CN107295081B - Combined routing optimization energy supplementing device and energy supplementing method thereof - Google Patents

Combined routing optimization energy supplementing device and energy supplementing method thereof Download PDF

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CN107295081B
CN107295081B CN201710476672.3A CN201710476672A CN107295081B CN 107295081 B CN107295081 B CN 107295081B CN 201710476672 A CN201710476672 A CN 201710476672A CN 107295081 B CN107295081 B CN 107295081B
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CN107295081A (en
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贾杰
陈剑
刘忠禹
熊喆
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Northeastern University China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/0215—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices
    • H04W28/0221—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices power availability or consumption
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/246—Connectivity information discovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • 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

Abstract

The invention belongs to the technical field of Internet of things, and particularly relates to a combined routing optimization energy supplementing device and an energy supplementing method thereof. The energy supplement device for joint routing optimization comprises: a link state acquirer: the method comprises the steps of obtaining link state information between nodes; a node position acquirer: the method comprises the steps of obtaining position information of nodes; a path encoder: carrying out genetic coding on a communication path between the internet of things in a tree mode based on the acquired link state information and the node position; genetic path evolver: optimizing a communication path of the Internet of things in a genetic algorithm mode, and constructing an optimal routing tree; a charging mode output device: a movement path for outputting the optimal wireless charging cart WCV; an optimal path output device: and the data routing path is used for outputting the optimal data routing path of the Internet of things. Meanwhile, an energy supplement method for joint path optimization is also provided. The optimal charging path obtained by the invention has higher energy supplement efficiency.

Description

Combined routing optimization energy supplementing device and energy supplementing method thereof
Technical Field
The invention belongs to the technical field of Internet of things, and particularly relates to a combined routing optimization energy supplementing device and an energy supplementing method thereof.
Background
The Internet of things has a very wide application prospect, is widely used in multiple fields such as military reconnaissance, medical monitoring, environmental monitoring, traffic management and the like at present, and is a research hotspot which is concerned at present. In the outdoor and some complex application environments, the nodes of the internet of things are generally deployed in a monitoring area in a mode of airplane broadcasting, shell ejection or artificial embedment, and after the nodes are deployed, a network is formed in a self-organizing multi-hop mode. Due to the energy limitation of the nodes, when some nodes exhaust energy, the network is fragmented and data in some sensing areas is not extracted and uploaded. How to prolong the working time of the network as far as possible becomes a key problem for the research of the internet of things.
In recent years, extensive research has been conducted to extend the lifetime of internet of things. Conventional research generally focuses on research of energy-saving technology, and reduces energy consumption in the internet of things from two aspects, namely, designing a hardware architecture with low power consumption, realizing software with low complexity, realizing wireless communication with effective energy, dynamic routing technology, mobile data acquisition and the like. While these solutions can effectively extend network life, network life is still dependent on limited battery power. Another type of solution is to utilize energy replenishment methods to alleviate the energy usage limitations of the internet of things. Recent advances in wireless energy transfer have made efficient transfer of electrical energy possible. Wireless energy transfer has been used to charge small appliances, and recent advances have also shown application prospects for medium-length range non-radiative energy transfer. When the induction coils with magnetism work at the same frequency, the induction coils can generate strong resonance coupling through non-radiative magnetic induction coupling, and under the resonance coupling, energy is efficiently transmitted from the source coil to the receiving coil, and meanwhile, the energy loss to the outside is small. Compared with the electromagnetic radiation method, the magnetic resonance coupling can provide higher energy transfer efficiency under the conditions of omnidirectional and non-line-of-sight and is less influenced by the surrounding environment. Commercial products using medium length range wireless energy transmission are currently on the market. The development of wireless energy transmission technology has provided a new feasible method for energy supplement of nodes of the internet of things, and is attracting wide attention.
Although some research works begin to research the energy supplement mechanism of the internet of things, the current energy supplement methods lack consideration on the data transmission mechanism of the internet of things, and the prior art has the problem of low energy supplement efficiency.
Disclosure of Invention
Technical problem to be solved
Aiming at the existing technical problems, the invention provides a combined routing optimization energy supplementing device, which can solve the problem of low supplementing efficiency in an energy supplementing mode of the Internet of things in the prior art.
Another objective of the present invention is to provide an energy supplementing method for joint path optimization.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
a combined routing-optimized energy replenishment device, comprising:
a link state acquirer: the method comprises the steps of obtaining link state information between nodes;
a node position acquirer: the node position information acquisition module is used for acquiring node position information;
a path encoder: carrying out genetic coding on communication paths among the Internet of things in a tree mode based on the acquired link state information and the node position information;
genetic path evolver: optimizing a communication path of the Internet of things in a genetic algorithm mode, and constructing an optimal routing tree;
a charging mode output device: the mobile path used for outputting the optimal wireless charging vehicle WCV in the internet of things;
an optimal path output device: and the data routing path is used for outputting the optimal data routing path of the Internet of things.
As a preferable mode of the above-mentioned joint route optimized energy supplement apparatus, the genetic path evolver includes:
a genetic operator composed of selection, crossover, compilation and substitution modes;
and the individual evaluator consists of a charging residence point selector, a charging residence point optimizer and an adaptive value evaluator.
A combined path optimization energy supplement method comprises the following steps:
step 1, collecting node position information;
step 2: collecting link state information;
and step 3: based on the node position information and the link state information, repeatedly using a routing tree construction algorithm to construct N individuals as an initial population of a genetic algorithm;
and 4, step 4: constructing an optimal routing tree by using a genetic algorithm;
and 5: and evaluating the adaptive value of the individual corresponding to the optimal routing tree to obtain the moving path of the optimal wireless charging vehicle WCV.
As a preferred scheme of the energy supplement method for the joint path optimization, the initialization method for each individual of the population is as follows:
step 3-1, setting the sign of the sink node in the routing tree to be 1, and setting the signs of other nodes to be 0;
step 3-2, for any node i, changing v to i, wherein i ∈ N;
step 3-3: judging the neighbor set, which specifically comprises the following steps
Step a: if v isjIs not empty, a node is randomly selected from the neighbor set and is marked as vj-1At the same time vj-1Removing from the neighbor set;
b, performing a step; if neighbor set is empty and vjNot equal to i, then order vj＝vj+1Re-executing the step 3;
step c: if neighbor set is empty and vjI, indicating that no feasible path exists under the current topology, and ending the algorithm;
step 3-4: if the mark of the node in the routing tree is not 1, setting the node mark as 1 and recording the path vj-1→vjOtherwise, turning to the step 3-3;
step 3-5: if the mark of the node in the routing tree is 1, indicating that a path from the sink node to the node is found, and turning to the step 3-6; otherwise, setting the mark of the node in the routing tree as 1, and enabling v to bej＝vj-1Turning to step 3-3;
step 3-6: recording a path from a sink node to a node i;
step 3-7: and constructing paths from the N sink nodes to the nodes into a chromosome code.
As a preferred solution of the energy supplement method for joint path optimization, in step 4, the method for constructing an optimal routing tree includes:
step 4-1: selecting operation, wherein the algorithm selects half of the original population as a parent population through a binary tournament algorithm;
step 4-2: performing cross operation, namely randomly selecting two individuals from the selected parent, randomly selecting a pair of alleles from the two individuals, performing cross operation on the alleles to obtain two new offspring individuals, and if the generated new individuals do not meet the routing tree condition of the Internet of things, adjusting the offspring individuals to enable the result to meet the routing tree condition;
4-3, performing mutation operation, namely randomly selecting an individual from the selected parent population, randomly selecting an allele to generate a new offspring individual by using single-point mutation, selecting a random node in the algorithm to perform local multicast tree adjustment, and if the mutated individual is not a routing tree, adjusting the mutated individual to become the routing tree;
and 4-5: combining the filial generation generated after crossing and mutation operations with the original seed population, evaluating the adaptive value of each individual in the combined population, and selecting the optimal N chromosomes to form a new population;
and 4-6: judging whether the termination condition is met, if not, returning to the step 4-1; otherwise, finding out the optimal individual in the optimal population.
As a preferable scheme of the energy supplement method for joint path optimization, the method for evaluating the individual adaptive value includes finding charging residence points in a monitoring area of the internet of things based on a given routing tree, and using a shortest hamilton loop connecting all the charging residence points and the service site length as the individual adaptive value,
as a preferable scheme of the energy supplement method for joint path optimization, in step 5, the method specifically includes the following steps:
step 5-1: selecting a charging residence point, wherein the charging residence point is used for selecting the charging residence point in the monitoring area;
step 5-2: an optimal Hamiltonian loop calculation step, which includes obtaining a shortest path connecting a service station and all Charging residence points by using an ant colony algorithm, and using the path as a moving path of an optimal Wireless Charging Vehicle WCV (Wireless Charging Vehicle);
step 5-3: and returning the current rest time ratio according to the Hamilton loop length and the routing tree structure, and taking the rest time ratio as an individual adaptive value.
As a preferred embodiment of the energy supplement method for joint path optimization, the method includes the following steps in the selection of the charging stagnation point:
step 5-1-1: acquiring initialization information of a network, including acquiring node coordinates (x, y), serving site S position (x)0,y0) Radius of charge rchThe sensor node Set not within any charging residence point charging range is equal to N, and the sensor node Set within a certain charging residence point charging range
Figure GDA0002386788590000051
Charging dwell point coordinate set
Figure GDA0002386788590000052
Step 5-1-2: finding the coordinate (x) of the node i nearest to the service site Si,yi) As a candidate position of a first charging residence point, finding a distance node i smaller than 2 rchA node set U;
step 5-1-3: if it is not
Figure GDA0002386788590000053
Selecting a node j farthest from the node i in the U, and taking a midpoint M (x) of a connecting line between the node j and the node iM,yM) As the first charging dwell point of WCV, it will be at M (x)M,yM) R ofchThe nodes in the node are deleted from the Set and addedTo set C, P ═ P ∪ (x)M,yM)，
If not, then,
connecting S with a node i, and selecting the distance node i as rchPoint (x) ofs,ys) As the charging stagnation point, Set-i, C-C ∪ i, and P-P ∪ (x)s,ys)；
Step 5-1-4: selecting a node k closest to the last charging residence point in the Set as a candidate position of the next charging residence point, and searching for a node k less than 2 r from the candidate node kchAll the nodes of (2) set Q;
step 5-1-5: if it is not
Figure GDA0002386788590000054
Searching a node w farthest from the node k in the Q, connecting the node k and the node w, and taking a midpoint M (x) of a connecting lineM,yM) As the next WCV charge dwell point, the charge dwell point (x)M,yM) Near rchNodes in the range are deleted from Set and added to the Set C, P ═ P ∪ (x)M,yM) And if not, the step (B),
the candidate node k is an independent node, a charging residence point and the node k are connected, and the distance node k is selected to be rchPosition (x) ofpos，ypos) As the next charging stagnation point of WCV, Set-k, C-C ∪ k, and P-P ∪ (x)pos，ypos)；
Step 5-1-6: repeating steps 5-1-4 and 5-1-5 until
Figure GDA0002386788590000064
C＝N。
As a preferred embodiment of the energy supplement method for joint path optimization, in step 5-1-5, the definition of the independent node is: a certain node i, except the node, is centered at 2 · rchIf no other node exists in the range, node i is called an independent node.
As a preferred scheme of the energy supplement method for the joint path optimization, according to the hamilton loop length and the routing tree structure, the current rest time ratio is returned, and the rest time ratio is used as an individual adaptive value, and the specific calculation method is as follows:
step 5-3-1: for any charging residence point i, the charging residence time t of the calculatoriThe specific calculation mode is that a charging residence point i is selected to cover the node j with the maximum energy consumption in the set, the energy consumption rate is Pj, and the residence time is
Figure GDA0002386788590000061
Wherein, is the charging period, and U is the charging rate;
step 5-3-2: calculate the total charge time and TE＝Σi∈MtiWhere M is a set of charging dwell points;
step 5-3-3: calculating walking time
Figure GDA0002386788590000062
Wherein L is the length of the Hamiltonian loop, and V is the speed of walking;
setp 5-3-4: calculating the time to rest ratio of
Figure GDA0002386788590000063
(III) advantageous effects
The invention has the beneficial effects that: according to the energy supplement device and method for joint routing optimization, the optimal charging path is obtained in a joint path mode, the network structure of the many-to-one communication of the Internet of things can be fully considered, and the energy supplement efficiency is higher.
Drawings
FIG. 1 is a schematic structural diagram of an energy supplement apparatus for joint routing optimization according to an embodiment of the present invention;
fig. 2 is a diagram of a multicast tree and a constructed chromosome code mapping relationship according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a routing tree crossing operation according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a routing tree mutation operation according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a charging stagnation point selection process according to an embodiment of the present invention;
fig. 6 is a curve of the rest time ratio and the number of nodes according to the embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
As shown in fig. 1, the present embodiment provides a combined routing-optimized energy supplement apparatus, which includes:
a link state acquirer: the method comprises the steps of obtaining link state information between nodes;
a node position acquirer: the node position information acquisition module is used for acquiring node position information;
a path encoder: carrying out genetic coding on communication paths among the Internet of things in a tree mode based on the acquired link state information and the node position information;
genetic path evolver: optimizing a communication path of the Internet of things in a genetic algorithm mode, and constructing an optimal routing tree;
a charging mode output device: the mobile path used for outputting the optimal wireless charging vehicle WCV in the internet of things;
an optimal path output device: and the data routing path is used for outputting the optimal data routing path of the Internet of things.
The genetic path evolver includes: a genetic operator composed in a selection, crossover, compilation and substitution manner and an individual evaluator composed of a charging dwell point selector, a charging dwell point optimizer and an adaptive value evaluator.
In order to further describe the energy supplement device for joint routing optimization, the present embodiment further provides an energy supplement method for joint routing optimization, which includes the following steps:
step 1, collecting node position information;
step 2: collecting link state information;
and step 3: based on the node position information and the link state information, repeatedly using a routing tree construction algorithm to construct N individuals as an initial population of a genetic algorithm;
and 4, step 4: constructing an optimal routing tree by using a genetic algorithm, wherein the construction method of the optimal routing tree is used for evaluating the adaptive value of each individual in the optimal population obtained by the genetic algorithm to obtain the optimal individual, and the evaluation of the adaptive value refers to the step 5;
and 5: and evaluating the adaptive value of the individual, searching a charging residence point in the monitoring area of the Internet of things based on a given routing tree, and taking the shortest Hamiltonian loop connecting all the charging residence points and the service station as the adaptive value of the individual to obtain the optimal charging path.
In step 3, the initialization method for each individual of the population is as follows:
step 3-1, setting the sign of the sink node in the routing tree to be 1, and setting the signs of other nodes to be 0;
step 3-2, for any node i, making v ═ i, wherein i ∈ N;
step 3-3: judging a neighbor set:
a) if v isjIs not empty, a node is randomly selected from the neighbor set and is marked as vj-1At the same time vj-1Removing from the neighbor set;
b) if neighbor set is empty and vjNot equal to i, then order vj＝vj+1Re-executing 3);
c) if neighbor set is empty and vjI, indicating that no feasible path exists under the current topology, and ending the algorithm;
step 3-4: if the mark of the node in the routing tree is not 1, setting the node mark as 1 and recording the path vj-1→vjOtherwise, turning to the step 3-3;
step 3-5: if the mark of the node in the routing tree is 1, indicating that a path from the sink node to the node is found, and turning to the step 3-6; otherwise, put the node's on-wayMarked by 1 in the tree, let vj＝vj-1Turning to step 3-3;
step 3-6: recording a path from a sink node to a node i;
step 3-7: and constructing paths from the N sink nodes to the nodes into a chromosome code. Specifically, the correspondence between the multicast book and the chromosome code shown in fig. 2 may be referred to.
In step 4, the method for constructing the optimal routing tree includes:
step 4-1: selecting operation, wherein the algorithm selects half of the original population as a parent population through a binary tournament algorithm;
step 4-2: performing cross operation, namely randomly selecting two individuals from the selected parent, randomly selecting a pair of alleles from the two individuals, performing cross operation on the alleles to obtain two new offspring individuals, and if the generated new individuals do not meet the routing tree condition of the Internet of things, adjusting the offspring individuals to enable the result to meet the routing tree condition;
referring to fig. 3, a specific procedure of a routing tree crossing operation is shown, in which a node No. 1 is a sink node, two routes of a first routing tree are (6 → 4 → 3 → 2 → 1) and (5 → 3 → 2 → 1), two routes of a second routing tree are (4 → 3 → 2 → 1) and (8 → 7 → 5 → 2 → 1), two routes of the first routing tree generated after the crossing operation are (8 → 7 → 5 → 2 → 1) and (6 → 4 → 3 → 2 → 1), and two routes of the second routing tree are (5 → 3 → 2 → 1) and (4 → 3 → 2 → 1), respectively.
And 4-3, performing mutation operation, namely randomly selecting an individual from the selected parent population, randomly selecting an allele to generate a new child individual by using single-point mutation, selecting a random node in the algorithm to perform local multicast tree regulation, and if the mutated individual is not the routing tree, regulating the mutated individual to become the routing tree.
As shown in fig. 4, the two routes before the mutation operation are (6 → 4 → 3 → 2 → 1) and (5 → 3 → 2 → 1), respectively, and the two routes after the mutation operation are (6 → 4 → 3 → 2 → 1) and (5 → 2 → 1), respectively.
And 4-5: combining the filial generation generated after crossing and mutation operations with the original seed population, evaluating the adaptive value of each individual in the combined population, and selecting the optimal N chromosomes to form a new population;
setp 4-6: judging whether the termination condition is met, if not, returning to the step 4-1; otherwise, finding out the optimal individual in the optimal population.
In step 5, the method specifically comprises the following steps:
step 5-1: selecting a charging residence point, wherein the charging residence point is used for selecting the charging residence point in the monitoring area;
step 5-2: an optimal Hamiltonian loop calculation step, which includes obtaining a shortest path connecting a service site and all Charging residence points by using an ant colony algorithm, and using the shortest path as a moving path of WCV (Wireless Charging Vehicle);
step 5-3: and returning the current rest time ratio according to the Hamilton loop length and the routing tree structure, and taking the rest time ratio as an individual adaptive value.
In the charging stagnation point selection, the following steps are included:
step 5-1-1: acquiring initialization information of a network, including acquiring node coordinates (x, y), serving site S position (x)0,y0) Radius of charge rchA node Set not within the charging range of any charging station is N, and a node Set within the charging range of a certain charging station is N
Figure GDA0002386788590000101
Charging dwell point coordinate set
Figure GDA0002386788590000102
Step 5-1-2: finding the coordinate (x) of the node i nearest to the service site Si,yi) As a candidate position of a first charging residence point, finding a distance node i smaller than 2 rchA node set U;
step 5-1-3: if it is not
Figure GDA0002386788590000103
Selecting a node j farthest from the node i in the U, and taking a midpoint M (x) of a connecting line between the node j and the node iM,yM) As the first charging dwell point of WCV, it will be at M (x)M,yM) R ofchThe nodes in the Set are deleted from the Set and added to the Set C, and P is PU (x)M,yM)，
If not, then,
connecting S with a node i, and selecting the distance node i as rchPoint (x) ofs,ys) As the charging stagnation point, Set-i, C-C ∪ i, and P-P ∪ (x)s,ys)；
Step 5-1-4: selecting a node k closest to the last charging residence point in the Set as a candidate position of the next charging residence point, and searching for a node k less than 2 r from the candidate node kchAll the nodes of (2) set Q;
step 5-1-5: if it is not
Figure GDA0002386788590000111
Searching a node w farthest from the node k in the Q, connecting the node k and the node w, and taking a midpoint M (x) of a connecting lineM,yM) As the next WCV charge dwell point, the charge dwell point (x)M,yM) Near rchNodes in the range are deleted from Set and added to the Set C, P ═ P ∪ (x)M,yM),
If not, then,
the candidate node k is an independent node, a charging residence point and the node k are connected, and the distance node k is selected to be rchPosition (x) ofpos，ypos) As the next charging stagnation point of WCV, Set-k, C-C ∪ k, and P-P ∪ (x)pos，ypos)；
Step 5-1-6: repeating steps 5-1-4 and 5-1-5 until
Figure GDA0002386788590000112
C＝N。
In step 5-1-5, the definition of an independent node is: a certain node i, except the node, is centered on the node, 2 · rchIf no other node exists in the range, node i is called an independent node.
FIG. 5 shows the completion of the charging dwell point selection process based on the above steps. WCV starting from service station S, charging residence point positions are obtained step by step, and all nodes are covered. As shown in FIG. 5(a), first, a node n is selected1As a candidate charging stay point, find a distance n1Less than 2. rchOf a neighbor node of (1), a node n in the graph satisfying the condition1N is to be1And n2As the final WCV charging dwell point P1N is to be1、n2Deleted from the set of nodes that were not charged. Continue to search for distance P1The nearest node, as shown in FIG. 5(b), finds node n3N is to be3As candidate charging stagnation points. Likewise, a distance n is sought3Less than 2. rchAll nodes n in the graph satisfying the condition4、n5Wherein n is5Distance candidate charging stay point n3At the most distant, n is3And n5Is taken as the second charging stagnation point P of WCV2A 1 is to P2Nearby in the charging range rchAll nodes within are deleted from the set of nodes that have not been charged, n3、n4、n5Satisfy the condition, delete n3、n4、n5. Continue to search for distance P2The nearest node, as shown in FIG. 5(c), finds node n6N is to be6As a candidate charging stay point, find a distance n6Is less than 2 rchHas no nodes satisfying the condition in the graph, so that the node n6Is an independent node to which is connected P2And n6Taking the distance n on the connecting line6Is rchAs a third charging dwell position P3Node n6Is covered. All nodes in the network can be supplemented with energy in a wireless charging mode.
According to the Hamilton loop length and the routing tree structure, returning the current rest time ratio, and taking the rest time ratio as an individual adaptive value, wherein the specific calculation method comprises the following steps:
step 5-3-1: for any charging residence point i, the charging residence time t of the calculatoriThe specific calculation mode is that a charging residence point i is selected to cover the node j with the maximum energy consumption in the set, the energy consumption rate is Pj, and the residence time is
Figure GDA0002386788590000121
Wherein, is the charging period, and U is the charging rate;
step 5-3-2: calculate the total charge time and TE＝Σi∈MtiWhere M is a set of charging dwell points;
step 5-3-3: calculating walking time
Figure GDA0002386788590000122
Wherein L is the length of the Hamiltonian loop, and V is the walking speed;
setp 5-3-4: calculating the time to rest ratio of
Figure GDA0002386788590000123
In the embodiment, the effect of the scheme is verified through a specific experiment, specifically, a plurality of sensor nodes are randomly distributed in a 500m × 500m network, the sink node coordinates are fixed to (250 ), the service sites are fixed to (0,0) coordinates, the sensor nodes are randomly distributed in the network, the numerical value of the sensing data generation rate is fixed to 6kb/s, different charging schemes are obtained through an energy supplement algorithm of joint path planning, a charging residence point selection algorithm with the shortest path and a hexagon division charging algorithm, and curves of the rest time ratio and the node number of each charging scheme are made, specifically referring to fig. 6, the charging effect provided by the embodiment is better than that of the other two schemes through the content displayed in fig. 6.
The technical principles of the present invention have been described above in connection with specific embodiments, which are intended to explain the principles of the present invention and should not be construed as limiting the scope of the present invention in any way. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive efforts, which shall fall within the scope of the present invention.

Claims (6)

1. A combined routing-optimized energy supplement device is characterized in that: the method comprises the following steps:
a link state acquirer: the method comprises the steps of obtaining link state information between nodes;
a node position acquirer: the node position information acquisition module is used for acquiring node position information;
a path encoder: carrying out genetic coding on communication paths among the Internet of things in a tree mode based on the acquired link state information and the node position information;
genetic path evolver: optimizing a communication path of the Internet of things in a genetic algorithm mode, constructing an optimal routing tree, and obtaining a mobile path of the optimal wireless charging vehicle WCV based on the optimal routing tree; the construction method of the optimal routing tree comprises the following steps:
step 4-1: selecting operation, wherein a genetic algorithm selects a half of an initial population as a parent population through a binary tournament algorithm;
step 4-2: performing cross operation, namely randomly selecting two individuals from the selected parent population, randomly selecting a pair of alleles from the two individuals, performing cross operation on the alleles to obtain two new offspring individuals, and if the generated new individuals do not meet the routing tree condition of the Internet of things, adjusting the offspring individuals to enable the result to meet the routing tree condition;
4-3, performing mutation operation, namely randomly selecting an individual from the selected parent population, randomly selecting an allele to generate a new offspring individual by using single-point mutation, selecting a random node in the genetic algorithm to perform local multicast tree regulation, and if the mutated individual is not a routing tree, regulating the mutated individual into the routing tree;
and 4-5: combining the filial generation generated after crossing and mutation operations with the initial population, carrying out adaptive value evaluation on each individual in the combined population, and selecting the optimal N chromosomes to form a new population;
and 4-6: judging whether the termination condition is met, if not, returning to the step 4-1; otherwise, finding out the optimal individual in the optimal population,
a charging mode output device: the mobile path used for outputting the optimal wireless charging vehicle WCV in the internet of things;
an optimal path output device: the optimal data routing path is used for outputting the Internet of things;
obtaining the optimal path of travel for the wireless charging cart WCV includes the steps of:
step 5-1: selecting a charging residence point, wherein the charging residence point is used for selecting the charging residence point in the monitoring area;
step 5-2: calculating an optimal Hamiltonian loop, wherein the shortest path connecting the service station and all charging residence points is obtained by utilizing an ant colony algorithm, and the path is taken as a moving path of the optimal wireless charging vehicle WCV;
step 5-3: and returning the current rest time ratio according to the Hamilton loop length and the routing tree structure, and taking the rest time ratio as an individual adaptive value.
2. The joint routing optimization energy supplement device of claim 1, wherein the genetic path evolver comprises:
a genetic operator composed of selection, crossover, compilation and substitution modes;
and the individual evaluator consists of a charging residence point selector, a charging residence point optimizer and an adaptive value evaluator.
3. A method for energy replenishment for joint path optimization, based on a joint route-optimized energy replenishment device according to claim 1 or 2, comprising the steps of:
step 1, collecting node position information;
step 2: collecting link state information;
and step 3: based on the node position information and the link state information, repeatedly using a routing tree construction algorithm to construct N individuals as an initial population of a genetic algorithm;
and 4, step 4: constructing an optimal routing tree by using a genetic algorithm;
and 5: evaluating the adaptive value of the individual corresponding to the optimal routing tree to obtain the moving path of the optimal wireless charging vehicle WCV;
and the individual corresponding to the optimal routing tree is a node.
4. The joint path optimized energy replenishment method of claim 3, comprising, in the charging stagnation point selection, the steps of:
step 5-1-1: acquiring initialization information of a network, including acquiring node coordinates (x, y), serving site S position (x)0,y0) Radius of charge rchThe sensor node Set not within the charging range of any charging residence point is equal to N, and the node Set within the charging range of a certain charging residence point
Figure FDA0002481141370000021
Figure FDA0002481141370000022
Charging dwell point coordinate set
Figure FDA0002481141370000023
Step 5-1-2: finding the coordinate (x) of the node i nearest to the service site Si,yi) As a candidate position of a first charging residence point, finding a distance node i smaller than 2 rchA node set U;
step 5-1-3: if it is not
Figure FDA0002481141370000031
Selecting a node j farthest from the node i in the U, and taking a midpoint M (x) of a connecting line between the node j and the node iM,yM) As the first charging dwell point of WCV, it will be at M (x)M,yM) R ofchThe nodes in the Set are deleted from the Set and added to the Set C, and P is P ∪ (x)M,yM)，
If not, then,
connecting S with a node i, and selecting the distance node i as rchPoint (x) ofs,ys) As the charging stagnation point, Set-i, C-C ∪ i, and P-P ∪ (x)s,ys)；
Step 5-1-4: selecting a node k closest to the last charging residence point in the Set as a candidate position of the next charging residence point, and searching for a node k less than 2 r from the candidate node kchAll the nodes of (2) set Q;
step 5-1-5: if it is not
Figure FDA0002481141370000032
Searching a node w farthest from the node k in the Q, connecting the node k and the node w, and taking a midpoint M (x) of a connecting lineM,yM) As the next WCV charge dwell point, the charge dwell point (x)M,yM) Near rchNodes in the range are deleted from Set and added to the Set C, P ═ P ∪ (x)M,yM),
If not, then,
the candidate node k is an independent node, a charging residence point and the node k are connected, and the distance node k is selected to be rchPosition (x) ofpos，ypos) As the next charging stagnation point of WCV, Set-k, C-C ∪ k, and P-P ∪ (x)pos，ypos)；
Step 5-1-6: repeating steps 5-1-4 and 5-1-5 until
Figure FDA0002481141370000033
C＝N。
5. According toThe joint path optimized energy replenishment method of claim 4, wherein in steps 5-1-5, the independent nodes are: a certain node i, except the node, is centered at 2 · rchIf no other node exists in the range, node i is called an independent node.
6. The joint path optimization energy supplement method according to claim 3, wherein according to the Hamiltonian loop length and the routing tree structure, the current rest time ratio is returned, and the rest time ratio is used as an individual adaptive value, and the specific calculation method comprises the following steps:
step 5-3-1: calculating charging residence time t for any charging residence point iiThe specific calculation mode is that a charging residence point i is selected to cover the node j with the maximum energy consumption in the set, the energy consumption rate is Pj, and the residence time is
Figure FDA0002481141370000041
Wherein, is the charging period, and U is the charging rate;
step 5-3-3: calculate the total charge time and TE＝Σi∈MtiWhere M is a set of charging residents;
step 5-3-4: calculating walking time
Figure FDA0002481141370000042
Wherein L is the length of the Hamiltonian loop, and V is the speed of walking;
step 5-3-5: calculating a rest time ratio of
Figure FDA0002481141370000043
Figure FDA0002481141370000044
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