AU2021106296A4 - Vehicle Computing Task Unloading Method Based on Blockchain Data Sharing - Google Patents
Vehicle Computing Task Unloading Method Based on Blockchain Data Sharing Download PDFInfo
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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
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
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.
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.
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
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.
Fig. 1 is a flowchart of a method for unloading vehicle computing tasks.
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;
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
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.
The higher the value, the higher the evaluation, and the specific evaluation mechanism
will be in the subsequent steps, and
price=init_price Xqua1ity,#(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) 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:
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 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;
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 ( ),#(5)
S is the size of the newly generated block, and n is the number of edge computing
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
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 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.
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
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;
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, 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
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
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
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
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
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;
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
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;
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
C,(p) = pi 1 - I A-p) + (1 - pj)p,#(14) k;Of
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
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.
By synthesizing the results of equations (13) and (14), the user vehicle i can get
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
optimal response method to calculate the optimal strategy pi that needs to be taken when
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;
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 servicelD 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: 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
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.
Claims (2)
1. A vehicle computing task unloading method based on blockchain data sharing
is characterized by comprising the following steps:
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:
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;
A: After receiving the service capability information sent by the vehicle j, the edge
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;
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:
record =(timestamp I recordD 1 provider I resource#(3) \1 trajectory I price I period U quality )#I
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,
pricezinit_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 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;
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: N\ h=Smod G2) #(5)
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. MEC) 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 1 4 vi = E t(timestamp I Data, l I CertgE, I Signye,(Datagec #(6)
DataGMEC, = EK (tmestamp | record, 1\ record2 || - 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
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;
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,max 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- 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
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
/ Pjdif h 2 R=WIoga 1+= == ,2(+ #(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
tI,E = ±t±j 4D +k1L r,#( tIO)+ A+ ,tg +Pd IhiI fE 1 1 +PidI hi 2 Wil921+ No WTlg O
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, 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.
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 = 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
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, f 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.. (t i+5ti'.) - (ti+5ti')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) r;(tt) +(1- p)q ri~max rimax
r,mx r((1-8) t ima) 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 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)
Pk represents the possible unloading strategies of other surrounding vehicles that
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.
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) 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
optimal response method to calculate the optimal strategy pi that needs to be taken when
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
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= (timestampl serviceIDl 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:
=(-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
evaluation of the user vehicle i after receiving the service vehicle j can be expressed as
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
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