CN112053043A - Block chain-based crowd sensing method and system - Google Patents

Block chain-based crowd sensing method and system Download PDF

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CN112053043A
CN112053043A CN202010847740.4A CN202010847740A CN112053043A CN 112053043 A CN112053043 A CN 112053043A CN 202010847740 A CN202010847740 A CN 202010847740A CN 112053043 A CN112053043 A CN 112053043A
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node
data
verifier
task
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CN112053043B (en
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邹仕洪
奚锦文
徐国爱
徐国胜
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Beijing University of Posts and Telecommunications
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Abstract

The embodiment of the specification provides a crowd sensing method and a crowd sensing system based on a block chain, wherein when a task requester issues a task to the block chain, a worker is selected, and an intelligent contract is selected by an executing verifier to select the verifier; the worker completes the task, when the sensing data is submitted to the task requester through the block chain, the worker hires an intelligent contract, and the verifier verifies the hiring relation between the worker and the task requester to complete the first consensus; after the task requester evaluates the data quality of the sensing data, submitting an evaluation result and the sensing data to the block chain, executing a data verification intelligent contract, verifying the sensing data by a verifier according to the sensing data submitted by the task requester and the sensing data submitted by workers during the first consensus, determining whether to execute a payment operation according to the evaluation result, and finishing the second consensus; if the payment operation can be executed, the worker is paid a corresponding reward. The method and system of the present specification can reduce the workload of the participating nodes.

Description

Block chain-based crowd sensing method and system
Technical Field
One or more embodiments of the present disclosure relate to the field of computer technologies, and in particular, to a method and system for crowd sensing based on a block chain.
Background
With the development of mobile communication, internet of things technology and the development of functional diversity of mobile equipment, the crowd sensing system is applied to the fields of monitoring environment, public infrastructure, social network and the like as a novel effective data collection technology.
In order to realize efficient acquisition Of sensing data in different scenes and ensure system security, the existing crowd sensing system generally adopts a workload proving mechanism (Proof Of Work) in the stage Of reaching consensus，POW), and a very complex encryption algorithm (e.g., a homomorphic encryption algorithm, secure multi-party computation, etc.) is adopted to protect privacy, which greatly increases the workload of the participating nodes and has high requirements on configuration of the participating nodes.
Disclosure of Invention
In view of this, one or more embodiments of the present disclosure provide a block chain-based crowd sensing method and system, which can reduce the workload of participating nodes and reduce the configuration requirements on the participating nodes.
In view of the above, one or more embodiments of the present specification provide a block chain-based crowd sensing method, including:
when a task requester issues a task to the block chain, selecting a worker, and selecting an intelligent contract to select a verifier by executing the verifier;
when the worker completes a task and submits perception data to the task requester through the block chain, executing a worker employment intelligent contract, and verifying the employment relationship between the worker and the task requester by the verifier to complete first consensus;
after the task requester evaluates the data quality of the sensing data submitted by the worker, submitting an evaluation result and the received sensing data submitted by the worker to the block chain, executing a data verification intelligent contract, verifying the sensing data by the verifier according to the sensing data submitted by the task requester and the sensing data submitted by the worker during the first consensus, determining whether to execute a payment operation according to the evaluation result, and completing the second consensus;
and if the worker determines that the payment operation can be executed, paying corresponding rewards to the worker.
Optionally, the selecting a verifier by executing the verifier to select the intelligent contract includes:
calculating the global reputation values of all nodes participating in the transaction;
and selecting the verifier according to the global reputation value of each node.
Optionally, the calculating a global reputation value of all nodes participating in the transaction includes: for each node, the following steps are performed:
calculating a local reputation value of the current node;
standardizing the local credit value to obtain a standardized local credit value;
according to the transaction rating between the current node and the neighbor node, performing aggregation processing on the normalized local credit value to obtain an aggregated local credit value; the neighbor node is a node which has transacted with the current node;
and calculating the global reputation value based on the aggregated local reputation value according to the transaction rating between the neighbor node and the neighbor node.
Optionally, the method for calculating the global reputation value includes:
Figure BDA0002643649120000021
wherein，
Figure BDA0002643649120000022
The global reputation vector for node i calculated for the (f + 1) th iteration,
Figure BDA0002643649120000023
the global reputation vector for node 1 calculated for the f-th iteration,
Figure BDA0002643649120000024
a global reputation vector for node n, i 1,2, …, n, j 1,2, …, n, γ being a constant from 0 to 1,
Figure BDA0002643649120000025
Figure BDA0002643649120000026
representing the satisfaction degree of the node j to the node i for the normalized local reputation value; p is a set of pre-trusted nodes,
Figure BDA0002643649120000027
and (4) local reputation vectors of the nodes i in the pre-trust node set.
Optionally, the method for calculating the normalized local reputation value includes:
Figure BDA0002643649120000028
wherein the content of the first and second substances,
Figure BDA0002643649120000029
pjis the local credit value of the node j in the pre-trust node set, | P | is the number of the pre-trust node set, Replocal(i, j) is a local reputation value, which represents the satisfaction degree of the node i to the node j, and the calculation formula is as follows:
Replocal(i,j)＝α*Sat(i,j)+(-β)*Unsat(i,j) (1)
wherein Sat (i, j) represents the number of satisfactory transactions achieved by the node i and the node j, Unsat (i, j) represents the number of dissatisfied transactions achieved by the node i and the node j, and α and β represent the weight occupied by the satisfactory transactions and the dissatisfied transactions, respectively.
Optionally, the task requester performs data quality evaluation on the sensing data, including:
calculating a true value of the perception data;
and calculating the distance between the perception data and the true value, and evaluating the data quality according to the distance.
Optionally, the method for calculating the true value of the perception data is;
Figure BDA0002643649120000031
where D is the perception data set submitted by all workers, and D is divided into intervals { D }s∈D|s＝1,2,…,m}，daThe interval at which the value is true, d is the set of sensory data currently submitted by the worker, and m is the number of verifiers.
Optionally, the method for calculating the distance between the perception data and the true value is:
Figure BDA0002643649120000032
Figure BDA0002643649120000033
is a worker wqThe value of the global reputation of (a),
Figure BDA0002643649120000034
is a worker wqThe perception data that is submitted is presented in a manner,
Figure BDA0002643649120000035
is the accuracy between the perception data and the true value.
Optionally, the sensing data submitted by the worker to the task requester through the blockchain is sensing data with a digital signature;
the executing the worker engaging in the intelligent contract, verifying, by the verifier, the engagement between the worker and the task requester to complete a first consensus, comprising:
generating a correspondence between the worker and the task requester;
taking the corresponding relation, the sensing data, the digital signature and the timestamp for submitting the sensing data as the work certification of a worker, storing the work certification in a newly generated work certification block, and adding the work block to a block chain;
the verifier inquires the working proof and judges whether the corresponding relationship exists, if so, the hiring relationship passes verification, otherwise, the verification fails;
if the employment relationship verification passes, verifying the validity of the perception data through the digital signature, and verifying the timeliness of the perception data through the time stamp.
An embodiment of the present specification further provides a crowd sensing system based on a block chain, including:
the task requester is used for issuing tasks to the block chain, receiving the sensing data submitted by workers, performing data quality evaluation on the sensing data to obtain an evaluation result, and sending the evaluation result and the received sensing data submitted by the workers to the block chain;
the block chain is used for selecting the worker when the task requester issues the task, and selecting the verifier by executing the verifier to select the intelligent contract; when the perception data submitted by the worker is received, executing a worker employment intelligent contract, and verifying the employment relationship between the worker and the task requester by the verifier to complete first consensus; the system comprises a task requester, a verifier and a controller, wherein the task requester is used for receiving an evaluation result and perception data submitted by the task requester, executing a data verification intelligent contract, verifying the perception data by the verifier according to the perception data submitted by the task requester and the perception data submitted by workers in the first consensus, determining whether to execute a payment operation according to the evaluation result, and completing the second consensus; and paying corresponding rewards to the workers if the second consensus determines to execute the payment operation.
As can be seen from the foregoing, in the block chain-based crowd sensing method and system provided in one or more embodiments of the present disclosure, when a task requester issues a task to a block chain, a worker is selected, an authenticator is selected by selecting an intelligent contract through the execution authenticator, when the worker completes the task and submits sensed data to the task requester through the block chain, the worker hires the intelligent contract, the authenticator verifies the hiring relationship between the worker and the task requester, a first consensus is completed, after the task requester evaluates the sensed data submitted by the worker, the evaluation result and the received sensed data submitted by the worker are submitted to the block chain, the data verification intelligent contract is executed, the authenticator verifies the sensed data according to the sensed data submitted by the task requester and the sensed data submitted by the worker during the first consensus, and determining whether to execute the payment operation according to the evaluation result, finishing the second consensus, and paying corresponding rewards to workers if determining that the payment operation can be executed. The method and the system can reduce the workload of the participating nodes, reduce the configuration requirements on the participating nodes, improve the system performance and ensure the transaction fairness.
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In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a schematic flow chart of a method according to one or more embodiments of the present disclosure;
FIG. 2 is a schematic diagram of a system topology according to one or more embodiments of the present disclosure;
FIG. 3 is a signal flow diagram of one or more embodiments of the present disclosure;
FIG. 4 is a flow diagram of a global reputation calculation method according to one or more embodiments of the present disclosure;
FIG. 5 is a schematic flow diagram of a data quality assessment method according to one or more embodiments of the present disclosure;
fig. 6 is a schematic structural diagram of an electronic device according to one or more embodiments of the present disclosure.
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in one or more embodiments of the specification is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
The participating nodes of the blockchain-based crowd-sourcing awareness system typically include a Task Requester (Task request), a blockchain, a Worker (Worker), and a verifier (mineworker). In order to collect relevant information of a specific area, a task requester formulates a task comprising task information and a corresponding reward, the task is issued to a block chain, and an intelligent contract is triggered after the task is issued. A blockchain recruiter, wherein the worker is a participant for completing a task issued by a task requester on a blockchain, and can select a task from a public blockchain or a private blockchain according to the personal privacy consideration degree; the method comprises the steps that a worker executes a task, perception data are collected, after the task is completed, the perception data are uploaded to a task requester through a block chain, after the task requester is confirmed, the block chain executes an intelligent contract, the worker obtains corresponding rewards, and due to the anonymity of the block chain, the worker is anonymous and does not reveal the true identity; the verifier, who is a participant in verifying the transaction between the worker and the task requester in the above-described process, needs to have a certain capability. Data collection and analysis tasks under the specific scene of the Internet of things can be completed through data interaction among the task requester, the block chain, the worker and the verifier.
The functions of the task requester, the worker and the verifier are realized based on the terminal equipment, the functions of the task requester, the worker and the verifier can be switched according to requirements, the user can serve as the task requester to issue a task, the user can serve as the worker to perform data collection, and the user can compete with the verifier.
With reference to fig. 1 to 3, a block chain-based crowd sensing method provided by one or more embodiments of the present specification includes:
s101: when a task requester issues a task to the block chain, selecting a worker, and selecting an intelligent contract to select a verifier by executing the verifier;
in this embodiment, when the task requester issues the task, the block chain selects a proper worker and a proper verifier, wherein the verifier selects the intelligent contract and selects the proper verifier by triggering the verifier, and the verifier selects the intelligent contract, so that the verifier verifies the transaction process.
The existing crowd sensing system selects the verifier based on a consensus mechanism of workload certification, has complex algorithm, higher requirement on the computing capacity of terminal equipment and higher resource allocation requirement, and increases the workload of the terminal equipment. In this embodiment, in order to reduce the configuration requirement on the terminal device and reduce the load of the terminal device, a consensus mechanism pogr (proof of Global reporting) based on Global reputation is provided, and a suitable verifier is selected according to a calculated Global reputation value by calculating a Global reputation value of each node in the system, so that the configuration requirement on the terminal device can be significantly reduced, and thus, the crowd sensing system can accommodate more light-weighted terminal devices, the resource utilization rate is improved, the system performance is improved, the task completion speed can be increased, and the data sensing efficiency is improved.
S102: when a worker completes a task and submits perception data to a task requester through a block chain, executing a worker employment intelligent contract, and verifying the employment relationship between the worker and the task requester by a verifier to complete first consensus;
in the embodiment, a worker executes a task, collects perception data according to the task requirement, and submits the perception data with digital signatures to a block chain after the task is completed and the perception data is digitally signed by using a private key of the worker; when the data is submitted, triggering a worker to employ an intelligent contract, wherein the worker employs the intelligent contract and is used for signing a contract comprising employment relation between a worker and a task requester according to perception data with digital signatures submitted by the worker, generating corresponding relation between the worker and the task requester, storing the corresponding relation and transaction information such as the perception data, the digital signatures, timestamps for submitting the perception data and the like as work evidences of the worker in a newly generated work evidencing block, and adding the work evidencing block to a block chain; the verifier inquires the work proof in the work proof block, verifies whether a employment relationship exists between the worker and the task requester by inquiring whether the corresponding relationship exists between the worker and the task requester, if so, the task requester really hires the worker, otherwise, the task requester does not hire the worker; then, verifying the validity of the submitted sensing data by verifying the digital signature (decrypting the digital signature by using a public key, if the decryption is successful, confirming that the sensing data is sent by a worker, and the sensing data is valid), and verifying whether the submitted sensing data exceeds the task time limit or not by using a timestamp, thereby verifying the timeliness of the sensing data; so far, the first consensus is completed.
S103: after the task requester evaluates the data quality of the sensing data submitted by the worker, submitting an evaluation result and the received sensing data submitted by the worker to the block chain, executing a data verification intelligent contract, verifying the sensing data by a verifier according to the sensing data submitted by the task requester and the sensing data submitted by the worker during the first consensus, determining whether to execute a payment operation according to the evaluation result, and finishing the second consensus;
in the embodiment, the worker submits the perception data to the task requester through the block chain, and the task requester performs data quality evaluation on the perception data after receiving the perception data submitted by the worker, so as to obtain an evaluation result.
The task requester submits an evaluation result and received sensing data submitted by workers to the block chain, and triggers a data verification intelligent contract, wherein the data verification intelligent contract is used for extracting a work certificate in a work certificate block, sending the work certificate, the sensing data submitted by the task requester and the evaluation result to a verifier, the verifier compares the sensing data in the work certificate with the sensing data submitted by the task requester, and verifies the authenticity and validity of the sensing data, and after the verification is passed, whether payment operation is executed or not is determined according to the evaluation result, so that secondary consensus is completed.
S104: if the second consensus process determines that a payment operation can be performed, the worker is paid a corresponding reward.
In this embodiment, when the verifier determines that the payment operation can be executed, the reward submitted by the task requester when the task is issued is distributed to the worker, after the payment is completed, the verifier saves the payment record in the newly generated payment transaction record block, and adds the payment transaction record block to the block chain, so that other participating nodes can conveniently perform query and trace, and whether fraudulent behaviors such as payment refusal exist can be conveniently identified.
The crowd sensing method based on the block chain comprises the steps that when a task requester issues a task to the block chain, a proper worker is selected, an intelligent contract is selected by an executing verifier, the intelligent contract is selected by the executing verifier, when the worker finishes the task and submits perception data to the task requester through the block chain, the worker hires the intelligent contract, the verifying relation between the worker and the task requester is verified by the verifier, first consensus is completed, after the task requester evaluates the data quality of the perception data submitted by the worker, an evaluation result and the perception data submitted by the worker and received by the task requester are submitted to the block chain, the intelligent contract is executed, the verifier verifies the real validity of the perception data according to the perception data in the first consensus and the perception data submitted by the task requester, and after the verification is passed, whether payment operation is executed or not is determined according to the evaluation result, and completing the second consensus, and paying corresponding rewards to workers if the payment operation can be executed.
According to the crowd sensing method, the novel verifier selection method is utilized, so that the system overhead can be reduced, and a lightweight system is realized; by adopting the data verification and the consensus of the first stage and the second stage, the problem of unfair payment can be solved; three intelligent contracts, namely a Verifier Selection intelligent Contract (VSC), a Worker Employment intelligent Contract (WEC) and a Data Verification intelligent Contract (DVC), are used for ensuring the normal and automatic operation of the system and limiting the behaviors of participants from multi-benefit subjects; based on the block chain technology, trust between users in a non-complete trusted network can be guaranteed, user identity privacy is protected, and data can not be tampered.
In some embodiments, the verifier selects the intelligent contract based on the global reputation consensus mechanism PoGR, and selects an appropriate verifier from the global reputation values of all nodes participating in the transaction in the computing system according to the global reputation values of all nodes. Namely, the global reputation value of each node in the crowd sensing process is calculated according to the historical transaction records of the nodes in the block chain, and the interaction history of all the nodes can be reflected. In PoGR, the verifier selects all transactions in the intelligent contract monitoring block chain, after each task is completed and all transactions are confirmed, the global credit values of all nodes are started, the global credit values of the nodes can be updated in real time along with the transaction process, and all nodes participate in the calculation of the global credit values in a distributed and safe mode without generating large overhead.
As shown in fig. 4, in this embodiment, the method for the verifier to select the intelligent contract to calculate the global reputation values of all nodes participating in the transaction includes: for each node, the following steps are performed:
s401: calculating a local reputation value of the current node;
in the crowd sensing system, the task requester can evaluate the sensing data submitted by the worker, and the worker can also feed back the evaluation result and the payment amount given by the task requester. For example, a task requester may be rated as satisfied or unsatisfactory with respect to received sensory data, and may be rated as unsatisfactory if the submission time requirements or other task requirements are not met. Due to the openness and transparency of the block chain, all transactions can be retrieved, and therefore, the satisfaction degree of the node i to the node j in the transaction time can be known for the node i and the node j with transaction relations. Based on this, for the current node (node i), the satisfaction degree of the node i to the node j is calculated, and the local reputation value Rep is usedlocal(i, j) is expressed by the following calculation formula:
Replocal(i,j)＝α*Sat(i,j)+(-β)*Unsat(i,j) (1)
where Sat (i, j) represents the number of satisfactory transactions achieved by node i and node j, unset (i, j) represents the number of unsatisfactory transactions achieved by node i and node j, and α and β represent the weights occupied by satisfactory transactions and unsatisfactory transactions, respectively, and in some ways, α ═ β ═ 1 may be set for simplifying the operation.
S402: standardizing the local credit value to obtain a standardized local credit value;
after the local reputation value is calculated, the local reputation value is subjected to standardization processing so as to prevent malicious collusion among malicious nodes and cause overhigh reputation of some malicious nodes. Normalized local reputation value
Figure BDA0002643649120000091
Comprises the following steps:
Figure BDA0002643649120000092
therefore, the value of the local credit value after standardization is between 0 and 1, and the problem that the credit of some malicious nodes is too high can be avoided.
S403: according to the transaction rating between the current node and the neighbor node, performing aggregation processing on the local credit value after the standardization processing to obtain a local credit value after the aggregation processing;
in this embodiment, in order to aggregate a reputation value in a wider range, obtain a transaction rating assigned to the current node by a neighboring node that has transacted with the current node, and a transaction rating assigned to the current node by a neighboring node of the neighboring node, and implement aggregation processing of a local reputation value of the current node, a calculation formula is:
Figure BDA0002643649120000093
the node i and the node j have a transaction relationship, the node j is a neighbor node of the node i, the node j and the node k have a transaction relationship, and the node k is a neighbor node of the node j;
Figure BDA0002643649120000094
representing the degree of satisfaction that node i estimates node k indirectly from transactions with neighbor node j, i.e., the aggregated local reputation value
Figure BDA0002643649120000095
Indicating how satisfied node i is with node k.
For a more intuitive representation, formula (3) can be represented in a matrix form, with R being defined as the matrix
Figure BDA0002643649120000096
And will be
Figure BDA0002643649120000097
Is defined as comprising
Figure BDA0002643649120000098
The vector of (a), namely:
Figure BDA0002643649120000099
wherein the content of the first and second substances,
Figure BDA00026436491200000910
is composed of
Figure BDA00026436491200000911
In the form of a matrix.
Vector quantity
Figure BDA00026436491200000912
Is in the form of sigmajai,jAnd isjai,j1 is ideal. Aggregating the local reputation value of the current node for transactions considering neighboring nodes helps each participating node to obtain a wider view of the blockchain network than for direct transactions considering only the current node.
S404: and calculating the global credit value of the current node based on the aggregated local credit value according to the transaction rating between the neighbor node and the neighbor node.
In this embodiment, the local reputation value after aggregation processing can reflect the transaction experience of the current node and its neighboring nodes, and does not cover the global blockchain network, so that the transactions generated between the neighboring nodes and their neighboring nodes are further considered, and iteration is performed in this manner to obtain:
Figure BDA0002643649120000101
where N is the number of iterations, and when N → large, i.e., after N iterations, node i will have a complete view of the blockchain network. Assuming that R is irreducible and aperiodic, when N is large enough, the global reputation vector will converge to the same vector for all nodes, i.e., to the left of RA feature vector. In other words,
Figure BDA0002643649120000102
is the global reputation vector of the system, the global reputation vector of node i
Figure BDA0002643649120000103
Can be used to quantify the trust level of the whole system for node i.
Referring to the normalized local reputation value shown in equation (2), for the newly added node, due to its Σjmax(ReplocalTo solve this problem, in some embodiments, considering that a node added first in the blockchain network may be considered trusted, a set of pre-trusted nodes P is introduced, defined as:
Figure BDA0002643649120000104
wherein if the node i is a node in the pre-trust node set, piAnd is the local reputation value of the node i, and | P | is the number of the pre-trust node set.
Based on equation (6), the local reputation value after the normalization process is retrieved as:
Figure BDA0002643649120000105
thus, if the node j is a newly added node, the node j is considered as a node in the pre-trust node set, and the local credit value of the node j after the standardization processing is carried out
Figure BDA0002643649120000106
And obtaining the local reputation value of the newly added node.
In view of the presence of malicious nodes, in some embodiments, a local reputation vector for a pre-trusted node in a set of pre-trusted nodes is defined
Figure BDA0002643649120000107
Local reputation vector for node i in a set of pre-trusted nodes
Figure BDA0002643649120000108
Redefining the global reputation vector as:
Figure BDA0002643649120000111
and (3) calculating the global reputation vector of the system according to the formula (8), wherein the convergence is faster, and the system can be effectively prevented from being damaged by malicious nodes.
In some embodiments, to prevent malicious collusion attacks, the following approach is taken:
global credit vector of f +1 th iteration calculation
Figure BDA0002643649120000112
Comprises the following steps:
Figure BDA0002643649120000113
where γ is a constant from 0 to 1.
The local reputation vector is updated as:
Figure BDA0002643649120000114
the calculation formula of the global reputation value of the node i in the pre-trust node set is as follows:
Figure BDA0002643649120000115
wherein the content of the first and second substances,
Figure BDA0002643649120000116
to normalize the satisfaction of the processed node j with the node i. In a blockchain network, each node is considered to be connected to other nodesThe transaction range of points is limited, therefore, a large fraction of σjiThe calculation can be simplified by 0.
Figure BDA0002643649120000117
The global reputation vector for node 1 calculated for the f-th iteration,
Figure BDA0002643649120000118
a global reputation vector for node n is computed for the f-th iteration.
In order to ensure the fairness of the transaction, in the embodiment, a novel Data Quality assessment Mechanism (QEM) is proposed based on a true phase discovery theory and an expectation maximization algorithm, and by adopting the Data Quality assessment Mechanism, the Quality of perception Data submitted by a worker can be effectively assessed, and a reward payment rating is calculated according to an assessment result.
As shown in fig. 5, the data quality evaluation method of the data quality evaluation mechanism includes:
s501: calculating a true value of perception data submitted by a worker;
in this embodiment, the truth value of the perception data submitted by the worker is calculated by using an Expectation-Maximization algorithm (EM algorithm) in consideration of the calculation amount and the data security.
Assuming that the number of verifiers is m, the perception data set D submitted by all workers is divided into a plurality of intervals { D }sE.g. D | s ═ 1,2, …, m }, where the true value belongs to DsIs defined as { psisE Ψ | s ═ 1,2, …, m }, as a latent variable for the EM algorithm, and is initialized as follows:
Figure BDA0002643649120000119
wherein, wsIs a worker, and W is a worker set.
When the truth value is consistent with the interval of submitting perception data by the worker, the truth value is equal to 1, and otherwise, the truth value is equal to 0. In order to estimate the reliability of the worker, with θ as a parameter of the model, the goal being to obtain a corresponding likelihood estimate of the model containing the perceptual data D and the latent variable Ψ, the likelihood function L (θ; D; Ψ) may be set as:
L(θ；D,Ψ)＝p(D,Ψ|θ)＝∑Ψp(D,Ψ|θ) (13)
is provided with
Figure BDA0002643649120000121
Indicates worker wqThe submitted perception data is in interval dbIn the interval d, the true valueaThe degree of matching between the sensing data submitted by the node and the true value, a is 1,2, …, m, b is 1,2, …, m; calculating the expected log-likelihood function is:
Figure BDA0002643649120000122
wherein the content of the first and second substances,
Figure BDA0002643649120000123
indicating the perception data submitted at interval dbIn the interval d, the true valueaWorker w in (1)qThe reliability of the operation of the system is improved,
Figure BDA0002643649120000124
is a worker wqThe submitted perception data is in interval daThe calculation formula is as follows:
Figure BDA0002643649120000125
to determine the parameters that maximize the likelihood function, it can be used as an estimate of θ after t iterations, i.e.:
θt+1＝argmax[L(θt；D,Ψ)](16) therefore, the true value of the perception data is calculated by weighted averaging as follows:
Figure BDA0002643649120000126
where D is the perception data set submitted by the current worker, and D is a subset of the perception data sets D submitted by all workers.
S502: and calculating the distance between the perception data submitted by the worker and the true value, and performing data quality evaluation according to the distance.
After the truth value is determined, in order to evaluate the data quality and distribute the task rewards, the accuracy between the sensing data and the truth value is calculated based on the K-means clustering algorithm and the preset clustering quantity, and the formula is as follows:
Figure BDA0002643649120000127
in order to encourage workers to submit high-quality perception data and maintain good performance, the global reputation value and the data accuracy of the node are combined to provide higher return for workers with higher reputation and performance, so that the Euclidean distance EDis is calculated by the following calculation formula:
Figure BDA0002643649120000131
Figure BDA0002643649120000132
indicates worker wqThe value of the global reputation of (a),
Figure BDA0002643649120000133
is a worker wqAnd submitting the perception data.
And taking the calculated Euclidean distance as an evaluation result, further dividing perception data submitted by the worker into different grades, and paying rewards to the worker according to the grade result. For example, the euclidean distance is in the first range, the perceptual data is qualified, the euclidean distance is in the second range, the perceptual data is general, and the euclidean distance is in the third range, the perceptual data is unqualified.
In the embodiment, by fusing the truth value discovery and the expectation maximization algorithm, the data quality of perception data submitted by workers can be effectively evaluated, the problem of payment refusal or unfair payment is avoided, the transaction fairness is ensured, malicious users can be effectively avoided, and the enthusiasm of user participation is improved.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
As shown in fig. 2, an embodiment of the present specification provides a block chain-based crowd sensing system, including:
the task requester is used for issuing tasks to the block chain, receiving the perception data submitted by workers, performing data quality evaluation on the perception data to obtain an evaluation result, and sending the evaluation result and the received perception data submitted by the workers to the block chain;
the block chain is used for selecting workers when the task requester issues the task, and selecting the verifier by executing the verifier to select the intelligent contract; when receiving the perception data submitted by the worker, executing the intelligent contract for the worker to hire, and verifying the hiring relationship between the worker and the task requester by a verifier to finish first consensus; the system comprises a task requester, a verifier and a payment server, wherein the task requester is used for receiving an evaluation result and sensing data submitted by the task requester, executing a data verification intelligent contract, verifying the sensing data by the verifier according to the sensing data submitted by the task requester and the sensing data submitted by workers in the first consensus, determining whether to execute payment operation according to the evaluation result, and completing the second consensus; and paying corresponding rewards to the workers if the second consensus determines to execute the payment operation.
The system of the foregoing embodiment is used to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
The data processing process of the crowd sensing system based on the blockchain is described in detail below with reference to specific embodiments, as shown in fig. 2 and 3.
All task requesters and workers who want to participate in the crowd sensing system register on the block chain first, the block chain distributes a pair of public key and private key for each participating node, the key of the participating node is associated with identity information and stored in a user pool, and other non-privacy registration information of the participating node is stored in a ledger of a public block chain as a transaction record. In the transaction process of the blockchain, the identity of the node is represented by a public key, and real identity information does not need to be reflected.
The task requester generates task information and corresponding rewards according to requirements, and broadcasts the tasks in the block chain to obtain perception data. The task requester carries out digital signature on the task information and the reward, attaches a public key of the task information and the reward, generates hash abstract data representing the task, and broadcasts the task.
And the block link receives the tasks issued by the task requester and selects workers and verifiers. The worker selecting method is that according to the working range, time limit requirement and worker quality requirement in the task, the worker meeting the requirement is determined, for example, the worker whose distance from the task site is within a certain range, global credit value is greater than a certain credit value and working time meets the requirement can be selected, and the selected proper worker is used for executing the task issued by the task requester. The method for selecting the verifier includes triggering the verifier to select an intelligent contract (VSC), based on a global reputation consensus mechanism, calculating global reputation values of all nodes participating in transaction in a computing system, and selecting a proper verifier according to the calculated global reputation values of the nodes, for example, selecting a certain proportion of nodes with higher global reputation values as the verifier according to the sequence of the global reputation values from high to low.
Workers perform tasks and collect perception data according to task requirements
Figure BDA0002643649120000141
Submitting the perception data to the task requester through the blockchain, wherein the submitted perception data is the worker wqPerception data Hash after digital signature by using private key thereofw
Figure BDA0002643649120000142
The perception data can represent perception data
Figure BDA0002643649120000143
Belonging to the workers wq. When the perception data is submitted, triggering a worker to employ an intelligent contract (WEC), and generating a perception data Hash comprising the corresponding relation between the worker and a task requesterw
Figure BDA0002643649120000144
The work certification of transaction information such as digital signatures, time stamps and the like is stored in a work certification block and added to a block chain; the verifier inquires the work testimony in the work testification block, verifies whether a hiring relation exists between the worker and the task requester through the corresponding relation between the worker and the task requester, if yes, verifies the validity of the sensing data according to the digital signature, and verifies the timeliness of the sensing data according to the timestamp; so far, the first consensus is completed. Wherein, the working testimony can be used in the subsequent data quality evaluation stage, and meanwhile, the denial attack of the task requester can be avoided.
After the first consensus is completed, if the employment relationship exists between the worker and the task requester and the perception data submitted by the worker is valid, the blockchain sends the perception data submitted by the worker to the task requester, and the task requester performs data quality evaluation on the submitted perception data by using a truth value discovery and expectation maximization algorithm and generates an evaluation result.
After the task requester completes the data quality evaluation, the task requester evaluates the result and the received perception data Hash submitted by the workerr
Figure BDA0002643649120000151
Sending the data to a verifier through a block chain, triggering a Data Verification Contract (DVC), extracting the working proof of a working proof block by the verifier, and Hash perception data submitted by workers in the first consensusw
Figure BDA0002643649120000152
Hash with sensing data submitted by task requestersr
Figure BDA0002643649120000153
Comparing the sensing data and the data to verify the validity of the sensing data, and if the sensing data and the data are consistent, further determining whether to execute payment operation according to the evaluation result, for example, if the evaluation result is qualified, the payment operation can be executed, and if the evaluation result is unqualified, the payment operation is not executed; thus, the second consensus is completed.
After the second consensus is completed, if the verifier confirms that the payment operation can be performed, the blockchain pays the corresponding reward to the worker, thereby completing the transaction process between the task requester and the worker.
On one hand, the consensus mechanism based on the global reputation selects the verifier, and replaces the existing consensus mechanism of workload certification, so that resource waste caused by computational competition in the process of mining is avoided, the workload and time overhead of inter-node transaction are reduced, the resource utilization rate is improved, the system performance is improved, the configuration requirement on terminal equipment is remarkably reduced, the crowd sensing system can accommodate more light-weight terminal equipment, the task completion speed can be increased, and the data sensing efficiency can be improved; on the other hand, by fusing the truth value discovery and the expectation maximization algorithm, the data quality evaluation is carried out on the perception data submitted by the workers, the problems of payment refusal and unfair payment can be avoided, the transaction fairness is ensured, the perception data quality is favorably improved, and the enthusiasm of user participation is promoted.
Fig. 6 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
Bus 1050 includes a path that transfers information between various components of the device, such as processor 1010, memory 1020, input/output interface 1030, and communication interface 1040.
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures, for simplicity of illustration and discussion, and so as not to obscure one or more embodiments of the disclosure. Furthermore, devices may be shown in block diagram form in order to avoid obscuring the understanding of one or more embodiments of the present description, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the one or more embodiments of the present description are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that one or more embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.

Claims (10)

1. A crowd sensing method based on block chains is characterized by comprising the following steps:
when a task requester issues a task to the block chain, selecting a worker, and selecting an intelligent contract to select a verifier by executing the verifier;
when the worker completes a task and submits perception data to the task requester through the block chain, executing a worker employment intelligent contract, and verifying the employment relationship between the worker and the task requester by the verifier to complete first consensus;
after the task requester evaluates the data quality of the sensing data submitted by the worker, submitting an evaluation result and the received sensing data submitted by the worker to the block chain, executing a data verification intelligent contract, verifying the sensing data by the verifier according to the sensing data submitted by the task requester and the sensing data submitted by the worker during the first consensus, determining whether to execute a payment operation according to the evaluation result, and completing the second consensus;
and if the worker determines that the payment operation can be executed, paying corresponding rewards to the worker.
2. The method of claim 1, wherein said selecting a verifier by executing a verifier-selection smart contract comprises:
calculating the global reputation values of all nodes participating in the transaction;
and selecting the verifier according to the global reputation value of each node.
3. The method of claim 2, wherein calculating a global reputation value for all nodes participating in a transaction comprises: for each node, the following steps are performed:
calculating a local reputation value of the current node;
standardizing the local credit value to obtain a standardized local credit value;
according to the transaction rating between the current node and the neighbor node, performing aggregation processing on the normalized local credit value to obtain an aggregated local credit value; the neighbor node is a node which has transacted with the current node;
and calculating the global reputation value based on the aggregated local reputation value according to the transaction rating between the neighbor node and the neighbor node.
4. The method of claim 3, wherein the global reputation value is calculated by:
Figure FDA0002643649110000011
wherein the content of the first and second substances,
Figure FDA0002643649110000012
the global reputation vector for node i calculated for the (f + 1) th iteration,
Figure FDA0002643649110000013
the global reputation vector for node 1 calculated for the f-th iteration,
Figure FDA0002643649110000021
a global reputation vector for node n, i 1,2, …, n, j 1,2, …, n, γ being a constant from 0 to 1,
Figure FDA0002643649110000022
Figure FDA0002643649110000023
representing the satisfaction degree of the node j to the node i for the normalized local reputation value; p is a set of pre-trusted nodes,
Figure FDA0002643649110000024
and (4) local reputation vectors of the nodes i in the pre-trust node set.
5. The method of claim 4, wherein the normalized local reputation value is calculated by:
Figure FDA0002643649110000025
wherein the content of the first and second substances,
Figure FDA0002643649110000026
pjis the local credit value of the node j in the pre-trust node set, | P | is the number of the pre-trust node set, Replocal(i, j) is a local reputation value, which represents the satisfaction degree of the node i to the node j, and the calculation formula is as follows:
Replocal(i,j)＝α*Sat(i,j)+(-β)*Unsat(i,j) (1)
wherein Sat (i, j) represents the number of satisfactory transactions achieved by the node i and the node j, Unsat (i, j) represents the number of dissatisfied transactions achieved by the node i and the node j, and α and β represent the weight occupied by the satisfactory transactions and the dissatisfied transactions, respectively.
6. The method of claim 5, wherein the task requester performs a data quality assessment on the sensory data, comprising:
calculating a true value of the perception data;
and calculating the distance between the perception data and the true value, and evaluating the data quality according to the distance.
7. The method of claim 6, wherein the method of calculating the true value of the perception data is;
Figure FDA0002643649110000027
where D is the perception data set submitted by all workers, and D is divided into intervals { D }s∈D|s＝1,2,…,m}，daIs trueThe interval of values, d is the set of sensory data currently submitted by the worker, and m is the number of verifiers.
8. The method of claim 7, wherein the distance between the perception data and the true value is calculated by:
Figure FDA0002643649110000031
Figure FDA0002643649110000032
is a worker wqThe value of the global reputation of (a),
Figure FDA0002643649110000033
is a worker wqThe perception data that is submitted is presented in a manner,
Figure FDA0002643649110000034
is the accuracy between the perception data and the true value.
9. The method of claim 1, wherein the sensory data submitted by the worker to the task requester over the blockchain is sensory data with a digital signature;
the executing the worker engaging in the intelligent contract, verifying, by the verifier, the engagement between the worker and the task requester to complete a first consensus, comprising:
generating a correspondence between the worker and the task requester;
taking the corresponding relation, the sensing data, the digital signature and the timestamp for submitting the sensing data as the work certification of a worker, storing the work certification in a newly generated work certification block, and adding the work block to a block chain;
the verifier inquires the working proof and judges whether the corresponding relationship exists, if so, the hiring relationship passes verification, otherwise, the verification fails;
if the employment relationship verification passes, verifying the validity of the perception data through the digital signature, and verifying the timeliness of the perception data through the time stamp.
10. A crowd sensing system based on block chains, comprising:
the task requester is used for issuing tasks to the block chain, receiving the sensing data submitted by workers, performing data quality evaluation on the sensing data to obtain an evaluation result, and sending the evaluation result and the received sensing data submitted by the workers to the block chain;
the block chain is used for selecting the worker when the task requester issues the task, and selecting the verifier by executing the verifier to select the intelligent contract; when the perception data submitted by the worker is received, executing a worker employment intelligent contract, and verifying the employment relationship between the worker and the task requester by the verifier to complete first consensus; the system comprises a task requester, a verifier and a controller, wherein the task requester is used for receiving an evaluation result and perception data submitted by the task requester, executing a data verification intelligent contract, verifying the perception data by the verifier according to the perception data submitted by the task requester and the perception data submitted by workers in the first consensus, determining whether to execute a payment operation according to the evaluation result, and completing the second consensus; and paying corresponding rewards to the workers if the second consensus determines to execute the payment operation.
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