CN111416735A - Federal learning-based safety QoS prediction method under mobile edge environment - Google Patents

Federal learning-based safety QoS prediction method under mobile edge environment Download PDF

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CN111416735A
CN111416735A CN202010135672.9A CN202010135672A CN111416735A CN 111416735 A CN111416735 A CN 111416735A CN 202010135672 A CN202010135672 A CN 202010135672A CN 111416735 A CN111416735 A CN 111416735A
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training
qos
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data
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CN111416735B (en
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金惠颖
张鹏程
吉顺慧
李清秋
张雅玲
魏芯淼
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Hohai University HHU
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14Network analysis or design
    • H04L41/147Network analysis or design for predicting network behaviour
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Computing arrangements based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0445Feedback networks, e.g. hopfield nets, associative networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computing arrangements based on biological models
    • G06N3/02Computing arrangements based on biological models using neural network models
    • G06N3/04Architectures, e.g. interconnection topology
    • G06N3/0454Architectures, e.g. interconnection topology using a combination of multiple neural nets
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14Network analysis or design
    • H04L41/145Network analysis or design involving simulating, designing, planning or modelling of a network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/28Restricting access to network management systems or functions, e.g. using authorisation function to access network configuration
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50Network service management, e.g. ensuring proper service fulfilment according to agreements

Abstract

The invention discloses a security QoS prediction method under a mobile edge environment based on federal learning, which comprises the steps of firstly collecting edge position information and a QoS data set, fusing to obtain a time-space edge user QoS data set, then dividing and extracting a public data set from edge areas, and combining public model training and personalized prediction to achieve the purpose of security.

Description

Federal learning-based safety QoS prediction method under mobile edge environment
Technical Field
The invention relates to a QoS prediction method, in particular to a safe QoS prediction method under a mobile edge environment, and belongs to the technical field of information.
Background
SOA (service Oriented architecture) is an application architecture in which all functions are defined as independent services, and Web services are one of the technologies for implementing the SOA, so that industry experts provide users with ever-changing demands by combining the services. In recent years, with the development of Web services, the non-functional attribute qos (quality of service) of the Web services has received more and more attention, and nowadays, a large number of Web services with the same or similar functions appear on the network, so it is important to select an appropriate Web service to meet the needs of users. On the other hand, with the advent of the 5G era, mobile edge computing has become more widely used, and since it is located at the edge of the network, in close proximity to users or information sources, the latency of responding to requests can be greatly reduced. Providing service response for users in the mobile edge environment is a current trend, but also brings security problems in the mobile edge environment.
The existing QoS security research work focuses mainly on two aspects: QoS privacy protection method and QoS security service. In terms of QoS privacy protection, the earliest scholars proposed a differential privacy encryption algorithm, and subsequently liu et al combined it with a collaborative filtering method to propose a QoS privacy protection method. Qi et al in 2017 proposed a locality-sensitive-hash-based distributed recommendation system. Shahriar et al propose a protection protocol for attribute value encryption and location hiding. Therefore, the existing QoS privacy protection method is mostly suitable for static environment, and the prediction precision is damaged after data encryption. On the other hand, since users predict by accessing each other's history data in the marginal environment, the data encryption rule is more easily broken as the number of interactions increases.
In terms of QoS security service, researchers have proposed various security mechanisms and criteria, Shen et al have proposed a distributed dynamic management system mechanism, but the mechanism is only applicable to a specific environment (e.g., when network traffic is light), Alessandro et al have proposed an integrated tool support method, which can achieve the maximum tradeoff between security and quality, but the method does not consider the change situation under a dynamic environment.
With the development of technology, a great deal of private information of users is acquired in more and more prediction methods, and safety becomes a great demand for users in the prediction process.
Disclosure of Invention
The invention aims to provide a security QoS prediction method under the mobile edge environment based on federal learning, which is used for solving the problems that the traditional method is not applicable under the mobile edge environment, the prediction precision is greatly reduced and the requirement of a user on the security is considered, a time-space edge user QoS data set is obtained by fusing the data sets, and the possibility of being cracked is fundamentally eliminated by adopting a public model training and personalized prediction mode, so that the security is ensured.
The technical scheme is as follows: in order to achieve the above object, the method for predicting the security QoS in the mobile edge environment based on federal learning according to the present invention includes the following steps:
step 1: collecting edge position information and a QoS data set;
step 2: fusing QoS data and edge position points by taking the user ID number as connection;
and step 3: obtaining a time-space edge user QoS data set after fusion;
and 4, step 4: the geographical position distribution of an edge server and a city district is considered, an original edge network area is divided into a plurality of edge areas, and public data set extraction is carried out;
step 5, performing public data training on the public data sets of all edge areas based on L STM to obtain a public model;
and 6, taking the public L STM weight parameter in the region to which the user belongs as an initial parameter of the private L STM, and continuously training the private L STM according to the private data so as to perform personalized prediction.
Preferably, the data collected in step 1 mainly includes two aspects: a QoS data set containing a user ID, a service ID, a time period ID, and an attribute value; a base station data set containing latitude and longitude information.
Preferably, the step 2 comprises the following steps:
step 21: sorting the QoS data sets in the order of the user ID, the time period ID, the service ID and the attribute value;
step 22: randomly selecting a corresponding number of edge position points in the base station data set according to the total number of the users in the step 21 and carrying out ID numbering;
step 23: counting longitude and latitude information of the edge position points, and positioning the position points of the base station through map service;
step 24: the total number of users in the QoS dataset and the total number of edge servers are equal, so the two datasets are merged with the user ID as the connection.
Preferably, the step 3 comprises the following steps:
step 31: counting the total time segment number of the fused data set and the total number of formed edge servers;
step 32: the total time period is abbreviated as 'hour', the total edge server is abbreviated as 'null', and a time-null edge user QoS data set is obtained after fusion.
Preferably, the step 4 comprises the following steps:
step 41: considering the geographical position distribution of an edge server and a municipal district, dividing the whole edge network area into k edge areas, wherein each edge area corresponds to 1-4 municipal districts, and k is more than or equal to 2 and is determined by the geographical distribution of the edge server;
step 42: taking out each edge region T1All users in the time period call attribute values of all services, and the attribute values are expressed in a service-user two-dimensional matrix form; the median of each row of data in the two-dimensional matrix is taken one by one to obtain a service-T1A column vector, which is taken as T1Public data of attribute values of the service called by the time period;
step 43: take T in sequence2、…、TnAll users in the time period call the attribute values of all services, and step 42 is repeated to obtain service-T2…, service-TnColumn vectors, where n ≧ 2;
step 44: and synthesizing the n column vectors to form a service-time period two-dimensional matrix for the public model training of the edge area.
Preferably, the step 5 comprises the following steps:
step 51, each edge region executes L STM training based on the common data set of each region, and a consensus is achieved between the learning rate and the training times, namely, the larger the learning rate is, the faster the error adjustment speed is, so the training times required to be executed are less when the same training effect is achieved;
step 52: setting the learning rate and the training times as a group of combination parameters for public model training, wherein the training times are gradually reduced when the learning rate is increased due to the negative correlation relationship between the learning rate and the training times;
step 53, calculating the final output through a forgetting gate, an input gate, the current time unit state and an output gate in L STM, and calculating a root mean square error value to estimate a loss function;
step 54: deriving the loss function and continuously adjusting the weight parameter; with the increase of the training iteration times, the training error is continuously reduced, and the optimal initial weight parameter is provided for personalized prediction.
Preferably, in the personalized prediction process, the step 6 executes L STM training once at intervals based on private data continuously generated by a user, so as to continuously update the weight parameters to meet the requirements of the edge environment on the real-time performance and accuracy of the data, thereby realizing the prediction of the safe QoS attribute value in the edge environment.
Preferably, the step 6 comprises the following steps:
step 61, the user carries out QoS prediction according to the initial weight parameter transmitted in the public L STM;
step 62: after the prediction of a time period is completed, adjusting model weight parameters according to the deviation of the predicted data and the actual QoS data;
and step 63: judging whether new data is generated in the next time period, if so, transmitting the latest weight parameter to the model prediction of the time period, and executing training so as to further adjust the weight parameter; therefore, the steps are repeated until new data are not generated any more, and the real-time personalized prediction is realized.
Has the advantages that: compared with the prior art, the method for predicting the safety QoS under the mobile edge environment based on the federal learning provided by the invention has the advantages that on one hand, the defect that the traditional encryption mode is easy to crack under the edge environment is overcome by combining the public model training and the personalized prediction, and on the other hand, the prediction precision under the edge environment is ensured by continuously updating the weight parameters while the safety is ensured.
Drawings
FIG. 1 is an overall step diagram of an embodiment of the present invention;
FIG. 2 is a flow chart of a method according to an embodiment of the present invention.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
As shown in fig. 1, the method for predicting the security QoS in the mobile edge environment based on federal learning disclosed in the embodiment of the present invention mainly includes 6 steps:
step 1: collecting an edge position and QoS data set with longitude and latitude information;
step 2: corresponding the edge position point and the QoS data set one by one through a user ID number and fusing the edge position point and the QoS data set;
and step 3: obtaining a QoS data set with geographic position attributes after fusion, and obtaining a time-space edge user QoS data set based on the mapped edge position points;
and 4, step 4: the geographical position distribution of an edge server and a municipal district is considered, an original edge network area is divided into a plurality of edge areas, and a common data set of each area is extracted for common model training;
step 5, performing public data training on the public data sets of all the areas based on L STM to obtain a public model, and transmitting a public weight parameter to a private user;
and 6, taking the public L STM weight parameter in the region to which the user belongs as an initial parameter of the private L STM, and executing private L STM training based on private data so as to continuously update the weight parameter for personalized prediction.
As shown in fig. 2, the method for predicting the security QoS in the mobile edge environment based on federal learning disclosed in the embodiment of the present invention includes the following specific steps:
step 101: and collecting edge position information and a QoS data set, wherein the edge position information mainly comprises the latitude and longitude of the base station, and the QoS data set mainly comprises representative attributes such as response time, throughput and the like.
In this step, a QoS data set including a user ID, a service ID, a time period ID, and an attribute value is collected from wsdream, which is a channel for collecting user information, and can provide Web service reliability and quality evaluation. Counting the total time period number of the QoS data set with the time attribute according to the time period ID number, wherein the user IDs in the data set are used for corresponding to the IDs of the edge position points one by one; a shanghai telecom base station data set containing latitude and longitude information is collected from the telecom, which contains 720 million internet access records of 9481 mobile phones collected by 3233 base stations.
Step 102: rearranging the QoS data set according to the order of the user ID, the time period ID, the service ID and the attribute value; the data size of the QoS data set in this step is 142 × 4500 × 64, that is, the attribute value data set containing response time and throughput formed by 142 users invoking 4500 services in 64 time periods. The QoS data sets are rearranged in the order of the user ID (0-141), the period ID (0-63), the service ID (0-4499), and the attribute value.
Step 103: randomly selecting a corresponding number (142 groups) of edge position points in the data set of the Shanghai telecommunication base station according to the total number (142) of users in the step 102 and carrying out ID numbering (0-141).
Step 104: counting longitude and latitude information of the edge position points, and positioning the position points of the base station through a map service (carefree map) to obtain the distribution condition of the edge server; this example 142 sets of data covers 8 municipalities, Min, Huangpu, irid, Pudong New, Xuhui, Songjiang, Qingpu and Silian, Shanghai.
Step 105: the total number of users in the QoS data set and the total number of edge servers are equal (both 142), so that the two data sets are fused by using user IDs (0-141) as connections and corresponding to the same IDs.
Compared to a conventional QoS dataset, a QoS dataset in an edge environment has spatial attributes, and in addition the dataset has temporal attributes. After the fused data set is mapped, obtaining an edge position point to obtain a time-space edge user QoS data set; the specific operation steps for acquiring the time-space edge user QoS data set are as follows:
step 106: and counting the total number of time segments (64 time segments, the interval of each time segment is 15 minutes) of the fused data set and the total number of formed edge servers (142), wherein the time segment ID (0-63) is abbreviated as 'hour', and counting the number of edge servers formed by positioning 142 groups of longitude and latitude information to the geographic position points, wherein the total number is 67. The geographical location distribution of the edge server is referred to as 'null', so that the two are correspondingly fused to obtain a time-null edge user QoS data set.
The dividing of the edge area and the extraction of the public data set are used for further enhancing the security, and the specific processing steps for dividing the edge area are as follows:
step 107: and considering the geographical position distribution of the edge server and the urban area, dividing the whole edge network area into k (wherein k is more than or equal to 2 and is determined by the geographical distribution of the edge server) edge areas, wherein each edge area corresponds to 1-4 urban areas. In the formed time-space edge user QoS data set, according to the formed 67 edge servers and the distribution situation of the Shanghai urban districts, the urban districts which are closer in geographic position are divided into an edge area because the edge servers which are closer in geographic position have a more similar edge environment. The Shanghai area is divided into three edge areas, edge area 1: minkout, 22 edge servers, edge area 2: huangpu zone, iris zone and purdong new zone, 24 edge servers, edge zone 3: xu hui district, Songjiang district, Qingpu district and quiet district, 21 edge server.
Step 108: taking out each region T1And all users call the attribute values of all services in the time period, wherein the attribute values comprise response time and throughput and are represented in the form of a service-user two-dimensional matrix. Such as: taking attribute values of all services called by all users in the time period 1 of the edge area 1 to form a T1A service-user matrix of a time period, and the median of each row of data in the two-dimensional matrix is taken one by one to obtain a service-T1A column vector, which is taken as T1The user of the time slot edge area 1 calls public data of the attribute value of the service; then taking the T of the edge region 1 in sequence2、…、T64All users in the time period call attribute values of all services to obtain service-T of the edge area 12service-T64A column vector. The edge region 2 and the edge region 3 can obtain service-time period column vectors in the same way; the 64 column vectors of the edge area 1 are synthesized to form a service-time period two-dimensional matrix, and the edge area 1 forms two common data sets of response time-time period and throughput-time period for common model training of the area. Edge region 2 and edge region 3 can likewise form a common data set by synthesis.
The L STM-based public model training can provide initial weight parameters for private users, and the specific processing steps of the public data training are as follows:
step 109: each edge region is based on a two-dimensional matrix of response time (rt) and throughput (tp) of the respective regionL STM training is carried out on the common data set, a consensus is achieved between the learning rate and the training times, namely, the higher the learning rate is, the faster the error adjustment speed is, and therefore, the less training times need to be carried out when the same training effect is achieved, the learning rate and the training times are set as a group of combined parameters of common model training, and because of the negative correlation between the learning rate and the training times, the training times are gradually reduced when the learning rate is increased, and if the learning rate is in the interval of 0.001 step length of 0.001,0.01]Taking the value above, the corresponding training times are in the interval [100,1000 ] by the step length of 100]Calculating the final output through a forgetting gate, an input gate, a unit state at the current moment and an output gate in L STM after parameter setting is finished, and estimating a loss function through calculating a root mean square error value, wherein L STM input predicts the QoS attribute value of each service at the next moment by using the QoS attribute value (response time or throughput) of each service at the previous moment, and the concrete model is expressed as (1) ft=σ(Wf·[ht-1,xt]+bf) Wherein W isfIs the weight matrix of the forgetting gate, ht-1And xtRespectively representing the output of the previous time and the input of the current time, bfL STM model selectively retains the unit state from the last time to the current time through the forgetting gate, (2) it=σ(Wi·[ht-1,xt]+bi) Wherein W isiIs a weight matrix of the input gate, biIs an offset item of an input gate that selectively saves the input cell state at the current time; (3) calculating the cell state c at the current timet. It is determined from the cell state c at the previous timet-1Multiplication by element of forget gate ftReuse the currently input cell stateMultiplying input Gate i by elementtFinally, the two products are added(4) Tong (Chinese character of 'tong')(3) reference L STM to current memoryAnd long term memory ct-1Combine to form a new cell state ct. Due to the control of the forgetting gate and the input gate, they can save information of a long time ago and avoid the current irrelevant contents from entering the memory. ot=σ(Wo·[ht-1,xt]+bo) The calculation process of output gate, it controls the influence of long-term memory on current output, (5) L STM final output is determined by output gate and unit statet=otοtanh(ct) (ii) a (6) Calculating a root mean square error valueTo measure a loss function, whereinIs tiThe output value of the STM at time instant L,is tiThe true value of the time, N is the total number of times.
Step 110: and obtaining a loss function of each prediction result, and continuously adjusting the weight parameter by differentiating the loss function. Therefore, with the increase of training iteration times, training errors are continuously reduced, weight parameters are gradually accurate, and finally 2 common models trained by the response time data set and the throughput data set in each region are obtained.
Step 111: and after the public model training is finished in each edge area, the public weight parameter is stored, and the weights of a forgetting gate, an input gate, a unit state and an output gate are provided for the private users in the area as the optimal initial weight parameter for personalized prediction.
Step 112, the user of each region makes QoS prediction according to the initial weight parameter transferred in STM of L common to the region, and the calculation process is as described in (1) - (5) of step 109, and T is used1,T2,…,T64Time of dayAnd predicting the sequence of the segments and obtaining a prediction result. And the weight parameters in the prediction model are continuously updated based on the derivation of the prediction error.
Step 113: when T is completed1After the time period is predicted, the weight parameter of the model is adjusted according to the deviation of the predicted data and the actual QoS data, and the weight parameter adjusting formula is as follows: weighti=weighti-l error, where weightiIs the initial weight parameter, l is the learning rate,is to take the derivative of the prediction error,is tiThe output value of the STM at time instant L,is tiThe true value of the time of day.
Step 114: the model is updated once each time training is completed for a time interval (which can be set according to system requirements and data conditions, such as 1 hour, 1 day, etc. in specific applications), the model at this moment is called a private model, and weight parameters in the model are only private for the user. Along with the continuous iteration of the training times, the private model is continuously perfected, and the weight parameters in the model are continuously updated.
Step 115: and when the training is finished each time, judging whether new data is generated in the next time period, if so, continuing to perform private prediction according to the method in the step 112, and obtaining a prediction result in the time period. If not, the training is finished.
Step 116: and outputting a final prediction result after the prediction and training of all time periods are finished.

Claims (8)

1. A method for predicting the safety QoS under the mobile edge environment based on the federal learning is characterized by comprising the following steps:
step 1: collecting edge position information and a QoS data set;
step 2: fusing QoS data and edge position points by taking the user ID number as connection;
and step 3: obtaining a time-space edge user QoS data set after fusion;
and 4, step 4: the geographical position distribution of an edge server and a city district is considered, an original edge network area is divided into a plurality of edge areas, and public data set extraction is carried out;
step 5, performing public data training on the public data sets of all edge areas based on L STM to obtain a public model;
and 6, taking the public L STM weight parameter in the region to which the user belongs as an initial parameter of the private L STM, and continuously training the private L STM according to the private data so as to perform personalized prediction.
2. The method of claim 1, wherein the data collection in step 1 mainly includes two aspects: a QoS data set containing a user ID, a service ID, a time period ID, and an attribute value; a base station data set containing latitude and longitude information.
3. The method for predicting the safety QoS under the mobile edge environment based on the federal learning in claim 1, wherein the step 2 comprises the following steps:
step 21: sorting the QoS data sets in the order of the user ID, the time period ID, the service ID and the attribute value;
step 22: randomly selecting a corresponding number of edge position points in the base station data set according to the total number of the users in the step 21 and carrying out ID numbering;
step 23: counting longitude and latitude information of the edge position points, and positioning the position points of the base station through map service;
step 24: the total number of users in the QoS dataset and the total number of edge servers are equal, so the two datasets are merged with the user ID as the connection.
4. The method of claim 1, wherein the step 3 comprises the following steps:
step 31: counting the total time segment number of the fused data set and the total number of formed edge servers;
step 32: the total time period is abbreviated as 'hour', the total edge server is abbreviated as 'null', and a time-null edge user QoS data set is obtained after fusion.
5. The method of claim 1, wherein the step 4 comprises the following steps:
step 41: considering the geographical position distribution of an edge server and a municipal district, dividing the whole edge network area into k edge areas, wherein each edge area corresponds to 1-4 municipal districts, and k is more than or equal to 2 and is determined by the geographical distribution of the edge server;
step 42: taking out each edge region T1All users in the time period call attribute values of all services, and the attribute values are expressed in a service-user two-dimensional matrix form; the median of each row of data in the two-dimensional matrix is taken one by one to obtain a service-T1A column vector, which is taken as T1Public data of attribute values of the service called by the time period;
step 43: take T in sequence2、…、TnAll users in the time period call the attribute values of all services, and step 42 is repeated to obtain service-T2…, service-TnColumn vectors, where n ≧ 2;
step 44: and synthesizing the n column vectors to form a service-time period two-dimensional matrix for the public model training of the edge area.
6. The method of claim 1, wherein the step 5 comprises the following steps:
step 51, each edge region executes L STM training based on the common data set of each region, and a consensus is achieved between the learning rate and the training times, namely, the larger the learning rate is, the faster the error adjustment speed is, so the training times required to be executed are less when the same training effect is achieved;
step 52: setting the learning rate and the training times as a group of combination parameters for public model training, wherein the training times are gradually reduced when the learning rate is increased due to the negative correlation relationship between the learning rate and the training times;
step 53, calculating the final output through a forgetting gate, an input gate, the current time unit state and an output gate in L STM, and calculating a root mean square error value to estimate a loss function;
step 54: deriving the loss function and continuously adjusting the weight parameter; with the increase of the training iteration times, the training error is continuously reduced, and the optimal initial weight parameter is provided for personalized prediction.
7. The method according to claim 1, wherein in the step 6, in the personalized prediction process, L STM training is performed every other time interval based on private data continuously generated by a user, so that weight parameters are continuously updated to meet the requirements of real-time performance and accuracy of data in the edge environment, and the prediction of the safety QoS attribute value in the edge environment is realized.
8. The method of claim 7, wherein the step 6 comprises the following steps:
step 61, the user carries out QoS prediction according to the initial weight parameter transmitted in the public L STM;
step 62: after the prediction of a time period is completed, adjusting model weight parameters according to the deviation of the predicted data and the actual QoS data;
and step 63: judging whether new data is generated in the next time period, if so, transmitting the latest weight parameter to the model prediction of the time period, and executing training so as to further adjust the weight parameter; therefore, the steps are repeated until new data are not generated any more, and the real-time personalized prediction is realized.
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