromone concentration, and finally finds a sink node, so that an optimal gradient path comprehensively considering distance and energy is formed.
Further, the improvement of the process of enhancing the path in step three specifically includes: according to the iteration process of the ant colony algorithm, an optimal Path is generated in each iteration process, under the pheromone updating mechanism, after the iteration times are finished, the optimal 3 paths are selected through the optimal Path ranking, namely, the optimal Path set is calculated and the paths are ordered from small to largebest＝{Pathbest1,Pathbest2,Pathbest3… …, selecting the first three, finding all the nodes passed by the path through the record, and enhancing the path process, the source node edgeThe data is sent in the gradient direction and the sink node will correspondingly strengthen the three best paths according to the pheromone level value.
The invention has the following advantages and beneficial effects:
1. according to the invention, through the improved ant colony algorithm, the route of the traditional directional diffusion protocol is optimized, the energy consumption and resource waste situation under the flooding diffusion mode is reduced, the improved pheromone gradient field is utilized to provide a redundant path for the model, and the robustness of the network is increased.
2. In the second step, the gradient establishing process is optimized, firstly, the improved ant colony algorithm is utilized to simulate the data sending process, the transition probability is optimized through the distance and the energy of the next hop node, and on the basis, the pheromone updating formula is optimized according to the global average energy and the path distance, and finally, the simulation of the improved ant colony algorithm is completed to form the gradient field network of the pheromone. The processing ensures that the path optimization considers the distance and node energy factors, and is beneficial to prolonging the life cycle of the network. In the directional diffusion gradient establishment, the original gradient establishment process is a large-scale data flooding exploration process, and then the gradient is established mainly at the speed of transmitting data to a convergent point. The ant colony algorithm has the concept of pheromone concentration, the improved algorithm considers the updated pheromone concentration under the conditions of distance and energy, and has the standard of guiding gradient establishment and the standard of guiding the optimal path.
3. The invention carries out redundancy improvement on the path enhancement process in the step three. And D, according to the iteration process of the improved ant colony algorithm in the step II, generating an optimal path in each iteration, counting and calculating the first three paths with the optimal ranking, and reinforcing. The processing is beneficial to the situation that the energy consumption is too high when the data transmission is in the optimal path for a long time in the later stage of network formation, the redundant path improves the life cycle of the network and simultaneously strengthens the robustness of the network, and the part utilizes the ant colony iterative exploration process in the second step, so that the optimal path can be found and the suboptimal path can be provided for the network through the iterative action. Compared with routing protocols such as HREEMR and the like, although suboptimal paths are searched for the network in the path enhancing process, extra complicated steps are needed, the method can directly utilize the exploration process of the improved ant colony algorithm to provide redundant paths, and unnecessary steps and energy consumption are avoided. In the method, three redundant paths increase the robustness of the network and improve the whole life cycle of the network.
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Fig. 1 is a flow chart of a directional diffusion routing method based on an improved ant colony algorithm in a wireless sensor network according to the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described in detail and clearly in the following with reference to the accompanying drawings. The described embodiments are only some of the embodiments of the present invention.
The technical scheme for solving the technical problems is as follows:
the technical scheme for solving the technical problems is as follows:
step 1: interest flooding;
no change is made according to the original directed diffusion protocol. The sink node broadcasts interest periodically to the whole network in a flooding way, and the target node receives the corresponding interest message and then determines the position of the source node.
Step 2: establishing a gradient;
according to a special network model of a directional diffusion protocol, an ant colony algorithm is improved, and a node energy factor and a global average energy factor are added, so that the ant colony algorithm is suitable for planning network routing. On the basis, the improved ant colony algorithm is utilized to carry out path optimization from the source node to the sink node. Starting from a source node, using a sink node as a destination node, operating an improved ant colony algorithm, initializing iteration times and the number of ants, initializing initial energy of each node, searching a next node according to the improved transition probability, simultaneously leaving pheromones on a path, continuously exploring by all the ants to finally iterate a gradient field of the pheromones, and sequentially finding nodes with the maximum gradient to form a gradient path in a gradient establishing stage.
The method specifically comprises the following steps:
step 2.1: running improved ant colony algorithm
An initialization operation is first performed. In the wireless sensor network, assuming that n nodes exist, the nodes are initialized firstly, and initial energy E is given to the nodes1，E2，E3，...，EnAnd n nodes at the initial time have the same energy. Initializing a node pheromone τ1，τ2，τ3...，τn. And the number of initialization iterations N is 0, and the number of ants is m. And running the improved ant colony algorithm.
(1) The improved ant colony algorithm transition probability is shown in the formulas (15) and (16):
Figure GDA0002482702510000071
Figure GDA0002482702510000072
in the formula (15), the reaction mixture is,
Figure GDA0002482702510000073
representing the transition probability of ant k from node i to node j at time t. Tau isij(t) represents the amount of information remaining on the path (i, j) at time t, ηijIs a heuristic function. Alpha is an information heuristic factor; beta is an expected heuristic factor, s represents a next hop node, tauis(t) pheromone from node i to node s in the current search, etaisAnd the heuristic function from the node i to the node s is explored at this time, and allowedk represents the set of nodes allowed to be selected by the ant k in the next step.
In the formula (16), EiAnd EjIs the residual energy value of node i, j. ETxIs the power consumption of the sensor node transmitting data, ERxThe power consumption of the sensor node receiving data is n, 2 or 4 is selected, and the two parameters are obtained according to a wireless sensor power consumption model, and are specifically shown in formulas (17) and (18):
the energy consumption of the kbit data sent by the sensor node is as follows:
Figure GDA0002482702510000074
the sensor node receives kbit data and consumes energy:
ERx(k)＝kEelec (11)
in the formula (17), d0D represents the Euclidean distance between two nodes as the distance threshold. EpsilonfsAnd εmpThe amplifier is powered down. EelecThe energy consumption is unit bit data.
And each ant selects the next hop node according to the transfer probability, modifies the tabu table and records the left pheromone after walking one node, and finishes one iteration until m ants all walk the path to reach the sink node. The pheromone is updated at this time.
(2) In the pheromone updating stage, the pheromone is updated in a way of combining and selecting the path and the energy. And defining the path with the shorter path and the higher average energy as the best path, whereby the concentration of pheromones on such path will be prominent compared to other paths. The updating method is shown in the formulas (19), (20) and (21):
τij(t+n)＝(1-ρ)τij(t)+Δτij best (12)
Figure GDA0002482702510000081
Figure GDA0002482702510000082
in the formula (19), ρ is a pheromone volatilization factor, 0 < ρ < 1, and Δ τij bestIndicating the quantity of pheromones to be added on the defined optimal path, indicating that the pheromones from i to j at time t + n are equal to the fraction retained after evaporation of the pheromone at time t and the pheromone increment on the optimal path under global conditionsAnd (c). PathbestA criterion representing a defined best path.
Figure GDA0002482702510000083
Representing the inverse of the average residual energy of all nodes on a path.
The definition of the optimal path also incorporates a path-averaged residual energy factor. E in formula (21)avgRepresents the path average residual energy factor, whose value is equal to the average of the energies of all nodes on the path. L represents a path distance, ω, of all possible paths0Is a weight used to control the importance between path length and average energy when defining an optimal path.
Step 2.2: establishment of a gradient field
According to the ant colony algorithm improved in the step 2.1, after the operation is finished, a gradient field determined by pheromone concentration is generated, in the gradient establishing process, a source node sequentially finds a next hop node along the direction with the maximum pheromone concentration, and finally finds a sink node, so that an optimal gradient path comprehensively considering distance and energy is formed.
And step 3: path enhancement; according to the iteration process of the ant colony algorithm, an optimal path is generated in each iteration process. Under the pheromone updating mechanism, after the iteration times are finished, the optimal 3 paths are selected through the optimal Path ranking, namely, the optimal Path set is calculated and the paths are ordered from small to largebest＝{Pathbest1,Pathbest2,Pathbest3… …, and selecting the top three, and finding all nodes corresponding to the path through the record. In the process of path enhancement, the source node sends data along the gradient direction, and the sink node correspondingly enhances the three optimal paths according to the pheromone level value. The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (4)

1. A directional diffusion routing method based on an improved ant colony algorithm in a wireless sensor network is characterized by comprising the following steps:
the method comprises the following steps: an interest flooding step; the sink node broadcasts interest to the wireless sensor network through full-network flooding periodically, and the target node determines the position of a source node after receiving the corresponding interest message;
step two: establishing a gradient; starting from a source node, using a sink node as a destination node, operating an improved ant colony algorithm, wherein the improved ant colony algorithm is mainly characterized in that a node energy factor and a global average energy factor are added into the ant colony algorithm, the method is embodied in an updating method of a heuristic function with an improved transition probability function and an pheromone updating function, initializing iteration times and ant number, initializing initial energy of each node, searching a next node according to the improved transition probability, and the improvement of the transition probability mainly lies in the improvement of the heuristic function, wherein the node energy factor is added, and the energy factor is sent and received; meanwhile, pheromones are left on the paths, pheromone concentration gradient fields are iterated finally through the continuous exploration of all ants, and nodes with the maximum gradient are sequentially found in the gradient establishing stage to form gradient paths;
step three: path enhancement; according to the pheromone concentration gradient field in the second step, calculating and counting the optimal first 3 paths under N times of iteration, enhancing and routing, wherein the enhancing method of the path enhancing process still comprises the steps that a sink node reversely sends an interest enhancing message, and the path is reversely established in the direction of the maximum pheromone concentration;
the second step of calculating the pheromone concentration gradient field comprises the following steps:
(1) the improved ant colony algorithm transition probability is shown in the formulas (15) and (16):
Figure FDA0003542609180000011
Figure FDA0003542609180000012
in the formula (15), the reaction mixture is,
Figure FDA0003542609180000013
represents the transition probability of the ant k from the node i to the node j at the time t, tauij(t) represents the amount of information remaining on the path (i, j) at time t, ηijFor the heuristic function, α is an information heuristic factor, β is an expectation heuristic factor, s denotes a next hop node, τis(t) pheromone from node i to node s in the current search, etaisExpressing an heuristic function from an i node to an s node in the exploration, wherein allowedk expresses a set of nodes allowed to be selected by ant k in the next step;
in the formula (16), EiAnd EjResidual energy values of nodes i, j, respectively, ETxIs the power consumption of the sensor node transmitting data, ERxThe sensor node receives data power consumption;
the energy consumption of the sensor node for sending kbit data is as follows:
Figure FDA0003542609180000021
the sensor node receives kbit data and consumes energy:
ERx(k)＝kEelec (4)
in the formula (17), d0D represents the Euclidean distance between two nodes, epsilonfsAnd εmpFor amplifier power consumption, EelecEnergy consumption is unit bit data;
each ant selects the next hop node according to the transition probability, modifies the taboo table and records the retained pheromone after walking one node, and one iteration is completed until m ants all walk the path to reach the sink node, and then the pheromone is updated;
(2) in the pheromone updating stage, the pheromone is updated by selecting a path and energy in a combined manner, and the path with a shorter path and the path with a higher average energy are defined as an optimal path, so that the concentration of the pheromone on the path is highlighted compared with other paths, and the updating method is shown in the formulas (19), (20) and (21):
τij(t+n)＝(1-ρ)τij(t)+Δτij best (5)
Figure FDA0003542609180000022
Figure FDA0003542609180000023
in the formula (19), ρ is a pheromone volatilization factor, Δ τij bestIndicating the amount of pheromone, omega, that needs to be added on the defined optimal path0And rho is more than 0 and less than 1, which represents that the pheromone from i to j at the moment t + n is equal to the sum of the part remained after the pheromone is volatilized at the moment t and the pheromone increment on the optimal path under the global condition; pathbestA criterion representing a defined best path;
Figure FDA0003542609180000024
representing the inverse of the average residual energy of all nodes on a path;
the definition of the optimal path also adds the path average residual energy factor, E in equation (21)avgRepresents the path average residual energy factor with a value equal to the average of the energies of all nodes on the path, L represents a path distance of all possible paths, ω0Is a weight used to control the importance between path length and average energy when defining an optimal path;
step 2.2: establishment of a gradient field
According to the ant colony algorithm improved in the step 2.1, after the operation is finished, a gradient field determined by pheromone concentration is generated, in the gradient establishing process, a source node sequentially finds a next hop node along the direction with the maximum pheromone concentration, and finally finds a sink node, so that an optimal gradient path comprehensively considering distance and energy is formed.
2. The method for directional diffusion routing based on the improved ant colony algorithm in the wireless sensor network according to claim 1, wherein the interest flooding step in the first step still takes the flooding process in the original directional diffusion protocol without change.
3. The directional diffusion routing method based on the improved ant colony algorithm in the wireless sensor network according to claim 1, wherein the initialization iteration number and the ant number in the second step and the initialization initial energy of each node are specifically as follows: in the wireless sensor network, assuming that n nodes exist, the nodes are initialized first, and initial energy E is given to the nodes1，E2，E3，...，EnInitializing the node pheromone tau with the same energy of n nodes at the initial time1，τ2，τ3...，τnThe number of initialization iterations N is 0, and the number of ants is m.
4. The method for directional diffusion routing based on the improved ant colony algorithm in the wireless sensor network according to claim 1, wherein the improvement of the process of enhancing the path in the third step specifically comprises: according to the iteration process of the ant colony algorithm, an optimal Path is generated in each iteration process, under the pheromone updating mechanism, after the iteration times are finished, the optimal 3 paths are selected through the optimal Path ranking, namely, the optimal Path set is calculated and the paths are ordered from small to largebest＝{Pathbest1,Pathbest2,Pathbest3… …, selecting the three nodes, finding all the nodes corresponding to the path, and under the process of enhancing the path, the source node sends data along the gradient direction, and the sink node correspondingly enhances the three best paths according to the pheromone level value.
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