CN115334164A - Parking vehicle resource allocation method based on mobile block chain - Google Patents

Parking vehicle resource allocation method based on mobile block chain Download PDF

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CN115334164A
CN115334164A CN202210959682.3A CN202210959682A CN115334164A CN 115334164 A CN115334164 A CN 115334164A CN 202210959682 A CN202210959682 A CN 202210959682A CN 115334164 A CN115334164 A CN 115334164A
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mec node
mec
vehicle
parked
representing
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许娟
刘昆
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Nanjing University of Aeronautics and Astronautics
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Nanjing University of Aeronautics and Astronautics
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • 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
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The invention discloses a parking vehicle resource allocation method based on a mobile block chain, which is used for efficiently utilizing idle computing resources of a parking vehicle. The MEC node collects transaction information in the blockchain network and gains income by contending for accounting right through computing resources of rented and parked vehicles. Parked vehicles may lease their own idle computing resources for revenue. The interactive relationship between the parked vehicle and the MEC node is modeled into a two-stage Stackelberg game. In the first phase, parked vehicles serve as leaders to adjust the pricing of computing resources. In the second stage, the MEC node adjusts demand for parking computing resources as a follower, maximizing the common benefit through gaming of MEC nodes and parked vehicles. The invention can effectively improve the total utility of the system in the parking vehicle auxiliary mobile block chain network.

Description

Parking vehicle resource allocation method based on mobile block chain
Technical Field
The invention belongs to the field of mobile block chains based on Internet of vehicles, relates to a method for resource allocation and profit maximization, and particularly relates to a method for resource allocation and profit maximization based on parking vehicle edge calculation
Background
In the context of the internet of vehicles, the rapid development of the automotive industry has brought about a huge dividend, but at the same time has brought about some problems. It is important to establish a trusted data sharing environment. Recently, blockchain techniques have received much attention because the collaboration mode in blockchains can establish trust in untrusted environments. Compared with the traditional centralized processing, the block chain technology greatly improves the security of the system. But the security of the blockchain system requires certain computational resources to guarantee.
Furthermore, with the development of hardware and communication technology, today's vehicles are no longer merely a mechanical combination, but rather a complex combination of software and hardware. It is anticipated that modern transportation is moving towards intelligent integrated infrastructure, providing richer applications for modern life. The utilization of abundant idle vehicle resources in vehicles is a current hot point.
Currently, much work has been done in utilizing the onboard resources of a moving vehicle. However, most of the time in the vehicle is in a parked state, greatly wasting on-board resources. Therefore, how to utilize the idle resources of the parked vehicles is a potential research direction.
The spare computing resources of parked vehicles may be used to increase the robustness of the blockchain network. However, because of the individuality between the parked vehicles and the MEC nodes, the parked vehicles and MEC nodes do not have enough power to provide computing resources and maintain the blockchain network, and therefore, how to design a reasonable scheme, the benefits of both maintenance parties become a huge challenge.
Disclosure of Invention
In order to solve the above problems, the present invention provides a parking vehicle resource allocation method based on a mobile block chain, which comprises the following specific steps:
in order to solve the technical problems, the invention adopts the following technical scheme:
step 1, constructing a parking vehicle resource allocation model based on a mobile block chain, an MEC node and a parking vehicle expected utility model. The parked vehicle assisted mobile blockchain model includes M MEC nodes, N parked vehicles, and blockchain users.
The MEC node collects transactions published to the network by blockchain users, and writes the transactions to the network and receives rewards by renting computing resources of parked vehicles to compete for billing rights.
Parked vehicles gain revenue by leasing unused computing resources to the MEC node. The MEC node maximizes the utility of the MEC node by adjusting the computation resource rented by the MEC node, and the maximization expression of the utility of the MEC node is as follows:
Figure BSA0000280646170000021
Figure BSA0000280646170000022
wherein f is i Set of policies representing the amount of computing resources purchased by MEC node i, F -i A set of policies representing the amount of computing resources purchased by nodes other than MEC node i, P represents a set of policies for price allocation of all parked vehicles, φ i Representing the expected utility of MEC node i, R represents a fixed reward, R represents a variable reward, s i Indicating the block size to be written by the ith MEC node,
Figure BSA0000280646170000027
indicates the success probability, omega, of the MEC node i contending for the accounting right ij Representing the probability, p, that MEC node i chooses to park vehicle j ij Representing the price of the MEC node i for purchasing the computing resource of the parked vehicle j, f ij Representing the amount of computing resources, D, of MEC node i purchasing a parked vehicle max Representing an upper limit for the MEC node to purchase computing resources.
The parked vehicle utility function may be expressed as:
Figure BSA0000280646170000023
s.t.0≤p ij ≤p max
wherein p is j Set of strategies, p, representing price configuration of parked vehicle j max Representing the upper limit of the price of the calculated resource in the system, P -j Representing the strategy set of price configuration of other parked vehicles except the parked vehicle j, F representing the strategy set of the quantity of computing resources purchased by all MEC nodes, and eta being a unit energy consumption price factor and setting the current round q =1.
And 2, adjusting and updating the calculation resource requirement of the MEC node.
And 3, updating the dual variable lambda.
And 4, adjusting the price configuration of the parked vehicle to maximize the utility of the parked vehicle.
And 5, repeating the steps 2 to 4 until one of the following termination conditions is reached: 1) The maximum cycle number is reached; 2) The absolute value of the difference between the total utility of the vehicle parked in the previous round and the current round is less than a given threshold. After stopping, an optimal unloading scheme can be obtained.
Further, in step 1, the probability of successful accounting right contending for by MEC node i
Figure BSA0000280646170000024
Write as:
Figure BSA0000280646170000025
wherein
Figure BSA0000280646170000026
Representing the MEC node i initial computing resources.
In step 1, the MEC node i selects the probability ω of parking the vehicle j ij The calculation method comprises the following steps:
Figure BSA0000280646170000031
wherein α and β are weighting factors, α + β =1;
Figure BSA0000280646170000032
represents the maximum computational resource owned by the parked vehicle j; b is a mixture of ij Represents the price incentive factor for parked vehicle j to MEC node i, expressed as:
b ij ＝p max -p ij 。
further, in step 2, the method for updating the computing resource demand of the MEC node i comprises
Figure BSA0000280646170000033
Wherein t is a time chartIndicating that the MEC node i does not update the computing resource requirement, and indicating that the MEC node has already updated the computing resource requirement at the time t + 1; wherein ε = t +1 when i > k and ε = t when i < k; ρ is a damping factor;
Figure BSA0000280646170000034
is the dual variable of MEC node i.
Further, in step 3, the method for updating the dual variable of the MEC node i includes:
Figure BSA0000280646170000035
further, in step 4, the method for updating the price configuration of the parked vehicle j includes:
Figure BSA0000280646170000036
has the advantages that: compared with the prior art, the method has the remarkable advantage that the problem of resource allocation of multiple MEC nodes for maintaining the robustness of the blockchain network by using idle computing resources of parked vehicles is considered for the first time. And the resource allocation problem is modeled into a Stackelberg game model, and the MEC node and the parked vehicles alternately optimize the utility of the MEC node and the parked vehicles, so that Nash balance is achieved. The invention not only can effectively improve the total utility of the system in the parking vehicle auxiliary mobile block chain network, but also has simple method and easy implementation.
Drawings
FIG. 1 is a flowchart of the algorithm as a whole in the present invention.
Fig. 2 is a graph showing the effect change of the MEC node in the iterative process of the algorithm.
FIG. 3 is a graph of the effective change of parked vehicles during the iteration of the algorithm.
Detailed Description
The present invention will be further described in detail with reference to the accompanying drawings, and the overall flow chart of the present invention is shown in FIG. 1.
Step 1: and constructing a parking vehicle auxiliary moving block chain model, an MEC node and a parking vehicle expected utility model. The parked vehicle assisted mobile blockchain model includes M MEC nodes, N parked vehicles, and blockchain users.
The MEC node collects transactions issued by blockchain users to the network, writes the transactions to the network and receives rewards by renting computing resources of parked vehicles to compete for billing rights.
Parked vehicles gain revenue by leasing unused computing resources to the MEC node. The MEC node maximizes the utility of the MEC node by adjusting the computation resource rented by the MEC node, and the maximization expression of the utility of the MEC node is as follows:
Figure BSA0000280646170000041
Figure BSA0000280646170000042
wherein f is i Set of policies representing the amount of computing resources purchased by MEC node i, F -i A set of policies representing the amount of computing resources purchased by nodes other than MEC node i, P represents a set of policies for price allocation of all parked vehicles, φ i Representing the expected utility of MEC node i, R represents a fixed reward, R represents a variable reward, s i Indicating the block size to be written by the ith MEC node,
Figure BSA0000280646170000049
indicates the success probability, omega, of the MEC node i contending for the accounting right ij Representing the probability, p, that MEC node i chooses to park vehicle j ij Representing the price of MEC node i to purchase the computing resource of parked vehicle j, f ij Representing the amount of computing resources, D, of MEC node i purchasing a parked vehicle max Representing an upper limit for the MEC node to purchase computing resources.
The parked vehicle utility function may be expressed as:
Figure BSA0000280646170000043
s.t.0≤p ij ≤p max
wherein p is j Set of strategies, p, representing price configuration of parked vehicles j max Representing the upper limit of the price of the computational resource in the system, P -j Representing the strategy set of price configuration of other parked vehicles except the parked vehicle j, F representing the strategy set of the quantity of computing resources purchased by all MEC nodes, and eta being a unit energy consumption price factor and setting the current round q =1.
(1) Probability of success of accounting right contended by MEC node i
Figure BSA0000280646170000044
Writing as follows:
Figure BSA0000280646170000045
wherein
Figure BSA0000280646170000046
Representing the MEC node i initial computational resources.
(2) Probability omega of MEC node i selecting parking vehicle j ij The calculation method comprises the following steps:
Figure BSA0000280646170000047
wherein α and β are weighting factors, α + β =1;
Figure BSA0000280646170000048
represents the maximum computational resource owned by the parked vehicle j; b is a mixture of ij Represents the price incentive factor of the parked vehicle j to the MEC node i, and is represented as:
b ij ＝p max -P ij 。
and 2, adjusting and updating the computing resource requirement of the MEC node. The method for updating the computing resource demand of the MEC node i comprises the following steps
Figure BSA0000280646170000051
The time t represents that the MEC node i does not update the computing resource requirement, and the time t +1 represents that the MEC node has already updated the computing resource requirement; wherein ε = t +1 when i > k and ε = t when i < k; ρ is a damping factor;
Figure BSA0000280646170000052
is the dual variable of MEC node i.
And 3, updating the dual variable lambda. The method for updating the dual variables of the MEC node i comprises the following steps:
Figure BSA0000280646170000053
and 4, adjusting the price configuration of the parked vehicle to maximize the utility of the parked vehicle. The method for updating the price configuration of the parked vehicle j comprises the following steps:
Figure BSA0000280646170000054
and 5, repeating the steps 2 to 4 until one of the following termination conditions is reached: 1) The maximum cycle number is reached; 2) The absolute value of the difference between the total utility of the vehicle parked in the previous round and the current round is less than a given threshold. After stopping, an optimal unloading scheme can be obtained.
The method is adopted to carry out simulation experiments, 3 MEC nodes are arranged, and 5 vehicles are parked. The results of the experiment are shown in FIGS. 2-3. Fig. 2 is a graph of the utility change of MEC nodes during the iteration of the algorithm. FIG. 3 is a graph of the effective change of parked vehicles during the iteration of the algorithm.
The invention is not described in detail, but is well known to those skilled in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (6)

1. A parking vehicle resource allocation method based on a mobile block chain is characterized by comprising the following specific operation steps:
step 1, constructing a parking vehicle resource allocation model based on a mobile block chain, an MEC node and a parking vehicle expected utility model;
the parked vehicle assisted mobile blockchain model comprises M MEC nodes, N parked vehicles and blockchain users;
the MEC node collects transactions issued by blockchain users to the network, and contends for accounting right through the computing resource of the rented and parked vehicle so as to write the transactions into the network and obtain rewards;
parking vehicles obtain revenue by renting idle computing resources to MEC nodes;
the MEC node maximizes the utility of the MEC node by adjusting the computation resource rented by the MEC node, and the maximization expression of the utility of the MEC node is as follows:
Figure FSA0000280646160000011
Figure FSA0000280646160000012
wherein f is i A policy set representing the amount of computing resources purchased by the MEC node i; f -i A strategy set representing the amount of computing resources purchased by other nodes except the MEC node i; p represents a policy set of all parked vehicle price configurations; phi is a unit of i Representing the expected utility of MEC node i; a fixed prize represented by R; r represents a variable award; s is i Represents the block size to be written by the ith MEC node;
Figure FSA0000280646160000014
representing the probability of success of the MEC node i competing for the accounting right; omega ij Representing the probability of selecting the parked vehicle j by the MEC node i; p is a radical of formula ij Representing the price of the MEC node i for purchasing the computing resource of the parking vehicle j; f. of ij Representing the amount of computing resources purchased by the MEC node i for parking the vehicle; d max Representing an upper limit for the MEC node to purchase computing resources;
the parked vehicle utility function may be expressed as:
Figure FSA0000280646160000013
s.t.0≤p ij ≤p max
wherein p is j A policy set representing a parked vehicle j price configuration; p is a radical of formula max Representing a calculated resource price upper limit in the system; p is -j A set of policies representing price configurations for parked vehicles other than parked vehicle j; f represents a strategy set of the quantity of computing resources purchased by all MEC nodes; eta is a unit energy consumption price factor; set current round q =1.
Step 2, adjusting and updating the computing resource requirements of the MEC nodes;
step 3, updating a dual variable lambda;
step 4, adjusting the price configuration of the parked vehicles to maximize the utility of the parked vehicles;
and 5, repeating the steps 2 to 4 until one of the following termination conditions is reached: 1) The maximum cycle number is reached; 2) The absolute value of the difference between the total utility of the vehicle parked in the previous round and the current round is less than a given threshold.
2. The method of claim 1, wherein in step 1, the MEC node i wins the probability of successful accounting right
Figure FSA0000280646160000021
Write as:
Figure FSA0000280646160000022
wherein
Figure FSA0000280646160000023
Representing the MEC node i initial computational resources.
3. The method for allocating parking vehicle resources based on mobile block chain as claimed in claim 1, wherein in step 1, the MEC node i selects the probability ω of parking vehicle j ij The calculation method comprises the following steps:
Figure FSA0000280646160000024
wherein α and β are weighting factors, α + β =1;
Figure FSA0000280646160000025
represents the maximum computational resource owned by the parked vehicle j; b ij Represents the price incentive factor of the parked vehicle j to the MEC node i, and is represented as:
b ij ＝p max -p ij 。
4. the method for allocating parking vehicle resources based on mobile block chain as claimed in claim 1, wherein in step 2, the method for updating the computing resource demand of the MEC node i is that
Figure FSA0000280646160000026
The time t represents that the MEC node i does not update the computing resource requirement, and the time t +1 represents that the MEC node finishes updating the computing resource requirement; wherein ε = t +1 when i > k and ε = t when i < k; ρ is a damping factor;
Figure FSA0000280646160000027
is the dual variable of MEC node i.
5. The method for allocating parking vehicle resources based on the mobile block chain as claimed in claim 1, wherein in step 3, the MEC node i dual variable updating method is as follows:
Figure FSA0000280646160000028
6. the method for allocating parking vehicle resources based on mobile block chain as claimed in claim 1, wherein in step 4, the updating method of the price allocation of the parking vehicle j comprises:
Figure FSA0000280646160000029
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