AU2021106296A4

AU2021106296A4 - Vehicle Computing Task Unloading Method Based on Blockchain Data Sharing

Info

Publication number: AU2021106296A4
Application number: AU2021106296A
Authority: AU
Inventors: Xuting Duan; Xu Han; Ping LANG; Chunmian Lin; Daxin Tian; Yunpeng Wang; Yuanhao Zhao; Jianshan Zhou
Original assignee: Beihang University
Current assignee: Beihang University
Priority date: 2021-08-21
Filing date: 2021-08-21
Publication date: 2021-11-04
Anticipated expiration: 2029-08-21

Abstract

A vehicle computing task unloading method based on blockchain data sharing aims at data sharing and safe transmission of computing task unloading between vehicles, and designs a sharing transmission mechanism of computing unloading service vehicle information in edge computing nodes by using blockchain technology, which can ensure that trusted service vehicle information can be released in a large range and promote effective matching between user vehicles and service vehicles. To solve the problem of limited roadside resources for computing task unloading between vehicles and roadside edge nodes, the computing unloading behavior of each vehicle is regarded as competition for edge server resources. Game theory framework is used to describe the competitive behavior of each vehicle in the scene. The computing unloading strategy of each vehicle is determined according to the resource occupation of edge nodes and the situation of peripheral service vehicles, so as to achieve a balanced state among the computing unloading strategies of each vehicle, which can improve the operation efficiency of edge computing system on the premise of effective utilization of resources. -43 FIGURES Start., icepoviingvehicles upload their owntsrice cpility ationtoroadideed computigncdes Roadside edp node: collect vehicle capabili ty inf aticn d u:se blochain technology to publi:h and shAre i ts upl1ink Pe-ce cxAabiity information in the blo- itwuedby vehides [the overall are of roadside edt ncdes The vehicle Inlye the peipheralser'ice vehicle information sent by the edr- ncde and de termines i ts own .pplicatonunloadingrequirement a ., Yesi! Figur e1 r - N Vehicle I acome out the servio vehide that is lo-es to its ownm drivingbtack rhe user vehtide I detecnim the d-ata bansmission rate with the recdsi.1de ed4 nodes und the error-free vehicle f ae on te Vehicle I estimates the time Fequired fortfs unloadinto edp acde and vehide I to exe-ute respectivel V ehicle I ggmleg r;adom numte b "ff IM and calculates the probabili ty of remote acdes during which sks are unloaded by ustn the optimal reP2tion6tsate of thepgme Pevehidle construction tAsk i's YO wde t te rodside edg node for hevehidle I ermcutes the tAk at the service vehicle 1, and the sevice vehicle I wants to upload the sevice information to the edge node POfT untoding the service, the vehicle I upomds the service :valua tion of the service v;ehidle I to the ed* noxde Figure I

Description

FIGURES

Start.,

icepoviingvehicles upload their owntsrice cpility ationtoroadideed computigncdes

Roadside edp node: collect vehicle capabili ty inf aticn d u:se blochain technology to publi:h andshAre i ts upl1ink

Pe-ce cxAabiity information in the blo- itwuedby vehides

[the overall are of roadside edt ncdes

The vehicle Inlye the peipheralser'ice vehicle information sent by the edr- ncde and determines i ts own .pplicatonunloadingrequirement a .,

Yesi!

Figur e1 r - N

Vehicle I acome out the servio vehide that is lo-es to its ownm drivingbtack

rhe user vehtide I detecnim the d-ata bansmission rate with the recdsi.1de ed4 nodes und the error-free vehicle f ae on te

Vehicle I estimates the time Fequired fortfs unloadinto edp acde and vehide I to exe-ute

respectivel

Vehicle I ggmleg r;adom numte b "ff IM and calculates the probabili ty of remote acdes during which sks are unloaded by ustn the optimal reP2tion6tsate of thepgme

Pevehidle construction tAsk i's YO wdet te rodside edg node for

hevehidle I ermcutes the tAk at the service vehicle 1, and the sevice vehicle I wants to upload the sevice information to the edge node

POfT untoding the service, the vehicle I upomds the service :valua tion of the service v;ehidle I to the ed* noxde

Figure I

Vehicle Computing Task Unloading Method Based on Blockchain Data Sharing

TECHNICAL FIELD

The invention relates to a vehicle computing task unloading technology in the

cross field of intelligent transportation system, edge computing technology and

blockchain technology, in particular to a vehicle computing task unloading method

based on blockchain information sharing in edge computing network.

BACKGROUND

With the continuous development of intelligent transportation system and

automatic driving technology, more and more on-board applications have emerged,

which cover driving safety, traffic efficiency and information entertainment. A large

number of computation-intensive and delay-sensitive applications make the

requirements of computing and storage capacity of vehicles constantly improve.

However, each vehicle is limited by physical space and economic cost, and the local

resources it provides cannot fully meet the needs of these applications.

To solve this problem, as a key technology in 5G networks, Multi-accessedge

computing (MEC) technology has been widely concerned by researchers. Different

from the traditional mobile cloud, MEC, a new architecture, migrates the cloud

computing resources located in the center of the network to the edge of the network to

reduce the end-to-end transmission delay of data and relieve the calculation and storage

pressure of vehicles or roadside intelligent facilities. Under this framework, intelligent

vehicles on the road can unload their difficult computing tasks to the peripheral network

edge nodes, and use the rich computing and storage resources of the edge nodes to complete these computing tasks within the specified time, thus ensuring the safe and stable operation of various vehicle-mounted applications.

In the above-mentioned edge computing network, the vehicles on the road can

choose to offload their own computing tasks to two types of nodes. The first type is the

roadside edge computing nodes generally known by people, which provide computing

services for a certain number of vehicles within their communication coverage by

deploying abundant computing and storage resources at the base station or the roadside

unit (RSU). The second category is the dynamic vehicles with certain computing power

in the road network. In order to achieve advanced functions such as automatic driving,

these vehicles often deploy more computing and storage resources than ordinary

intelligent vehicles, and can use their redundant resources to provide computing

services for surrounding vehicles.

However, the premise of realizing the unloading of vehicle-shop computing tasks

is that intelligent vehicles can accurately obtain the information of peripheral trusted

service vehicles. If only the traditional service node broadcasting method is used to

provide the information of the service node, the information cannot be sent to the distant

vehicles, which may cause the loss of potential users of the service vehicles. Moreover,

the broadcast method can't guarantee the security of computing task unloading, and the

user vehicle can't accurately identify the trusted service vehicle, which makes it difficult

to guard against possible malicious node attacks.

In recent years, the emerging blockchain technology provides a new idea for

decentralized storage and secure sharing of all kinds of data. Blockchain is a kind of accounting technology, which is jointly maintained by many parties, adopts cryptography technology to ensure the security of transmission and access, and can realize consistent data storage, be difficult to tamper with and prevent repudiation, also known as distributed ledger technology. Blockchain is essentially a multi-party synchronous database. By abstracting different types of information into transactions and storing them in a block-chain structure, it can establish trust relationship at low cost in an untrustworthy competitive environment, realize real-time data sharing, ensure traceability of information, and effectively prevent malicious nodes. In the application of vehicle computing and unloading, blockchain can be used to realize the sharing of service vehicle information among different edge nodes, broadcast the service vehicle information in a wide range, and ensure the effective matching between user vehicles and service vehicles. At the same time, blockchain can also establish the trust relationship between user vehicles and service vehicles, and prevent attacks from malicious service nodes.

At the same time, the resources of roadside edge servers are not unlimited. The

continuous increase of the number of vehicles or computing tasks will lead to the load

of edge servers exceeding its maximum limit, which makes it impossible to guarantee

the service quality for each vehicle, and the vehicles will not benefit from computing

unloading. Each vehicle needs to comprehensively consider the execution time

requirement of its application, the resource occupation of the edge server and the

calculation of the price of the unloading service, so as to comprehensively determine to

unload the task to the roadside edge server or the surrounding service vehicles for execution, thus ensuring the accurate and stable execution of its on-board application and improving the intelligent level of the vehicle.

Therefore, how to make effective use of the existing blockchain and edge

computing technology principles to establish a service information sharing mechanism

for serving vehicles in edge computing networks, and to solve the problem of vehicle

selection decision in computing unloading, is an urgent problem to be solved in edge

computing networks of intelligent transportation systems.

SUMMARY

In the scene of multi-vehicle computing task unloading, the control of vehicle

unloading strategy can generally be centralized and distributed. Through the overall

planning of the server, the centralized control scheme allocates appropriate edge

computing resources to each vehicle according to its application requirements, so as to

realize the optimal utilization of resources. In the distributed control scheme, each

vehicle depends on the interaction between vehicles to judge the occupancy of

resources in the current environment, and decides whether to calculate and unload on

the premise of maximizing its own utility. Because the centralized control scheme needs

to occupy more extra resources, it is more suitable to adopt the distributed computing

offload control method in large-scale vehicle networking applications. However, the

vehicle computing unloading methods widely used in current research are simply based

on delay or reliability indicators to make optimization decisions, without considering

the information sharing and transmission security protection in the unloading of

computing tasks between vehicles and workshops, and without considering the limited roadside computing resources in the unloading of computing tasks between vehicles and roadside edge nodes, so it is difficult to make specific application and deployment.

The invention designs a vehicle computing task unloading method based on

blockchain data sharing. Aiming at the problems of data sharing and safe transmission

of computing task unloading between vehicles, a sharing transmission mechanism of

computing unloading service vehicle information in edge computing nodes is designed

by using blockchain technology, which can ensure that trusted service vehicle

information can be released in a large range, promote the effective matching between

user vehicles and service vehicles, and improve the efficiency of vehicle computing

unloading in intelligent transportation systems. To solve the problem of limited

roadside resources for computing task unloading between vehicles and roadside edge

nodes, the invention regards the computing unloading behavior of each vehicle as

competition for edge server resources, describes the competition behavior of each

vehicle in the scene by using game theory framework, determines the computing

unloading strategy of each vehicle according to the resource occupation situation of

edge nodes and peripheral service vehicles, realizes the balanced state among the

computing unloading strategies of each vehicle, and can improve the operation

efficiency of the edge computing system on the premise of effective utilization of

resources.

The invention discloses a method for building vehicle computing task unloading

based on blockchain information sharing, which is characterized in that in a vehicle

edge computing network, each edge computing node shares the information of computing service vehicles through blockchain and broadcasts the information to the vehicles in its coverage area respectively, and each vehicle needing computing task unloading makes a comprehensive decision based on game according to the received service vehicle information and its surrounding roadside edge node information, and finally unloads the task to roadside edge nodes or peripheral service vehicles for execution, thus ensuring the safe and stable operation of vehicle-mounted intelligent applications.

Step1: A service providing vehicle uploads self service capability information to

a roadside edge computing node;

A: Within the road range covered by the edge node MECm network, an intelligent

vehicles with redundant computing resources, which intends to provide computing task

unloading service, determines the computing capacity resources, the time period, the

planned driving trajectory and the init _ pricej for its service;

B. the vehicle i establishes a communication link with the edge node MECm, and

sends its computing capacity resources capable of providing services, its time period

capable of providing services, its planned driving track trajectory j in this period of time,

and its initial pricing initprice j for its services to the edge node MECm in an encrypted

manner, with the vehicle's own pseudonym certificate and signature of the message,

which is specifically shown as follows:

v - M ECm = EKPA timestamp H DataH,,,g I Cert,,,ISigne,,(Damp,,(1)

In which MEC represents the public key cipher of MECm, timestamp represents

the time stamp of message generation, Psealf represents the s-th pseudonym of vehicle j, Cer,, represents the certificate corresponding to the s-th pseudonym of vehicle j, ign,.f(X) represents the signature of message X by vehicle j with the s th pseudonym identity, and E K (X) represents the encryption of message X with key

K,

Data,. = Ep 1 initprice; I periodj)#,(2) (timestamp 0 resource; I trajectory;

P""Irepresents the private key password corresponding to the s-th pseudonym

of vehicle j.

Step 2: Roadside edge nodes collect vehicle capability information, and use

blockchain technology to publish and share its uplink;

A: After receiving the service capability information sent by the vehicle j, the edge

node MECm decrypts the message in formula (1) with its own private key cipher Mem

Certp s obtains the certificate C corresponding to the s-th pseudonym of the vehicle j KP" senujPseu through the decrypted message, analyzes the public key cipher of

contained in the certificate, and verifies the signature SignI ata,.u) of KP" the message. Further deciphering formula (2) by using '"" edge nodes to obtain

service capability information provided by vehicle j;

B: The edge node MECm identifies various service capability information provided

by the vehicle j, and stores these information as service capability records in the form

of blockchain transaction records. The specific format is as follows:

I resource# record =(timestamp H recordlD 1 provider \1r trajectoryI price I period H quality )(

The recordID is the number of this service capability record, the provider is the

provider of computing unloading service, that is, the service providing vehicle, the resource, trailer and period are the computing resources that the service providing vehicle can provide, the running track of the vehicle and the service duration, respectively, and the quality is the evaluation of the service quality of the service providing vehicle by the user vehicle, which is within [0,1], with an initial value of 1.

The higher the value, the higher the evaluation, and the specific evaluation mechanism

will be in the subsequent steps,

price init_price X quality,#(4)

In which initprice is the initial service pricing provided by the service providing

vehicle. Equation (4) indicates that the edge node uses the intelligent contract

mechanism of blockchain to dynamically price its service according to the service

quality of the service providing vehicle;

C: Edge computing nodes (i.e., blockchain nodes in the process of information

sharing) share the service capability records within a certain period of time by adopting

the blockchain consensus mechanism combining Proof-of-Service and practical

Byzantine fault tolerance (PBFT), and ensure the data security and prevent attacks by

malicious nodes. The specific implementation steps are as follows:

I. Service capability record Broadcast: edge computing nodes (e.g., MECm) record

the service capability analyzed in step B in the blockchain network (i.e., the network

composed of all edge computing nodes) for broadcasting, and each blockchain node

collects and stores the service capability records sent by other nodes;

II. Master node Selection: after a certain time t, select a master node in the

blockchain system to organize the recently generated service capability records into a block, and link the block to the current blockchain after passing the consensus mechanism between nodes, and share it as the latest block in the system. Here, the master node is selected based on the Proof-of-Service mechanism, that is, considering the current computing capacity of each blockchain node, the node with rich redundant computing resources is selected as the master node to generate the block. The specific steps are as follows:

1) Each edge node broadcasts its own redundant computing resources in the

blockchain network, such as the current idle CPU core number and corresponding

frequency;

2) Each edge node sorts the redundant computing resources of all edge nodes, and

the nodes located in the first half randomly select the h node as the master node based

on the size of the generated block, and the determination method of h is as follows:

h=Smod(D,)#(S)

S is the size of the newly generated block, and n is the number of edge computing

nodes in the network;

III. Block allocation (pre-prepare): each node confirms whether it is a master node

according to the calculation method in II, and if it is a selected master node, broadcasts

its generated block and its own verification result as a pre-prepare message to all edge

nodes in the blockchain network;

IV. Prepare: after receiving the pre-prepare message sent by the master node, the

edge nodes except the master node verify the authenticity of the master node and the validity of the content in the block, and broadcast the verification result as the prepare message in the blockchain network;

V. Block commit: after receiving the prepare message sent by other nodes, each

edge node makes a comprehensive judgment based on its own verification results, and

votes on whether the block is successfully generated. If the total number of results

verified as valid blocks exceeds 2f(f is the maximum number of malicious nodes

tolerated by the blockchain system), the edge node broadcasts a commit message to all

other nodes in the blockchain network to indicate its voting results;

VI. Reply: After receiving the commit message sent by other nodes, each edge

node judges according to its own voting results. If the number of votes in favor of the

generated block is above 2f+1 (including the current node's own vote), it is considered

that the blockchain system has reached a consensus on the generation of the block and

sent the consensus result to the master node.

VII. Store: the master node receives the consensus result of each edge node,

completes the generation of the final block, and sends the block to all edge nodes in the

blockchain system for data sharing and storage.

Step 3: The roadside edge node issue the service capability information in the

block to the vehicles in the coverage area;

A: After the roadside edge node (e.g. MEC1 ) completes the blockchain information

sharing, it parses out the latest service capability information record in the block, and

sends the parsed service capability information record to the vehicles (e.g. vi) within its communication coverage area through encrypted transmission, which is specifically shown as follows:

MEC, -+ v = E (timestamp I Datamc I Certrc,I SinpEc 1(DatamEc,)) #(6)

DatamEc = E,,J (timestamp I record, 1 record? 1 - H record,),#(7)

Record 1 .. .record z are the service capability information record in the block.

B: After receiving the service capability information record sent by the edge node

MEC 1, the vehicle vi decrypts the message in formula (6) by using its own private key

"Pseu-, SignEc(Data,~oe cipher K verifies the signatureSign aaE) of MEC1 to the message, and KPU further decrypts formula (7) by using the public key cipher MCI of MECi obtained

in the message to obtain the latest peripheral service providing vehicle information.

Combined with the service chain information introduced in the following step 4, judge

whether there is a service vehicle that can unload the vehicle workshop calculation task,

so as to make the subsequent calculation unloading decision;

Step 4: The user vehicle makes a calculation and unloading decision based on the

service capability information of edge nodes and peripheral service vehicles;

A: The user vehicle i that needs to perform calculation unloading determines the

input data size Li, the calculation complexity ai and the tolerable maximum execution

time ti,mx of the task to be calculated. If there is no service vehicle that can provide

computing unloading service around the user's vehicle i, the vehicle i directly unloads

the computing task to the edge node. If there is more than one service vehicle around

the user vehicle i, the vehicle i screens out the service vehicle j that is closest to its own

driving track, and executes the subsequent calculation and unloading decision process;

B: The user vehicle i establishes a communication model based on the distance

between the user vehicle i and the roadside edge node and the service vehicle j, and

determines the data transmission rate of the corresponding communication link.

Specifically, the data transmission rate between the user vehicle and the edge node is

Pid-O|hi|2 RL. = WIg 1082 1 + N(8)

W i,e represent the bandwidth of communication link between vehicle i and

roadside edge nodes, P i represents the signal transmission power of vehicle i, di,e are

the communication distance between vehicle i and roadside edge nodes, 0 is the channel

path fading index, hi is the channel path fading coefficient, and N 0 represents the

Gaussian white noise of communication environment. Similarly, the data transmission

rate between the user vehicle and the peripheral service providing vehicle is

/ Pjd a h2 Rj= Wfj o0g(1 + #(9)

Wi, j represent the bandwidth of communication link between vehicle i and service

providing vehicle j, and di, j represent the communication distance between vehicle i

and service providing vehicle j;

C: Based on the computing power of roadside edge nodes, the application model

of the task and the data transmission rate between the task and the edge nodes, the user

vehicle i estimates that the total time required for unloading the task to the edge nodes

for execution is

tPd h = +i|2+ fPidL,#|2 NoE j E W( logz 1 + PiNh);hi2) W log2 9 21 +

T i, u represent the estimated time required for data uplink transmission during

calculation unloading, t i, d represent the estimated time required for calculation result

downlink backhaul during calculation unloading, ri, eare the task execution time of

roadside edge nodes, P i, u are the overhead factors of data uplink transmission, P i,d are

the ratio of calculation output data size to input data size and the comprehensive factors

of downlink overhead, and f E is the calculation frequency of roadside edge node CPU.

At the same time, the user vehicle i estimates that the total time required for the

task to be unloaded to the service providing vehicle j for execution is

tj =j='Tq + TIJ+ ti' ,reqj !iLi +( PidifI hJl2 h}Pd||| Wi lo0z1 + NJ Wi o 2 ( 1 Noi+

Ti, req represent estimated unloading request time, ti, res represent estimated

calculation result return time, i,j represent specific execution time of task in service

vehicle j, Pi, req are transmission overhead factors of unloading request data, Pi, res are

comprehensive factors of calculation output result data size to input data size and return

overhead of service vehicle, fj is CPU calculation frequency of service providing

vehicle j;

D: Based on the total task execution time estimated and calculated in step c, the

user vehicle i respectively judges the value corresponding to the delay caused by

unloading the calculated task to the edge node and the service vehicle by using the game

theory, and in order to consider the reciprocity of competition among vehicles, the value

is expressed in the form of quadratic function as follows

ri (ti) =;-2ti.n= (t i+6ti..) - (t i+6ti.) 2,# (12)

Among them, ti E {ti, e, ti, j} is the total unloading time of the specific computing

task, and 6 is the value adjustment factor, which can dynamically adjust the delay under

which the task obtains the maximum value;

E: Based on the game theory framework, the user vehicle i judges the benefits and

expenses it can get under the mixed strategy, and then obtains the payment function of

its game. Specifically, the revenue that the user vehicle i can get under its mixed

strategy pi is as follows

U,(p) rg(tE) +( - pi) qjr(tj)(13) rija ryma

mx r((1- 6) t .) is the maximum value that can be obtained by

calculation unloading, pi is the probability that the vehicle i unloads the calculation task

to the roadside edge nodes for execution, and accordingly, (1-pi) is the probability that

the vehicle i unloads the calculation task to the peripheral service vehicles for

execution, and qjis the service quality of the service vehicle j, which will be evaluated

by the user nodes after each unloading task is completed. In addition, the cost of user

vehicle i under its mixed strategy pi is

C (p) = pJ2I-JjJ(1- AkPk) + (1 - p~pp#(14)

Pk represents the possible unloading strategies of other surrounding vehicles that

need to be unloaded, and k is the estimated average generation rate of each vehicle's

P=pricej calculation task, and i "" is the ratio of the price of the user's vehicle unloading

the task to the vehicle j to the roadside edge node. The left part of the plus sign in

formula (14) can be regarded as the cost caused by the competition of user nodes for computing resources of roadside edge nodes, and the right part is the cost required to unload tasks to surrounding vehicles.

By synthesizing the results of equations (13) and (14), the user vehicle i can get

its payment function as follows

u (p) = U (p) - C(p)

=Pir tE+(1-O T(tn)( Mq -AkPk)]- (1-_PJ 1 .#(15)

F: Based on the payment function of formula (15), the user vehicle i uses the

optimal response method to calculate the optimal strategy pi that needs to be taken when

getting the maximum payment, which is

pi = arg maxu (p)= - A,;- k + ,)2[1 #(16) P 2[1H-l~(1 -0p)]

Among them, indicates that the value of pi is limited to [0 ,], so that the

user vehicle i can get the computing task unloading strategy pi which can reach Nash

equilibrium in most cases. Based on pi, the user vehicle can finally decide whether to

unload the task to the roadside edge node or the surrounding service vehicle for

execution;

Step 5: The service providing vehicle uploads the ongoing service information,

and the edge node updates the service chain;

If the user vehicle that needs to unload the computing task has selected a service

providing vehicle to unload the computing task, the service-providing vehicle will

upload the generated current service information to the peripheral edge nodes at the

same time of starting to execute the specific computing task. Similar to the operations

from Step 1 to Step 3, the edge nodes package a plurality of service information into blocks and link them to the service chain parallel to the service provider chain mentioned above through the consensus mechanism. The service information of the current service providing vehicle is shared through the service chain to assist the user in decision-making. Since the specific uplink step of the information is the same as that of the service providing vehicle information, this step only gives the specific format of the service record Service on the chain as follows: service= (timestamp serviceID provider requester| duration),#(17)

ServicelD is the number of the service record, provider represents the specific

provider of the service, requester represents the specific requester of the service, and

duration is the estimated duration of the service.

Step 6: The user vehicle evaluates the service quality of the service vehicle, and

the edge nodes dynamically price based on the evaluation;

A: After receiving the calculation result returned by the service vehicle j, the user

vehicle i evaluates the service quality of the service vehicle j by using the subjective

logic framework. specifically, the user vehicle evaluates the service quality by using

the following three trust variables:

bi-i = (1 - i- .,"

di-j= (i - Ut-A ai-j+ fi

uj =-s g,# (20)

Bi_.j represents the trust degree of user vehicle i to service vehicle j, and dij

represents user vehicle i to service vehicle

The suspicion degree of vehicle j, ui.j represents the uncertainty degree of

evaluation, ai_.j is the historical number of successful unloading events between two

vehicles,

pi_.j is the historical number of unloading failure events, including errors and

timeout events, and si-j is the reliability of the link, that is, the data packet becomes

Work arrival rate. Based on these three subjective logic variables, the final

evaluation of the user vehicle i after receiving the service vehicle j can be expressed as

qi j=bi +kuis,0# (2 1)

4 is the influence weight of uncertainty on evaluation;

B: The user vehicle sends the evaluation result to the roadside edge node to

complete the whole calculation unloading process of the calculation task. The edge

node updates the service capability record of the corresponding service vehicle, and

links the information when the next block is generated, so that the intelligent contract

is used to dynamically price the computing and unloading service of the service vehicle

on the chain, and the service capability information is continuously provided to the user

node.

Compared with the existing computing task unloading technology, the invention

has the following advantages and positive effects:

1. The service vehicle information sharing method based on blockchain provided

by the invention provides an effective scheme for large-scale broadcast sharing of

unloading service vehicle information, promotes service matching between user

vehicles and service vehicles in edge computing networks, and reduces the computing pressure of roadside edge nodes. In addition, the method utilizes the endogenous security mechanism of blockchain to ensure the consistency and non-tamperability of shared data. At the same time, a data security transmission mechanism between vehicles and roadside edge nodes is established by cryptography, which improves the security level of vehicle computing task unloading.

2. Based on the game theory framework, the invention designs a comprehensive

decision-making scheme for vehicle computing task unloading in the vehicle

networking edge computing network, which can achieve Nash equilibrium of vehicle

computing unloading strategy under the environment that multiple vehicles compete

for computing unloading of roadside edge computing nodes, reduce resource pressure

of roadside edge nodes, realize load balance between roadside edge nodes and

computing unloading service vehicles, improve the overall operation efficiency of the

vehicle networking edge computing network, meet the demand of intelligent vehicles

for computing and storage services, ensure safe and stable operation of intelligent

vehicles in-vehicle English, and promote automatic driving.

BRIEF DESCRIPTION OF THE FIGURES

Fig. 1 is a flowchart of a method for unloading vehicle computing tasks.

DESCRIPTION OF THE INVENTION

The real-time mode of the present invention will be further explained with

reference to the accompanying drawings.

As shown in fig. 1, the vehicle computing task unloading method based on

blockchain data sharing of the present invention is realized by the following steps:

Step 1: A service providing vehicle uploads self service capability information to

a roadside edge computing node;

A: Within the road range covered by edge node MECm network, intelligent

vehicle j with redundant computing resources that intends to provide computing task

unloading service determines its computing capacity resources, time period, planned

driving track trajectory and initial pricing initpricej for its service;

B: The vehicle i establishes a communication link with the edge node MECm, and

sends its computing power resources, time period, planned driving track trajectory and

initial pricing initpricej to the edge node MECm through encryption, with the

pseudonym certificate of the vehicle itself and the signature of the message, which is

specifically shown as follows:

Vi - MECM = EFu timestamp I Oata,, |1 Cert,w, \1 Signpe; (Datap))#. (1)

Km'""-em represents the public key cipher of MECm, timestamp represents the time

stamp of message generation, PseI represents the s-th pseudonym of vehicle j, Cert,I represents the certificate corresponding to the s-th pseudonym of vehicle j, Signpme(X) represents the signature of message X by vehicle j with the s-th

pseudonym identity, EK(X) represents the encryption of message X with key K,

Data, = E< (times source | trajectory || initriceg I periodi), (2)

I' represents the private key password corresponding to the s-th pseudonym

of vehicle j.

Step 2: Roadside edge nodes collect vehicle capability information, and use

blockchain technology to publish and share its uplink;

node MECm decrypts the message in formula (1) with its own private key cipher

K rm, obtains the certificate Certpet! corresponding to the s-th pseudonym of the KPU vehicle j through the decrypted message, analyzes the public key cipher "' of

PSeu contained in the certificate, and verifies the signature of PSeu~s KP,UA to the message. At K, the edge node further decrypts the formula (2) to

obtain the service capability information provided by the vehicle j;

B: The edge node MECm identifies various service capability information

provided by the vehicle j, and stores these information as service capability records in

the form of blockchain transaction records, with the specific format as follows:

recordd timestamp trajectory || price ||| provider || recordlD || resource\#(3) period || quality ,3

The recordlD is the number of this service capability record, the provider is the

provider of computing unloading service, that is, the service providing vehicle, the

resource, trailer and period are the computing resources that the service providing

vehicle can provide, the running track of the vehicle and the service duration,

respectively, and the quality is the evaluation of the service quality of the service

providing vehicle by the user vehicle, which is within [0,1], with an initial value of 1.

will be in the subsequent steps, and

price=init_price Xqua1ity,#(4)

vehicle. Equation (4) indicates that the edge node uses the intelligent contract mechanism of blockchain to dynamically price its service according to the service quality of the service providing vehicle;

C: Edge computing nodes (i.e., blockchain nodes in the process of information

sharing) use the blockchain consensus mechanism combining service proof and

practical Byzantine fault tolerance (PBFT) to share the service capability records within

a certain period of time, and ensure the data security and prevent attacks by malicious

nodes. The specific implementation steps are as follows:

collects and stores the service capability records sent by other nodes;

II. Master node Selection: after a certain time t, select a master node in the

blockchain system to organize the recently generated service capability records into a

block, and link the blocktothecurrentblockchain after passing the consensus

mechanism between nodes, and share it as the latest block in the system. Here, the

master node is selected based on the Proof-of-Service mechanism, that is, considering

the current computing capacity of each blockchain node, the node with rich redundant

computing resources is selected as the master node to generate the block. The specific

steps are as follows:

1) Each edge node broadcasts its own redundant computing resources in the

blockchain network, such as the current idle CPU core number and corresponding

frequency;

h=Smod ( ),#(5)

nodes in the network;

III. Block prepare: each node confirms whether it is the master node according to

the calculation method in II, and if it is the selected master node, broadcasts the

generated block and its own verification result as a prepare message to all edge nodes

in the blockchain network;

IV. Prepare: after receiving the prepare message sent by the master node, the edge

nodes except the master node verify the authenticity of the master node and the validity

of the content in the block, and broadcast the verification result as the prepare message

in the blockchain network;

V. Block commit: after receiving the prepare message sent by other nodes, each

verified as valid blocks exceeds 2f(f is the maximum number of malicious nodes

other nodes in the blockchain network to indicate its voting results;

VI. Reply: After receiving the commit message sent by other nodes, each edge

node judges according to its own voting results. If the number of votes in favor of the generated block is above 2f+1 (including the current node's own vote), it is considered that the blockchain system has reached a consensus on the generation of the block and sent the consensus result to the master node.

VII. Store: the master node receives the consensus result of each edge node,

blockchain system for data sharing and storage.

Step 3: The roadside edge node issue the service capability information in the

block to the vehicles in the coverage area;

A: After completing blockchain information sharing, roadside edge nodes (e.g.,

MEC) parse out the latest service capability information records in the block, and send

the parsed service capability information records to vehicles (e.g., vi) within their

communication coverage by encrypted transmission, which is specifically shown as

follows:

A1ECJ-V =E.,~Pu (t imest amp 11D tam*~cl 11CertH,11USignMEC 1(D ataMEC))#(6)

DataMEc, = EKP (timestamp || record1 1 record2 || - |||record,),#(7)

Recordi...recordz are the service capability information record in the block.

KPrel SigIMEr,(DItaMEC!) cipher ", verifies the signature of MECi to the message, and

decrypts formula (7) by using the public key cipher uscl of MEC 1 obtained in the

message to obtain the latest peripheral service providing vehicle information.

Combined with the service chain information introduced in the following step 4, it is judged whether there is a service vehicle that can unload the vehicle workshop computing task, so as to make subsequent computing unloading decisions;

service capability information of edge nodes and peripheral service vehicles;

input data size Li, calculation complexity ai and tolerable maximum execution time ti

and max of the task to be calculated. If there is no service vehicle that can provide

B: The user vehicle i establishes a communication model based on the distance

determines the data transmission rate of the corresponding communication link.

Ri, =Wig 10g2 1Id+ No )#(8)

Wi,E represents the bandwidth of communication link between vehicle i and

roadside edge nodes, Pi represents the signal transmission power of vehicle i, di,E is the

communication distance between vehicle i and roadside edge nodes, 0 is the channel

path fading index, hi is the channel path fading coefficient, and NO represents the

Gaussian white noise of communication environment. Similarly, the data transmission rate between the user vehicle and the peripheral service providing vehicle is

Rj = W log2 1 + N ,,, #(9)

Wij represents the bandwidth of communication link between vehicle i and service

providing vehicle j, and di,j is the communication distance between vehicle i and

service providing vehicle j;

C: Based on the computing power of roadside edge nodes, the application model

for execution is

t6= t0 + TIX+ =_____&0___Li___+ !iLi + DI#(10)

W tog2 1 + No d ) h 2 Wi 1092 (1+ No.rIhi1

In which ti,u represent the estimated time required for data uplink transmission

during calculation unloading, ti,D represent the estimated time required for calculation

result downlink backhaul during calculation unloading, Ti,E are task execution time of

roadside edge nodes, pi,u are overhead factors of data uplink transmission, pi,D are

comprehensive factors of the ratio of calculation output result data size to input data

size and downlink overhead, and fE is calculation frequency of roadside edge node

CPU.

At the same time, the user vehicle i estimates that the total time required for

unloading the task to the service providing vehicle j for execution is based on the

computing power of the service providing vehicle j, the application model of the task

and the data transmission rate between the task and the vehicle j.

/ ,Pd hg|; fJ 'P d ~fhg Wt log I + NrW g 1 N1

+ In which ti,eq represent estimated unloading request time, ti,es represent estimated

calculation result return time, Tij represent specific execution time of task in service

vehicle j, pi,,eg are transmission overhead factors of unloading request data, and pi,res are

services

The vehicle calculates the ratio of the output result data size to the input data size

and the comprehensive factor of the return cost, and fj is the CPU calculation frequency

of the service providing vehicle j;

is expressed in the form of quadratic function as follows

r, (t j) = 2t j. (t i +Rt[&L) - (ti +6t , 14 #(12)

ti E {ti, E, ti, j} is the total unloading time of the specific computing task, and

6 is the value adjustment factor, which can dynamically adjust the delay under which

the task obtains the maximum value;

strategy pi is as follows

C,(p) = pi 1 - I A-p) + (1 - pj)p,#(14) k;Of

need to be unloaded, and )k is the estimated average generation rate of each vehicle's

calculation task, which is the ratio of the price of the user's vehicle unloading the task

to the vehicle j to the roadside edge node. The left part of the plus sign in formula (14)

can be regarded as the cost caused by the competition of user nodes for computing

resources of roadside edge nodes, and the right part is the cost required to unload tasks

to surrounding vehicles.

its payment function as follows

Ui(p) = U (p) - C(p) ri(tur) + pi q;jr(ty) - p2 1 - (1 - Apk) - (1 - pJp,.#(15)

F: Based on the payment function of formula (15), the user vehicle i uses the

getting the maximum payment, which is

pi = arg max u (p)= - r(r)/r +P, #(16) ,, 2[1- [kJ(1- Ak OI 1

The value of pi is limited to [0,1], so that the user vehicle i can get the computing

task unloading strategy pi which can reach Nash equilibrium in most cases. Based on

pi, the user vehicle can finally decide whether to unload the task to the roadside edge

node or the surrounding service vehicle for execution;

Step 5, the service providing vehicle uploads the ongoing service information, and

the edge node updates the service chain;

from Step 1 to Step 3, the edge nodes package a plurality of service information into

blocks and link them to the service chain parallel to the service provider chain

mentioned above through the consensus mechanism. The service information of the

current service providing vehicle is shared through the service chain to assist the user

in decision-making. Since the specific uplink step of the information is the same as that

of the service providing vehicle information, this step only gives the specific format of

the service record Service on the chain as follows:

service= (timestamp servicelD provider requester duration),#(17)

ServicelD is the number of the service record, provider represents the specific

duration is the estimated duration of the service.

the edge nodes dynamically price based on the evaluation;

vehicle i evaluates the service quality of the service vehicle j by using the subjective logic framework. Specifically, the user vehicle evaluates the service quality by using the following three trust variables: b = (1 - up1 ) ,'#(18)

11 i -. j I - S i1+t4 (20

In which bij indicates the trust degree of user vehicle i to service vehicle j, dij

indicates the suspicion degree of user vehicle i to service vehicle j, ui-j indicates the

uncertainty degree of evaluation, ai_.j is the historical number of successful unloading

events between two vehicles,

pi-.j is the historical number of unloading failure events, including errors and

timeout events, and si.j is the reliability of the link, that is, the successful arrival rate

of data packets. Based on these three subjective logic variables, the final evaluation of

the user vehicle i after receiving the service vehicle j can be expressed as

C' . b -+kLugj#(21)

Among them, 4 is the influence weight of uncertainty on evaluation;

B: The user vehicle sends the evaluation result to the roadside edge node to

node.

Claims

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:

1. A vehicle computing task unloading method based on blockchain data sharing

is characterized by comprising the following steps:

a roadside edge computing node;

A: Within the road range covered by the edge node MECm network, an intelligent

planned driving trajectory and the init _ pricej for its service;

B: The vehicle i establishes a communication link with the edge node MECm, and

which is specifically shown as follows:

vj - MECm= = EKZu (timestamp N Data,,,, I Cert,, 11i Sign,,s(Data,,g)#,(1) K MM

In which urc represents the public key cipher of MECm, timestamp represents

the time stamp of message generation, """l represents the s-th pseudonym of

vehicle j, I represents the certificate corresponding to the s-th pseudonym of

vehicle j, Sign,.I(X) represents the signature of message X by vehicle j with the s- th pseudonym identity, and E K (X) represents the encryption of message X with key

K,

Dataps = EPr (timestamp I resources trajectory; I initprice;i period;)#,(2) seu peid,# KPT ""Pfrepresents the private key password corresponding to the s-th pseudonym

of vehicle j.

Step 2: Roadside edge nodes collect vehicle capability information, and use

blockchain technology to publish and share its uplink;

KPT node MECm decrypts the message in formula (1) with its own private key cipher MrCm

Certp s obtains the certificate C corresponding to the s-th pseudonym of the vehicle j K~P through the decrypted message, analyzes the public key cipher K, of sel Pseui Signp"',(Datap,,u contained in the certificate, and verifies Pe the signature SigOf

the message; Further deciphering formula (2) by using '" edge nodes to obtain

service capability information provided by vehicle j;

of blockchain transaction records; The specific format is as follows:

record =(timestamp I recordD 1 provider I resource#(3) \1 trajectory I price I period U quality )#I

vehicle can provide, the running track of the vehicle and the service duration, respectively, and the quality is the evaluation of the service quality of the service providing vehicle by the user vehicle, which is within [0,1], with an initial value of 1;

will be in the subsequent steps,

pricezinit_price X quality,#(4)

vehicle; Equation (4) indicates that the edge node uses the intelligent contract

quality of the service providing vehicle;

C: Edge computing nodes (i.e., blockchain nodes in the process of information

the blockchain consensus mechanism combining Proof-of-Service and practical

malicious nodes; The specific implementation steps are as follows:

collects and stores the service capability records sent by other nodes;

II. Master node Selection: after a certain time t, select a master node in the

block, and link the blocktothecurrentblockchain after passing the consensus

mechanism between nodes, and share it as the latest block in the system; Here, the master node is selected based on the Proof-of-Service mechanism, that is, considering the current computing capacity of each blockchain node, the node with rich redundant computing resources is selected as the master node to generate the block; The specific steps are as follows:

1) Each edge node broadcasts its own redundant computing resources in the

blockchain network, such as the current idle CPU core number and corresponding

frequency;

on the size of the generated block, and the determination method of h is as follows: N\ h=Smod G2) #(5)

nodes in the network;

nodes in the blockchain network;

edge nodes except the master node verify the authenticity of the master node and the

validity of the content in the block, and broadcast the verification result as the prepare

message in the blockchain network;

V. Block commit: after receiving the prepare message sent by other nodes, each

votes on whether the block is successfully generated; If the total number of results

verified as valid blocks exceeds 2f(f is the maximum number of malicious nodes

other nodes in the blockchain network to indicate its voting results;

VI. Reply: After receiving the commit message sent by other nodes, each edge

node judges according to its own voting results; If the number of votes in favor of the

sent the consensus result to the master node.

VII. Store: the master node receives the consensus result of each edge node,

blockchain system for data sharing and storage.

Step 3: The roadside edge node issue the service capability information in the

block to the vehicles in the coverage area;

A: After the roadside edge node (e.g. MEC) completes the blockchain information

sends the parsed service capability information record to the vehicles (e.g. vi) within its

communication coverage area through encrypted transmission, which is specifically

shown as follows:

MEC 1 4 vi = E t(timestamp I Data, l I CertgE, I Signye,(Datagec #(6)

DataGMEC, = EK (tmestamp | record, 1\ record2 || - record,),#(7)

O~r cipher verifies the signature iOMEQaMEC "Pv, of MEC 1 to the message, and K'" further decrypts formula (7) by using the public key cipher I"I. of MECi obtained

in the message to obtain the latest peripheral service providing vehicle information;

so as to make the subsequent calculation unloading decision;

service capability information of edge nodes and peripheral service vehicles;

time ti,max of the task to be calculated; If there is no service vehicle that can provide

the computing task to the edge node; If there is more than one service vehicle around

B: The user vehicle i establishes a communication model based on the distance

between the user vehicle i and the roadside edge node and the service vehicle j, and determines the data transmission rate of the corresponding communication link;

Pid- ht|2 R, := WLEbog2 1 +PZJ- (1 #(8)

W i,e represent the bandwidth of communication link between vehicle i and

roadside edge nodes, P i represents the signal transmission power of vehicle i, d i,e are

Gaussian white noise of communication environment; Similarly, the data transmission

rate between the user vehicle and the peripheral service providing vehicle is

/ Pjdif h 2 R=WIoga 1+= == ,2(+ #(9)

Wi,j represent the bandwidth of communication link between vehicle i and service

and service providing vehicle j;

C: Based on the computing power of roadside edge nodes, the application model

for execution is

tI,E = ±t±j 4D +k1L r,#( tIO)+ A+ ,tg +Pd IhiI fE 1 1 +PidI hi 2 Wil921+ No WTlg O

calculation unloading, t i,d represent the estimated time required for calculation result

downlink backhaul during calculation unloading, Ti, eare the task execution time of roadside edge nodes, P i, u are the overhead factors of data uplink transmission, P i,dare the ratio of calculation output data size to input data size and the comprehensive factors of downlink overhead, and f E is the calculation frequency of roadside edge node CPU.

task to be unloaded to the service providing vehicle j for execution is

tj = tiq+ Tj+ ti~re +?ijeq + iesLi (1 P hl ;P Ihi1 PId,d" Wi 102 1+ N WhO92 1+ Nj

Ti,req represent estimated unloading request time, ti, res represent estimated

calculation result return time, Ti,j represent specific execution time of task in service

overhead of service vehicle, f is CPU calculation frequency of service providing

vehicle j;

is expressed in the form of quadratic function as follows

ri (ti) =2ti.. (t i+5ti'.) - (ti+5ti')2,# (12)

which the task obtains the maximum value;

its game; Specifically, the revenue that the user vehicle i can get under its mixed

strategy pi is as follows

U (p) r;(tt) +(1- p)q ri~max rimax

r,mx r((1-8) t ima) is the maximum value that can be obtained by

execution, and gi is the service quality of the service vehicle j, which will be evaluated

by the user nodes after each unloading task is completed; In addition, the cost of user

vehicle i under its mixed strategy pi is

C(p)= p 1-f7(1-AkPk) +(1- pa)pp#(14)

need to be unloaded, and Ak is the estimated average generation rate of each vehicle's

= price) calculation task, and Pr"e is the ratio of the price of the user's vehicle unloading

the task to the vehicle j to the roadside edge node; The left part of the plus sign in

formula (14) can be regarded as the cost caused by the competition of user nodes for

computing resources of roadside edge nodes, and the right part is the cost required to

unload tasks to surrounding vehicles.

its payment function as follows

u;(p) = U (p) - C(p) rrr2kk Pi rL(t!E+ +(1-a PI) qjrF(ti'V) Pt n( - J-~k (I -PJPp-#01S)

F: Based on the payment function of formula (15), the user vehicle i uses the

getting the maximum payment, which is

p = arg maxu(p)= p= m ) ri (t/r., - qiri tt,)/rmaX+ p - Ap)] 1 ,#(16)

Among them, indicates that the value of pi is limited to [0 ,], so that the

equilibrium in most cases; Based on pi, the user vehicle can finally decide whether to

execution;

Step 5: The service providing vehicle uploads the ongoing service information,

and the edge node updates the service chain;

same time of starting to execute the specific computing task; Similar to the operations

mentioned above through the consensus mechanism; The service information of the current service providing vehicle is shared through the service chain to assist the user in decision-making; Since the specific uplink step of the information is the same as that of the service providing vehicle information, this step only gives the specific format of the service record Service on the chain as follows: service= (timestampl serviceIDl provider requester| duration),#(17)

ServicelD is the number of the service record, provider represents the specific

duration is the estimated duration of the service.

the edge nodes dynamically price based on the evaluation;

logic framework; specifically, the user vehicle evaluates the service quality by using

the following three trust variables:

=(-i- (1 -9) i~ l

u 2=- ,# (20)

Bij represents the trust degree of user vehicle i to service vehicle j, and di.j

represents user vehicle i to service vehicle

The suspicion degree of vehicle j, ui.j represents the uncertainty degree of

evaluation, ai-j is the historical number of successful unloading events between two

vehicles, pij is the historical number of unloading failure events, including errors and timeout events, and sij is the reliability of the link, that is, the data packet becomes

Work arrival rate; Based on these three subjective logic variables, the final

q ij= b -+ku-, # (2 1)

4 is the influence weight of uncertainty on evaluation;

B: The user vehicle sends the evaluation result to the roadside edge node to

complete the whole calculation unloading process of the calculation task;The edge node

updates the service capability record of the corresponding service vehicle, and links the

information when the next block is generated, so that the intelligent contract is used to

dynamically price the computing and unloading service of the service vehicle on the

chain, and the service capability information is continuously provided to the user node.

FIGURES -43-

Figure 1