CN112261704A - Repeated game routing method based on rechargeable wireless sensor network - Google Patents

Repeated game routing method based on rechargeable wireless sensor network Download PDF

Info

Publication number
CN112261704A
CN112261704A CN202011144441.0A CN202011144441A CN112261704A CN 112261704 A CN112261704 A CN 112261704A CN 202011144441 A CN202011144441 A CN 202011144441A CN 112261704 A CN112261704 A CN 112261704A
Authority
CN
China
Prior art keywords
node
nodes
forwarding
cluster head
sink
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.)
Granted
Application number
CN202011144441.0A
Other languages
Chinese (zh)
Other versions
CN112261704B (en
Inventor
刘贵云
林家豪
钟晓静
李君强
彭智敏
舒聪
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.)
Guangzhou University
Original Assignee
Guangzhou University
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 Guangzhou University filed Critical Guangzhou University
Priority to CN202011144441.0A priority Critical patent/CN112261704B/en
Publication of CN112261704A publication Critical patent/CN112261704A/en
Application granted granted Critical
Publication of CN112261704B publication Critical patent/CN112261704B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a repeated game routing method based on a rechargeable wireless sensor network, which comprises the following steps: arranging a plurality of nodes with the same initial energy value in a monitoring area of the wireless sensor network, and performing area clustering on all the nodes according to the area and the sink nodes; in each round of data transmission, a node is randomly selected as a source node, and a data packet is sent to a sink node; other nodes in the area where the source node is located perform strategy game on whether the forwarding work of the source node data packet is performed or not, and provide utility values of self-election cluster head nodes; the source node selects the same cluster node with the maximum utility value from the election nodes as a cluster head and forwards the data information to the cluster head; the cluster head transmits information to the sink node, and after the data information is successfully received, the unmanned aerial vehicle is informed to charge the cluster head of the current round. The invention effectively improves the cooperation of the wireless sensor network nodes, solves the problems of node energy consumption and information transmission routes, and prolongs the service life of the whole network.

Description

Repeated game routing method based on rechargeable wireless sensor network
Technical Field
The invention relates to the technical field of wireless sensor networks, in particular to a repeated game routing method based on a rechargeable wireless sensor network.
Background
At present, Wireless Sensor Network (WSN) technology has been applied to many emerging fields and plays an important role therein, such as being applied to the smart home industry, the national military safety field, the field of automated industrial production, the smart agriculture regulation and control system, daily human health monitoring, etc., the Wireless Sensor network technology is inevitably applied, and the problem of the solution urgently needed at present is to find a method for prolonging the service life of the Wireless Sensor network. The current main research direction is to develop an operation method which can save the energy consumption of the sensor node as much as possible so as to improve the cruising ability of the sensor node. In a practical application scene, the wireless sensor nodes are used for collecting information instead of people, so that the wireless sensor nodes are mostly arranged in places with severe environmental conditions, and in order to ensure that collected data information has certain comprehensiveness and accuracy, the number of the sensor nodes required to be arranged in a wireless sensor network is generally large, and frequent manual charging is difficult to realize technically. Therefore, it is meaningful to explore how the sensor nodes operate in the most energy-saving mode without affecting the overall performance of the wireless sensor network.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides a repeated game routing method based on a rechargeable wireless sensor network, game theory knowledge and a WSN technology are combined, a design is carried out aiming at selection of a next hop node in a data forwarding stage, and a repeated game routing protocol is formed.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a repeated game routing method based on a rechargeable wireless sensor network, which comprises the following steps:
arranging a plurality of nodes with the same initial energy value in a region monitored by the wireless sensor network, and performing region clustering on all the nodes according to the area and the position of the sink node;
after clustering is completed, a utility value model and a reward mechanism combining charging are established;
in each round of data transmission, a node is randomly selected as a source node, and a data packet is sent to a sink node;
other nodes in the area where the source node is located perform strategy game on whether the forwarding work of the source node data packet is performed or not, and provide utility values of self-election cluster head nodes;
the source node selects the same cluster node with the maximum utility value from the election nodes as a cluster head and forwards the data information to the cluster head;
the cluster head transmits information to the sink node, and after the sink node successfully receives the data information, the Unmanned Aerial Vehicle (UAV) is informed to charge the cluster head of the current round.
As a preferred technical scheme, the method for performing region clustering on all nodes according to the area size and the position of the sink node comprises the following specific steps:
taking a sink node as a center, equally dividing square regions with the same area and shape to the periphery of the sink node, wherein the side length of each region is smaller than a preset multiple of an inter-node communication threshold, and the number of the regions is determined by the size of the area needing to be covered and monitored by the wireless sensor network;
the nodes are clustered according to the areas where the nodes are located, and the nodes distributed in the same area are classified into the same cluster; the clustering model has the preconditions that: the positions of the nodes are not changed after the nodes are arranged, the positions of all the nodes can be known, and each node knows all the information of other nodes in the same cluster.
As a preferred technical scheme, the method for constructing the utility value model comprises the following specific steps:
when a source node is determined, all nodes in the same cluster give utility values of self-evaluation election cluster head nodes to forwarding requests of the source node, and the source node selects one node as a next hop node to assist in data forwarding;
constructing a utility function of a node election cluster head according to the distance from a node to a source node, the distance from the node to a sink node, the distance from the node to a region center and the energy change when selecting a strategy of forwarding or not forwarding a data packet, wherein the utility function is specifically represented as follows:
not forwarding the data packet:
Figure BDA0002739264250000031
and forwarding the data packet:
Figure BDA0002739264250000032
wherein E isnoseRepresenting the remaining energy of the node to be selected, ErecRepresenting the energy consumed by the process of receiving the source node data packet, EtransRepresenting the energy consumed in the process of successfully forwarding the data packet to the sink node after the node to be selected becomes a cluster head node, p represents the probability of successful forwarding, E0Representing the initial energy of the node, Di-sinkRepresents the distance from the node i to be selected to the sink node, Dsink-maxRepresents the maximum distance from the node to the sink in the region, Di-centerRepresenting the distance from the node i to be selected to the center of the area, Dcenter-maxRepresents the maximum distance from the node to the center of the area, Di-sourceRepresenting the distance from the node i to be selected to the source node, Dsource-maxRepresenting the maximum distance from the node in the region to the source node.
As a preferred technical solution, the building of the reward mechanism combined with charging specifically includes the steps of:
when the node to be selected in the current round selects the cooperation strategy, charging reward is given to the node before the next round of information transmission simulation starts, so that the consumed energy of the node is supplemented, and the obtained benefit exceeds the cost paid by the current action of selecting the cooperation strategy and is used as the reward for the node;
the expected values for the energy obtained for successful forwarding and successful charging are:
(Echarge-Etrans)*p*q+(Echarge-0)*p*(1-q)+(0-Etrans)*(1-p)
*q+(0-0)*(1-p)(1-q)＝(Echarge*p-Etrans*q)
the revenue function is defined as:
Figure BDA0002739264250000041
wherein E ischarge NRepresenting that the node to be selected in each round becomes a cluster head node and obtains supplemented energy after successfully forwarding the data packet, wherein p is the probability of successful forwarding, and q represents the probability of successful charging;
updating utility functions of all nodes in the area where the source node is located;
the specific definition is as follows:
not forwarding the data packet:
Figure BDA0002739264250000042
and forwarding the data packet:
Figure BDA0002739264250000043
will reward mechanism EchargeThe magnitude of the value is defined as:
Echarge＝1.05555556*Etrans+Erec
wherein E istransRepresenting the energy consumed in forwarding the packet.
As a preferred technical scheme, the method includes randomly selecting a node as a source node, sending a data packet to a sink node, specifically performing data transmission in a two-hop mode of node forwarding, and selecting a cluster head node to forward source node data.
As a preferred technical solution, the charging is specifically performed by: unmanned aerial vehicle charges for appointed node.
As a preferred technical scheme, the method further comprises a step of constructing a calculation model of the energy consumption of the sensor node, and the specific calculation formula is as follows:
the energy consumption model of the node for transmitting information is as follows:
Figure BDA0002739264250000051
the energy consumption model of the node for receiving the information is as follows:
ERx＝l*Eelec
where l denotes the length of the data packet and di,jIndicating the communication distance between nodes, EelecRepresenting transmission energy per unit length, epsilonfsAnd εmpRespectively representing a free space transmission parameter and a multipath fading transmission parameter.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) the invention combines game theory knowledge with WSN technology, designs aiming at selection of next hop node in data forwarding stage, forms a repeated game routing protocol which can effectively improve cooperation of wireless sensor network nodes, combines UAV charging technology to make network continuously run and energy consumption balanced, solves the problems of node energy consumption and information transmission route, and prolongs the service life of the whole network.
(2) According to the invention, the reward mechanism is designed by combining with the UAV charging factor, and after each round of information transmission is successful, the UAV rewards the cooperative nodes for energy supplement, so that on one hand, the enthusiasm of selecting a cooperative strategy by the nodes is stimulated, and the working performance of the whole network is improved; on the other hand, the nodes can supplement working energy and can continuously work; by combining the definition of the utility function, the node with the most comprehensive performance, which has the advantages of large residual energy value and short total communication distance, in each round of selection area is used as a cluster head, and the sink node successfully receives information and rewards the information for energy, thereby effectively improving the energy consumption balance performance of the whole wireless sensor network.
Drawings
Fig. 1 is a schematic flowchart of a repeated game routing method based on a rechargeable wireless sensor network according to the present embodiment;
FIG. 2 is a schematic diagram of a process for constructing a utility function of the utility value model according to the embodiment;
FIG. 3 is a schematic diagram illustrating a construction process of a utility function under the reward mechanism of the present embodiment;
fig. 4 is a schematic diagram of a data forwarding path according to the present embodiment;
FIG. 5 is a schematic diagram of communication power consumption in the present embodiment;
FIG. 6 is a schematic diagram illustrating the process of energy consumption according to the present embodiment;
FIG. 7 is a schematic diagram illustrating simulation of node sequence numbers according to this embodiment;
FIG. 8 is a cluster head node history map according to the present embodiment;
FIG. 9 is a diagram of an information transmission route according to the present embodiment
FIG. 10 is a histogram of the node residual energy in the present embodiment;
FIG. 11 is a histogram comparing the energy consumption of the present embodiment;
fig. 12 is a histogram of the energy consumption balance in the present embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
As shown in fig. 1, the present embodiment provides a repeated game routing method based on a rechargeable wireless sensor network, including the following steps:
s1: arranging N nodes with the same initial energy value in a region monitored by the wireless sensor network, and then performing region clustering on all the nodes according to the area and the position of the sink node;
in the node clustering stage, the clustering method adopted in this embodiment is: taking a sink node as a center, equally dividing square regions with the same area and shape to the periphery of the sink node, wherein the side length of each region is less than √ 2 times of an inter-node communication threshold, and the number of the regions is determined by the size of the area needing to be covered and monitored by the WSN; the nodes are clustered according to the areas where the nodes are located, and the nodes distributed in the same area are classified into the same cluster; the clustering model has the preconditions that: the positions of the nodes are not changed after the nodes are arranged, the positions of all the nodes can be known, each node knows all information such as ID numbers, positions, residual energy and the like of other nodes in the same cluster, and enters a node information transmission stage after the clustering stage is finished, and the routing protocol is designed in the stage by combining with relevant knowledge of a repeated game theory, wherein the specific content comprises utility value model design, Nash equilibrium solution and reward mechanism design;
s11: constructing a utility value model:
compared with a routing protocol with a layered structure, the most typical variables which can be used as game factors comprise the distance between nodes, the residual energy of the nodes and the like; for the nodes, the nodes themselves benefit as rational nodes, and if the cost of the data forwarding is larger than the benefit, cooperation is not selected. On the contrary, if the benefit is greater than the cost, the node selects the cooperation strategy behavior to maximize the utility value of the node, and then the node actively selects to forward the data packet;
when a source node is determined, all nodes in the same cluster give utility values of self-evaluation election cluster head nodes to forwarding requests of the source node, and the source node selects one node as a next hop node to assist in data forwarding; considering that the distance between each node and the source node is different, the energy consumed when receiving the data packet of the source node is different; the distance between each node and the sink node is different, and the consumption is different when the data packet is forwarded; each node may choose to have a different policy of "forward packets" or "not forward packets" and consume a different amount of energy.
Therefore, as shown in fig. 2, a utility function of a node election cluster head is constructed for distances from nodes in each region to a source node, distances from the source node to a sink node, distances from the center of the region, and energy changes when a policy of forwarding or not forwarding a data packet is selected, and the utility function is specifically defined as follows:
not forwarding the data packet:
Figure BDA0002739264250000071
and forwarding the data packet:
Figure BDA0002739264250000081
(a1＞0，a2＞0，a3＞0，a1+a2+a3＝1)
wherein E isnodeRepresenting the remaining energy of the node to be selected, ErecRepresenting the energy consumed by the process of receiving the source node data packet, EtransRepresenting the energy consumed in the process of successfully forwarding the data packet to the sink node after the node to be selected becomes a cluster head node, p represents the probability of successful forwarding, E0Representing the initial energy (maximum energy value), D, of the nodei-sinkRepresents the distance from the node i to be selected to the sink node, Dsink-maxRepresents the maximum distance from the node to the sink in the region, Di-centerRepresenting the distance from the node i to be selected to the center of the area, Dcenter-maxRepresents the maximum distance from the node to the center of the area, Di-sourceRepresenting the distance from the node i to be selected to the source node, Dsource-maxRepresenting the maximum distance from the node in the region to the source node.
Utility value parameter analysis:
(1)Dsink-max: after the 100 nodes are distributed at random in a simulation mode, the positions are fixed, and the positions of the nodes in each round are not changed. Thus Dsink-maxThe value of (a) is effectively a constant value that is associated only with the node in each region that is farthest from the sink node;
(2)Dsource-max: when the positions of 100 nodes are fixed, the distances between the nodes are determined. The distance between the source node of each round as any one of 100 nodes and all nodes in the area where the source node is located is a fixed value, and the distance is only related to the source node.
(3)Di-sink+Di-source: indicating the sum of the distances from the respective nodes in the area of the source node to the sink node and to the source node, and in order to reduce power consumption, it is desirable that the smaller the value, the better.
(4)
Figure BDA0002739264250000082
When D is presenti-sink+Di-sourceThe time is taken as short as possible,
Figure BDA0002739264250000083
is also as small as possible, so that it can be ensured that
Figure BDA0002739264250000084
The value of (a) is as large as possible, and the value of the utility function can be maximized.
(5)Dcenter-max: similarly, when 100 nodes are fixed after being randomly distributed in the simulation, the distance between the node in each area and the center of the area is also fixed.
(6)Di-center: when the distance between the node to be selected and the center of the area is closer, the distance between the node to be selected and other nodes in the area is relatively compromised. The energy consumed by the cluster head node for data forwarding is smaller.
(7)
Figure BDA0002739264250000091
When the distance between the node to be selected and the center of the area is moreNear time, Di-centerWill be as small as possible, and
Figure BDA0002739264250000092
will be as small as possible and will therefore be as small as possible
Figure BDA0002739264250000093
The value of (c) is made as large as possible and the value of the utility function can be maximized.
When the nodes can be supplemented with energy, the whole network can continuously and normally operate, so that the process of information transmission by the source node and data forwarding by the selected cluster head node is a continuously repeated process. For a certain node i, assuming that the node i participates in the N-round repeated game in an accumulated mode, the accumulated utility of the node i is as follows:
Figure BDA0002739264250000094
in the model of this embodiment, since the node i can be charged by the UAV to supplement energy, the life cycle of the entire network can be regarded as infinite, and according to the theoretical knowledge about the infinite repeated game, the average utility of the node i is:
Figure BDA0002739264250000095
namely, the net present value is normalized, the net present value is reduced by a factor (1-delta), and the analysis of perfect equilibrium of the sub game in the infinite repetition game is simplified by average utility.
S12: in a game process, the strategy selection of one party participating in the game is not influenced by the strategy selection of the other party, and when two participating objects in the game select respective optimal strategies, the strategy combination is defined as Nash balance. Thus, after reaching nash equilibrium, changes in either party alone do not lend themselves to any other benefit. Thus, a determination of whether a result is nash equilibrium can generally be made as to whether any party in the game can benefit from it by changing policy behavior unilaterally, and if not, the policy is nash equilibrium solution.
In the model of the present embodiment, it is assumed that more energy components E will be consumed because the nodes choose to forward the datatransP, this will result in:
Enode-Erec＞Enode-(Erec+Etrans*p)
namely:
uiR＞uis
that is, the node selects the policy action of "not forwarding packets" to obtain a larger utility value. Therefore, it can be speculated that the nodes reasonably consider themselves, and in order to maximize the utility value of the nodes themselves, the nodes to be selected in each round (i.e. all nodes in the area where the source node is located) choose not to forward the data packet sent by the source node. Therefore, the strategic behavior of game participant object selecting "not forward data packet" is nash equilibrium of the game at this stage.
However, in the model of this embodiment, selecting a relay node and playing a game on "whether to forward a data packet" is a continuously repeated process, and if a selfish behavior (not forwarding a data packet) is selected by a cluster head node in each round, a sink node cannot receive information collected by a source node, the information cannot be transmitted to a user, and the performance of the whole wireless sensor network becomes very poor, thereby losing the significance of the information. In order to improve the performance of the wireless sensor network and ensure that data collected by each round of source nodes can be successfully transmitted to the sink node, a reward mechanism is established by considering the combination of special chargeable factors in the model.
S13: constructing a reward mechanism in combination with charging:
in order to save energy of the candidate nodes (all nodes in the area where the source node is located), the candidate nodes select 'not to forward data packets which need to be transmitted by the source node' from a rational point of view. The selected node only considers the benefits of the selected node in the game, but does not consider the influence of the behavior of the selected node on the performance of the whole wireless sensor network. In order to ensure that the candidate nodes in each round can actively select the cooperation strategy and successfully forward the data packet, the following rewarding measures are taken:
when the current round of the candidate node selects the 'forward data packet' (cooperation strategy), charging reward is given to the node before the next round of information transmission simulation starts, so that the consumed energy of the node is supplemented, namely the obtained benefit exceeds the cost paid by the current strategy of selecting the 'forward data packet', and the cost is used as the reward for the node. Through the reward measure giving energy supplement, inducibility to the candidate nodes is generated, and the candidate nodes in each round can be prompted to actively adopt a cooperative strategy of 'forwarding data packets' in data forwarding.
Because there is a certain probability of failure in forwarding a data packet between two hops of a node, the probability of success in forwarding between two hops is defined as p, and it can be known based on literature that: the probability of successful transmission by a node in the two-hop range is approximately 0.9, so the value of p is set to 0.9. The UAV also has a certain failure probability in charging the node, and defines the probability of successful charging as q, and based on literature, it can be known that: the probability of successful charging is approximately 0.95, so the q value is set to 0.95.
The expected values of energy obtained for successful forwarding and successful charging are:
(Echarge-Etrans)*p*q+(Echarge-0)*p*(1-q)+(0-Etrans)*(1-p)
*q+0-0)*(1-p)(1-q)＝(Echarge*p-Etrans*q)
the revenue function (pay-off) is defined as:
Figure BDA0002739264250000111
wherein Echarge NRepresenting that the node to be selected in each round becomes a cluster head node and obtains supplemented energy after the data packet is successfully forwarded, wherein p is the probability of successful forwarding, and q represents the probability of successful charging.
Due to the charging factor, the utility function value of the node is changed. For the proposed reward mechanism, when each round of data needs to be forwarded, the utility functions of all nodes in the area where the source node is located are given again, the construction idea is shown in fig. 3, and the new utility functions are specifically defined as follows:
not forwarding the data packet:
Figure BDA0002739264250000121
and forwarding the data packet:
Figure BDA0002739264250000122
(a1＞0，a2＞0，a3＞0，a1+a2+a3＝1)
the cumulative utility of each node i to be selected is:
not forwarding the data packet:
Figure BDA0002739264250000123
and forwarding the data packet:
Figure BDA0002739264250000124
the i average utility of a node is:
not forwarding the data packet:
Figure BDA0002739264250000125
and forwarding the data packet:
Figure BDA0002739264250000126
nash equalization under the reward mechanism:
assuming that all the front x wheels of the node i are adopted when the node i is selected as a cluster head nodeUncooperative forwarding policy that obtains a utility value of u1：
(a1＞0，a2＞0，a3＞0，a1+a2+a3＝1)
Figure BDA0002739264250000131
Adopting a cooperative forwarding strategy after the round (x + 1) begins to select a cluster head, wherein the obtained utility value is u2：
(a1＞0，a2＞0，a3＞0，a1+a2+a3＝1)
Figure BDA0002739264250000132
The average utility at this time is:
(a1＞0，a2＞0，a3＞0，a1+a2+a3＝1)
Figure BDA0002739264250000133
assuming that the cluster head node i always adopts a cooperative data forwarding strategy, the average utility is as follows:
(a1＞0，a2＞0，a3＞0，a1+a2+a3＝1)
Figure BDA0002739264250000141
if the cluster head node is required to be prompted to select the cooperation strategy and actively forward data, the utility value of the node adopting the cooperation strategy is required to be greater than the utility value of the node adopting the non-cooperation strategy, namely the requirement is that:
(a1＞0，a2＞0，a3＞0，a1+a2+a3＝1)；u’iav-uiav＞0
namely, the requirements are as follows:
Figure BDA0002739264250000142
Figure BDA0002739264250000143
1-delta > 0 because 0 < delta < 1,
and due to deltax-1＞0，a1>0, so there are:
(Echarge*p-Etrans*q)＞0
and because p is 0.9, q is 0.95,
thus, it is possible to provide
0.9*Echarge＞0.95*Etrans
Can obtain the product
Echsrge＞1.05555556*Etrans
Namely, when the energy consumed in the process of forwarding the data packet is obtained, which is more than 1.05555556 times of the supplementary energy, after each round of finishing the data transmission of the cluster head node, the cluster head node can keep the cooperation balance.
In the Nth round, Echarge N＞1.05555556*Etrans NAnd at the moment, the cluster head node i selects cooperative forwarding of the data packet as the unique Nash equilibrium solution of the game at the stage. When it can be ensured that each round has Echarge N＞1.05555556*Etrans NIn time, u can be ensuredis＞uiRI.e. by
Figure BDA0002739264250000151
I.e., greater utility value obtained by selecting a cooperative forwarding policy. According to game theory knowledge, the 'cooperative forwarding data packet' is selected as the unique Nash balance of each stage of the game of the cluster head node i. At the moment, the cluster head node i can be ensured to simulate to beat in each roundAnd a cooperation forwarding strategy is selected in the chess playing process, so that the utility of the chess reaches the maximum value.
Because the cluster head node also consumes energy when receiving the data packet of the source node, in order to maintain the cluster head node at a certain energy level, the reward mechanism EchargeThe magnitude of the value is defined as:
Echarge＝1.05555556*Etrans+Erec
therefore, the cluster head node can obtain enough energy reward after being successfully forwarded, the cluster head node is induced to actively adopt a cooperative strategy action, and a utility function value given by the node to be selected when participating in election is considered as the following formula:
and forwarding the data packet:
Figure BDA0002739264250000152
s2: after the clustering work is finished, 1 node is randomly selected as a source node in each round of data transmission work, and a data packet of the node needs to be sent to a sink node; in order to reduce the incapability of communication or excessive energy consumption caused by distance exceeding as much as possible, as shown in fig. 4, in a smaller area, the embodiment performs data transmission in a node forwarding two-hop manner, that is, selects a cluster head node to forward source node data;
s3: other nodes in the area where the source node is located perform strategy game on whether the forwarding work of the source node data packet is performed or not, and provide utility values of self-election cluster head nodes;
s4: the source node selects the same cluster node with the maximum utility value from the election nodes as a cluster head and forwards the data information to the cluster head;
s5: the cluster head transmits information to the sink node, and after the sink node successfully receives the data information, the Unmanned Aerial Vehicle (UAV) is informed to charge the cluster head of the current round;
s6: the steps S2 through S5 are repeated until interrupted or the simulation ends.
Energy consumption is mainly concentrated on communication energy consumption and data fusion energy consumption, wherein in order to realize the functions of data receiving and sending between nodes, as shown in fig. 5, most energy of communication energy consumption is mainly consumed in a transmitting circuit, a power amplifier, a receiving circuit and the like; after the cluster head node receives the data packet from the source node, the cluster head node needs to add own data information on a secondary basis, combine the data packet into a large data packet, and then send the combined data packet to the sink node. In the process, the energy consumed by data fusion is unified to 50 nJ/bit.
As shown in fig. 6, combining the energy consumption in the above two aspects, a calculation model of the energy consumption of the following sensor nodes is established:
the energy consumption model of the node for transmitting information is as follows:
Figure BDA0002739264250000161
the energy consumption model of the node for receiving the information is as follows:
ERx＝l*Eelec
in the communication process energy consumption model, l represents the length of a data packet, di,jIndicating the communication distance between nodes, EelecRepresenting transmission energy per unit length, epsilonfsAnd εmpRespectively representing a free space transmission parameter and a multipath fading transmission parameter.
The transmission energy consumption of the nodes is related to the distance: when the signal is smaller than the threshold value, a free space channel model is adopted, and when the signal is larger than the threshold value, a multipath fading channel model is adopted. Assume a threshold value of d0If d < d0Then get d2(ii) a If d is greater than or equal to d0Then get d4. In the above formula dmaxMaximum communication radius of sensor node, d0Is a distance threshold and has:
Figure BDA0002739264250000171
the repeated game forwarding model provided by the embodiment is designed based on a clustering routing protocol, information among nodes is completely disclosed, and the residual electric quantity of the wireless sensor network nodes is in a heterogeneous characteristic in normal operation. The main contents of the repeat game routing protocol are the following three parts:
(1) a clustering mechanism: after the initial work of the nodes distributed in the region is finished, all the nodes are classified into clusters according to the positions of the sensor nodes by adopting a mode of dividing the clusters according to the region. Meanwhile, each node establishes a self same cluster node ID list to prepare for the operation of a game mechanism and the like.
(2) The game mechanism comprises the following steps: after the ID number of the source node of each round is determined, the cluster where the source node is located is judged firstly, then all the nodes of the same cluster give the utility value of the cluster head node which is selected by the nodes in competition to serve as a response, and the game of the cluster head node which is selected in competition is carried out according to the size of the utility value. And finally forming a set by the utility function values of all nodes in the same cluster of one source node, and selecting the source node as the final next hop according to the set with the maximum value.
(3) The reward mechanism is as follows: considering that rational nodes participating in election can select selfish behaviors based on self consideration, in order to promote a cluster head node to actively select a strategy behavior of data forwarding, the embodiment designs that after the cluster head node successfully forwards data to a sink node, the cluster head node can obtain corresponding energy supplement rewards before the next round of data transmission starts; and carrying a charging plate to the upper space near the node by the UAV with the wireless communication function, and wirelessly charging the specific node. And selecting cooperative cluster head nodes, wherein the profit can be maximized, so that inducibility is generated for the cluster head nodes, and the nodes are promoted to actively select cooperative forwarding strategy behaviors.
Compared with other repeated game routing protocols, the routing protocol provided by the embodiment has the following improvements:
(1) comprehensively considering node residual energy, forwarding income, forwarding success rate and node position, fusing several factors and designing a utility function of the node for cluster head competition;
(2) UAV charging is merged in the design of a punishment mechanism of node cooperation, and the cooperation of the nodes is improved through obtaining charging reward. For the cluster head nodes adopting the cooperative forwarding strategy, a certain amount of electric quantity reward can be obtained after information is successfully transmitted to the sink node (E in a reward mechanism)chargeValue), the charging mode is to charge the designated node for the UAV, which also enables the total energy of the whole wireless sensor network to be supplemented.
(3) The success probability of information forwarding of a data packet between two hops of a node and the success probability of charging the node by the UAV are considered: defining the success probability of the communication between two hops of the node as p, and setting the value of p as 0.9; the probability of successful charging is defined as q, and the q value is set to 0.95.
The routing protocol is characterized in that a reward mechanism is designed by combining with UAV charging factors, and after each round of information transmission is successful, the UAV rewards the cooperative nodes for energy supplement. On one hand, the initiative of selecting a cooperation strategy by the node is stimulated, and the working performance of the whole network is improved; on the other hand, the nodes are supplemented with working energy and can continuously work. And combining the definition of the utility function, selecting the node with the most energy value, short total communication distance and best comprehensive performance in each round as a cluster head, and successfully receiving information at the sink node to reward the energy. The reward mechanism designed by combining the UAV charging can enable the energy of each node in the area to be basically maintained at the same level in each time period, embodies the concept of 'more labor and more labor for the energetic person' together with the design of the utility function, and effectively improves the energy consumption balance performance of the whole wireless sensor network.
In this embodiment, Matlab is used to perform experimental simulation, a wireless sensor network model is established to simulate the selection of the next hop when the repeated game routing protocol provided by this embodiment is used to perform information transmission, and finally, the obtained data and images are rationally analyzed to complete the verification of the performance of the repeated game routing protocol of this embodiment.
In a network model set for simulation, three types of nodes are shared according to functional classification:
(1) a sink node: as the center of the simulation area; the data collecting module is responsible for receiving data collected by other nodes and summarizing the data to a user; informing the source node to transmit data again; and informing the UAV to carry out charging reward to the cooperative node.
(2) And (3) common nodes: selecting the source node as the probability; and participating in the game process of competing for cluster head nodes and forwarding data packets.
(3) A source node: after each stage is started, a path needs to be selected to send a data packet to the sink node.
In the simulation, the experimental region is set to be 300 × 300m2The area is divided into 4 parts on average, and 100 sensor nodes are randomly distributed in the area. The region centers (150 ) are marked as sink nodes. After the random distribution is completed, the positions of all the nodes are fixed and do not change any more, the position information of the nodes is broadcasted outwards, and the sink node determines the areas of all the nodes according to the position information broadcasted by the nodes and records the areas. After the simulation is started, one of the 100 sensor nodes is randomly selected as a source node for each round of information transmission, and the simulation needs to transmit information to the sink node. However, when considering the inter-node communication, when the communication distance exceeds the threshold, the consumed energy is far larger than the energy consumption of the inter-node communication within the threshold. Therefore, in order to reduce energy consumption as much as possible, information of the source node is forwarded by the cluster head node in a manner of selecting the cluster head node in the area where the source node is located, and information is transmitted in a manner of two hops between the nodes. Within the model, the 100 nodes can be energy supplemented by UAVs, provided that the nodes pick up cluster heads and successfully forward data to sink nodes.
The wireless sensor network initialization parameter settings are shown in table 1 below:
TABLE 1 table of initial values of parameters
Figure BDA0002739264250000191
Figure BDA0002739264250000201
For the above model, 2000 rounds of simulations of information transmission were performed, with the following results:
(1) node distribution diagram
100 sensor nodes are randomly distributed in a 300-300 area, and unique ID numbers are marked beside the nodes, as shown in FIG. 7, so that node serial number simulation results are obtained.
Cluster head node history map
In the 2000 simulation runs, nodes elected cluster heads in each run are labeled with a "+" label, as shown in fig. 8, and a historical record of cluster head nodes is plotted.
The image shows that the number of cluster head nodes of each cluster is different, and is particularly related to the distribution of the nodes and the residual energy of the nodes. And defined by the utility function of the previous chapter, the nodes which have more comprehensive residual energy and shorter total communication distance have higher probability to be selected as cluster head nodes. Secondly, the position factor of the node is considered, specifically the sum of the distances of the source node, the cluster head node and the sink node, and the node with smaller distance has higher probability to be selected as the cluster head node.
(3) Information transmission route and source node historical record chart
As shown in fig. 9, in the 2000-round simulation, the nodes that perform information transmission as source nodes in each round are denoted by ″, and routes of "source node, cluster head node, sink node" are drawn.
(4) Residual energy map of 100 nodes
As shown in fig. 10, after 2000 rounds of simulation, the residual energy at 100 nodes is plotted as a histogram for comparison, and as seen from the image and data, each round of node passes through the game, a cooperation strategy is selected, and the reward is carried out according to a reward mechanism. After 2000 rounds of simulation, the energy of the nodes can still be basically maintained at a higher level, no dead nodes appear temporarily, and the network can still maintain normal operation.
(5) Network energy consumption comparison graph
As shown in fig. 11, the total network energy consumption of "reward mechanism is adopted" and "no reward mechanism is adopted" after 2000 rounds of simulation are plotted as bar graphs for comparison, and it can be known from image and data comparison that the energy consumption of the wireless sensor network adopting the reward mechanism is greatly reduced. According to the algorithm content, the nodes of the wireless sensor network adopting the reward mechanism can obtain energy reward after information transmission is completed cooperatively, so that the energy of the whole network can be continuously supplemented, the total energy is maintained at a certain level, and the difference between the total energy and the total energy of the network in an initial state is not large.
(6) Network energy consumption balance comparison graph
As shown in fig. 12, after 2000 rounds of simulations, the average residual energy of the nodes in different clusters in the wireless sensor networks with the reward mechanism and the reward mechanism not adopted is plotted as a histogram for comparison, and it can be seen from images and data that after the reward mechanism is adopted, the average residual energy distribution of the nodes in four clusters is relatively even and is substantially maintained at a level close to 1J of full energy. In contrast, when the reward mechanism is not adopted, the average residual energy difference of the nodes of each cluster is large, and especially the energy level of the C cluster is far lower than that of other clusters. The image and data can be seen visually, the repeated game routing protocol provided by the embodiment can greatly improve the node energy consumption balance performance of the whole wireless sensor network.
According to the repeated game routing protocol based on the layered structure wireless sensor network, on the basis of dividing nodes in the network into clusters, a source node searches the best cluster head node of the next hop through a game based on utility values given by competitive participants. Simulation experiment results show that the routing protocol of the embodiment can effectively improve the cooperation of the nodes, reduce the energy consumption of the network for information transmission, and improve the network energy consumption balance.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (7)

1. A repeated game routing method based on a rechargeable wireless sensor network is characterized by comprising the following steps:
arranging a plurality of nodes with the same initial energy value in a region monitored by the wireless sensor network, and performing region clustering on all the nodes according to the area and the position of the sink node;
after clustering is completed, a utility value model and a reward mechanism combining charging are established;
in each round of data transmission, a node is randomly selected as a source node, and a data packet is sent to a sink node;
other nodes in the area where the source node is located perform strategy game on whether the forwarding work of the source node data packet is performed or not, and provide utility values of self-election cluster head nodes;
the source node selects the same cluster node with the maximum utility value from the election nodes as a cluster head and forwards the data information to the cluster head;
the cluster head transmits information to the sink node, and after the sink node successfully receives the data information, the Unmanned Aerial Vehicle (UAV) is informed to charge the cluster head of the current round.
2. The repeated game routing method based on the rechargeable wireless sensor network as claimed in claim 1, wherein the area clustering is performed on all nodes according to the area and the position of the sink node, and the specific steps include:
taking a sink node as a center, equally dividing square regions with the same area and shape to the periphery of the sink node, wherein the side length of each region is smaller than a preset multiple of an inter-node communication threshold, and the number of the regions is determined by the size of the area needing to be covered and monitored by the wireless sensor network;
the nodes are clustered according to the areas where the nodes are located, and the nodes distributed in the same area are classified into the same cluster; the clustering model has the preconditions that: the positions of the nodes are not changed after the nodes are arranged, the positions of all the nodes can be known, and each node knows all the information of other nodes in the same cluster.
3. The repeated game routing method based on the rechargeable wireless sensor network as claimed in claim 1, wherein the specific steps of constructing the utility value model are as follows:
when a source node is determined, all nodes in the same cluster give utility values of self-evaluation election cluster head nodes to forwarding requests of the source node, and the source node selects one node as a next hop node to assist in data forwarding;
constructing a utility function of a node election cluster head according to the distance from a node to a source node, the distance from the node to a sink node, the distance from the node to a region center and the energy change when selecting a strategy of forwarding or not forwarding a data packet, wherein the utility function is specifically represented as follows:
not forwarding the data packet:
Figure FDA0002739264240000021
and forwarding the data packet:
Figure FDA0002739264240000022
wherein E isnodeRepresenting the remaining energy of the node to be selected, ErecRepresenting the energy consumed by the process of receiving the source node data packet, EtransRepresenting the energy consumed in the process of successfully forwarding the data packet to the sink node after the node to be selected becomes a cluster head node, p represents the probability of successful forwarding, E0Representing the initial energy of the node, Di-sinkRepresents the distance from the node i to be selected to the sink node, Dsink-maxRepresents the maximum distance from the node to the sink in the region, Di-centerRepresenting the distance from the node i to be selected to the center of the area, Dcenter-maxRepresents the maximum distance from the node to the center of the area, Di-sourceRepresenting the distance from the node i to be selected to the source node, Dsource-maxRepresenting the maximum distance from the node in the region to the source node.
4. The repeated game routing method based on the rechargeable wireless sensor network as claimed in claim 1, wherein the step of constructing a reward mechanism combined with charging comprises the following specific steps:
when the node to be selected in the current round selects the cooperation strategy, charging reward is given to the node before the next round of information transmission simulation starts, so that the consumed energy of the node is supplemented, and the obtained benefit exceeds the cost paid by the current action of selecting the cooperation strategy and is used as the reward for the node;
the expected values for the energy obtained for successful forwarding and successful charging are:
(Echarge-Etrans)*p*q+(Echarge-0)*p*(1-q)+(0-Etrans)*(1-p)*q+(0-0)*(1-p)(1-q)＝(Echarge*p-Etrans*q)
the revenue function is defined as:
Figure FDA0002739264240000031
wherein E ischarge NRepresenting that the node to be selected in each round becomes a cluster head node and obtains supplemented energy after successfully forwarding the data packet, wherein p is the probability of successful forwarding, and q represents the probability of successful charging;
updating utility functions of all nodes in the area where the source node is located;
the specific definition is as follows:
not forwarding the data packet:
Figure FDA0002739264240000032
and forwarding the data packet:
Figure FDA0002739264240000033
will reward mechanism EchargeDimensioning of valuesMeaning as follows:
Echarge＝1.05555556*Etrans+Erec
wherein E istransRepresenting the energy consumed in forwarding the packet.
5. The repeated game routing method based on the rechargeable wireless sensor network as claimed in claim 1, wherein a node is randomly selected as a source node, a data packet is sent to a sink node, data transmission is performed specifically by adopting a two-hop mode of node forwarding, and a cluster head node is selected for forwarding source node data.
6. The repeated game routing method based on the rechargeable wireless sensor network according to claim 1, wherein the recharging is performed in a specific manner as follows: unmanned aerial vehicle charges for appointed node.
7. The repeated game routing method based on the rechargeable wireless sensor network as claimed in claim 1, further comprising a step of constructing a calculation model of energy consumption of the sensor nodes, wherein the specific calculation formula is as follows:
the energy consumption model of the node for transmitting information is as follows:
Figure FDA0002739264240000041
the energy consumption model of the node for receiving the information is as follows:
ERx＝l*Eelec
where l denotes the length of the data packet and di，jIndicating the communication distance between nodes, EelecRepresenting transmission energy per unit length, epsilonfsAnd εmpRespectively representing a free space transmission parameter and a multipath fading transmission parameter.
CN202011144441.0A 2020-10-23 2020-10-23 Repeated game routing method based on rechargeable wireless sensor network Active CN112261704B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011144441.0A CN112261704B (en) 2020-10-23 2020-10-23 Repeated game routing method based on rechargeable wireless sensor network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011144441.0A CN112261704B (en) 2020-10-23 2020-10-23 Repeated game routing method based on rechargeable wireless sensor network

Publications (2)

Publication Number Publication Date
CN112261704A true CN112261704A (en) 2021-01-22
CN112261704B CN112261704B (en) 2022-12-09

Family

ID=74263588

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011144441.0A Active CN112261704B (en) 2020-10-23 2020-10-23 Repeated game routing method based on rechargeable wireless sensor network

Country Status (1)

Country Link
CN (1) CN112261704B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114786145A (en) * 2022-03-04 2022-07-22 云南电网有限责任公司玉溪供电局 Cable intermediate joint temperature sensor networking method based on NB-IoT
CN117896740A (en) * 2024-03-13 2024-04-16 武汉科技大学 Intelligent household wireless sensor network deployment method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170188409A1 (en) * 2015-12-24 2017-06-29 Seoul National University R&Db Foundation Method for Network Self-Healing in Cluster-Tree Structured Wireless Communication Networks
CN107948983A (en) * 2017-11-13 2018-04-20 南京邮电大学 A kind of small base station resource distribution method of energy acquisition based on Game with Coalitions
CN109451556A (en) * 2018-11-28 2019-03-08 广州大学 The method to be charged based on UAV to wireless sense network
CN111787500A (en) * 2020-05-11 2020-10-16 浙江工业大学 Multi-target charging scheduling method for mobile charging vehicle based on energy priority

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170188409A1 (en) * 2015-12-24 2017-06-29 Seoul National University R&Db Foundation Method for Network Self-Healing in Cluster-Tree Structured Wireless Communication Networks
CN107948983A (en) * 2017-11-13 2018-04-20 南京邮电大学 A kind of small base station resource distribution method of energy acquisition based on Game with Coalitions
CN109451556A (en) * 2018-11-28 2019-03-08 广州大学 The method to be charged based on UAV to wireless sense network
CN111787500A (en) * 2020-05-11 2020-10-16 浙江工业大学 Multi-target charging scheduling method for mobile charging vehicle based on energy priority

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
SHENSHEN MAO等: "An Energy Efficient Charging Scheme for UAV-aided Wireless Sensor Networks", 《2019 IEEE 5TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC)》 *
刘贵云等: "基于博弈的能量获取传感器网络分布式估计算法", 《广州大学学报( 自然科学版)》 *
王慧娇等: "基于演化博弈的无线传感器网络分簇算法", 《计算机工程与应用》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114786145A (en) * 2022-03-04 2022-07-22 云南电网有限责任公司玉溪供电局 Cable intermediate joint temperature sensor networking method based on NB-IoT
CN114786145B (en) * 2022-03-04 2024-04-05 云南电网有限责任公司玉溪供电局 Cable intermediate joint temperature sensor networking method
CN117896740A (en) * 2024-03-13 2024-04-16 武汉科技大学 Intelligent household wireless sensor network deployment method
CN117896740B (en) * 2024-03-13 2024-05-28 武汉科技大学 Intelligent household wireless sensor network deployment method

Also Published As

Publication number Publication date
CN112261704B (en) 2022-12-09

Similar Documents

Publication Publication Date Title
Arjunan et al. Lifetime maximization of wireless sensor network using fuzzy based unequal clustering and ACO based routing hybrid protocol
Wang et al. Energy efficient cluster based routing protocol for WSN using firefly algorithm and ant colony optimization
CN112261704B (en) Repeated game routing method based on rechargeable wireless sensor network
CN109492887B (en) Mobile crowd sensing and exciting method based on potential game theory
CN105636143A (en) Wireless sensor network clustering collaborative routing algorithm based on cooperative game
Priyadarshani et al. An efficient partial charging scheme using multiple mobile chargers in wireless rechargeable sensor networks
CN103052147A (en) Energy effectiveness multistage ring-type networking method based on wireless sensor network
Qu et al. An energy-efficient dynamic clustering protocol for event monitoring in large-scale WSN
Bhanderi et al. Machine learning for wireless sensor network: a review, challenges and applications
Jabeur A firefly-inspired micro and macro clustering approach for wireless sensor networks
Muthukkumar et al. A genetic algorithm-based energy-aware multi-hop clustering scheme for heterogeneous wireless sensor networks
Nobahary et al. A Credit-based Method to Selfish Node Detection in Mobile Ad-hoc Network.
Prakash et al. Metaheuristic algorithm for energy efficient clustering scheme in wireless sensor networks
Ramesh et al. Firefly and Grey Wolf search based multi-criteria routing and aggregation towards a generic framework for LEACH
Lekhraj et al. Multi criteria decision making based energy efficient clustered solution for wireless sensor networks
Abasikeleş‐Turgut DiCDU: Distributed clustering with decreased uncovered nodes for WSNs
Senthilkumar et al. Advertisement timeout driven bee's mating approach to maintain fair energy level in sensor networks
Bongale et al. Firefly algorithm inspired energy aware clustering protocol for wireless sensor network
Ya-qiong et al. A routing protocol for wireless sensor networks using K-means and Dijkstra algorithm
Singh et al. Multi-parameter based load balanced clustering in WSN using MADM technique
CN112512001B (en) Potential game topology method of chargeable wireless sensor network
Revanesh et al. Retracted article: Deep learning-based algorithm for optimum cluster head selection in sustainable wireless communication system
CN113938978A (en) Heterogeneous wireless sensor path finding method based on reinforcement learning
Korteby et al. Multi dimensional analysis of sensor communication processes
CN113993177A (en) Game theory-based energy consumption balancing wireless sensor network clustering routing method

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
GR01 Patent grant
GR01 Patent grant