CN113747535B - Genetic algorithm-based energy heterogeneous wireless sensor routing system - Google Patents
Genetic algorithm-based energy heterogeneous wireless sensor routing system Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
- H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses an energy heterogeneous wireless sensor routing system based on a genetic algorithm, and belongs to the technical field of network management. The cluster head multi-hop path planning system comprises a cluster head election module, a cluster construction module and a cluster head multi-hop path planning module; the cluster head election module screens out optimal individuals by calculating individual adaptation values, intersecting and mutating the individuals and carrying out iterative computation, and the screened multiple optimal individuals form an optimal individual set, and the cluster head election module is used for electing the optimal individual set.
Description
Technical Field
The invention relates to the technical field of network management, in particular to an energy heterogeneous wireless sensor routing system based on a genetic algorithm.
Background
In energy-constrained wireless sensor networks, clustering routing algorithms can effectively improve network lifetime while meeting load balancing requirements, LEACH is a classical clustering algorithm that selects cluster heads based on round-robin concepts, but the randomness of cluster head elections can lead to problems that can further lead to premature death of sensor nodes and an imbalance in energy consumption, followed by a significant reduction in network lifetime.
In a wireless sensor network, a large number of densely distributed sensor nodes consume most energy to collect, process and forward sensing data, so that the energy consumption is overlarge, the service life of the network is reduced, the network performance is reduced, in the wireless sensor network, single-hop communication is generally adopted, the time delay of data communication in a large-scale network is greatly increased, and when sensor nodes with insufficient energy are selected as cluster heads, the existing algorithm only considers the residual energy, and the influence of the distance and the node density is ignored, so that the cluster heads are unevenly distributed.
Disclosure of Invention
The invention aims to provide an energy heterogeneous wireless sensor routing system based on a genetic algorithm, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the cluster head multi-hop path planning system comprises a cluster head election module, a cluster construction module and a cluster head multi-hop path planning module;
the cluster head election module screens the sensor nodes, forms an optimal individual set from a plurality of screened optimal individuals, transmits the elected optimal individual set to the cluster construction module, and is used for electing the optimal individual set;
the cluster construction module is used for receiving the optimal individual set transmitted by the cluster head election module, the optimal individual sets form a cluster by searching sensor nodes, and the cluster construction module is used for selecting proper common sensor nodes with the cluster head as the center to form a new cluster;
the cluster head multi-hop path planning module is used for planning the shortest path from the cluster head to the base station by traversing all the cluster head nodes and combining the energy factors to plan the shortest path from the cluster head to the base station.
Further, the cluster head election module comprises an initial data receiving unit, a population initializing unit, a coding unit, an individual adaptation value calculating unit, a crossing unit, a mutation unit and an iterative calculating unit;
the initial data receiving unit inquires node information of all sensors in a base station broadcast and sensor network and sends the inquired node information to the base station, the base station stores the received node information of all sensors, marks each sensor node and transmits the stored node information of all sensors to the population initializing unit, and the initial data receiving unit is used for transmitting the node information of the sensors to the base station and the population initializing unit;
the population initialization unit receives node information of all the sensors transmitted by the initial data receiving unit, counts the specific number of the sensor nodes, sets population scale according to the specific number of the sensor nodes, randomly generates initial population according to the population scale, transmits the randomly generated initial population to the coding unit, and randomly generates the initial population by the population initialization unit according to the set population scale;
the coding unit receives the initial population transmitted by the population initializing unit, each individual in the population is a variable sequence, called a chromosome or a gene string, codes the chromosome gene corresponding to each node in a decimal coding mode, each node is provided with a unique serial number corresponding to the chromosome gene code of the node, the coded individual is transmitted to the individual adaptation value calculating unit, the decimal coding is directly numbered in a digital form, the optimal population problem is solved, the coding unit is used for expressing each individual in a serial number form, and the chromosome gene change of the individual is convenient to observe and judge intuitively;
the individual adaptation value calculation unit calculates the adaptation value of each coded individual according to the adaptation function, arranges each individual in the population from high to low according to the adaptation value, transmits the rearranged population to the intersection unit, receives newly generated individuals which do not reach the maximum iteration number or the adaptation value error value within the allowable range, calculates, selects, crosses and varies again, and is used for calculating the adaptation value of each coded individual;
the cross unit receives the rearranged population, randomly sets a cross point in the code string of each individual in the population, randomly selects one individual to exchange partial chromosomes of two individuals at the cross point, transmits the cross-treated population to the mutation unit, and is used for carrying out cross treatment on the partial chromosomes in the individuals, namely exchanging the chromosome genes of the individuals, and selecting a new surviving individual formed after the cross treatment as a mutation target individual;
the mutation unit receives the population processed by the crossing unit, carries out mutation operation on part of chromosomes in the crossed individuals to generate new individuals, and transmits the new individuals generated after mutation to the iterative computation unit, wherein the mutation unit is used for carrying out mutation treatment on the crossed individuals, waiting for a period of time before the mutation treatment of the individuals, avoiding incomplete crossing treatment of the individuals, and the mutation unit is used for reversing the chromosome gene sequences in the new individuals surviving the crossing treatment to realize individual mutation and selecting the new individuals surviving the mutation treatment as iterative targets;
the iteration calculation unit receives new individuals generated after the mutation processing, carries out iteration processing on the new individuals, outputs the new individuals which reach the maximum iteration times or the adaptive value error value within the allowable range to form an optimal individual set, conveys the newly generated individuals which do not reach the maximum iteration times or the adaptive value error value within the allowable range to the individual adaptive value calculation unit for carrying out correlation processing again, and conveys the optimal individual set to the cluster construction module, wherein the iteration calculation unit is used for selecting a plurality of optimal individuals in a population, namely selecting a plurality of optimal cluster heads.
Further, the fitness function of the individual fitness value calculating unit is:
wherein Fitness represents the adaptation value size, d to BS is the distance between the node and the base station, neighbor is the number of Neighbor nodes, E n_res The rest energy of the node is alpha, beta and theta are weight coefficients, and the weight coefficients satisfy alpha+beta+θ=1, the weight coefficient is adjusted according to the magnitude of the influence fitness value, i represents the number corresponding to each node, and N represents the total number of nodes.
Further, the initial cross probability is set to be 0.6, the cross probability of the next round is obtained by means of the cross probability of the previous round, and a specific calculation formula of the cross probability is as follows:
wherein P is c Represents the crossover probability, P c1 Represents the initial crossover probability, P c2 Represents the crossover probability obtained by the last iterative crossover process, f max Is the fitness value of the optimal individual in the population, f ang Is the average fitness of the population and f' is the larger fitness of the two individuals to be crossed.
Further, the initial variation probability is 0.1, the variation probability of the next round is obtained by means of the variation probability of the previous round, and a specific calculation formula of the variation probability is as follows:
wherein P is m Representing the probability of variation, P m1 Represents the initial mutation probability, P m2 Representing the variation probability obtained after the last iteration variation treatment, f max Is the fitness value of the optimal individual in the population, f ang Is the average fitness value of the population, f "represents the fitness value of the variant individual, and the initial variation probability can also be set to a value less than 0.1.
Further, the cluster construction module receives the optimal individual set transmitted by the cluster head election module, the individuals in the set broadcast the message which becomes the cluster head, the common sensor nodes receive the message transmitted by the individuals, select to join a certain cluster head in the set, the cluster head and all the common sensor nodes which join the cluster head form a cluster, meanwhile, in the construction process, the common sensor nodes judge which cluster is particularly added according to the relation among the residual energy of the cluster head, the distance between the cluster head and the base station and the distance between the sensor nodes and the cluster head, and the cluster construction module is used for selecting the appropriate common sensor nodes with the cluster head as the center to form a new cluster.
Further, the following formula relation is satisfied among the cluster head residual energy, the distance between the cluster head and the base station and the distance between the sensor node and the cluster head, and the specific formula is as follows:
wherein E is i Indicated is the energy of the i-th cluster head node,represents the average energy of h cluster heads, d toCH Distance d is the distance from the common node to the cluster head toBS Lambda is the distance between the target cluster head node and the base station 1 、λ 2 And lambda (lambda) 3 Is a proportionality coefficient lambda 1 ＝0.55，λ 2 ＝0.3，λ 3 ＝0.15，T h Indicating the composite index.
Further, the common sensor node is used for detecting the total index T h The specific steps for judging which cluster to add are as follows:
step one: selecting a common node, and carrying out calculation on the distance information between each cluster head and the common node, the distance information between the cluster head and the base station and the residual energy of the cluster head into a comprehensive index calculation formula;
step two: arranging the comprehensive indexes obtained after calculation according to the sequence from big to small, ensuring that all cluster heads are selected and the calculation is completed, and avoiding the phenomenon that the cluster heads are omitted;
step three: after the calculation is completed, the comprehensive indexes calculated between the same common sensor node and different cluster heads are compared, the cluster to which the common sensor node is added is judged by comparing the magnitude of the comprehensive indexes, and whether the node is added to the cluster head is judged by the comprehensive indexes, so that the energy consumption is reduced;
step four: and traversing all the common sensor nodes, and repeating the steps, thereby realizing the division and formation of clusters.
Further, the cluster head multi-hop path planning module plans the shortest path from the cluster head to the base station through the Floyd algorithm and combining the cluster head energy consumption, and a specific shortest path calculation formula is as follows:
distance judgment conditions:
D i_to ^2+D to_BS ^2＜D i_to_BS ^2；
wherein D is i-to D is the distance between the current node and the next hop node to_BS D is the distance between the next hop node and the base station i_to_BS The distance between the current node and the base station;
when the distance judging condition is met, the energy consumption of the current node reaching the base station through the jump node is lower than the energy consumption of the current node directly reaching the base station, the path of the current node finally reaching the base station through the intermediate nodes which meet the judging condition is expressed as the shortest path, the data transmission distance and the consumed energy show a quadratic relation, the distance judging condition is in the form of the 2 nd power of the Euclidean distance, and the energy factor is indirectly considered by the way;
multi-hop transmission judgment conditions:
wherein d 0 Epsilon is the critical distance from the cluster head to the base station fs Is the energy consumption coefficient epsilon of a power amplifier in a free space channel model amp Is the power amplification factor of the multipath fading channel model, if the distance between the end node and the base station is greater than d 0 The end node cannot transmit the node information to the base station, and the data information of the end node needs to be transmitted to the base station through the intermediate node at the moment, when the distance between the end node and the base station is smaller than d 0 And in the process, multi-hop transmission is not needed, so that the communication can be directly performed, and the energy consumption is reduced by adopting multi-hop communication.
Further, the specific steps of the Floyd algorithm for planning the shortest path by combining the cluster head energy consumption are as follows:
step1: numbering the cluster heads, and putting the source node numbers into a path set S;
step2: judging whether the shortest path between the source node and the base station is obtained, outputting a path set S if the shortest path is obtained, and searching a vertex U which is not in the set S and has the minimum distance with the source node if the shortest path is not completely obtained, wherein the vertex U also needs to meet a distance judging condition;
step3: if the vertex U is found, the vertex U is merged into the set S, the operation of the second step is repeated for the new set, the U is temporarily used as a source node to solve the next hop, and if the vertex U is not found, whether the distance between the current node and the base station is smaller than d is judged 0 If less than d 0 Then the self is the last hop, if greater than d 0 Traversing the neighbor node to find the nearest point which can be directly communicated with the base station as the last hop;
step4: and adding the number of the last hop into the set S to obtain the shortest path.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the cluster head nodes are uniformly distributed, so that the sensor nodes added with the corresponding cluster heads and the sensor nodes with less uniformly distributed cluster head residual energy and the cluster heads have more residual energy, and further, when the sensor nodes are used as the cluster head relay points, the consumption energy for collecting, processing and forwarding the cluster heads and the sensor node data is less, and the network service life and the network performance are further prolonged.
2. The invention replaces the single-hop communication of the cluster head by the multi-hop communication, and the cluster head data packet communication is carried out in a multi-hop mode, so that the energy consumed from the cluster head to the base station is less and is smaller than the energy consumption of direct communication, and the energy consumption of data communication in a large-scale network is further reduced by planning the shortest hop path.
3. According to the invention, the cluster head and the sensor nodes added into the cluster head accord with the principle of 'energy consumption' by taking the cluster head residual energy, the distance between the cluster head and the base station and the distance between the sensor nodes and the cluster head into consideration in the construction of the cluster, so that the load of the network is more balanced.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a genetic algorithm workflow diagram of an energy heterogeneous wireless sensor routing system based on a genetic algorithm of the present invention;
FIG. 2 is a flow chart of the Fluedel algorithm in a cluster head multi-hop path planning module of the energy heterogeneous wireless sensor routing system based on a genetic algorithm;
FIG. 3 is a schematic diagram of the number of rounds of death occurring at a first node of an energy heterogeneous wireless sensor routing system based on a genetic algorithm according to the present invention;
FIG. 4 is a schematic diagram of the change in the number of cycles of death of a first node in the energy heterogeneous wireless sensor routing system based on a genetic algorithm under the condition of a change in the density of sensor nodes;
FIG. 5 is a graph comparing the number of rounds of death of a first node of the energy heterogeneous wireless sensor routing system based on a genetic algorithm under the condition of the change of the width of a network area;
FIG. 6 is a diagram showing the overall remaining energy of a network of the heterogeneous wireless sensor routing system according to the present invention as a function of the number of rounds based on a genetic algorithm;
FIG. 7 is a graph showing the comparison of the change of network energy residual rate under the condition of the change of sensor node density in the energy heterogeneous wireless sensor routing system based on the genetic algorithm;
FIG. 8 is a graph showing the comparison of the change of network energy residual rate under the condition of the change of the network area width of the energy heterogeneous wireless sensor routing system based on the genetic algorithm;
FIG. 9 is a schematic diagram showing the number of remaining surviving nodes of the energy heterogeneous wireless sensor routing system according to the present invention according to the round change;
FIG. 10 is a graph of the survival rate change of nodes compared with the change of the sensor node density of the energy heterogeneous wireless sensor routing system based on the genetic algorithm;
FIG. 11 is a graph showing the survival rate change of nodes of the energy heterogeneous wireless sensor routing system based on the genetic algorithm under the condition of the change of the network area width;
FIG. 12 is a schematic diagram of network delay conditions corresponding to different pause times of an energy heterogeneous wireless sensor routing system based on a genetic algorithm according to the present invention;
FIG. 13 is a graph of average time delay contrast of a network of the energy heterogeneous wireless sensor routing system based on a genetic algorithm under the condition of sensor node density change;
FIG. 14 is a graph of mean time delay contrast of a network of the energy heterogeneous wireless sensor routing system of the present invention under varying network area widths based on a genetic algorithm;
FIG. 15 is a graph comparing throughput in different density networks of an energy heterogeneous wireless sensor routing system based on a genetic algorithm according to the present invention under the condition of sensor node density variation;
fig. 16 is a graph of throughput versus throughput in different density networks for a genetic algorithm-based heterogeneous wireless sensor routing system of the present invention with varying network area widths.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples: the invention provides a uniform clustering routing algorithm based on a genetic algorithm, namely a GACR algorithm, a region with the length of 100m is selected, 100 sensor nodes are randomly distributed in the region, the initial energy of the sensor nodes is 0.02J, the data packet size of the sensor nodes is 4000bit, the GACR algorithm and the LEACH-P algorithm adopt a multi-hop network to plan the shortest path, the KAEC algorithm and the LEACH algorithm adopt a single-hop network to plan the shortest path, the LEACH is a classical clustering algorithm, cluster heads are selected based on the concept of round, the LEACH-P is a chained routing algorithm, the multi-hop path planning of the cluster heads is completed through a multi-hop network, and the KAEC algorithm is improved by a K-Means algorithm to solve the problem of uniform coverage of the cluster heads.
Comparative example one: referring to fig. 3-5, under the condition that other set values are unchanged, analyzing the turn condition of death of the first node in the four algorithms by respectively changing the number of the sensor nodes and the area of the network area;
<1> referring to fig. 3, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, if the set value satisfies the first embodiment, part of the sensor nodes in the LEACH algorithm simulation die prematurely due to too fast energy consumption, and the GACR algorithm prolongs the service life of the sensor nodes;
<2> in the four algorithm simulations of the GACR, KAEC, LEACH-P and LEACH under the condition that the number of sensor nodes is increased from 40 to 200 and other set values satisfy the first embodiment, as the sensor nodes are increased, the death time of the first node in the simulation of the GACR algorithm is later and later, and in the other three algorithms, the death time of the first node is earlier and earlier, so that the GACR algorithm is more suitable for a large-scale network than the other three algorithms, and the GACR algorithm is not suitable for a small-scale network because in the small-scale network, due to the location and the non-sustainability of the nodes, excellent nodes satisfying the energy conditions, the neighbor node density and the distance from a base station become less, and the like, the nodes need to be continuously selected as cluster heads in the GACR algorithm, which can lead to the fact that the alternative nodes cannot satisfy the demands, and thus the death time of the nodes is too early;
<3> under the condition that the network area is enlarged from 20x20m to 140mx140m, and other set values meet the condition of the first embodiment, referring to fig. 5, since the node under test dies rapidly when the network area width is greater than 150m, the network load is more balanced by selecting to finally enlarge the area to 140x140m, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, as the network is continuously enlarged, the KAEC curve, LEACH-P curve and LEACH algorithm curve are basically overlapped, compared with the fact that the GACR algorithm delays the death time of the first node;
in summary, under the basic condition or the condition of increasing the number of sensor nodes and enlarging the area of a network area, the death time of the first node in the simulation of the GACR algorithm is the latest, and the GACR algorithm is more suitable for a large-scale network.
Comparative example two: referring to fig. 6-8, under the condition that other set values are unchanged, the network energy remaining conditions in the four algorithms are analyzed by respectively changing the number of sensor nodes and the area of a network area;
<1> referring to fig. 6, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, the energy consumption of the LEACH-P algorithm in the first half of the simulation is better than that of the LEACH-P algorithm, and the remaining energy in the LEACH-P algorithm is greater than that in the KAEC algorithm in the later stage of the network simulation, and the remaining energy in the GACR algorithm is at most about 3-5 times as compared with the other three algorithms after 600 rounds of simulation;
<2> referring to fig. 7, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH under the condition that the number of sensor nodes is increased from 40 to 200 and other set values satisfy the first embodiment, as the node density is continuously increased, the remaining energy of the four algorithms of GACR, KAEC, LEACH-P and LEACH is gradually increased, but the remaining energy of the GACR algorithm is the greatest under the condition that the node density is the same, and the trend of increasing the remaining energy is more obvious as the sensor node density is increased;
<3> under the condition that the network area is enlarged from 20x20m to 140mx140m and other set values meet the condition of the first embodiment, referring to fig. 8, in simulation of four algorithms, GACR, KAEC, LEACH-P and LEACH, as the width of the network area is continuously increased, the energy residual rate of the four algorithms is gradually reduced, but the GACR algorithm always keeps the advantage, and the residual energy is larger;
in summary, under the basic condition or the condition of increasing the number of sensor nodes and enlarging the area of the network area, the energy remaining amount in the simulation of the GACR algorithm is more compared with other three algorithms.
Comparative example three: referring to fig. 9-11, under the condition that other set values are unchanged, the number of the remaining surviving nodes in the four algorithms is analyzed along with the change condition of the turn by respectively changing the number of the sensor nodes and the area of the network area;
<1> referring to fig. 9, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, most of the sensor nodes in the simulation of the GACR algorithm survive, and when 600 rounds of simulation are performed, the number of surviving sensor nodes using the GACR algorithm is the largest and the survival rate is higher, indicating the balance of energy consumption;
<2> under the condition that the number of sensor nodes is increased from 40 to 200 and other set values meet the condition of the first embodiment, referring to fig. 10, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, when there are only 40 nodes in the network, the overhead is increased due to network sparseness, and the remaining energy of each algorithm is smaller at this time, so that the survival rate of the nodes is low, and as the number of the nodes increases, the survival rate of the nodes in each algorithm gradually increases, and the survival rate of the nodes in the GACR algorithm simulation is highest;
<3> under the condition that the network area is enlarged from 20x20m to 140mx140m, and other set values meet the condition of the first embodiment, in the simulation of four algorithms, GACR, KAEC, LEACH-P and LEACH, the survival rate of the nodes is in a descending trend under the condition that the network area is continuously enlarged, and under the condition of the network area of 140mx140m, the node survival rate of the GACR algorithm is 60 percent and is obviously superior to other algorithms;
in summary, under the basic condition or the condition of expanding the area of the network area, the node survival rate in the GACR algorithm simulation is higher than that of other three algorithms, but the main body is in a descending trend, and under the condition of increasing the sensor nodes, the node survival rate in the GACR algorithm simulation is obviously higher than that of the other three algorithms, and the survival rate is in an ascending trend.
Comparative example four: referring to fig. 12-14, under the condition that other set values are unchanged, analyzing the average delay condition of the network in the four algorithms by respectively changing the number of the sensor nodes and the area of the network area;
<1> under the condition that the set value satisfies the first embodiment, please refer to fig. 12, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, in terms of delay, the average delay of the multi-hop network is not better than that of the single-hop network, both the GACR and LEACH-P algorithms have higher delay, and in combination with fig. 6, it can be seen that the GACR algorithm is worse than the LEACH algorithm in terms of average delay, but the GACR algorithm is more reasonable and efficient in terms of energy utilization and distribution, thereby prolonging the service life of the node, and is more excellent and efficient in terms of network load balancing;
<2> in the case where the number of sensor nodes is increased from 40 to 200 and other set values satisfy the condition of the first embodiment, referring to fig. 13, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, as the number of sensor nodes is increased, the delay conditions of the LEACH-P algorithm and the GACR algorithm are similar, the overall trend is kept stable, and the delay time is higher compared with the KAEC algorithm and the LEACH algorithm;
<3> under the condition that the network area is enlarged from 20x20m to 140mx140m and other set values satisfy the condition of the first embodiment, referring to fig. 14, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, as the network area is continuously increased, in a small-scale network, the delay time of the GACR algorithm is smaller than LEACH-P, and in a large-scale network, the delay time performance of the GACR algorithm is worst;
in summary, in fig. 12, 13 and 14, the delay time rising trend of the GACR algorithm is not obvious in the case of enlarging the area of the network area or increasing the number of sensor nodes, but is not better than the other three algorithms in terms of average delay.
Comparative example five: referring to fig. 13 and 14, under the condition that other set values are unchanged, the throughput conditions under different network densities in the four algorithms are analyzed by respectively changing the number of sensor nodes and the area of the network area;
<1> in the simulation of four algorithms of GACR, KAEC, LEACH-P and LEACH, referring to fig. 15, under the condition that the number of sensor nodes increases from 40 to 200 and other set values satisfy the first embodiment, as the number of sensor nodes increases, the throughput of the GACR algorithm is significantly better than that of the three algorithms of KAEC, LEACH-P and LEACH, and as the network density increases, the throughput of the GACR algorithm increases significantly;
<2> referring to fig. 16 under the condition that the network area is enlarged from 20x20m to 140mx140m and other set values satisfy the condition of the first embodiment, in four algorithm simulations of GACR, KAEC, LEACH-P and LEACH, as the network area is continuously increased, throughput of KAEC, LEACH-P and LEACH algorithms is gradually increased, but throughput in the GACR algorithm is the largest and is less affected by the network density;
in summary, the throughput of the GACR algorithm is higher than the other three algorithms in the case of expanding the network area or increasing the number of sensor nodes.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An energy heterogeneous wireless sensor routing system based on a genetic algorithm is characterized in that: the cluster head multi-hop path planning system comprises a cluster head election module, a cluster construction module and a cluster head multi-hop path planning module;
the cluster head election module screens the sensor nodes, forms an optimal individual set from a plurality of screened optimal individuals, and transmits the elected optimal individual set to the cluster construction module;
the cluster construction module receives the optimal individual set transmitted by the cluster head election module, and the optimal individuals form a cluster by searching sensor nodes;
the cluster head multi-hop path planning module plans the shortest path from the cluster head to the base station by traversing all cluster head nodes and combining energy factors;
the cluster head election module comprises an initial data receiving unit, a population initializing unit, a coding unit, an individual adaptation value calculating unit, a crossing unit, a variation unit and an iterative calculating unit;
the initial data receiving unit queries node information of all sensors in a base station broadcast and sensor network, and transmits the queried node information to the base station, and the base station stores the received node information of all sensors, marks each sensor node and transmits the stored node information of all sensors to the population initializing unit;
the population initializing unit receives node information of all the sensors transmitted by the initial data receiving unit, counts the specific number of the sensor nodes, sets population scale according to the specific number of the sensor nodes, randomly generates an initial population according to the population scale, and transmits the randomly generated initial population to the encoding unit;
the coding unit receives the initial population transmitted by the population initializing unit, each individual in the population is a variable sequence, called a chromosome or a gene string, codes the chromosome gene corresponding to each node in a decimal coding mode, each node has a unique serial number corresponding to the chromosome gene code of the node, and transmits the coded individual to the individual adaptation value calculating unit;
the individual fitness value calculating unit calculates the fitness value of each coded individual according to the fitness function, arranges each individual in the population from high to low according to the fitness value, transmits the rearranged population to the intersecting unit, receives newly generated individuals which do not reach the maximum iteration times or the fitness value error value within the allowable range, and calculates, selects, intersects and varies again;
the cross unit receives the rearranged population, randomly sets a cross point in the code string of each individual in the population, randomly selects one individual to exchange partial chromosomes of two individuals at the cross point, and transmits the cross-treated population to the mutation unit;
the mutation unit receives the population processed by the crossing unit, performs mutation operation on part of chromosomes in the individuals after the crossing processing to generate new individuals, and transmits the new individuals generated after the mutation to the iterative calculation unit;
the iteration calculation unit receives new individuals generated after the mutation processing, carries out iteration processing on the new individuals, outputs the new individuals which reach the maximum iteration times or the adaptive value error value within the allowable range to form an optimal individual set, conveys the new individuals which do not reach the maximum iteration times or the adaptive value error value within the allowable range to the individual adaptive value calculation unit for carrying out correlation processing again, and conveys the optimal individual set to the cluster construction module;
the fitness function of the individual fitness value calculating unit is as follows:
；
wherein,representing the adaptation value size, +.>For the distance of the node from the base station, < > j->For the number of neighbor nodes,for the remaining energy of the node, +.>、/>And->Is a weight coefficient, and the weight coefficient satisfies +.>，/>Reference numerals indicating correspondence of each node +.>Representing the total number of nodes;
the initial cross probability is set to be 0.6, the cross probability of the next round is obtained by means of the cross probability of the previous round, and the specific calculation formula of the cross probability is as follows:
；
wherein,represents the crossover probability->Representing the initial crossover probability, ++>Represents the crossover probability obtained by the last iterative crossover process, < >>Is the fitness of the optimal individual in the population, +.>Is the average fitness of the population,/->Is the larger adaptation value in the two individuals to be crossed;
the initial variation probability is 0.1, the variation probability of the next round is obtained by the variation probability of the previous round, and the specific calculation formula of the variation probability is as follows:
；
wherein,representing mutation probability->Representing the initial mutation probability, < >>Representing the mutation probability obtained after the last iteration of the mutation treatment,/->Is the fitness of the optimal individual in the population, +.>Is the average fitness of the population,/->The initial mutation probability may also be set to a value less than 0.1, which represents the fitness value of the mutated individual。
2. The genetic algorithm-based heterogeneous wireless sensor routing system of claim 1, wherein: the cluster construction module receives the optimal individual set transmitted by the cluster head election module, the individuals in the set broadcast the information of becoming the cluster heads, the common sensor nodes receive the information transmitted by the individuals, a certain cluster head in the set is selected to be added, the cluster head and all the common sensor nodes added in the cluster head are selected to form a cluster, and meanwhile, in the construction process, the cluster head selects whether to accept the common sensor nodes to enter the cluster or not according to the relation among the residual energy of the cluster head, the distance between the cluster head and the base station and the distance between the sensor nodes and the cluster head.
3. The genetic algorithm-based heterogeneous wireless sensor routing system of claim 2, wherein: the cluster head residual energy, the distance between the cluster head and the base station and the distance between the sensor node and the cluster head satisfy the following formula relation, and the specific formula is as follows:
；
wherein,indicated is the energy of the i-th cluster head node,/->Represents the average energy of h cluster heads, +.>Distance from common node to cluster head, +.>For the distance between the target cluster head node and the base station, < >>、/>And->Is a proportional coefficient->Indicating the composite index.
4. The genetic algorithm-based heterogeneous wireless sensor routing system of claim 2, wherein: the common sensor node is used for detecting the total indexThe specific steps for judging which cluster to add are as follows:
step one: selecting a common node, and carrying out calculation on the distance information between each cluster head and the common node, the distance information between the cluster head and the base station and the residual energy of the cluster head into a comprehensive index calculation formula;
step two: arranging the calculated comprehensive indexes in the order from big to small;
step three: after the calculation is completed, comparing the calculated comprehensive indexes between the same common sensor node and different cluster heads, and judging which cluster the common sensor node is added into by comparing the magnitude of the comprehensive indexes;
step four: and traversing all the common sensor nodes, and repeating the steps.
5. The genetic algorithm-based heterogeneous wireless sensor routing system of claim 1, wherein: the cluster head multi-hop path planning module plans the shortest path from the cluster head to the base station through the Floyd algorithm and the energy consumption of the cluster head, and a specific shortest path calculation formula is as follows:
distance judgment conditions:
；
wherein,for the distance between the current node and the next hop node, < > the next hop node>For the distance between the next hop node and the base station, and (2)>The distance between the current node and the base station;
when the distance judging condition is met, the energy consumption of the current node reaching the base station after passing through the jump node is lower than the energy consumption of the current node directly reaching the base station, and the path of the current node finally reaching the base station after passing through the intermediate nodes which meet the judging condition is expressed as the shortest path;
multi-hop transmission judgment conditions:
；
wherein,for critical distance of cluster head to base station, +.>Is the energy consumption coefficient of the power amplifier in the free space channel model,is the power amplification factor of the multipath fading channel model, if the distance between the end node and the base station is greater than +.>The end node cannot trust the nodeThe information is transmitted to the base station, at this time, the data information of the end node is transmitted to the base station through the intermediate node, when the distance between the end node and the base station is smaller than +.>In this case, the communication can be performed directly without performing multi-hop transmission.
6. The genetic algorithm-based heterogeneous wireless sensor routing system of claim 5, wherein: the Floyd algorithm is combined with cluster head energy consumption to plan the shortest path, and the method comprises the following specific steps of:
step1: numbering the cluster heads, and putting the source node numbers into a path set S;
step2: judging whether the shortest path between the source node and the base station is obtained, outputting a path set S if the shortest path is obtained, and searching a vertex U which is not in the set S and has the minimum distance with the source node if the shortest path is not completely obtained, wherein the vertex U also needs to meet a distance judging condition;
step3: if the vertex U is found, the vertex U is merged into the set S, the operation of the second step is repeated for the new set, the U is temporarily used as a source node to solve the next hop, and if the vertex U is not found, whether the distance between the current node and the base station is smaller than the threshold value is judgedIf less than->The self is the last hop if greater than +.>Traversing the neighbor node to find the nearest point which can be directly communicated with the base station as the last hop;
step4: and adding the number of the last hop into the set S to obtain the shortest path.
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