CN111107506A - Network resource safety sharing method based on block chain and auction game - Google Patents

Network resource safety sharing method based on block chain and auction game Download PDF

Info

Publication number
CN111107506A
CN111107506A CN202010002832.2A CN202010002832A CN111107506A CN 111107506 A CN111107506 A CN 111107506A CN 202010002832 A CN202010002832 A CN 202010002832A CN 111107506 A CN111107506 A CN 111107506A
Authority
CN
China
Prior art keywords
resource
user
transaction
resources
computing
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
CN202010002832.2A
Other languages
Chinese (zh)
Other versions
CN111107506B (en
Inventor
朱晓荣
张秀贤
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.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
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 Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202010002832.2A priority Critical patent/CN111107506B/en
Publication of CN111107506A publication Critical patent/CN111107506A/en
Application granted granted Critical
Publication of CN111107506B publication Critical patent/CN111107506B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/35—Services specially adapted for particular environments, situations or purposes for the management of goods or merchandise
    • 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
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/104—Peer-to-peer [P2P] networks
    • 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
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/1095—Replication or mirroring of data, e.g. scheduling or transport for data synchronisation between network nodes
    • 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
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
    • H04L9/3247—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving digital signatures
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W12/00—Security arrangements; Authentication; Protecting privacy or anonymity
    • H04W12/10—Integrity
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2209/00—Additional information or applications relating to cryptographic mechanisms or cryptographic arrangements for secret or secure communication H04L9/00
    • H04L2209/56—Financial cryptography, e.g. electronic payment or e-cash
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computer Security & Cryptography (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention provides a network resource security sharing method based on a block chain and an auction game, which comprises the steps of establishing a mobile edge computing network resource security sharing model based on a block chain technology, and providing an optimization model of resource distribution according to bandwidth resource distribution user income, subcarrier and power combined distribution benefit, computing and storing resource distribution user income; based on an optimization model, a method for jointly allocating frequency spectrums and computing resources is provided, and the method comprises the following steps: (1) allocating frequency spectrum resources by adopting an auction method; (2) sub-carriers and capacity are distributed by adopting a Lagrange algorithm; (3) and allocating computing and storage resources by adopting an auction game model.

Description

Network resource safety sharing method based on block chain and auction game
Technical Field
The invention relates to the technical field of information communication, in particular to a network resource safety sharing method based on a block chain and an auction game.
Background
With the development of the internet of things, particularly the intelligent internet of things (AIoT), various novel intelligent devices are emerging continuously, and massive data are generated. For example, surveillance cameras are already ubiquitous (statistically, one surveillance camera for every 14 individuals in london), and generate large amounts of video data each day. Each autonomous vehicle will generate up to 5TB of data each day. It is not affordable today for clouds and networks to transmit all of this data to the cloud for processing. Second, new scenarios and applications require the data to be processed locally. For example, automatic driving and industrial automation have high requirements for real-time data processing. Network delay caused by data transmission often cannot meet the requirement of real-time performance, and if a network fails, disastrous results can be brought. For another example, people are increasingly concerned about personal privacy, and many data (video, pictures, audio, etc.) contain a great deal of personal privacy. The best way to protect the privacy of an individual is to perform data processing locally and not to transmit the individual data to the network. Also, and importantly, the rapid development of hardware has made intelligent edge computing possible. With the increasing maturity of AI algorithms, people begin to design and manufacture special AI chips, especially AI chips specially used for deep learning model inference, which not only have strong data processing capability, but also have small size, low power consumption and low price, and can be applied to various edge devices, thereby providing a solid hardware foundation for intelligent edge computing. However, a single edge cloud has limited service capability, and a plurality of distributed edge clouds are required to work cooperatively to improve network access and service capability. However, at present, each edge cloud belongs to different operators, enterprises or third parties, and data of the edge cloud of each enterprise serves a certain industry or application, so that each edge cloud forms a data island, and cross-row, cross-network and cross-boundary fusion cannot be realized.
Therefore, the invention provides a credible resource security sharing model of mobile edge computing based on a block chain and an auction game, and solves the problem of 'island' of each edge cloud in a mobile edge computing network.
Disclosure of Invention
The purpose of the invention is as follows: the invention provides a network resource safety sharing method based on a block chain and an auction game, which effectively solves the problem of 'island' of each edge cloud in a mobile edge computing network, realizes resource sharing among the edge clouds and improves the resource utilization rate.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
a network resource security sharing method based on a block chain and an auction game comprises the following steps:
step 1, establishing a mobile edge computing network resource security sharing model based on a block chain technology;
the model is divided into a cloud layer, an edge cloud layer and a terminal access layer; the cloud layer is a core network CN and comprises a group of large servers for providing core cloud resources of the whole system; the edge cloud layer comprises an MEC server and a Resource Proxy Server (RPS); the MEC server is a small cloud computing resource pool deployed at the edge of the network by a telecommunication operator and used for supplementing a central server so that tasks can be processed at a place close to a user; the terminal access layer comprises a base station and intelligent terminal equipment; the base station provides network access service for terminal equipment only;
step 2, realizing edge cloud layer data sharing by adopting a block chain, which specifically comprises the following steps:
step 2.1, transaction flow; the transaction process comprises offline transaction and online transaction based on a lightning network;
the offline transaction flow comprises the following steps:
① resource applicant and resource provider create a channel;
② when the two parties are doing business, the resource applicant sends an incomplete transaction to the resource provider;
③ the resource provider sends the corresponding incomplete transaction to the resource applicant after receiving the incomplete transaction;
④ resource provider provides service;
the online transaction process comprises the following steps:
① the resource applicant pre-stores the corresponding amount to the block chain after selecting the trading object;
② the resource application party creates transaction and sends the pre-stored amount transaction to the peer node, wherein the peer node represents the resource proxy server;
③ Peer node writes pre-stored transaction into global account book;
④ peer node notifies resource applicant of transaction completion;
⑤ the resource applicant notifies the resource provider that payment is complete;
⑥ the resource provider checks from the global account book whether the transaction is completed, and if so, provides the service;
step 2.2, data uplink process;
step 2.2.1, the resource application party applies for the trade uplink;
step 2.2.2, after receiving the transaction, the peer node calls the uplink chain code and broadcasts the transaction to a plurality of endorsement nodes;
step 2.2.3, the endorsement node checks and verifies the transaction, signs and returns the signature to the peer node of the transaction proposal;
step 2.2.4, the peer node of the transaction proposal collects the endorsement and checks whether the collected endorsement is enough, and after the collected endorsement is enough, the transaction containing the endorsement is submitted to the sequencing node;
step 2.2.5 the sorting node sorts all received transactions in a prescribed order and then packs them for distribution in blocks. Once the size of the blocks collected by the sorting node is sufficient, or the maximum time is reached, the blocks will be broadcast to the master node;
step 2.2.6, the master node verifies the block and records the block into a global account book, and synchronizes to all other peer nodes;
step 2.3, detection flow
Step 2.3.1, after the payment of the resource applicant is completed, informing the peer node of the completion of the payment, and simultaneously sending the data to be processed to the resource provider; the peer node reads the global account book and checks whether the payment is finished; calling the transaction monitoring chain code when payment is completed; if the payment is not completed, the priority of the resource application party is reduced; when the priority of the resource application party is reduced to 0, no resource is distributed to the resource application party;
step 2.3.2, the resource provider receives the data of the resource applicant and processes the data;
step 2.3.3, after the data processing is finished, the resource provider returns the data processing result to the resource applicant, and meanwhile, the peer node is informed of the completion of the transaction;
step 2.3.4, after the resource application party receives the processed data, the resource application party informs the peer node of completing the transaction;
step 2.3.5, after the Peer node receives the transaction completion notice of the resource sender and the resource applicant, the transaction is ended; only receiving the transaction completion message of the resource provider, but not receiving the transaction success message of the resource applicant, sending an inquiry to the resource applicant to determine whether the transaction is completed; if the applicant replies that the transaction is completed, the transaction is ended; if the reply transaction is not completed, broadcasting that the resource provider has a fault in the blockchain, and removing the fault from the blockchain; the resource provider needs to reapply for joining after fault repair is completed;
a resource joint allocation method based on the mobile edge computing network resource security sharing model of claim 1, comprising: an optimization model for resource joint allocation is established based on user spectrum resource auction revenue, time delay and power consumption benefits saved by unloading user tasks to edge cloud processing and user auction calculation and storage resource revenue as follows:
(1) the benefit function of the bandwidth resource auction problem is as follows:
Figure BDA0002354137680000031
wherein m isijIs a binary variable indicating whether base station j allocates resources to end users i, mijE {0,1}, when mijWhen 1, it means that resources are allocated, mij0 means no resource is allocated; w is aijIndicating the amount of bandwidth application resources;
Figure BDA0002354137680000032
indicating the time of occupied bandwidth;
Figure BDA0002354137680000033
the bid price of unit time when the user i applies for the bandwidth resource from the base station j is represented;
Figure BDA0002354137680000034
the user i applies for a bid price of the bandwidth resource in unit time to the base station j, and gamma represents a normalization coefficient of the bandwidth resource auction benefit;
(2) the time delay and power consumption benefit function saved when the user task is unloaded to the edge cloud processing is as follows:
Figure BDA0002354137680000041
wherein, ti＝Di/fiIndicating the time required for local execution, DiComputing quantities for tasks, fiThe main frequency of the intelligent terminal is;
Figure BDA0002354137680000042
to offload tasks to the time required for MEC execution,
Figure BDA0002354137680000043
is the uplink communication delay, S, required for the transmission of data from user i to base station jiApply for storage resource size, r, for user iijThe transmission rate from user i to base station j;
Figure BDA0002354137680000044
data processing delay, fikRepresenting the size of the computing resource allocated by the resource provider k for the user i, α being a time benefit normalization coefficient, Ei＝ui*DiRepresenting the energy consumption consumed by the locally executing terminal device, where ui＝10-11fi 2；
Figure BDA0002354137680000045
Indicates willTraffic off-loaded to MEC to perform energy consumption of terminal device consumption, wherein cijnChannel capacity, p, for user i to base station j at subcarrier nijnβ represents the energy consumption benefit normalization coefficient;
(3) the calculation and storage resource allocation problem has a benefit function of:
Figure BDA0002354137680000046
wherein m isikIs a binary variable indicating whether the computing resource provider k allocates resources to the end user i, mikE {0,1}, when mikWhen 1, it means that resources are allocated, mik0 means no resource is allocated; f. ofikRepresenting the size of the computing resource allocated by the resource provider k for the user i;
Figure BDA0002354137680000047
data processing delay;
Figure BDA0002354137680000048
the bid price of a unit calculation resource unit time is applied to a resource provider k by a user i;
Figure BDA0002354137680000049
indicating that user i applies for bid price per unit of computing resource unit time from resource provider k, SiApplying for the size of a storage resource for a user i;
Figure BDA00023541376800000410
the bid price of unit storage resource unit time is applied to the resource provider k by the user i;
Figure BDA00023541376800000411
expressing that a user i applies for a bid price per unit time of a unit storage resource to a resource provider k, and delta represents a normalization coefficient of the MEC resource auction benefit;
under the conditions of bandwidth total amount limitation, emission power limitation, resource cost limitation and user application delay limitation, an optimization model of resource joint distribution is represented as follows:
Figure BDA00023541376800000412
the constraint conditions include:
Figure BDA0002354137680000051
Figure BDA0002354137680000052
Figure BDA0002354137680000053
Figure BDA0002354137680000054
Figure BDA0002354137680000055
Figure BDA0002354137680000056
Figure BDA00023541376800000513
Figure BDA0002354137680000057
Figure BDA0002354137680000058
wherein the condition (1) indicates that the total bandwidth resources allocated are less than the total bandwidth of the base station
Figure BDA0002354137680000059
The condition (2) indicates that the bid price per unit bandwidth of the applied base station is larger than the cost; condition (3) indicates that one channel is allocated to only one user; condition (4) indicates that the total power of the channel allocations is less than the maximum power PiAnd the power is greater than 0; condition (5) indicates that the system delay is less than
Figure BDA00023541376800000510
The condition (6) indicates that the total amount of computing resources for applying for the MEC is less than the total amount of computing resources for the MEC
Figure BDA00023541376800000511
The condition (7) indicates that the total amount of the storage resources of the application MEC is less than the total amount of the storage resources of the MEC
Figure BDA00023541376800000512
Condition (8) indicates that the bid price of the computing resource applying MEC unit is larger than the cost; the condition (9) indicates that the bid price applied for the MEC unit storage resource is larger than the cost.
Further, jointly allocating the frequency spectrum and the computing resources based on the optimization model of the joint allocation of the resources; the resource joint allocation algorithm comprises the following steps:
(1) allocating frequency spectrum resources by adopting an auction method;
(2) sub-carriers and capacity are distributed by adopting a Lagrange algorithm;
(3) and allocating computing and storage resources by adopting an auction game model.
Further, the spectrum resource allocation algorithm based on the auction method is as follows:
based on the user spectrum resource auction profit maximization, the multi-user spectrum resource allocation adopts an anonymous second price auction game model and a greedy algorithm; allocating the unit bandwidth with high price first until the resource allocation is completed or all users are satisfied;
s1.1, arranging the bid prices of unit bandwidth unit time in a descending order;
step S1.2, using the user with the highest price as a winning bid user, judging whether the residual frequency spectrum of the base station is larger than the frequency spectrum quantity applied by the winning bid user, judging whether the winning bid price is larger than the cost of the frequency spectrum, when the conditions are met, successfully distributing the frequency spectrum, removing the winning bid user from the application user set, and removing the frequency spectrum quantity applied by the winning bid user from the residual frequency spectrum quantity; if any one of the conditions is not met, the allocation is unsuccessful, and the winning user is removed from the application user set;
s1.3, when the number of the residual frequency spectrum resources is more than 0, repeatedly executing the steps S1.1-S1.2; and when the resource user set is not empty, repeatedly executing the steps S1.1-S1.2.
Further, the algorithm for allocating the subcarriers and the capacity based on the lagrangian algorithm is as follows:
for the user with successfully distributed spectrum resources, calculating the execution time of the task according to the uplink transmission time delay from the user to the spectrum and the total time delay of the user for applying to unload the processing task, and calculating the quantity of the applied calculation resources according to the task quantity and the execution time of the task;
based on the time delay and power consumption benefit maximization principle saved by unloading user tasks to edge cloud processing, relaxing 0,1 discrete variables distributed by user subcarriers linked with a base station to a (0,1) interval, and when the power distributed by each channel is constant, obtaining the distribution method of the subcarriers by a Lagrange multiplier method;
s2.1, arranging the capacities of the sub-carriers to be distributed corresponding to the users to be distributed in a descending order, and obtaining the maximum capacity of the sub-carriers of each user;
s2.2, arranging the maximum subcarrier capacity of each user in a descending order to obtain the maximum subcarrier capacity corresponding to all users;
s2.3, allocating the sub-carrier to the user with the maximum capacity, and removing the sub-carrier from the sub-carrier to be allocated; when the number of the sub-carriers allocated by the user reaches the requirement after the sub-carriers are allocated, removing the user from the user to be allocated; when the requirement is not met, allocating the sub-carrier to the user and removing the sub-carrier from the sub-carrier to be allocated;
s2.4, judging whether the sub-carrier to be distributed and the user to be distributed are empty or not; when any set is empty, the distribution is finished;
and S2.5, repeating the steps S2.1-S2.4.
Further, the calculation and storage resources are allocated by adopting an auction game model, as follows:
based on a user calculation and storage resource auction profit maximization principle, an improved anonymous second price auction game model is adopted for multi-user calculation and storage resource allocation, calculation and storage resources are preferentially allocated to users with high unit bandwidth price through an improved greedy algorithm until resource allocation is completed or all users are satisfied;
s3.1, dividing users applying for resources from the edge cloud into 3 priorities according to different positions of user access networks relative to the edge cloud, wherein the local area network is the 1 st priority, the wide area network is the 2 nd priority, and the metropolitan area network is the 3 rd priority;
s3.2, calculating the average value of unit calculation resource and storage resource bidding prices and the average value of cost in unit time;
s3.3, arranging the average bidding prices of the users with the 1 st priority in the edge cloud in a descending order; taking the user with the highest price as a winning bid user, and selecting the user price with the price of both the computing resource and the storage resource smaller than the price of the winning bid user and the average price highest as the winning bid price; when the edge cloud residual computing and storage resources are larger than the computing and spectrum resource quantity applied by the winning bid user and the winning bid price is larger than the cost of the resources, allocating the resources to the winning bid user, removing the winning bid user from the application user set and removing the resource quantity applied by the winning bid user from the residual resource quantity; otherwise, if the allocation is unsuccessful, removing the winning user from the application user set;
step S3.4, when the application user set with the 1 st priority is empty, the operation of the step S3.3 is further executed on the user with the 2 nd priority;
and step 3.5, when the application user set with the priority of 2 is empty, further executing the operation of the step 3.3 on the user with the priority of 3.
Has the advantages that:
the invention has the following advantages:
(1) a credible resource security sharing model of mobile edge computing based on block chains and auction games is provided, and the network model realizes resource sharing of each edge cloud in a mobile edge cloud network.
(2) And providing a resource allocation optimization model, and calculating and storing the resource allocation user profit according to the bandwidth resource allocation user profit and the subcarrier and power combined allocation benefit.
(3) A resource allocation solution is provided, frequency spectrum resources are allocated by adopting an auction method, subcarriers and capacity are allocated by adopting a Lagrange algorithm, and calculation and storage resources are allocated by adopting an auction game model.
Drawings
FIG. 1 is a schematic diagram of a mobile edge computing network resource security sharing model provided by the present invention;
FIG. 2 is a schematic diagram of an offline transaction model provided by the present invention;
FIG. 3 is a schematic diagram of an online transaction model provided by the present invention;
FIG. 4 is a flow chart of data uplink provided by the present invention;
fig. 5 is a schematic diagram of a transaction detection process provided by the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, a network resource security sharing method based on a blockchain and an auction game includes the following steps:
step 1, establishing a mobile edge computing network resource security sharing model based on a block chain technology.
The principle of establishing the model is that a block chain technology is applied to resource allocation among the mobile edge clouds to solve the problem of islanding among the mobile edge clouds, and each mobile edge cloud and a resource proxy server of the center cloud form a federation chain based on a hyper ledger (hyper folder fabric). The model can be divided into three layers, namely a cloud layer, an edge cloud layer and a terminal access layer; the cloud layer is a Core Network (CN), and the Core Network includes a group of large servers, which are central cloud resource providers of the whole system; the edge cloud mainly comprises an MEC server and a Resource Proxy server (Resource Proxy Server RPS), wherein the MEC server is a small cloud computing Resource pool which is deployed at the edge of a network by a telecommunication operator and is used for supplementing a central server so that tasks can be processed at a place close to a user, and the MEC server can be deployed near a base station or inside the base station to provide resources such as computing, storage and the like for a mobile terminal; the terminal access layer includes: base station, various intelligent terminal equipment, such as: smart home devices, smart cars, smart phones, sensors, and the like. The base station provides network access service for the intelligent terminal.
In the system model, the RPS is a key device for resource sharing of each edge cloud, is a device in the communication network, is a node of the blockchain network, is an important component of the blockchain network, and has two main functions: 1) storing resource information which corresponds to the edge cloud and the base station and can be sold to the outside; 2) and as a peer node of the block chain, operating the intelligent contract, namely the chain code, of the block chain, providing an interface for an application program to access the block chain, and storing a global account book and the like. A block chain formed by RPS and mechanisms of spectrum management, identity authentication and the like of some third parties constructs a resource sharing bridge between the whole edge cloud and the cloud, and the intelligent terminal can broadcast a resource application chain code to the block chain through the adjacent resource proxy server, so that the edge cloud and the cloud in the whole network can know the resource application chain code and provide service for the intelligent terminal according to user requirements, and information sharing between the edge clouds is realized.
Step 2, realizing edge cloud layer data sharing by adopting a block chain, which specifically comprises the following steps:
step 2.1, transaction flow; the transaction flow includes offline and online transactions based on the lightning network, as shown in fig. 2.
The offline transaction flow is shown in fig. 2, and includes:
① resource applicant and resource provider create a channel;
② when the two parties are doing business, the resource applicant sends an incomplete transaction to the resource provider;
③ the resource provider sends the corresponding incomplete transaction to the resource applicant after receiving the incomplete transaction;
④ resource provider provides service;
the online transaction process is shown in fig. 3, and includes:
① the resource applicant pre-stores the corresponding amount to the block chain after selecting the trading object;
② the resource application party creates transaction and sends the pre-stored amount transaction to the peer node, wherein the peer node represents the resource proxy server;
③ Peer node writes pre-stored transaction into global account book;
④ peer node notifies resource applicant of transaction completion;
⑤ the resource applicant notifies the resource provider that payment is complete;
⑥ the resource provider checks from the global account book whether the transaction is completed, and if so, provides the service;
step 2.2, the data uplink process is shown in fig. 4:
step 2.2.1, the resource application party applies for the trade uplink;
step 2.2.2, after receiving the transaction, the peer node calls the uplink chain code and broadcasts the transaction to a plurality of endorsement nodes;
step 2.2.3, the endorsement node checks and verifies the transaction, signs and returns the signature to the peer node of the transaction proposal;
step 2.2.4, the peer node of the transaction proposal collects the endorsement and checks whether the collected endorsement is enough, and after the collected endorsement is enough, the transaction containing the endorsement is submitted to the sequencing node;
step 2.2.5 the sorting node sorts all received transactions in a prescribed order and then packs them for distribution in blocks. Once the size of the blocks collected by the sorting node is sufficient, or the maximum time is reached, the blocks will be broadcast to the master node;
step 2.2.6, the master node verifies the block and records the block into a global account book, and synchronizes to all other peer nodes;
step 2.3, the detection process is shown in fig. 5, and specifically includes:
step 2.3.1, after the payment of the resource applicant is completed, informing the peer node of the completion of the payment, and simultaneously sending the data to be processed to the resource provider; the peer node reads the global account book and checks whether the payment is finished; calling the transaction monitoring chain code when payment is completed; if the payment is not completed, the priority of the resource application party is reduced; when the priority of the resource application party is reduced to 0, no resource is distributed to the resource application party;
step 2.3.2, the resource provider receives the data of the resource applicant and processes the data;
step 2.3.3, after the data processing is finished, the resource provider returns the data processing result to the resource applicant, and meanwhile, the peer node is informed of the completion of the transaction;
step 2.3.4, after the resource application party receives the processed data, the resource application party informs the peer node of completing the transaction;
step 2.3.5, after the Peer node receives the transaction completion notice of the resource sender and the resource applicant, the transaction is ended; only receiving the transaction completion message of the resource provider, but not receiving the transaction success message of the resource applicant, sending an inquiry to the resource applicant to determine whether the transaction is completed; if the applicant replies that the transaction is completed, the transaction is ended; if the reply transaction is not completed, broadcasting that the resource provider has a fault in the blockchain, and removing the fault from the blockchain; the resource provider needs to reapply for joining after fault repair is completed;
and 3, establishing an optimization model of resource joint distribution based on the user income of bandwidth resource distribution, the joint distribution income of subcarriers and power, and the calculation and storage of the user income of resource distribution. The optimization model with the maximum user utility comprises three parts, namely user frequency spectrum resource auction benefit, time delay and power consumption benefit saved by unloading user tasks to edge cloud processing, and user auction calculation and storage resource benefit.
The benefit function of the bandwidth resource auction problem is as follows:
Figure BDA0002354137680000101
mijis a binary variable indicating whether base station j allocates resources to end users i, mijE {0,1}, when mijWhen 1, it means that resources are allocated, mij0 means no resource is allocated; w is aijIndicating the amount of bandwidth application resources;
Figure BDA0002354137680000102
indicating the time of occupied bandwidth;
Figure BDA0002354137680000103
the bid price of unit time when the user i applies for the bandwidth resource from the base station j is represented;
Figure BDA0002354137680000104
the user i applies for the bid price of the bandwidth resource unit time to the base station j, and gamma represents the normalization coefficient of the bandwidth resource auction benefit.
The problem of joint allocation of spectrum resources and computing resources is solved, and the benefit function is as follows:
Figure BDA0002354137680000105
wherein, ti＝Di/fiIndicating the time required for local execution, DiComputing quantities for tasks, fiThe main frequency of the intelligent terminal is;
Figure BDA0002354137680000106
time required for offloading the task to MEC execution, wherein
Figure BDA0002354137680000107
Is the uplink communication delay, S, required for the transmission of data from user i to base station jiApply for storage resource size, r, for user iijThe transmission rate from user i to base station j;
Figure BDA0002354137680000108
data processing delay, fikRepresenting the size of the computing resource allocated by the resource provider k for the user i, α being a time benefit normalization coefficient, Ei＝ui*DiRepresenting the energy consumption consumed by the locally executing terminal device, where ui＝10-11fi 2；
Figure BDA0002354137680000111
Representing the energy consumption consumed by offloading the task to the MEC execution terminal device, wherein cijnChannel capacity, p, for user i to base station j at subcarrier nijnAnd β represents the energy consumption benefit normalization coefficient.
The calculation and storage resource allocation problem has the following benefit function:
Figure BDA0002354137680000112
mikis a binary variable indicating whether the computing resource provider k allocates resources to the end user i, mikE {0,1}, when mikWhen 1, it means that resources are allocated, mik0 means no resource is allocated; f. ofikRepresenting the size of the computing resource allocated by the resource provider k for the user i;
Figure BDA0002354137680000113
data processing delay;
Figure BDA0002354137680000114
the bid price of a unit calculation resource unit time is applied to a resource provider k by a user i;
Figure BDA0002354137680000115
indicating that user i applies for bid price per unit of computing resource unit time from resource provider k, SiApplying for the size of a storage resource for a user i;
Figure BDA0002354137680000116
the bid price of unit storage resource unit time is applied to the resource provider k by the user i;
Figure BDA0002354137680000117
the user i applies for the bid-winning price of unit storage resource unit time to the resource provider k, and the delta represents the normalization coefficient of the MEC resource auction revenue.
Under the conditions of bandwidth total amount limitation, emission power limitation, resource cost limitation and user application delay limitation, an optimization model of resource joint distribution is represented as follows:
Figure BDA0002354137680000118
the constraint conditions include:
Figure BDA0002354137680000119
Figure BDA00023541376800001110
Figure BDA00023541376800001111
Figure BDA00023541376800001112
Figure BDA00023541376800001113
Figure BDA0002354137680000121
Figure BDA0002354137680000122
Figure BDA0002354137680000123
Figure BDA0002354137680000124
wherein the condition (1) indicates that the total bandwidth resources allocated are less than the total bandwidth of the base station
Figure BDA0002354137680000125
The condition (2) indicates that the bid price per unit bandwidth of the applied base station is larger than the cost; condition (3) indicates that one channel is allocated to only one user; condition (4) indicates that the total power of the channel allocations is less than the maximum power PiAnd the power is greater than 0; condition (5) indicates that the system delay is less than
Figure BDA0002354137680000126
The condition (6) indicates that the total amount of computing resources for applying for the MEC is less than the total amount of computing resources for the MEC
Figure BDA0002354137680000127
The condition (7) indicates that the total amount of the storage resources of the application MEC is less than the total amount of the storage resources of the MEC
Figure BDA0002354137680000128
Condition (8) indicates that the bid price of the computing resource applying MEC unit is larger than the cost; the condition (9) indicates that the bid price applied for the MEC unit storage resource is larger than the cost. The optimization problem is a mixed integer non-linear assignment problem, which is an NP-hard problem.
Jointly allocating the frequency spectrum and the computing resources based on the optimization model of the joint allocation of the resources; the resource allocation algorithm comprises: (1) allocating frequency spectrum resources by adopting an auction method; (2) sub-carriers and capacity are distributed by adopting a Lagrange algorithm; (3) and allocating computing and storage resources by adopting an auction game model.
1. And (3) bandwidth resource allocation algorithm based on auction.
Based on the user spectrum resource auction profit maximization, the multi-user spectrum resource allocation adopts an anonymous second price auction game model and a greedy algorithm; allocating the unit bandwidth with high price first until the resource allocation is completed or all users are satisfied. As shown in algorithm 1 provided in table 1 below,
TABLE 1 Algorithm 1
Figure BDA0002354137680000129
Figure BDA0002354137680000131
Wherein BETAijFor the set of bid prices for user i at base station j,
Figure BDA0002354137680000132
the maximum allocatable bandwidth for the base station, I is the user set,
Figure BDA0002354137680000133
cost per unit bandwidth of base station, WijApplying a set of bandwidth resource quantity to a base station j for a user; i isjAllocating a successful user set for the base station j; pijAnd (5) winning a bid price set for the user. Firstly, BETAijArranged in descending order, judges whether the residual bandwidth of the base station is larger than the bandwidth resource, BETA, applied by the user with the highest bid priceijWhether the second price in (1) is greater than the base station cost csijIf both conditions are satisfied, the user with the highest bid price is placed in the successful bidding user set I as the successful bidder, and the user with the second highest bid price is placed in the successful bidding price set P as the successful bidderijIn, return I and Pij。
2. And adopting a Lagrange algorithm to allocate the subcarriers and the capacity.
According to the resource joint allocation optimization model:
Figure BDA0002354137680000134
the optimization model for joint allocation of subcarriers and power based on the time delay and power consumption benefits saved by user task offloading to edge cloud processing can be converted to:
Figure BDA0002354137680000135
Figure BDA0002354137680000136
Figure BDA0002354137680000137
Figure BDA0002354137680000138
when c is going toijnAnd pijnAt fixation, the original optimization problem is fikA concave function of (a). According to the constraint (5)
Figure BDA0002354137680000141
The optimal solution of (a) is:
Figure BDA0002354137680000142
will be provided with
Figure BDA0002354137680000143
Bring in PITThe problem turns into:
Figure BDA0002354137680000144
Figure BDA0002354137680000145
Figure BDA0002354137680000146
wherein the content of the first and second substances,
Figure BDA0002354137680000147
is a constant, molecule
Figure BDA0002354137680000148
Is about cijnConvex function of, denominator rij(cijn,pijn) Is about cijnThus, the spectrum allocation is divided into two parts, subcarrier allocation and power allocation.
(1) Subcarrier allocation model
When p isijnWhen it is constant, the condition cijnE {0,1} relaxes to 0<cijn<1, then problem PIT1 is equivalent to PIT2：
Figure BDA0002354137680000149
s.t.Ui(cijn)≤γi
Figure BDA00023541376800001410
Figure BDA00023541376800001411
0<cijn<1
Figure BDA00023541376800001412
Order to
Figure BDA00023541376800001413
Due to Ui(cijn)≤γiIs equivalent to hi(cijn)-γirijThe content is less than or equal to 0:
Figure BDA0002354137680000151
obtained from KTT conditions:
Figure BDA0002354137680000152
Figure BDA0002354137680000153
Figure BDA0002354137680000154
Figure BDA0002354137680000155
Figure BDA0002354137680000156
finishing to obtain:
Figure BDA0002354137680000157
when epsiloninWhen equal to 0, cijn>0；εin>0，cijn＝0
So if c is causedijn>0, only if epsiloninWhen 0, we get:
Figure BDA0002354137680000158
to maximize the merit function, λ should be madeinAt a maximum, i.e.
Figure BDA0002354137680000159
The specific algorithm is shown as algorithm 2:
TABLE 2 Algorithm 2
Figure BDA00023541376800001510
Figure BDA0002354137680000161
Wherein, N represents the subcarrier number set applied by user I, and I represents the set of user I applying for subcarrier.
Figure BDA0002354137680000162
The set representing the channel capacity of the user i to the base station j on the subcarrier n is a two-dimensional variable for the base station j, wherein the row represents the user and the column represents the channel; m ═ M1,m2,...,mIUser applies for subchannel number, IacRepresenting successfully allocated user set, NijRepresents the assignment to IacIs the set of which channels, σ: { i, n: cijn∈Cijn}→{i:i∈Iac}→{n:n∈NijIs Cijn,IacAnd NijThe mapping relationship of (2). Firstly, C is firstlyinjIn descending order, and then CinjIn descending order, then CinjThe first element is the maximum channel capacity, and the user corresponding to the maximum channel capacity is stored in IacIn (1), the corresponding channel number is stored in NijIn, then delete CinjIn the capacity of each user of all allocated channels, if CinjIf the user corresponding to the first element has allocated enough bandwidth, the user corresponding to this element is in CinjUntil the channel is allocated or the resources are allocated to all the users to be allocated.
(2) Power distribution model
The optimization problem is decomposed into I sub-problems. Each sub-problem optimizes the power allocation of a certain user to minimize the time delay of the user, and the solution is solved by adopting a water injection algorithm, which can be expressed as:
Figure BDA0002354137680000163
Figure BDA0002354137680000164
pijn≥0,n∈Nij
wherein r isijDenotes the total rate from user i to base station j, W denotes the sub-carrier bandwidth, NijSet of subchannels, p, allocated by user iijnDenotes the power, H, allocated to the nth sub-carrier from user i to base station jijnFor the channel gain of the nth channel, σ2Representing the Gaussian white noise power spectral density, PiRepresenting the total power of user i, i.e. the total water injection.
The convex optimization problem is solved by adopting a Lagrange multiplier method to obtain:
Figure BDA0002354137680000171
the optimal solution obtained by the KTT condition is as follows:
Figure BDA0002354137680000172
wherein (·)+Indicating taking a positive value.
3. And allocating computing and storage resources by adopting an auction game model.
Based on the principle of maximizing the profit of user computing and storage resource auction, an improved anonymous second price auction game model is adopted for multi-user computing and storage resource allocation, computing and storage resources are preferentially allocated to users with high unit bandwidth price through an improved greedy algorithm until the resource allocation is completed or all the users are satisfied, as shown in algorithm 3:
TABLE 3 Algorithm 3
Figure BDA0002354137680000173
Figure BDA0002354137680000181
Wherein the content of the first and second substances,
Figure BDA0002354137680000182
apply for a set of bid prices for a computing resource for user i,
Figure BDA0002354137680000183
a bid price set for a storage resource is applied for user i,
Figure BDA0002354137680000184
are the remaining computing resources of the MEC,
Figure BDA0002354137680000185
is the remaining storage resource of the MEC, fikAnd sikThe amount of computing and storage resources applied for user i,
Figure BDA0002354137680000186
and
Figure BDA0002354137680000187
cost price of the computing resources and storage resources of the MEC respectively,
Figure BDA0002354137680000188
and
Figure BDA0002354137680000189
is the set of the number of the remaining computing and storage resources of the edge cloud k, pi is the priority set of the user I on the MEC, IkIn order to allocate a set of users of the resource,
Figure BDA00023541376800001810
in order to calculate the bid price for the resource,
Figure BDA00023541376800001811
for bid price of the storage resource.
The resource proxy server calculates the average value of the unit price of the computing resource and the unit price of the storage resource of the bidder
Figure BDA00023541376800001812
Figure BDA00023541376800001813
Separating vector BETAikArranged in descending order, first orderThe resource applicant of (2) is used as an auction winner, and the user price with the computing resource unit price and the storage resource unit price both smaller than the winning user price and the maximum average price is selected as the auction price.
Based on the model, the users applying for resources from the edge cloud are divided into 3 priorities according to the different positions of the user access network relative to the edge cloud, the local area network is the 1 st priority, the wide area network is the 2 nd priority, and the metropolitan area network is the 3 rd priority. And calculating the average value of the bid prices of the unit computing resource and the storage resource in the unit time and the average value of the cost.
Arranging the average bidding prices of the users with the 1 st priority in the edge cloud in a descending order; taking the user with the highest price as a winning bid user, and selecting the user price with the price of both the computing resource and the storage resource smaller than the price of the winning bid user and the average price highest as the winning bid price; when the edge cloud residual computing and storage resources are larger than the computing and spectrum resource quantity applied by the winning bid user and the winning bid price is larger than the cost of the resources, allocating the resources to the winning bid user, removing the winning bid user from the application user set and removing the resource quantity applied by the winning bid user from the residual resource quantity; otherwise, the allocation is unsuccessful, and the winning user is removed from the application user set.
And when the application user set of the 1 st priority is empty, further executing the same operation on the 2 nd priority user.
And when the application user set of the 2 nd priority is empty, further executing the same operation on the users of the 3 rd priority.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (6)

1. A network resource security sharing method based on block chains and auction games is characterized in that: the method comprises the following steps:
step 1, establishing a mobile edge computing network resource security sharing model based on a block chain technology;
the model is divided into a cloud layer, an edge cloud layer and a terminal access layer; the cloud layer is a core network CN and comprises a group of large servers for providing core cloud resources of the whole system; the edge cloud layer comprises an MEC server and a Resource Proxy Server (RPS); the MEC server is a small cloud computing resource pool deployed at the edge of the network by a telecommunication operator and used for supplementing a central server so that tasks can be processed at a place close to a user; the terminal access layer comprises a base station and intelligent terminal equipment; the base station provides network access service for terminal equipment only;
step 2, realizing edge cloud layer data sharing by adopting a block chain, which specifically comprises the following steps:
step 2.1, transaction flow; the transaction process comprises offline transaction and online transaction based on a lightning network;
the offline transaction flow comprises the following steps:
① resource applicant and resource provider create a channel;
② when the two parties are doing business, the resource applicant sends an incomplete transaction to the resource provider;
③ the resource provider sends the corresponding incomplete transaction to the resource applicant after receiving the incomplete transaction;
④ resource provider provides service;
the online transaction process comprises the following steps:
① the resource applicant pre-stores the corresponding amount to the block chain after selecting the trading object;
② the resource application party creates transaction and sends the pre-stored amount transaction to the peer node, wherein the peer node represents the resource proxy server;
③ Peer node writes pre-stored transaction into global account book;
④ peer node notifies resource applicant of transaction completion;
⑤ the resource applicant notifies the resource provider that payment is complete;
⑥ the resource provider checks from the global account book whether the transaction is completed, and if so, provides the service;
step 2.2, data uplink process;
step 2.2.1, the resource application party applies for the trade uplink;
step 2.2.2, after receiving the transaction, the peer node calls the uplink chain code and broadcasts the transaction to a plurality of endorsement nodes;
step 2.2.3, the endorsement node checks and verifies the transaction, signs and returns the signature to the peer node of the transaction proposal;
step 2.2.4, the peer node of the transaction proposal collects the endorsement and checks whether the collected endorsement is enough, and after the collected endorsement is enough, the transaction containing the endorsement is submitted to the sequencing node;
step 2.2.5 the sorting node sorts all received transactions in a prescribed order and then packs them for distribution in blocks. Once the size of the blocks collected by the sorting node is sufficient, or the maximum time is reached, the blocks will be broadcast to the master node;
step 2.2.6, the master node verifies the block and records the block into a global account book, and synchronizes to all other peer nodes;
step 2.3, detection flow
Step 2.3.1, after the payment of the resource applicant is completed, informing the peer node of the completion of the payment, and simultaneously sending the data to be processed to the resource provider; the peer node reads the global account book and checks whether the payment is finished; calling the transaction monitoring chain code when payment is completed; if the payment is not completed, the priority of the resource application party is reduced; when the priority of the resource application party is reduced to 0, no resource is distributed to the resource application party;
step 2.3.2, the resource provider receives the data of the resource applicant and processes the data;
step 2.3.3, after the data processing is finished, the resource provider returns the data processing result to the resource applicant, and meanwhile, the peer node is informed of the completion of the transaction;
step 2.3.4, after the resource application party receives the processed data, the resource application party informs the peer node of completing the transaction;
step 2.3.5, after the Peer node receives the transaction completion notice of the resource sender and the resource applicant, the transaction is ended; only receiving the transaction completion message of the resource provider, but not receiving the transaction success message of the resource applicant, sending an inquiry to the resource applicant to determine whether the transaction is completed; if the applicant replies that the transaction is completed, the transaction is ended; if the reply transaction is not completed, broadcasting that the resource provider has a fault in the blockchain, and removing the fault from the blockchain; and re-applying for joining is needed after the fault repair of the resource provider is completed.
2. A resource joint allocation method based on the mobile edge computing network resource security sharing model of claim 1, characterized in that: an optimization model for resource joint allocation is established based on user spectrum resource auction revenue, time delay and power consumption benefits saved by unloading user tasks to edge cloud processing and user auction calculation and storage resource revenue as follows:
(1) the benefit function of the bandwidth resource auction problem is as follows:
Figure FDA0002354137670000021
wherein m isijIs a binary variable indicating whether base station j allocates resources to end users i, mijE {0,1}, when mijWhen 1, it means that resources are allocated, mij0 means no resource is allocated; w is aijIndicating the amount of bandwidth application resources;
Figure FDA0002354137670000022
indicating the time of occupied bandwidth;
Figure FDA0002354137670000023
the bid price of unit time when the user i applies for the bandwidth resource from the base station j is represented;
Figure FDA0002354137670000024
the user i applies for a bid price of the bandwidth resource in unit time to the base station j, and gamma represents a normalization coefficient of the bandwidth resource auction benefit;
(2) the time delay and power consumption benefit function saved when the user task is unloaded to the edge cloud processing is as follows:
Figure FDA0002354137670000031
wherein, ti＝Di/fiIndicating the time required for local execution, DiComputing quantities for tasks, fiThe main frequency of the intelligent terminal is;
Figure FDA0002354137670000032
to offload tasks to the time required for MEC execution,
Figure FDA0002354137670000033
is the uplink communication delay, S, required for the transmission of data from user i to base station jiApply for storage resource size, r, for user iijThe transmission rate from user i to base station j;
Figure FDA0002354137670000034
data processing delay, fikRepresenting the size of the computing resource allocated by the resource provider k for the user i, α being a time benefit normalization coefficient, Ei＝ui*DiRepresenting the energy consumption consumed by the locally executing terminal device, where ui＝10-11fi 2；
Figure FDA0002354137670000035
Representing the energy consumption consumed by offloading the task to the MEC execution terminal device, wherein cijnChannel capacity, p, for user i to base station j at subcarrier nijnβ represents the energy consumption benefit normalization coefficient;
(3) the calculation and storage resource allocation problem has a benefit function of:
Figure FDA0002354137670000036
wherein m isikIs a binary variable indicating whether the computing resource provider k allocates resources to the end user i, mikE {0,1}, when mikWhen 1, it means that resources are allocated, mik0 means no resource is allocated; f. ofikRepresenting the size of the computing resource allocated by the resource provider k for the user i;
Figure FDA0002354137670000037
data processing delay;
Figure FDA0002354137670000038
the bid price of a unit calculation resource unit time is applied to a resource provider k by a user i;
Figure FDA0002354137670000039
indicating that user i applies for bid price per unit of computing resource unit time from resource provider k, SiApplying for the size of a storage resource for a user i;
Figure FDA00023541376700000310
the bid price of unit storage resource unit time is applied to the resource provider k by the user i;
Figure FDA00023541376700000311
expressing that a user i applies for a bid price per unit time of a unit storage resource to a resource provider k, and delta represents a normalization coefficient of the MEC resource auction benefit;
under the conditions of bandwidth total amount limitation, emission power limitation, resource cost limitation and user application delay limitation, an optimization model of resource joint distribution is represented as follows:
Figure FDA0002354137670000041
the constraint conditions include:
(1)
Figure FDA0002354137670000042
(2)
Figure FDA0002354137670000043
(3)
Figure FDA0002354137670000044
(4)
Figure FDA0002354137670000045
(5)
Figure FDA0002354137670000046
(6)
Figure FDA0002354137670000047
(7)
Figure FDA0002354137670000048
(8)
Figure FDA0002354137670000049
(9)
Figure FDA00023541376700000410
wherein the condition (1) indicates that the total bandwidth resources allocated are less than the total bandwidth of the base station
Figure FDA00023541376700000411
The condition (2) indicates that the bid price per unit bandwidth of the applied base station is larger than the cost; condition (3) indicates that one channel is allocated to only one user; condition (4) indicates that the total power of the channel allocations is less than the maximum power PiAnd the power is greater than 0; condition (5) indicates that the system delay is less than
Figure FDA00023541376700000412
The condition (6) indicates that the total amount of computing resources for applying for the MEC is less than the total amount of computing resources for the MEC
Figure FDA00023541376700000413
The condition (7) indicates that the total amount of the storage resources of the application MEC is less than the total amount of the storage resources of the MEC
Figure FDA00023541376700000414
Condition (8) indicates that the bid price of the computing resource applying MEC unit is larger than the cost; the condition (9) indicates that the bid price applied for the MEC unit storage resource is larger than the cost.
3. The resource joint allocation method based on the mobile edge computing network resource security sharing model according to claim 2, wherein: jointly allocating the spectrum and the computing resources based on the optimization model for joint allocation of resources of claim 2; the resource joint allocation algorithm comprises the following steps:
(1) allocating frequency spectrum resources by adopting an auction method;
(2) sub-carriers and capacity are distributed by adopting a Lagrange algorithm;
(3) and allocating computing and storage resources by adopting an auction game model.
4. The method of claim 3, wherein the resource joint allocation method based on the mobile edge computing network resource security sharing model is characterized in that: the spectrum resource allocation algorithm based on the auction method is as follows:
based on the user spectrum resource auction profit maximization, the multi-user spectrum resource allocation adopts an anonymous second price auction game model and a greedy algorithm; allocating the unit bandwidth with high price first until the resource allocation is completed or all users are satisfied;
s1.1, arranging the bid prices of unit bandwidth unit time in a descending order;
step S1.2, using the user with the highest price as a winning bid user, judging whether the residual frequency spectrum of the base station is larger than the frequency spectrum quantity applied by the winning bid user, judging whether the winning bid price is larger than the cost of the frequency spectrum, when the conditions are met, successfully distributing the frequency spectrum, removing the winning bid user from the application user set, and removing the frequency spectrum quantity applied by the winning bid user from the residual frequency spectrum quantity; if any one of the conditions is not met, the allocation is unsuccessful, and the winning user is removed from the application user set;
s1.3, when the number of the residual frequency spectrum resources is more than 0, repeatedly executing the steps S1.1-S1.2; and when the resource user set is not empty, repeatedly executing the steps S1.1-S1.2.
5. The method of claim 3, wherein the resource joint allocation method based on the mobile edge computing network resource security sharing model is characterized in that: the algorithm for distributing the sub-carriers and the capacity based on the Lagrange algorithm is as follows:
for the user with successfully distributed spectrum resources, calculating the execution time of the task according to the uplink transmission time delay from the user to the spectrum and the total time delay of the user for applying to unload the processing task, and calculating the quantity of the applied calculation resources according to the task quantity and the execution time of the task;
based on the time delay and power consumption benefit maximization principle saved by unloading user tasks to edge cloud processing, relaxing 0,1 discrete variables distributed by user subcarriers linked with a base station to a (0,1) interval, and when the power distributed by each channel is constant, obtaining the distribution method of the subcarriers by a Lagrange multiplier method;
s2.1, arranging the capacities of the sub-carriers to be distributed corresponding to the users to be distributed in a descending order, and obtaining the maximum capacity of the sub-carriers of each user;
s2.2, arranging the maximum subcarrier capacity of each user in a descending order to obtain the maximum subcarrier capacity corresponding to all users;
s2.3, allocating the sub-carrier to the user with the maximum capacity, and removing the sub-carrier from the sub-carrier to be allocated; when the number of the sub-carriers allocated by the user reaches the requirement after the sub-carriers are allocated, removing the user from the user to be allocated; when the requirement is not met, allocating the sub-carrier to the user and removing the sub-carrier from the sub-carrier to be allocated;
s2.4, judging whether the sub-carrier to be distributed and the user to be distributed are empty or not; when any set is empty, the distribution is finished;
and S2.5, repeating the steps S2.1-S2.4.
6. The method of claim 3, wherein the resource joint allocation method based on the mobile edge computing network resource security sharing model is characterized in that: and allocating computing and storage resources by adopting an auction game model as follows:
based on a user calculation and storage resource auction profit maximization principle, an improved anonymous second price auction game model is adopted for multi-user calculation and storage resource allocation, calculation and storage resources are preferentially allocated to users with high unit bandwidth price through an improved greedy algorithm until resource allocation is completed or all users are satisfied;
s3.1, dividing users applying for resources from the edge cloud into 3 priorities according to different positions of user access networks relative to the edge cloud, wherein the local area network is the 1 st priority, the wide area network is the 2 nd priority, and the metropolitan area network is the 3 rd priority;
s3.2, calculating the average value of unit calculation resource and storage resource bidding prices and the average value of cost in unit time;
s3.3, arranging the average bidding prices of the users with the 1 st priority in the edge cloud in a descending order; taking the user with the highest price as a winning bid user, and selecting the user price with the price of both the computing resource and the storage resource smaller than the price of the winning bid user and the average price highest as the winning bid price; when the edge cloud residual computing and storage resources are larger than the computing and spectrum resource quantity applied by the winning bid user and the winning bid price is larger than the cost of the resources, allocating the resources to the winning bid user, removing the winning bid user from the application user set and removing the resource quantity applied by the winning bid user from the residual resource quantity; otherwise, if the allocation is unsuccessful, removing the winning user from the application user set;
step S3.4, when the application user set with the 1 st priority is empty, the operation of the step S3.3 is further executed on the user with the 2 nd priority;
and step 3.5, when the application user set with the priority of 2 is empty, further executing the operation of the step 3.3 on the user with the priority of 3.
CN202010002832.2A 2020-01-02 2020-01-02 Network resource safety sharing method based on block chain and auction game Active CN111107506B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010002832.2A CN111107506B (en) 2020-01-02 2020-01-02 Network resource safety sharing method based on block chain and auction game

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010002832.2A CN111107506B (en) 2020-01-02 2020-01-02 Network resource safety sharing method based on block chain and auction game

Publications (2)

Publication Number Publication Date
CN111107506A true CN111107506A (en) 2020-05-05
CN111107506B CN111107506B (en) 2022-05-10

Family

ID=70425794

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010002832.2A Active CN111107506B (en) 2020-01-02 2020-01-02 Network resource safety sharing method based on block chain and auction game

Country Status (1)

Country Link
CN (1) CN111107506B (en)

Cited By (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111797173A (en) * 2020-09-10 2020-10-20 联通(浙江)产业互联网有限公司 Alliance chain sharing system, method and device, electronic equipment and storage medium
CN111950858A (en) * 2020-07-20 2020-11-17 深圳供电局有限公司 Edge cloud collaborative computing resource transaction method, computer equipment and storage medium
CN112134959A (en) * 2020-09-24 2020-12-25 北京工业大学 Heterogeneous edge resource sharing method based on block chain
CN112188560A (en) * 2020-09-08 2021-01-05 北京科技大学 Edge collaborative computing resource allocation method
CN112330019A (en) * 2020-11-04 2021-02-05 安徽师范大学 Data transmission optimization method of block chain transaction channel
CN112492603A (en) * 2020-11-23 2021-03-12 重庆邮电大学 Multistage dynamic spectrum sharing method based on block chain technology
CN112511619A (en) * 2020-11-26 2021-03-16 北京工业大学 Method for matching transactions among resource nodes in wireless edge block chain scene
CN112532676A (en) * 2020-07-24 2021-03-19 北京航空航天大学 Vehicle calculation task unloading method based on block chain data sharing
CN112615919A (en) * 2020-12-16 2021-04-06 中国联合网络通信集团有限公司 Resource allocation method, resource allocation device and block chain
CN112616149A (en) * 2020-11-11 2021-04-06 南京大学 Heterogeneous operator block chain spectrum dynamic sharing method for Internet of vehicles
CN113888327A (en) * 2021-09-30 2022-01-04 天津大学 Energy internet transaction method and system based on reinforcement learning block chain energizing
CN113918645A (en) * 2021-09-02 2022-01-11 上海柚子工道物联技术有限公司 Heterogeneous asset transaction system and method based on block chain and computer device
CN116599966A (en) * 2023-05-09 2023-08-15 天津大学 Edge cloud service parallel resource allocation method based on block chain sharing

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108616596A (en) * 2018-05-09 2018-10-02 南京邮电大学 It is adaptively known together method based on the block chain that dynamic authorization and network environment perceive
CN108734575A (en) * 2018-04-03 2018-11-02 平安科技（深圳）有限公司 Financing method, system based on block chain and storage medium
CN109165972A (en) * 2018-07-30 2019-01-08 广东工业大学 A kind of mobile block chain resource allocation methods of task based access control migration mechanism
CN109600849A (en) * 2018-11-30 2019-04-09 东南大学 A kind of simultaneous resource distribution method based on mobile edge calculations system
US20190318377A1 (en) * 2019-06-25 2019-10-17 Alef Edge, Inc. System and method for a digital coin exchange

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108734575A (en) * 2018-04-03 2018-11-02 平安科技（深圳）有限公司 Financing method, system based on block chain and storage medium
CN108616596A (en) * 2018-05-09 2018-10-02 南京邮电大学 It is adaptively known together method based on the block chain that dynamic authorization and network environment perceive
CN109165972A (en) * 2018-07-30 2019-01-08 广东工业大学 A kind of mobile block chain resource allocation methods of task based access control migration mechanism
CN109600849A (en) * 2018-11-30 2019-04-09 东南大学 A kind of simultaneous resource distribution method based on mobile edge calculations system
US20190318377A1 (en) * 2019-06-25 2019-10-17 Alef Edge, Inc. System and method for a digital coin exchange

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
XUELIAN LIU: "Efficient Auction Mechanism for Edge Computing Resource Allocation in Mobile Blockchain", 《 2019 IEEE 21ST INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS; IEEE 17TH INTERNATIONAL CONFERENCE ON SMART CITY; IEEE 5TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND SYSTEMS (HPCC/SMARTCITY/DSS)》 *
宋丽华: "博弈论在区块链中的应用研究", 《密码学报》 *
邓爱民: "基于区块链的供应链"智能保理"业务模式及博弈分析", 《管理评论》 *
陈美娟等: "基于区块链的物联网设备标识研究", 《物联网学报》 *
高洁: "基于区块链的边缘计算IIoT架构研究", 《计算机应用研究》 *

Cited By (20)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111950858A (en) * 2020-07-20 2020-11-17 深圳供电局有限公司 Edge cloud collaborative computing resource transaction method, computer equipment and storage medium
CN111950858B (en) * 2020-07-20 2023-09-15 深圳供电局有限公司 Edge cloud collaborative computing resource transaction method, computer equipment and storage medium
CN112532676A (en) * 2020-07-24 2021-03-19 北京航空航天大学 Vehicle calculation task unloading method based on block chain data sharing
CN112532676B (en) * 2020-07-24 2021-09-28 北京航空航天大学 Vehicle calculation task unloading method based on block chain data sharing
CN112188560A (en) * 2020-09-08 2021-01-05 北京科技大学 Edge collaborative computing resource allocation method
CN111797173A (en) * 2020-09-10 2020-10-20 联通(浙江)产业互联网有限公司 Alliance chain sharing system, method and device, electronic equipment and storage medium
CN112134959B (en) * 2020-09-24 2022-10-28 北京工业大学 Heterogeneous edge resource sharing method based on block chain
CN112134959A (en) * 2020-09-24 2020-12-25 北京工业大学 Heterogeneous edge resource sharing method based on block chain
CN112330019A (en) * 2020-11-04 2021-02-05 安徽师范大学 Data transmission optimization method of block chain transaction channel
CN112330019B (en) * 2020-11-04 2024-03-15 安徽师范大学 Data transmission optimization method for block chain transaction channel
CN112616149A (en) * 2020-11-11 2021-04-06 南京大学 Heterogeneous operator block chain spectrum dynamic sharing method for Internet of vehicles
CN112492603A (en) * 2020-11-23 2021-03-12 重庆邮电大学 Multistage dynamic spectrum sharing method based on block chain technology
CN112511619B (en) * 2020-11-26 2022-11-18 北京工业大学 Method for matching transactions among resource nodes in wireless edge block chain scene
CN112511619A (en) * 2020-11-26 2021-03-16 北京工业大学 Method for matching transactions among resource nodes in wireless edge block chain scene
CN112615919B (en) * 2020-12-16 2021-11-26 中国联合网络通信集团有限公司 Resource allocation method, resource allocation device and block chain
CN112615919A (en) * 2020-12-16 2021-04-06 中国联合网络通信集团有限公司 Resource allocation method, resource allocation device and block chain
CN113918645A (en) * 2021-09-02 2022-01-11 上海柚子工道物联技术有限公司 Heterogeneous asset transaction system and method based on block chain and computer device
CN113888327A (en) * 2021-09-30 2022-01-04 天津大学 Energy internet transaction method and system based on reinforcement learning block chain energizing
CN116599966A (en) * 2023-05-09 2023-08-15 天津大学 Edge cloud service parallel resource allocation method based on block chain sharing
CN116599966B (en) * 2023-05-09 2024-05-24 天津大学 Edge cloud service parallel resource allocation method based on block chain sharing

Also Published As

Publication number Publication date
CN111107506B (en) 2022-05-10

Similar Documents

Publication Publication Date Title
CN111107506B (en) Network resource safety sharing method based on block chain and auction game
CN109814951B (en) Joint optimization method for task unloading and resource allocation in mobile edge computing network
CN101945369B (en) Auction and satisfaction model-based dynamic frequency spectrum allocation method
CN109600798B (en) Multi-domain resource allocation method and device in network slice
CN110519370B (en) Edge computing resource allocation method based on facility site selection problem
CN111405527A (en) Vehicle-mounted edge computing method, device and system based on volunteer cooperative processing
CN110856228A (en) WiFi unloading excitation method based on dynamic programming algorithm and reverse auction
CN110856227B (en) Data unloading method based on greedy algorithm and reverse auction
CN112751936A (en) Alliance chain system and alliance chain system deployment method
Dou et al. Mobile edge computing based task offloading and resource allocation in smart grid
Ma et al. Cognitive ran slicing resource allocation based on stackelberg game
CN110366194B (en) Resource calling method, device and system
CN108391257B (en) Resource allocation method based on auction theory in social network D2D scene
Huang et al. Game theory for spectrum sharing
Dai et al. Edge trusted sharing: task-driven decentralized resources collaborate in IoT
CN109673055B (en) Resource allocation method for joint communication and calculation based on two-dimensional region filling
CN112911653A (en) Priority B5G communication system resource scheduling method based on user grade
AbdelFadeel et al. Carrier aggregation‐based dynamic spectrum access framework for LTE‐A primary operators
Lin et al. Computation task scheduling and offloading optimization for collaborative mobile edge computing
Oloyede et al. Spectrum Auction for Dynamic Spectrum Access with Energy Efficiency
Kane The role of markets in spectrum policy
CN117676710A (en) Multistage excitation method for mobile general calculation integrated edge gateway
CN117707797B (en) Task scheduling method and device based on distributed cloud platform and related equipment
CN112054924B (en) Resource allocation method of integrated power grid
CN116684980B (en) Method, device and storage medium for processing data based on multiple satellites

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
CB02 Change of applicant information

Address after: 210003 Gulou District, Jiangsu, Nanjing new model road, No. 66

Applicant after: NANJING University OF POSTS AND TELECOMMUNICATIONS

Address before: Yuen Road Qixia District of Nanjing City, Jiangsu Province, No. 9 210003

Applicant before: NANJING University OF POSTS AND TELECOMMUNICATIONS

CB02 Change of applicant information
GR01 Patent grant
GR01 Patent grant