CN117336818A - WSN clustering routing algorithm based on locust optimization FCM - Google Patents
WSN clustering routing algorithm based on locust optimization FCM Download PDFInfo
- Publication number
- CN117336818A CN117336818A CN202311320231.6A CN202311320231A CN117336818A CN 117336818 A CN117336818 A CN 117336818A CN 202311320231 A CN202311320231 A CN 202311320231A CN 117336818 A CN117336818 A CN 117336818A
- Authority
- CN
- China
- Prior art keywords
- cluster
- nodes
- algorithm
- locust
- clustering
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 25
- 241000854291 Dianthus carthusianorum Species 0.000 claims abstract description 70
- 230000005540 biological transmission Effects 0.000 claims abstract description 12
- 230000004927 fusion Effects 0.000 claims abstract description 5
- 235000008694 Humulus lupulus Nutrition 0.000 claims abstract description 3
- 230000000739 chaotic effect Effects 0.000 claims description 10
- 230000000737 periodic effect Effects 0.000 claims description 4
- 230000003247 decreasing effect Effects 0.000 claims description 3
- 230000036961 partial effect Effects 0.000 claims description 3
- 238000000034 method Methods 0.000 abstract description 14
- 230000000694 effects Effects 0.000 abstract description 5
- 238000013507 mapping Methods 0.000 description 16
- 238000005265 energy consumption Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 5
- 241000282461 Canis lupus Species 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 241000238814 Orthoptera Species 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 239000002245 particle Substances 0.000 description 2
- 230000002035 prolonged effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000282326 Felis catus Species 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000002860 competitive effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/46—Cluster building
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Abstract
The invention discloses a WSN clustering routing algorithm based on locust optimization FCM, which comprises two stages of clustering and data transmission, wherein the clustering stage firstly clusters nodes in a network, and selects proper cluster heads according to the current residual energy of the nodes, the distance between the nodes and a cluster center and the distance between the nodes and a base station. In the data transmission stage, the nodes in the cluster transmit the collected data to the cluster head, the cluster head performs data fusion after receiving the data, and the data is transmitted to the base station through single hop or multiple hops. According to the method, the fuzzy c-means algorithm is optimized through improving the locust algorithm, and the convergence speed and the accuracy of the locust optimization algorithm are improved, so that the clustering effect of the fuzzy c-means is improved, and the problem that the fuzzy c-means is easy to fall into a local optimal solution is solved. And selecting proper cluster head and relay node according to the node residual energy and the distance between the cluster head and sink node.
Description
Technical Field
The invention relates to the related field of algorithms, in particular to a WSN clustering routing algorithm based on locust optimization FCM.
Background
The wireless sensor network (Wireless Sensor Network, WSN) is an ad hoc network composed of a large number of sensor nodes, and is widely used in the fields of agriculture, industry, medical treatment and the like. WSN nodes are energy-limited and it is often difficult to replace or supplement the energy of the nodes. Therefore, how to fully utilize the energy of the nodes and extend the life cycle of the network is an extremely important issue.
WSN routing protocols can be divided into two types, planar routing protocols and clustered routing protocols. The former sensor node has similar functions when the network is running, but it requires a large overhead for establishing and maintaining routes. The latter has better expansibility by clustering the nodes, and is also suitable for larger networks. Therefore, many scholars at home and abroad research on clustering routing and put forward a large number of improved algorithms.
The most typical clustering routing protocol LEACH selects cluster heads through random rotation, so that each node in the cluster can be a cluster head node in a fixed period, but the randomness also causes the waste of node energy. To solve these problems, the literature proposes an improved LEACH algorithm. In the clustering stage, the algorithm optimizes the selection of cluster head nodes by using the residual energy and distance parameters of the nodes, and in the data transmission stage, the cluster head selects the data forwarding nodes according to the node spacing and the residual energy, so that the energy consumption of the nodes is reduced and the data transmission amount of a network is increased. The literature proposes an improved LEACH algorithm based on double cluster heads, and the optimal cluster head number is calculated and the main cluster head and the auxiliary cluster head are introduced for rotation, so that the number of cluster head competitive selection times is reduced, the network life cycle is prolonged, and the auxiliary cluster head is too random to be selected first, so that energy loss is caused to a certain extent. The document divides the network into a plurality of areas through dynamic clustering based on the original LEACH algorithm, so that the condition of uneven distribution of cluster head nodes is reduced to a certain extent, and the service life of the nodes and the network coverage rate are improved. However, the improved LEACH algorithm still has larger randomness, so a lot of researchers optimize the problem by using a cluster intelligent and clustering algorithm, the literature proposes a clustering routing algorithm POFCA based on particle swarm optimization fuzzy c-means, the problem that the fuzzy c-means is sensitive to an initial clustering center is improved by particle swarm optimization, cluster heads are dynamically updated according to node energy and relative distance, and a cat swarm optimization algorithm is used for searching an optimal path for the cluster heads, so that the load of the cluster heads is balanced and the load of relay nodes is not increased. The literature provides an energy-saving hierarchical clustering routing method based on fuzzy c-means, the fuzzy c-means is promoted and applied to a three-level clustering structure, cluster heads are selected through three parameters of mass centers of clusters, distances among nodes and residual energy of the nodes, and a grid and cluster formation in a dynamic mode is adopted, so that the communication distance of nodes in the clusters is reduced, and the network life is prolonged. The literature proposes an algorithm FIGWO that calculates the gray wolf prey location weight information using fitness values and introduces initial clusters to improve the election of cluster head nodes. The literature proposes an optimal cluster preference algorithm of a hybrid optimization algorithm based on a gray wolf and crow search. The algorithm improves the position updating mechanism, the self-adaptive balance probability strategy and the change strategy of control parameters of the algorithm by mixing the two optimization algorithms of the sirius and the crow, balances the local search and the global search of the algorithm, improves the problem of premature convergence, reduces the network energy consumption and prolongs the network service life. The literature provides a centralized clustering routing algorithm based on a wolf algorithm, an optimal cluster head is selected through the wolf algorithm, a relay node with the minimum energy consumption is calculated for the cluster head node far away from a base station, and the energy consumption of the cluster head node far away from the base station is reduced. The protocol reduces network energy consumption to a certain extent and prolongs network life cycle, but the cluster selection process needs further improved research due to the problems of algorithm convergence, search strategy and the like.
Disclosure of Invention
The invention aims to provide a WSN clustering routing algorithm based on locust optimization FCM so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: WSN clustering routing algorithm based on locust optimization FCM comprises the following parts:
in the first part, the clustering stage of the algorithm firstly clusters nodes in the network, and selects proper cluster heads according to the current residual energy of the nodes, the distance between the cluster heads and the base station.
And in the second part, in the data transmission stage, the nodes in the cluster transmit the collected data to the cluster head, the cluster head performs data fusion after receiving the data, and the data is transmitted to the base station through single hop or multiple hops.
Preferably, lambda in the first partial cluster head selection 1 And lambda (lambda) 2 The sum of the weight parameter and the weight parameter is 1.
Preferably, said E total Is the total energy in the current round network, E cost Is the total energy already consumed, lambda as the energy is consumed 1 Gradually increase from 0.5 to 1, lambda 2 Gradually decreasing from 0.5 to 0.
Preferably, the method is characterized in that: the whole flow comprises the following steps:
step 1: the network and its parameters are initialized.
Step 2: initializing parameters of a locust algorithm, and obtaining an optimal group of cluster centers by improving the locust algorithm, wherein the optimal group of cluster centers are used as initial cluster centers of a fuzzy c-means algorithm.
Step 3: initializing parameters of a fuzzy c-means algorithm, and clustering all surviving nodes of the network in the round through the fuzzy c-means algorithm.
Step 4: after the clustering is completed, cluster election is carried out on each cluster, and the most suitable node is selected to be the cluster head according to the formula (23).
Step 5: after the cluster head collects the data information in the cluster, the cluster head directly or indirectly transmits the data to the base station.
Step 6: after all nodes in the network complete one round of data transmission, firstly judging whether all the nodes die, and if not, judging whether the nodes die in the previous round. If the node dies, returning to the step 2 to perform clustering again and select the cluster head; if no node dies, returning to the step 4 to reselect the cluster head in the original cluster. Until all nodes in the network die.
Preferably, the chaotic map used by the locust optimization algorithm is a ent map.
Preferably, the value of beta in the content map is (0, 1), but when the value of beta is 0.5, the system is in a short period state, beta does not generally take 0.5, and the initial value z of the system k Cannot be equal to beta. Because when z k And β, the system becomes a periodic system.
Compared with the prior art, the invention has the beneficial effects that:
1. the characteristic of chaotic mapping improves the convergence speed and precision of the locust optimization algorithm, and the initial clustering center of the fuzzy c-means algorithm is optimized by improving the locust algorithm, so that the problem that the fuzzy c-means is easy to fall into a local optimal solution is solved, and the clustering effect of the algorithm is improved;
2. and selecting a cluster head according to the node residual energy and the distance between the cluster head and the cluster center and the sink node, and selecting a proper relay node according to the distance between the cluster head and the sink node.
Detailed Description
The following description of the technical solutions in the embodiments of the present invention will be clear and complete, and it is obvious that the described embodiments 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.
The invention provides a technical scheme that: WSN clustering routing algorithm based on locust optimization FCM comprises the following parts:
the first part, improving locust optimization algorithm: the chaos mapping is introduced, and the uniformity of locust individuals in the whole solution space is increased through the ergodic property, the randomness and the overall stability of the chaos sequence, so that the global searching performance and the convergence speed of the algorithm are improved.
Second part, cluster preference: adding each scale feature map with other two scale feature maps, fusing the attention weights of the channels, and then outputting updated three scale feature maps;
third part, data transmission: the method is divided into intra-cluster communication and inter-cluster communication. The cluster head firstly allocates a time slot for each node in the cluster in a broadcasting mode, then the nodes in the cluster send the data collected by the nodes in the cluster to the cluster head node in own time slots, and the rest time is in a standby state so as to reduce the energy consumption of the nodes. After receiving the data sent by the nodes in the cluster, the cluster head node performs data fusion, and then sends the fused data to the base station or the relay node.
Lambda in first partial cluster head selection 1 And lambda (lambda) 2 The sum of the weight parameter and the weight parameter is 1.
E total Is the total energy in the current round network, E cost Is the total energy already consumed, lambda as the energy is consumed 1 Gradually increase from 0.5 to 1, lambda 2 Gradually decreasing from 0.5 to 0.
The method is characterized in that: the whole flow comprises the following steps:
step 1: the network and its parameters are initialized.
Step 2: initializing parameters of a locust algorithm, and obtaining an optimal group of cluster centers by improving the locust algorithm, wherein the optimal group of cluster centers are used as initial cluster centers of a fuzzy c-means algorithm.
Step 3: initializing parameters of a fuzzy c-means algorithm, and clustering all surviving nodes of the network in the round through the fuzzy c-means algorithm.
Step 4: after the clustering is completed, cluster election is carried out on each cluster, and the most suitable node is selected to be the cluster head according to the formula (23).
Step 5: after the cluster head collects the data information in the cluster, the cluster head directly or indirectly transmits the data to the base station.
Step 6: after all nodes in the network complete one round of data transmission, firstly judging whether all the nodes die, and if not, judging whether the nodes die in the previous round. If the node dies, returning to the step 2 to perform clustering again and select the cluster head; if no node dies, returning to the step 4 to reselect the cluster head in the original cluster. Until all nodes in the network die.
The chaotic map used by the locust optimization algorithm is the ent map.
the value range of beta in the ent map is (0, 1), but when the value of beta is 0.5, the system is in a short period state, beta does not generally take 0.5, and the initial value z of the system k Cannot be equal to beta. Because when z k And β, the system becomes a periodic system.
The algorithm is specifically described as follows:
in the first part, the locust optimization algorithm divides acting forces among the locusts into attractive force, repulsive force and comfortable areas according to the distances among different locusts, so that the method has higher searching efficiency and higher convergence rate, and the algorithm can balance global and local searching processes to a certain extent by a special self-adaptive mechanism, so that the method has better optimizing precision and relatively stable algorithm effect. However, the global searching capability is not strong, the algorithm lacks of random numbers, so that the behavior mode is relatively single, and the effect is not good when facing complex problems. The method aims at the problems of the locust algorithm, and the chaos mapping is introduced, so that the uniformity of the locust individual in the whole solution space is increased through the ergodic property, the randomness and the overall stability of the chaos sequence, and the global searching performance and the convergence speed of the algorithm are improved.
A group intelligent optimization algorithm typically starts an initial solution for optimization with a random set of solutions as the algorithm, and the optimization performance of the algorithm is closely related to the distance between the initial solution and the optimal solution. The locust optimization algorithm performs iterative optimization by generating a random initial population, and the stability and the ergodic performance of the population cannot be ensured. The chaos mapping is introduced, and the initial population of the algorithm is generated through the chaos mapping, so that the ergodic performance of the initial population is improved, the convergence speed of the algorithm is improved, and the capability of jumping out of a local optimal solution is improved. In various common chaotic mapping sets, the logical mapping and the content mapping have better uniformity, but the mapping sequence of the logical mapping has higher density at the position where the value is close to 0 and 1, and has lower value density in the middle area, so the content mapping is adopted in the text, and the mapping is shown as a formula (14):
wherein, the value of beta is (0, 1), but when the value of beta is 0.5, the system is in a short period state, so beta does not generally take 0.5, and the initial value z of the system k Cannot be equal to beta. Because when z k And β, the system becomes a periodic system.
The algorithm improves the uniformity of population distribution through the ergodic property of chaotic mapping. First an initial solution is generated and mapped to the interval of (0, 1), the mapping function is as shown in equation (15):
wherein n is the individual length of the population, namely the number of nodes in the WSN; t is the number of population individuals; x is x min And x max Is the search range of the solution space. Generating a chaotic population through the ent mapping, as shown in a formula (16):
and inversely mapping the sequence of the chaotic population to the original solution space range according to the obtained chaotic population, so as to obtain a chaotic initial population. In order to avoid the situation that the algorithm falls into local optimum in the later period, disturbance variation is added to population position update, diversity of the population in the iterative process is increased, and the capability of the algorithm to jump out of the local optimum is improved. By introducing a cauchy variation mechanism during population individual position updating, global optimizing performance of an algorithm in the early stage is enhanced, probability of the algorithm falling into local optimum in the later stage is reduced to a certain extent, and convergence speed of the algorithm is increased. The individual position updating mode is shown in the formula (17):
wherein X is best Is the optimal solution in the locust population, cauchy is a cauchy operator, and the one-dimensional cauchy distribution probability density function expression is shown as a formula (18):
and obtaining the position of the locust individual after disturbance, comparing the position with the individual before disturbance, calculating the fitness function of the locust individual, and reserving the higher individual. The updating mode of the population individual positions is shown as a formula (19):
the design of the fitness function is an important part of the locust algorithm and is a standard for evaluating the quality of the locust individuals in the algorithm. The fitness function is designed according to the average value of the distances between all nodes and the cluster head in the network and the uniformity of clustering, so that the initial cluster center obtained by the locust optimization algorithm is more reasonable, the final clustering effect of the algorithm is improved, and the computation of the fitness function is shown in formulas (20) to (22):
f＝f 1 (x n )+f 2 (x n ) (22)
the second part, the cluster head node, which is usually responsible for collecting the data in the cluster, is more loaded, while the load of the nodes in the cluster is less. In order to balance the load of the network nodes, the cluster head is selected by taking the current residual energy of the nodes, the distance from the cluster center and the distance from the base station into consideration. When the network operates, according to the current network clustering condition, the energy condition and the position of the nodes are comprehensively considered, the cluster head is dynamically updated in each round, the energy consumption of the network is reduced, and the load is balanced [18] . The specific calculation formula is shown as formula (23):
wherein lambda is 1 And lambda (lambda) 2 Is a weight parameter, the sum of the weight parameter and the weight parameter is 1, E rem For the current remaining energy of the node E init For node initial energy, d (CM) j BS) is the distance between node j and the base station, d (CM) j ，C i ) For nodes j and clusters to which they belongDistance of cluster center. As the network operates, the remaining energy of the node is smaller and smaller, λ 1 And the proportion of the rest energy when the cluster heads are selected is increased along with the gradual increase of the energy consumption of the network so as to balance the load of the network. Lambda (lambda) 1 And lambda (lambda) 2 The calculations of (a) are shown in formulas (24) and (25):
λ 2 ＝1-λ 1 (25)
wherein E is total Is the total energy in the current round network, E cost Is the total energy already consumed, lambda as the energy is consumed 1 Gradually increase from 0.5 to 1, lambda 2 Gradually reducing from 0.5 to 0, so that when the total energy of the network is low, the node with more residual energy is selected as the cluster head.
And the third part, the data transmission stage is divided into two parts of intra-cluster communication and inter-cluster communication. The cluster head firstly allocates a time slot for each node in the cluster in a broadcasting mode, then the nodes in the cluster send the data collected by the nodes in the cluster to the cluster head node in own time slots, and the rest time is in a standby state so as to reduce the energy consumption of the nodes. After receiving the data sent by the nodes in the cluster, the cluster head node performs data fusion, and then sends the fused data to the base station or the relay node. The overall flow steps of the algorithm presented herein are as follows:
step 1: the network and its parameters are initialized.
Step 2: initializing parameters of a locust algorithm, and obtaining an optimal group of cluster centers by improving the locust algorithm, wherein the optimal group of cluster centers are used as initial cluster centers of a fuzzy c-means algorithm.
Step 3: initializing parameters of a fuzzy c-means algorithm, and clustering all surviving nodes of the network in the round through the fuzzy c-means algorithm.
Step 4: after the clustering is completed, cluster election is carried out on each cluster, and the most suitable node is selected to be the cluster head according to the formula (23).
Step 5: after the cluster head collects the data information in the cluster, the cluster head directly or indirectly transmits the data to the base station.
Step 6: after all nodes in the network complete one round of data transmission, firstly judging whether all the nodes die, and if not, judging whether the nodes die in the previous round. If the node dies, returning to the step 2 to perform clustering again and select the cluster head; if no node dies, returning to the step 4 to reselect the cluster head in the original cluster. Until all nodes in the network die.
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.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. WSN clustering routing algorithm based on locust optimization FCM, which is characterized by comprising the following parts:
in the first part, the clustering stage of the algorithm firstly clusters nodes in the network, and selects proper cluster heads according to the current residual energy of the nodes, the distance between the cluster heads and the base station.
And in the second part, in the data transmission stage, the nodes in the cluster transmit the collected data to the cluster head, the cluster head performs data fusion after receiving the data, and the data is transmitted to the base station through single hop or multiple hops.
2. The locust-based optimized FCM WSN clustering routing algorithm of claim 1, wherein: lambda in the first partial cluster head selection 1 And lambda (lambda) 2 The sum of the weight parameter and the weight parameter is 1.
3. The locust-based optimized FCM WSN clustering routing algorithm of claim 1, wherein: e (E) total Is the total energy in the current round network, E cost Is the total energy already consumed, lambda as the energy is consumed 1 Gradually increase from 0.5 to 1, lambda 2 Gradually decreasing from 0.5 to 0.
4. The locust-based optimized FCM WSN clustering routing algorithm of claim 1, wherein: the whole flow comprises the following steps:
step 1: the network and its parameters are initialized.
Step 2: initializing parameters of a locust algorithm, and obtaining an optimal group of cluster centers by improving the locust algorithm, wherein the optimal group of cluster centers are used as initial cluster centers of a fuzzy c-means algorithm.
Step 3: initializing parameters of a fuzzy c-means algorithm, and clustering all surviving nodes of the network in the round through the fuzzy c-means algorithm.
Step 4: after the clustering is completed, cluster election is carried out on each cluster, and the most suitable node is selected to be the cluster head according to the formula (23).
Step 5: after the cluster head collects the data information in the cluster, the cluster head directly or indirectly transmits the data to the base station.
Step 6: after all nodes in the network complete one round of data transmission, firstly judging whether all the nodes die, and if not, judging whether the nodes die in the previous round. If the node dies, returning to the step 2 to perform clustering again and select the cluster head; if no node dies, returning to the step 4 to reselect the cluster head in the original cluster. Until all nodes in the network die.
5. The locust-based optimized FCM WSN clustering routing algorithm of claim 1, wherein: the chaotic map used is a ent map.
6. The locust-based optimized FCM WSN clustering routing algorithm of claim 1, wherein: the value range of beta in the ent map is (0, 1), but when the value of beta is 0.5, the system is in a short period state, beta does not generally take 0.5, and the initial value z of the system k Cannot be equal to beta. Because when z k And β, the system becomes a periodic system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311320231.6A CN117336818A (en) | 2023-10-12 | 2023-10-12 | WSN clustering routing algorithm based on locust optimization FCM |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311320231.6A CN117336818A (en) | 2023-10-12 | 2023-10-12 | WSN clustering routing algorithm based on locust optimization FCM |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117336818A true CN117336818A (en) | 2024-01-02 |
Family
ID=89294949
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311320231.6A Pending CN117336818A (en) | 2023-10-12 | 2023-10-12 | WSN clustering routing algorithm based on locust optimization FCM |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117336818A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117808034A (en) * | 2024-02-29 | 2024-04-02 | 济南农智信息科技有限公司 | Crop yield prediction optimization method based on wolf bird optimization algorithm |
| CN117808034B (en) * | 2024-02-29 | 2024-05-10 | 济南农智信息科技有限公司 | Crop yield prediction optimization method based on wolf bird optimization algorithm |
-
2023
- 2023-10-12 CN CN202311320231.6A patent/CN117336818A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117808034A (en) * | 2024-02-29 | 2024-04-02 | 济南农智信息科技有限公司 | Crop yield prediction optimization method based on wolf bird optimization algorithm |
| CN117808034B (en) * | 2024-02-29 | 2024-05-10 | 济南农智信息科技有限公司 | Crop yield prediction optimization method based on wolf bird optimization algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112469100B (en) | Hierarchical routing algorithm based on rechargeable multi-base-station wireless heterogeneous sensor network | |
| CN108712767B (en) | Inter-cluster multi-hop routing control method with balanced energy consumption in wireless sensor network | |
| CN111698705B (en) | Non-uniform clustering routing method of wireless sensor network based on energy optimization | |
| CN102158927A (en) | Efficient inhomogeneous clustering routing method of wireless sensor network | |
| CN110121200B (en) | Energy-efficient networking method in heterogeneous sensor network | |
| CN111818553B (en) | Fuzzy logic-based data transmission method for improving multi-hop LEACH protocol of wireless sensor network | |
| CN110769444B (en) | Transmission method of wireless energy-carrying communication based on power distribution | |
| CN113709841B (en) | WSN routing protocol method based on cuckoo algorithm and K-means algorithm | |
| CN108541038B (en) | Routing method and system of wireless sensor network | |
| CN110149672B (en) | Improved I-LEACH route communication method | |
| CN108566658B (en) | Clustering algorithm for balancing energy consumption in wireless sensor network | |
| CN103561457B (en) | A kind of multi-target networks power distribution method in heterogeneous wireless network collaboration communication | |
| CN115001537B (en) | Routing networking method of carrier communication system based on clustering algorithm | |
| CN111372296B (en) | Method for optimizing election threshold of wireless sensor network routing protocol | |
| Xiao et al. | Minimization of energy consumption for routing in high-density wireless sensor networks based on adaptive elite ant colony optimization | |
| Tukisi et al. | Multi-hop PSO based routing protocol for Wireless Sensor Networks with Energy Harvesting | |
| CN114501576A (en) | SDWSN optimal path calculation method based on reinforcement learning | |
| Wang et al. | Distributed energy-efficient clustering routing protocol for wireless sensor networks using affinity propagation and fuzzy logic | |
| Fan et al. | Energy balance based uneven cluster routing protocol using ant colony taboo for wireless sensor networks | |
| CN117336818A (en) | WSN clustering routing algorithm based on locust optimization FCM | |
| CN111447658B (en) | SDWSN-based clustering routing method | |
| Prakash et al. | Best cluster head selection and route optimization for cluster based sensor network using (M-pso) and Ga algorithms | |
| CN113923802B (en) | Energy-efficient hierarchical topology control method in software-defined wireless sensor network | |
| Yari et al. | Energy-efficient topology to enhance the wireless sensor network lifetime using connectivity control | |
| CN112822747B (en) | Routing strategy method based on genetic algorithm and ant colony algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication |