CN111556549A - WSNs routing method for power distribution network combining membrane calculation and ant colony algorithm - Google Patents

WSNs routing method for power distribution network combining membrane calculation and ant colony algorithm Download PDF

Info

Publication number
CN111556549A
CN111556549A CN202010570178.5A CN202010570178A CN111556549A CN 111556549 A CN111556549 A CN 111556549A CN 202010570178 A CN202010570178 A CN 202010570178A CN 111556549 A CN111556549 A CN 111556549A
Authority
CN
China
Prior art keywords
path
membrane
node
sub
nodes
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
Application number
CN202010570178.5A
Other languages
Chinese (zh)
Inventor
高健文
黄友锐
韩涛
徐善永
唐超礼
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui University of Science and Technology
Original Assignee
Anhui University of Science and Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Anhui University of Science and Technology filed Critical Anhui University of Science and Technology
Priority to CN202010570178.5A priority Critical patent/CN111556549A/en
Publication of CN111556549A publication Critical patent/CN111556549A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/002—Biomolecular computers, i.e. using biomolecules, proteins, cells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
    • H02J13/00006—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
    • H02J13/00022—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission
    • H02J13/00026—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment using wireless data transmission involving a local wireless network, e.g. Wi-Fi, ZigBee or Bluetooth
    • 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
    • H04L45/12—Shortest path evaluation
    • 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
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Theoretical Computer Science (AREA)
  • Signal Processing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biophysics (AREA)
  • Computing Systems (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computational Linguistics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Organic Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Power Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a WSNs routing method for a power distribution network, which integrates membrane calculation and improves an ant colony algorithm. The method improves the traditional ant colony algorithm and introduces membrane calculation to optimize the algorithm. By introducing dynamic compensation factors into the state transfer function, the phenomenon that the algorithm is prematurely stopped due to overhigh pheromones is avoided; the parallel capability of the intra-membrane operation and the inter-membrane operation is calculated by utilizing the membranes, and the introduced optimal path measurement formula is combined to perform multi-path parallel search to obtain the optimal path, so that the local and global convergence capability of the algorithm is improved; by defining a route repair mechanism, the algorithm avoids route holes. The method avoids the searching complexity, accelerates the searching speed, obviously enhances the reliable routing of the data, realizes the energy-saving requirement and prolongs the service life of the network.

Description

WSNs routing method for power distribution network combining membrane calculation and ant colony algorithm
Technical Field
The invention relates to the field of data transmission in intelligent power distribution networks (WSNs), in particular to a data routing method integrating film calculation and ant colony algorithm.
Background
As the country proposed the concept of "smart grid," the national grid began to shift from the traditional grid to the smart grid. The intelligent power grid has the advantages that the power grid can be subjected to online detection through intelligent equipment, so that the safety and the stability of the power grid are greatly enhanced, the wireless sensor network can conveniently and quickly collect the information and the operation data of the power distribution gateway equipment to realize real-time monitoring, and the wireless sensor network has obvious advantages when being introduced into the intelligent power distribution network. But since a wireless sensor network consists of a large number of sensors, the energy of its nodes depends on its limited stored energy, once installed deployed in a power distribution network, it will not change any more, and recharging will be more cumbersome when the energy is exhausted. Some routing methods for prolonging the service life of the WSNs network are proposed. The existing routing method for data transmission is easy to trap in local optimal searching of routing paths, and the searching mode is complex, so that energy consumption of sensor nodes in the power distribution network is excessive, routing holes are easy to cause, data acquisition and transmission efficiency in the intelligent power distribution network is reduced, and the effect is not ideal.
Disclosure of Invention
In view of the above problems, the present invention aims to provide a routing method for a power distribution network WSNs combining film calculation and ant colony algorithm to perform data transmission in a smart power distribution network. On the basis of reducing the node energy consumption and the data transmission reliability, the node energy consumption in the network is balanced, the operation and maintenance cost is reduced, and the service life of the network is prolonged.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a WSNs routing method for a power distribution network combining membrane calculation and ant colony algorithm is characterized by comprising the following steps:
step 1: initializing initial node energy E in a network0Initial pheromone concentration τij(t), distance between nodes dijAnd environmental information；
Step 2: the membrane structure in the initialization membrane calculation is [ 2 ]1[2]2[3]3[4]4]1The membrane 1 is a main membrane; membranes 2,3,4 are submembranes;
and step 3: before the algorithm starts iteration, n ants are placed at an initial node in each sub-membrane for preparing routing path selection;
and 4, step 4: after the preparation work is finished, ants on the initial nodes in each sub-membrane according to the probability
Figure BDA0002549309890000011
Selecting the next hop node to construct the path according to the probability
Figure BDA0002549309890000021
And the dynamic compensation factor m (i, j) is designed according to the formulas (1) and (2). The dynamic compensation factor is introduced to prevent the search from falling into a local optimal solution due to the fact that the pheromone content is considered to be excessive when the next-hop node is selected. And simultaneously recording the node energy information passed by the ants, and thus, searching the path according to the flow.
Figure BDA0002549309890000022
Figure BDA0002549309890000023
In the formulae (1) and (2), τij(t) denotes the pheromone concentration between nodes i, j, ηij(t) represents a distance dijReciprocal of (a), lmaxAnd lminRepresents the longest path and the shortest path of all ants in the round, respectively,/(k)Indicating the length of the path taken by ant k.
And 5: when the ants in all the sub-membranes 2,3 and 4 complete the path search, a plurality of searched paths exist in each sub-membrane. The probability C is then measured in terms of the path according to the evolution rule of the membrane(r,t)The path in the respective filmAnd sequencing, and finally selecting the first five paths to be sent into the main film 1. Path metric probability C(r,t)Designing according to the formula (3):
Figure BDA0002549309890000024
in the formula (3), Eave(r, t) is the average energy of the nodes on one of the paths, and L (r, t) is the length of one of the paths.
The evolution rules in the daughter membranes are designed according to equations (4), (5), (6):
Figure BDA0002549309890000025
Figure BDA0002549309890000026
Figure BDA0002549309890000027
in the formulas (4), (5) and (6), taking the sub-membrane 2 as an example,
Figure BDA0002549309890000028
represents a first path in the sub-film 2;
Figure BDA0002549309890000029
representing the sorted path sequence; in1 denotes the feeding of the first five sequenced paths into the primary film 1; the evolution rules in other sub-membranes are similar as in sub-membrane 2.
Step 6: when the sub-films send the first 5 paths in the respective films into the main film 1, the optimal path is selected according to the formula (3), and pheromone is updated. The updating of the pheromone is designed according to the formulas (7) and (8):
Figure BDA0002549309890000031
Figure BDA0002549309890000032
in the formulae (7) and (8), HkThe hop count of the ant K search path is represented; ekRepresenting node energy; l iskRepresents the distance traveled by the ant k; q represents a constant coefficient; rho represents the pheromone volatilization coefficient on the path; tau ismaxRepresenting the pheromone maximum threshold.
And 7: and repeating the steps 4-6 until the algorithm ending condition is met.
And 8: and outputting the optimal path, and randomly sending the selected optimal path into one sub-film to be constructed once again so as to confirm whether the transmission requirement is met. The evolution rule of the main membrane 1 is designed according to the formula (9):
Figure BDA0002549309890000033
in the formula (9), the reaction mixture,
Figure BDA0002549309890000034
indicating the optimal path, i ∈ (1, 15); inj indicates feeding the optimal path into the secondary film, j ∈ (2,3, 4).
And step 9: defining a path repairing mechanism, checking the state of nodes on a path at intervals, and replacing the nodes meeting the energy requirement if the nodes not meeting the requirement appear. D0 is used as the radius of the search circle region, and the nodes in the secondary circle region are used as candidate nodes. And connecting the circle center with the target node, and in order to prevent the selection of the node behind the replaced node, namely, the node far away from the target node, regulating the angle range between the connecting line of the circle center and the replacement node and the connecting line of the circle center and the target node to be limited within 60 degrees left and right of the connecting line of the circle center and the target node.
The invention has the beneficial effects that:
the invention combines the improved ant colony algorithm with the membrane calculation, and utilizes the characteristics of distributed and parallel calculation of the membrane calculation to simultaneously search the paths of ants in a plurality of sub-membranes. The path searching speed is accelerated while the ant searching precision is not influenced; and the paths searched out from each sub-membrane are selected with good quality and sent into the main membrane for optimal path selection by setting an inter-membrane evolution rule of membrane calculation, so that the situation that the paths are trapped in local optimal solutions is avoided. Therefore, the whole algorithm can reduce unnecessary energy loss in route searching, prolong the life cycle of the network, improve the efficiency and reliability of data transmission in the intelligent power distribution network and save the operation cost.
Drawings
FIG. 1 is an algorithm workflow diagram of the algorithm of the present invention.
FIG. 2 is a schematic diagram of the membrane structure and workflow for the algorithm of the present invention.
Fig. 3 is a schematic diagram of the selection range of the path repair mechanism replacing nodes.
Detailed Description
The invention is further explained below with reference to the drawings.
As shown in fig. 1, a method for routing WSNs in a smart distribution network by using an ant colony algorithm based on fusion membrane calculation includes the following steps:
step 1: initializing initial node energy E in a network0Initial pheromone concentration τij(t), distance between nodes dijAnd environmental information;
step 2: the membrane structure in the initialization membrane calculation is [ 2 ]1[2]2[3]3[4]4]1The membrane 1 is a main membrane; membranes 2,3,4 are submembranes;
and step 3: before the algorithm starts iteration, n ants are placed at an initial node in each sub-membrane for preparing routing path selection;
and 4, step 4: after the preparation work is finished, ants on the initial nodes in each sub-membrane according to the probability
Figure BDA0002549309890000041
Selecting the next hop node to construct the path according to the probability
Figure BDA0002549309890000042
And the dynamic compensation factor m (i, j) is according to the disclosureThe formulas (1) and (2) are designed. The dynamic compensation factor is introduced to prevent the search from falling into a local optimal solution due to the fact that the pheromone content is considered to be excessive when the next-hop node is selected. And simultaneously recording the node energy information passed by the ants, and thus, searching the path according to the flow.
Figure BDA0002549309890000043
Figure BDA0002549309890000044
In the formulae (1) and (2), τij(t) denotes the pheromone concentration between nodes i, j, ηij(t) represents a distance dijReciprocal of (a), lmaxAnd lminRepresents the longest path and the shortest path of all ants in the round, respectively,/(k)Indicating the length of the path taken by ant k.
And 5: as shown in fig. 2, after the ants in all the sub-films 2,3,4 complete the path search, there are multiple searched paths in each sub-film. The probability C is then measured in terms of the path according to the evolution rule of the membrane(r,t)And sequencing the paths in the respective membranes, and finally selecting the first five paths to be sent into the main membrane 1. Path metric probability C(r,t)Designing according to the formula (3):
Figure BDA0002549309890000045
in the formula (3), Eave(r, t) is the average energy of the nodes on one of the paths, and L (r, t) is the length of one of the paths.
The evolution rules in the daughter membranes are designed according to equations (4), (5), (6):
Figure BDA0002549309890000046
Figure BDA0002549309890000051
Figure BDA0002549309890000052
in the formulas (4), (5) and (6), taking the sub-membrane 2 as an example,
Figure BDA0002549309890000053
represents a first path in the sub-film 2;
Figure BDA0002549309890000054
representing the sorted path sequence; in1 denotes the feeding of the first five sequenced paths into the primary film 1; the evolution rules in other sub-membranes are similar as in sub-membrane 2.
Step 6: when the sub-films send the first 5 paths in the respective films into the main film 1, the optimal path is selected according to the formula (3), and pheromone is updated. The updating of the pheromone is designed according to the formulas (7) and (8):
Figure BDA0002549309890000055
Figure BDA0002549309890000056
in the formulae (7) and (8), HkThe hop count of the ant K search path is represented; ekRepresenting node energy; l iskRepresents the distance traveled by the ant k; q represents a constant coefficient; rho represents the pheromone volatilization coefficient on the path; tau ismaxRepresenting the pheromone maximum threshold.
And 7: and repeating the steps 4-6 until the algorithm ending condition is met.
And 8: as shown in fig. 2, the optimal path is outputted, and the selected optimal path is randomly sent to a sub-film to be constructed again to confirm whether the transmission requirement is satisfied. The evolution rule of the main membrane 1 is designed according to the formula (9):
Figure BDA0002549309890000057
in the formula (9), the reaction mixture,
Figure BDA0002549309890000058
indicating the optimal path, i ∈ (1, 15); inj indicates feeding the optimal path into the secondary film, j ∈ (2,3, 4).
And step 9: as shown in fig. 3, a path repair mechanism is defined, the state of nodes on a path is checked at intervals, and if nodes which do not meet requirements appear, the nodes which meet the energy requirements are replaced. D0 is used as the radius of the search circle region, and the nodes in the secondary circle region are used as candidate nodes. And connecting the circle center with the target node, and in order to prevent the selection of the node behind the replaced node, namely, the node far away from the target node, regulating the angle range between the connecting line of the circle center and the replacement node and the connecting line of the circle center and the target node to be limited within 60 degrees left and right of the connecting line of the circle center and the target node.
The whole method can balance the energy consumption of the nodes in the network, so that the reliability of data transmission of the WSNs of the intelligent power distribution network is ensured.

Claims (1)

1. A WSNs routing method for a power distribution network combining membrane calculation and ant colony algorithm is characterized by comprising the following steps:
step 1: initializing initial node energy E in a network0Initial pheromone concentration τij(t), distance between nodes dijAnd environmental information;
step 2: the membrane structure in the initialization membrane calculation is [ 2 ]1[2]2[3]3[4]4]1The membrane 1 is a main membrane; membranes 2,3,4 are submembranes;
and step 3: before the algorithm starts iteration, n ants are placed at an initial node in each sub-membrane for preparing routing path selection;
and 4, step 4: after the preparation work is finished, ants on the initial nodes in each sub-membrane according to the probability
Figure FDA0002549309880000011
Selecting the next hop node to construct the path according to the probability
Figure FDA0002549309880000012
And the dynamic compensation factor m (i, j) is designed according to the formulas (1) and (2). The dynamic compensation factor is introduced to prevent the search from falling into a local optimal solution due to the fact that the pheromone content is considered to be excessive when the next-hop node is selected. And simultaneously recording the node energy information passed by the ants, and thus, searching the path according to the flow.
Figure FDA0002549309880000013
Figure FDA0002549309880000014
In the formulae (1) and (2), τij(t) denotes the pheromone concentration between nodes i, j, ηij(t) represents a distance dijReciprocal of (a), lmaxAnd lminRepresents the longest path and the shortest path of all ants in the round, respectively,/(k)Indicating the length of the path taken by ant k.
And 5: when the ants in all the sub-membranes 2,3 and 4 complete the path search, a plurality of searched paths exist in each sub-membrane. The probability C is then measured in terms of the path according to the evolution rule of the membrane(r，t)And sequencing the paths in the respective membranes, and finally selecting the first five paths to be sent into the main membrane 1. Path metric probability C(r，t)Designing according to the formula (3):
Figure FDA0002549309880000015
in the formula (3), Eave(r, t) is the average energy of the nodes on one of the paths, and L (r, t) is the length of one of the paths.
The evolution rules in the daughter membranes are designed according to equations (4), (5), (6):
Figure FDA0002549309880000021
Figure FDA0002549309880000022
Figure FDA0002549309880000023
in the formulas (4), (5) and (6), taking the sub-membrane 2 as an example,
Figure FDA0002549309880000024
represents a first path in the sub-film 2;
Figure FDA0002549309880000025
representing the sorted path sequence; in1 denotes the feeding of the first five sequenced paths into the primary film 1; the evolution rules in other sub-membranes are similar as in sub-membrane 2.
Step 6: when the sub-films send the first 5 paths in the respective films into the main film 1, the optimal path is selected according to the formula (3), and pheromone is updated. The updating of the pheromone is designed according to the formulas (7) and (8):
Figure FDA0002549309880000026
Figure FDA0002549309880000027
in the formulas (7) and (8), slicekThe hop count of the ant K search path is represented; ekRepresenting node energy; l iskRepresents the distance traveled by the ant k; q represents a constant coefficient; rho represents the pheromone volatilization coefficient on the path; tau ismaxRepresenting the pheromone maximum threshold.
And 7: and repeating the steps 4-6 until the algorithm ending condition is met.
And 8: and outputting the optimal path, and randomly sending the selected optimal path into one sub-film to be constructed once again so as to confirm whether the transmission requirement is met. The evolution rule of the main membrane 1 is designed according to the formula (9):
Figure FDA0002549309880000028
in the formula (9), the reaction mixture,
Figure FDA0002549309880000029
indicating the optimal path, i ∈ (1, 15); inj indicates feeding the optimal path into the secondary film, j ∈ (2,3, 4).
And step 9: defining a path repairing mechanism, checking the state of nodes on a path at intervals, and replacing the nodes meeting the energy requirement if the nodes not meeting the requirement appear. D0 is used as the radius of the search circle region, and the nodes in the secondary circle region are used as candidate nodes. And connecting the circle center with the target node, and in order to prevent the selection of the node behind the replaced node, namely, the node far away from the target node, regulating the angle range between the connecting line of the circle center and the replacement node and the connecting line of the circle center and the target node to be limited within 60 degrees left and right of the connecting line of the circle center and the target node.
CN202010570178.5A 2020-06-21 2020-06-21 WSNs routing method for power distribution network combining membrane calculation and ant colony algorithm Pending CN111556549A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010570178.5A CN111556549A (en) 2020-06-21 2020-06-21 WSNs routing method for power distribution network combining membrane calculation and ant colony algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010570178.5A CN111556549A (en) 2020-06-21 2020-06-21 WSNs routing method for power distribution network combining membrane calculation and ant colony algorithm

Publications (1)

Publication Number Publication Date
CN111556549A true CN111556549A (en) 2020-08-18

Family

ID=72007067

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010570178.5A Pending CN111556549A (en) 2020-06-21 2020-06-21 WSNs routing method for power distribution network combining membrane calculation and ant colony algorithm

Country Status (1)

Country Link
CN (1) CN111556549A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113259251A (en) * 2021-06-17 2021-08-13 广东电网有限责任公司湛江供电局 Routing networking method and device for multimode converged communication

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070094166A1 (en) * 2002-08-05 2007-04-26 Edwin Addison Knowledge-based methods for genetic network analysis and the whole cell computer system based thereon
CN104050390A (en) * 2014-06-30 2014-09-17 西南交通大学 Mobile robot path planning method based on variable-dimension particle swarm membrane algorithm
CN111273562A (en) * 2020-01-15 2020-06-12 安徽理工大学 Method for realizing optimization of underground robot path based on membrane calculation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070094166A1 (en) * 2002-08-05 2007-04-26 Edwin Addison Knowledge-based methods for genetic network analysis and the whole cell computer system based thereon
CN104050390A (en) * 2014-06-30 2014-09-17 西南交通大学 Mobile robot path planning method based on variable-dimension particle swarm membrane algorithm
CN111273562A (en) * 2020-01-15 2020-06-12 安徽理工大学 Method for realizing optimization of underground robot path based on membrane calculation

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
何伊妮等: "基于膜计算和蚁群算法的电网云平台资源配置方法", 《电力工程技术》 *
徐浙君等: "基于膜计算和蚁群算法的融合算法在云计算资源调度中的研究", 《计算机测量与控制》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113259251A (en) * 2021-06-17 2021-08-13 广东电网有限责任公司湛江供电局 Routing networking method and device for multimode converged communication

Similar Documents

Publication Publication Date Title
CN110149671B (en) Routing method of unmanned aerial vehicle swarm network
CN109547966B (en) Routing planning and fault diagnosis backup method for wireless sensor network of power transmission line
CN112469100B (en) Hierarchical routing algorithm based on rechargeable multi-base-station wireless heterogeneous sensor network
CN101325544B (en) Method for establishing multi-path route based on link multiple characteristic values in wireless sensor network
CN109547965A (en) A kind of wireless sensor network paths planning method based on service priority
CN107911242A (en) A kind of cognitive radio based on industry wireless network and edge calculations method
CN107846719B (en) A kind of wireless sensor network routing method based on improvement gam algorithm
CN110461018B (en) Opportunistic network routing forwarding method based on computable AP
CN111148176B (en) Routing method and device for wireless ad hoc network
CN111556549A (en) WSNs routing method for power distribution network combining membrane calculation and ant colony algorithm
CN112672300B (en) Power consumption data vine type transmission optimization method for medium and low voltage switch cabinet
CN102238561A (en) Node deployment method for energy efficient hierarchical collaboration coverage model
CN110808911B (en) Networking communication routing method based on ant colony pheromone
CN113411766A (en) Intelligent Internet of things comprehensive sensing system and method
CN112423360A (en) Hardware framework of sensor node
Logambigai et al. QEER: QoS aware energy efficient routing protocol for wireless sensor networks
Rekha Defective Motes Uncovering and Retrieval for Optimized Network
CN115987867A (en) Photoelectric-radar sensor network data collection method based on equal clustering
CN113347561B (en) Multidimensional scale node positioning method based on improved particle swarm
CN112822653B (en) Clustering routing method in wireless sensor network
Matsuura Maximizing lifetime of multiple data aggregation trees in wireless sensor networks
Liu et al. Collision-constrained minimum energy node-disjoint multipath routing in ad hoc networks
Bai et al. EBTM: An energy-balanced topology method for wireless sensor networks
Zhao et al. An Improved Cluster Header Switch Gateway Routing Protocol
CN113938975B (en) Mobile sensing network route optimization method based on competition window ant colony clustering

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20200818

WD01 Invention patent application deemed withdrawn after publication