CN110691126B - Reliable code distribution strategy for improving code coverage rate in Internet of things - Google Patents

Reliable code distribution strategy for improving code coverage rate in Internet of things Download PDF

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CN110691126B
CN110691126B CN201910909785.7A CN201910909785A CN110691126B CN 110691126 B CN110691126 B CN 110691126B CN 201910909785 A CN201910909785 A CN 201910909785A CN 110691126 B CN110691126 B CN 110691126B
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李婷
刘安丰
谢尚晟
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Abstract

The invention discloses a reliable code distribution strategy for improving code coverage rate in a novel Internet of things. In the strategy of the method, a genetic algorithm is utilized to select vehicles with high reliability and coverage rate for code propagation, namely: each group of gene sequences in the gene algorithm is a vehicle group for code propagation, wherein two factors, namely coverage rate and reliability, are used as evaluation indexes to judge the fitness of each group of gene sequences and represent the value of the vehicle group; the higher the fitness, the higher the value representing the vehicle group, and finally the vehicle group in the gene sequence with the highest fitness is selected for spreading the update codes. By the method, the coverage rate of the updated code can reach more than 23.16% on the basis of improving the reliability of the vehicle.

Description

Reliable code distribution strategy for improving code coverage rate in Internet of things
Technical Field
The invention relates to a reliable code distribution strategy for improving code coverage rate in a novel Internet of things, which is characterized in that when updated codes are distributed to a large number of sensor devices in the Internet of things, the updated codes are spread by using vehicles with high reliability, and the coverage rate of the updated codes is improved.
Background
The internet of things is a network connected with objects based on the internet. A huge number of sensor devices are distributed in the Internet of things, and the sensor devices are widely applied to various special environments such as military, industrial detection, forests, oceans and the like and are closely related to the life of people. Meanwhile, the sensor equipment upgrades the system and service functions of the sensor equipment by updating codes, and provides higher-quality service for related users. In recent years, vehicles have attracted considerable attention as important information communication and transmission media for specific applications. As an important component of the Internet of things, vehicles with high mobility can distribute update codes to sensor equipment so as to ensure the timeliness of spreading the update codes. Therefore, expanding the coverage rate of the updated codes is an important aspect for ensuring and improving the service quality of the internet of things; when the code coverage rate is guaranteed to be updated, the fact that a reliable vehicle is hired for code propagation is an important research direction for guaranteeing and improving the service quality of the sensor equipment in the Internet of things, and the method has important research significance.
A large number of sensor devices in the internet of things network can be used for sensing surrounding information and data, such as weather data, and returning the information and the data to the control center; the control center receives the information and provides the relevant service to the corresponding user group. However, the functions of the sensor devices are required to be continuously improved and improved to meet the user demands of different situations, and thus, the built-in codes thereof are required to be updated in time. The vehicle, as an important component of the internet of things, can be used to propagate the update code at a lower consumption. And in the process of spreading the update codes by the vehicle, the vehicle receives the update codes issued from the control center, and completes the task of spreading the update codes according to the opportunity network. However, the distribution of the vehicles is not uniform, i.e.: the number of vehicles passing through the city center area is large, so that the probability that the sensor equipment in the city center area receives the update code is high; the number of vehicles passing through the city edge area is small, and therefore it is difficult for the sensor devices of the edge area to receive the update code. Therefore, expanding the coverage of update codes has important research significance.
Meanwhile, there is a problem of reliability of the vehicle, that is: unreliable vehicles can tamper or even steal code information, so that sensor equipment in the Internet of things cannot normally receive updated codes, and the service quality is reduced. Therefore, how to ensure the reliability of the vehicle has important research significance and value based on the updated code coverage rate.
Disclosure of Invention
The invention relates to a novel reliable code distribution strategy for improving code coverage rate in the Internet of things, which is characterized in that when updated codes are distributed to equipment in the Internet of things, vehicles with high reliability are used for spreading, and the coverage rate of the updated codes is improved. The method aims to solve the problem that the reliability of updating codes and propagating vehicles is difficult to receive by sensor equipment in the edge area in the existing code propagation process, so that the coverage rate of the updating codes is increased on the basis of ensuring the reliability, and the service performance of the Internet of things is improved.
A reliable code distribution strategy for improving code coverage rate in a novel Internet of things is characterized in that a control center issues an update code to update and improve the service performance of a large number of sensor devices in the Internet of things; the method comprises the steps of firstly training the vehicles according to historical data of the vehicles by using a genetic algorithm, screening the vehicles with high reliability, and secondly selecting the vehicles with larger track coverage areas from the vehicles with high reliability for code propagation. After the vehicle completes the code dissemination, the control center needs to pay the vehicle a reward.
Wherein, the genetic algorithm in the machine learning method is used to select the vehicle with spread code, namely: and on the basis of limited reward sum paid by the control center, selecting the vehicle with the optimal comprehensive capacity to complete the task of code propagation by comprehensively calculating the trust value and the coverage rate of the vehicle. Wherein the reward for each vehicle is related to the time that the vehicle is serviced, i.e.:
Figure BDA0002213864130000021
wherein the content of the first and second substances,
Figure BDA0002213864130000022
is a vehicle viValue of reward of LiIs a vehicle viTime of terminating service, FiIs a vehicle viThe time to start propagating the code. ξ is a fixed value representing the reward due by the control center per hour. In a city, there are a total of n vehicles.
The integrated calculation of the trust value and coverage rate of the vehicle is obtained by the following formula:
Figure BDA0002213864130000023
wherein phi isiIs a vehicle viThe larger the value of the overall evaluation value of (2), the better.
Figure BDA0002213864130000024
Is a vehicle viThe coverage of the base station is reduced,
Figure BDA0002213864130000025
representative vehicle viThe trust value of (c). α is a constant number between 0 and 1, is
Figure BDA0002213864130000026
The influence factor of (c). Wherein the content of the first and second substances,
Figure BDA0002213864130000027
the value of (d) is obtained by the following formula:
Figure BDA0002213864130000028
wherein the content of the first and second substances,
Figure BDA0002213864130000029
and
Figure BDA00022138641300000210
are all between 0 and 1. N (v)i) Representative vehicle viThe number of areas crossed during driving, l is the total number of city divisions. For a vehicle viBased on the value of the reward
Figure BDA00022138641300000212
It can choose whether to participate in the task of code propagation, with 0 representing no participation and 1 representing participation, as follows:
Figure BDA00022138641300000211
thus, a set of gene sequences in a genetic algorithm represents a set of vehicles participating in code dissemination, and the number of 1's in a sequence represents the number of vehicles participating in code dissemination. Thus, for a set of gene sequences
Figure BDA0002213864130000031
Its adaptive value (which can be understood as the propagation quality of the vehicle) can be calculated by the following formula:
Figure BDA0002213864130000032
wherein phi isiRepresentative vehicle viA combined evaluation value of the trust value and the coverage rate,
Figure BDA0002213864130000033
representative vehicle viWhether or not to participate in the task of code propagation and, therefore,
Figure BDA0002213864130000034
representative Gene sequences
Figure BDA0002213864130000035
I.e. the quality of the spread of the vehicles participating in the spread of the code is high or low. Within the limited remuneration, the user is,
Figure BDA0002213864130000036
the larger the value of (a), the stronger the fitness representing the gene sequence, the higher the quality of the code spread representing the vehicle group. The individuals (gene sequences) with the strongest fitness are screened and reserved in each round; and carrying out gene crossing, recombination and mutation to form a new gene sequence. According to the formula, in limited total reward, fitness calculation is carried out on each group of newly formed gene sequences, and finally the gene sequence with the highest fitness in all rounds is selected; wherein the group of the genes with the mark 1 is the vehicle group participating in the code transmission.
Advantageous effects
The invention provides a reliable code distribution strategy for improving code coverage rate in a novel Internet of things. In the internet of things, the mobility of vehicles is utilized to spread update codes for a large number of sensor devices in the internet of things, so that functions and service quality of the sensor devices are updated and improved. In the process of code propagation in the past, two problems of low coverage rate of code propagation and low reliability of vehicles are easy to occur. Therefore, the method proposed by the invention screens the vehicle group for code propagation by using the genetic algorithm in machine learning, improves the reliability of the propagated vehicle on the basis of expanding the coverage rate of updated codes, and achieves higher code propagation coverage rate within limited reward.
The coverage rate of code propagation is greatly optimized in the whole view, and the reliability of a vehicle for propagating codes is improved while the coverage rate is improved.
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FIG. 1 is a general block diagram of the process of the present invention;
FIG. 2 is a schematic track diagram of a vehicle propagating an update code in an Internet of things network to which the present invention is applied;
FIG. 3 is a schematic track diagram of a vehicle spreading update codes in an existing Internet of things network;
FIG. 4 is a schematic diagram comparing coverage areas of code propagation using the method of the present invention and a prior art method;
FIG. 5 is a schematic illustration of a reliable comparison of a code-dissemination vehicle employing the method of the present invention and a prior art method;
FIG. 6 is a comparison of the number of vehicles with high reliability values using the method of the present invention and a prior art method;
FIG. 7 is a graphical comparison of the performance of a prior art method using the method of the present invention;
Detailed Description
The present invention will be further described with reference to specific examples.
The utility model provides a improve reliable code distribution strategy of code coverage in novel thing networking, its aim at overcomes current code propagation in-process, and marginal area's sensor equipment is difficult to in time receive the reliability problem of updating the code and propagating the vehicle to this enlarges the coverage of updating the code on the basis of guaranteeing the reliability, makes the sensor equipment in the thing networking in time update and promote self function, thereby improves the service performance of thing networking.
The invention utilizes a genetic algorithm in a machine learning method to select vehicles participating in code propagation, namely: and on the basis of limited reward sum paid by the control center, selecting the vehicle with the optimal comprehensive capacity to complete the task of code propagation by comprehensively calculating the trust value and the coverage rate of the vehicle. Wherein the reward for each vehicle is related to the time that the vehicle is serviced, i.e.:
Figure BDA0002213864130000041
wherein the content of the first and second substances,
Figure BDA0002213864130000042
is a vehicle viValue of reward of LiIs a vehicle viTime of terminating service, FiIs a vehicle viThe time to start propagating the code. ξ is a fixed value representing the reward due by the control center per hour.
The integrated calculation of the trust value and coverage rate of the vehicle is obtained by the following formula:
Figure BDA0002213864130000043
wherein phi isiIs a vehicle viThe larger the value of the overall evaluation value of (2), the better.
Figure BDA0002213864130000044
Is a vehicle viThe coverage of the base station is reduced,
Figure BDA0002213864130000045
representative vehicle viThe trust value of (c). α is a constant number between 0 and 1, is
Figure BDA0002213864130000046
The influence factor of (c). Wherein the content of the first and second substances,
Figure BDA0002213864130000047
the value of (d) is obtained by the following formula:
Figure BDA0002213864130000048
wherein the content of the first and second substances,
Figure BDA0002213864130000049
and
Figure BDA00022138641300000410
are all between 0 and 1. N (v)i) Representative vehicle viThe number of areas crossed during driving, l is the total number of city divisions. For a vehicle viBased on the value of the reward
Figure BDA0002213864130000051
It can choose whether to participate in the task of code propagation, with 0 representing no participation and 1 representing participation, as follows:
Figure BDA0002213864130000052
thus, a set of gene sequences in a genetic algorithm represents a set of vehicles participating in code dissemination, and the number of 1's in a sequence represents the number of vehicles participating in code dissemination. Thus, for a set of gene sequences
Figure BDA0002213864130000053
Its adaptive value (which can be understood as the propagation quality of the vehicle) can be calculated by the following formula:
Figure BDA0002213864130000054
wherein phi isiRepresentative vehicle viA combined evaluation value of the trust value and the coverage rate,
Figure BDA0002213864130000055
representative vehicle viWhether or not to participate in the task of code propagation and, therefore,
Figure BDA0002213864130000056
representative Gene sequences
Figure BDA0002213864130000057
I.e. the quality of the spread of the vehicles participating in the spread of the code is high or low. Within the limited remuneration, the user is,
Figure BDA0002213864130000058
the larger the value of (a), the stronger the fitness representing the gene sequence, the higher the quality of the code spread representing the vehicle group. Individuals with the strongest fitness (a group of gene sequences) are screened and reserved in each round; meanwhile, gene crossing, recombination and variation are carried out on the gene sequences meeting the conditions to form new gene sequences. According to the formula, in limited total reward, fitness calculation is carried out on each group of newly formed gene sequences, and finally the gene sequence with the highest fitness in all rounds is selected; wherein the group of the genes with the mark 1 is the vehicle group participating in the code transmission.
FIG. 1 shows a general block diagram of the process of the present invention. In the internet of things network, vehicles have high mobility, so that the vehicles can be used for spreading update codes for a large number of sensor devices in the internet of things, and functions of the sensor devices are updated and improved. However, the distribution of the vehicles is not uniform, so that some sensor devices cannot receive the update code in a timely manner.
FIG. 2 shows the trajectory distribution of vehicles participating in code propagation under the present invention in the Internet of things; fig. 3 shows the trajectory distribution of vehicles participating in code propagation under the existing method. By contrast, the track coverage rate under the method is obviously greater than that of the existing method, so that vehicles participating in code propagation under the method can cover more sensor devices through comprehensive screening of vehicle groups, a better updating effect is achieved, the code updating probability of the sensor devices in the edge area is particularly improved, and the service quality of the sensor devices in the Internet of things is improved.
Figure 4 shows a comparison of the number of covered areas per vehicle using the method of the present invention and a prior art method. The experimental results show that the vehicle selection based on the genetic algorithm can achieve good effect, and the number of the coverage areas of the vehicles is greatly improved.
FIG. 5 shows a reliability comparison of a vehicle employing the method of the present invention and a prior art method. Compared with a random selection strategy of the vehicle, the reliability of the vehicle selected based on the genetic algorithm is greatly improved, so that the safety of updating codes is improved, the sensor equipment in the Internet of things can be transmitted more safely, and the service quality of the sensor equipment is improved. Fig. 6 shows a comparison of the number of vehicles with high reliability values, based on the prior art method and the method according to the invention, respectively. It is clear that the number of vehicles with high reliability values under the inventive method is greater than the number of vehicles with high reliability values under the existing strategy.
FIG. 7 shows a schematic comparison of the combined performance of the method of the present invention and the prior art method. Namely: based on the comprehensive evaluation of the coverage rate and the reliability value, the method of the invention compares the comprehensive performance of the vehicle selected by the genetic algorithm with the comprehensive performance of the vehicle selected by the prior method. Based on the method, the vehicle with high coverage rate and high reliability value is selected for code propagation, and experiments show that the vehicle selected by the method has higher reliability and can improve the coverage rate of updating the code.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-described embodiments. Modifications and variations that may occur to those skilled in the art without departing from the spirit and scope of the invention are to be considered as within the scope of the invention.

Claims (1)

1. A novel reliable code distribution method for improving code coverage rate in the Internet of things is characterized in that vehicles moving in cities are used as the Internet of things distributed in the citiesThe sensor device in (1) distributes and updates the code; in order to ensure the reliability and the maximum coverage of the updated code, a vehicle with high reliability is selected for spreading through analysis of a vehicle historical track, and meanwhile, the coverage rate of the updated code is improved; the method comprises the following steps that a vehicle group with high reliability and large track coverage rate is screened through a genetic algorithm to transmit an update code, so that sensor equipment receives the update code in time; the genetic algorithm is specifically implemented as follows: in the internet of things, vehicle viIt is possible to choose whether to participate in the task of code propagation,
Figure FDA0003251354100000011
a value of 0 indicates that the catalyst is not involved,
Figure FDA0003251354100000012
is 1 for participation, as shown in equation (1):
Figure FDA0003251354100000013
thus, a set of 0-1 sequences consisting of n vehicles is a set of gene sequences; thus, a set of gene sequences in the genetic algorithm represents a set of vehicles participating in code dissemination, and the number of 1's in the sequence represents the number of vehicles participating in code dissemination; thus, for a set of gene sequences
Figure FDA0003251354100000014
Its adaptive value (which can be understood as the propagation quality of the vehicle) can be calculated by equation (2):
Figure FDA0003251354100000015
wherein phi isiIs a vehicle viBy the vehicle's trust value
Figure FDA0003251354100000016
And coverage rate
Figure FDA0003251354100000017
The calculation results are that,
Figure FDA0003251354100000018
representative vehicle viWhether to participate in the task of code propagation; therefore, the temperature of the molten metal is controlled,
Figure FDA0003251354100000019
representative Gene sequences
Figure FDA00032513541000000110
The total value of (1), i.e. the propagation quality of the vehicles participating in the code propagation is high and low; within the limited remuneration, the user is,
Figure FDA00032513541000000111
the greater the value of (a), the stronger the fitness representing the gene sequence, the higher the quality of code propagation representing the vehicle group; the individuals (gene sequences) with the strongest fitness are screened and reserved in each round; carrying out gene crossing, recombination and mutation to form a new gene sequence; according to a formula (2), in limited total reward, fitness calculation is carried out on each group of newly formed gene sequences, and finally, the gene sequence with the highest fitness in all rounds is selected; wherein the unit group marked as 1 in the group of gene sequences is a vehicle group participating in code transmission;
wherein the overall evaluation value phi of the vehicle described in the formula (2)iTrust value by vehicle
Figure FDA00032513541000000112
And coverage rate
Figure FDA00032513541000000113
The composition and the calculation method are as follows: first, the trust value of the vehicle
Figure FDA00032513541000000114
Can be calculated from equation (3):
Figure FDA00032513541000000115
in the formula (3), the first and second groups,
Figure FDA00032513541000000116
is between 0 and 1; the reliability of the vehicle with regular track is greater than that of the vehicle with irregular track by analyzing the historical track of the vehicle, so that the method obtains the trust value of the vehicle by analyzing and calculating the historical parking place of the vehicle, thereby reflecting the reliability of the vehicle; wherein the content of the first and second substances,
Figure FDA0003251354100000021
representative vehicle viNumber of stops at a fixed parking place, D (v)i) Representative vehicle viThe number of days of trace collection;
second, the coverage of the vehicle
Figure FDA0003251354100000022
Can be calculated from equation (4):
Figure FDA0003251354100000023
in the formula (4), the first and second groups,
Figure FDA0003251354100000024
is between 0 and 1; n (v)i) Representative vehicle viThe number of areas covered during driving, l is the total number of urban subdivisions.
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