CN116016571A - Distributed storage method, system, equipment and storage medium based on RCRT - Google Patents
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Abstract
The invention discloses a distributed storage method, a system, equipment and a storage medium based on RCRT, and the related method comprises the following steps: selecting prime numbers according to the number of source nodes to form corresponding moduli of the source nodes; each source node perceives the detected physical data, compresses the perceived data by combining with the self modulus, and stores and transmits the compressed perceived data to the mobile base station; the mobile base station receives compressed sensing data of a part of source nodes, selects any one of the compressed sensing data as a reference for adjustment, obtains adjusted data, and then uses RCRT to reconstruct the sensing data. The scheme is a distributed storage scheme which simultaneously meets the requirements of limited storage resources of the sensor nodes, reliability, lightweight encryption, higher coding rate and tolerance of small measurement errors, and can solve the problems that in the actual situation, the data flow is lost due to limited storage resources of the sensor nodes and the difference between the obtained recovery data and the true value of the measured physical quantity is too large due to interference of the measurement errors.
Description
Technical Field
The present invention relates to the field of distributed storage technologies, and in particular, to a distributed storage method, system, device, and storage medium based on RCRT.
Background
Wireless sensor networks (Wireless sensor networks, WSNs) are a type of multi-hop ad hoc network system that has been successfully used in various fields such as data collection, security monitoring, and intelligent factories. To improve sensing accuracy and coverage, a large number of sensor nodes are typically deployed in a monitoring area, and the high density feature enables the same physical quantity to be measured by multiple Source nodes (Source nodes), resulting in the same or similar data streams and stored in each Source node separately. Wireless sensor networks are typically deployed in harsh environments that are adverse to humans, can access Source nodes by a mobile base station, such as a drone, by polling, and download the Source nodes' perceived data via wireless communications. Because of the energy possessed by Source nodes, storage and computational resources are limited, and in order to avoid loss of data flow due to under-storage, the data may be split into segments, each segment being a piece of perceived data compression.
Aiming at the storage problem caused by limited storage resources of Source nodes in a high-density scene, a distributed storage technology based on remainder system coding is generated. In 2012, chessa et al proposed a distributed storage scheme based on a non-paired mutual element remainder system (RNS-NPM), which achieved good results. Since the amount of data stored is greatly reduced, the loss of data stream due to limited storage resources is effectively reduced. In addition, the scheme has the characteristics of reliability, lightweight encryption and tolerance of measurement errors.
However, the scheme of Chessa et al has some problems, and the most important is that the upper bound of the coding rate in the scheme is only 1/2, so that the perceptual data can be further compressed. In order to solve the problem, researchers have proposed a distributed storage scheme based on the chinese remainder theorem, such as the reliable distributed storage scheme based on the redundant remainder system (Redundant Residue Number System, RRNS) proposed by Yatskiv et al, where the upper bound of the coding rate can reach 1 and can also meet the reliability and lightweight encryption, but the chinese remainder theorem has no robustness, i.e. it is required that the data perceived by all Source nodes is the same, which is difficult to guarantee in reality due to the perceived accuracy and interference of various faults.
It is therefore necessary to design a distributed storage scheme that simultaneously satisfies the limited storage resources of the sensor nodes, reliability, lightweight encryption, higher encoding rates, and tolerance to the presence of measurement errors.
Disclosure of Invention
The invention aims to provide a distributed storage method, a system, equipment and a storage medium based on RCRT, which are a distributed storage scheme simultaneously meeting the requirements of limited storage resources of sensor nodes, reliability, lightweight encryption, higher coding rate and tolerance of small measurement errors.
The invention aims at realizing the following technical scheme:
a distributed storage method based on RCRT, comprising:
selecting prime numbers with corresponding numbers according to the number of the source nodes to form a corresponding modulus of each source node;
each source node perceives the detected physical data, compresses the perceived data by combining with the self modulus, and stores and transmits the compressed perceived data to the mobile base station;
after receiving compressed sensing data of a part of source nodes, the mobile base station selects the compressed sensing data of any source node as a reference, adjusts the received compressed sensing data based on RCRT, reconstructs sensing data by utilizing the adjusted data, and RCRT represents the robust Chinese remainder theorem.
A distributed storage based on RCRT comprising:
a modulus selecting part for selecting prime numbers of corresponding numbers according to the number of the source nodes to form a modulus corresponding to each source node;
the sensing data compression storage part senses the physical data to be measured by each source node, compresses the sensing data by combining with the self modulus, and stores and transmits the compressed sensing data to the mobile base station;
and the perceived data reconstruction part is used for selecting the compressed perceived data of any source node as a reference after the mobile base station receives the compressed perceived data of a part of source nodes, adjusting the received compressed perceived data based on RCRT, reconstructing the perceived data by utilizing the adjusted data, wherein RCRT represents the robust Chinese remainder theorem.
A processing apparatus, comprising: one or more processors; a memory for storing one or more programs;
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the aforementioned methods.
A readable storage medium storing a computer program which, when executed by a processor, implements the method described above.
As can be seen from the technical scheme provided by the invention, 1) saving storage resources: each source node only stores compressed data, namely fragments of perceived data; 2) Reliability: segment erasure problems due to failure or wireless network unreliability can be tolerated; 3) Lightweight encryption: the compression of the perceived data by the source node is essentially a lightweight encryption, and if the corresponding modulus is not known, the perceived data cannot be obtained even if the segment is obtained; 4) Higher coding rate: the coding rate is improved and the length of the segment is further shortened compared to the scheme of Chessa et al; 5) Tolerance measurement errors exist: compared with the solution of Yatskiv et al, the method can tolerate small measurement errors and better meets the real requirement of a wireless sensor network.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a distributed storage method based on RCRT according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a distributed storage system based on RCRT according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a processing apparatus according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
The terms that may be used herein will first be described as follows:
the terms "comprises," "comprising," "includes," "including," "has," "having" or other similar referents are to be construed to cover a non-exclusive inclusion. For example: including a particular feature (e.g., a starting material, component, ingredient, carrier, formulation, material, dimension, part, means, mechanism, apparatus, step, procedure, method, reaction condition, processing condition, parameter, algorithm, signal, data, product or article of manufacture, etc.), should be construed as including not only a particular feature but also other features known in the art that are not explicitly recited.
The following describes in detail a distributed storage method, system, device and storage medium based on RCRT. What is not described in detail in the embodiments of the present invention belongs to the prior art known to those skilled in the art. The specific conditions are not noted in the examples of the present invention and are carried out according to the conditions conventional in the art or suggested by the manufacturer.
Example 1
The embodiment of the invention provides a distributed storage method based on RCRT (robust Chinese Remainder Theorem ), which is a distributed storage scheme capable of simultaneously meeting the limited storage resources of sensor nodes, reliability, lightweight encryption, higher coding rate and tolerating small measurement errors, and solves the problems of data stream loss caused by limited storage resources of the sensor nodes and overlarge difference between the obtained recovery data and the true value of the measured physical quantity caused by measurement error interference in the actual situation. As shown in fig. 1, the method mainly comprises the following steps:
1. and selecting prime numbers with corresponding numbers according to the number of the source nodes to form corresponding moduli of the source nodes.
The method mainly comprises selecting modulus, recording Source node number as n, selecting n different prime numbers Γ i I is more than or equal to 1 and less than or equal to n, and the modulus M corresponding to the ith source node i ＝mΓ i Where m > 4 μ, μ is a known positive integer.
The fragments are considered erased given that some source nodes may fail or that the unreliability of the wireless network during the download may result in the mobile base station not having access to some fragments. Assuming that at most f < n segments are erased, i.e. f source nodes are abnormally operated, all measurement errors are small errors, in order to be able to obtain a reasonable approximation of the true value of the measured physical quantity, it is necessary to make the true value X of the measured physical quantity satisfy X ε [ mu, B), wherein the parameter quantity
lcm (-) represents a function of the least common multiple, t= (M 1 ，M 2 ，...，M n ) I.e. a set of moduli corresponding to all source nodes; />
n-f indicates that the mobile base station receives compressed sensing data of n-f source nodes, ++>
Represents a subset of n-f bases selected from the set T, S represents a set of n-f source nodes modulo, s|represents the number of elements in the set S.
2. Each source node perceives the detected physical data, compresses the perceived data by combining with the self modulus, and stores and transmits the compressed perceived data to the mobile base station.
In the embodiment of the invention, in order to save storage resources, the sensing data is compressed by the source node. For the ith source node, its perceived data is X because of measurement error i ＝X+e i ，e i Measurement error for the ith source node; the i-th source node compresses the sensing data by combining the self modulus:
wherein ,/>
Compressed sensing data representing the ith source node, r i Representing compressed perceptual data without measurement error, mod being a residual function, symbol≡and symbol mod being combined to represent the congruence meaning, M i The modulus corresponding to the ith source node.
3. After receiving compressed sensing data of a part of source nodes, the mobile base station selects the compressed sensing data of any source node as a reference, adjusts the received compressed sensing data based on RCRT (robust Chinese residual theorem), and reconstructs sensing data by using the adjusted data.
Assuming that the mobile base station receives k more than or equal to n-f compressed sensing data in total, assuming that the mobile base station is the first k remainder, and recording the compressed sensing data of k source nodes as a set
The modulus of k source nodes is recorded as a set T A ＝(M 1 ，M 2 ，...，M k ) Wherein->
Ms represents the compressed sensing data and the modulus of the s-th source node, and s is more than or equal to 1 and less than or equal to k.
Compressed sensing data of the s-th source node selected by the mobile base station
As a reference, the adjustment is performed to obtain adjusted data G (s, a) = (λ) 1 ，λ 2 ，...，λ k), wherein ,λs Representing the compressed sensing data of the s-th source node after adjustment; constructing a congruence equation system by using the adjusted data G (s, A), and solving the solution of the congruence equation system by using RCRT>
I.e. reconstructed perceptual data; specific:
1) Compressed sensing data of the s-th source node selected by the mobile base station
As a reference, the adjustment is performed to obtain adjusted data G (s, a) = (λ) 1 ，λ 2 ，…，λ k ) Comprising the following steps:
and (3) calculating:
where m > 4. Mu.m, is a known positive integer, mod is a residual function,
compressed sensing data representing the first source node.
Then, calculation:
wherein ,
representing a round up->
ξ ss 、ξ sl Are all intermediate parameters.
And (3) recalculating:
wherein ,λl Compressed sensing data for the adjusted first source node;
finally obtaining the compressed sensing data lambda of the k source nodes after adjustment 1 ，λ 2 ，...，λ k Obtaining adjusted data G (s, A) = (lambda) 1 ，λ 2 ，…，λ k )。
2) Constructing a congruence equation system by using the adjusted data G (s, A), and solving a solution of the congruence equation system by using RCRT
Comprising the following steps:
constructing a congruence equation set by using the adjusted data G (s, A):
solving for a system of congruence equations using RCRT
Wherein the parameters are
Parameter r c ≡λ l (moa m), parameter->
Γ l To calculate modulus M l Prime number, parameter selected at the time->
Parameter->
m > 4 mu, mu is a known positive integer and mod is a remainder function.
Perceptual data reconstructed by mobile base station
Therefore->
I.e. < ->
Is a reasonable approximation of X.
The following description is directed to the principles of the RCRT that are involved in the above-described scheme of the present invention.
For the following set of congruence equations:
for a pair of
Modulus M i ＝mΓ i Wherein m > 1 and Γ i Is a prime number different from each other, and there is +.>
gcd(M i ，M j ) =m, where lcm (.) represents a function of the least common multiple and gcd (.) represents a function of the greatest common divisor.
Let r= (R) 1 ，r 2 ，...，r n ) According to the general Chinese remainder theorem, if R is uniform, i.e
m|(r i -r j ) I.e. m integer divided (r i -r j ) Then the above system of equations has a unique solution X, X ε [0, M).
When the remainder has no error, the original value X can be recovered; but since the source node is independently measured, an independent measurement error e is introduced i . The remainder at this time is
Is provided with->
The positive integer mu satisfies 4 mu < m. Called abs (e) i ) Mu is less than or equal to mu, otherwise, the error is unlimited, wherein abs (degree) represents absolute value. Assuming that the measurement errors are small errors, the recovered values are +.>
There may be a large error or no solution to the system of equations at all compared to the original value X, so a remainder of +.>
The adjustment was performed as a reference to obtain an adjusted value, denoted +.>
The process is as follows:
without loss of generality, the calculation is performed with the first remainder, i.e. s=1, as reference:
1) And (3) calculating:
2) According to formula (3):
3) Calculating a new remainder according to equation (4):
according to the adjustment process, there are
According to->
Obtaining a new congruence equation set:
it is easy to know according to the adjustment process,
is consistent, can uniquely solve ++according to the generalized Chinese remainder theorem>
And satisfy->
Thus, satisfy->
Is->
Is a reasonable approximation of X.
The above aspects of the invention are described below in connection with a specific example.
1. And (5) selecting a modulus.
Let the measured physical quantity be x= 3456789, n=5 source nodes total, μ=4, f=1. Taking m=17, Γ from μ and X 1 ＝17，Γ 2 ＝19，Γ 3 ＝23，Γ 4 ＝29，Γ 5 =31, then there is M 1 ＝289，M 2 ＝323，M 3 ＝391，M 4 ＝493，M 5 ＝527，T＝(M 1 ，M 2 ，...，M 5 ) Therefore, it is
2. Compression of the sense data.
Without loss of generality, assume that the measurement error is e 1 ＝-4，e 2 ＝-2，e 3 ＝1，e 4 ＝3，e 5 =2, so there is
3. Reconstruction of the perceptual data.
Without loss of generality, it is assumed that the data of the 5 th source node is erased, and thus received by the mobile base station
Suppose that the mobile base station picks +.>
Adjustment was performed as a reference to obtain:
according to the information, the mobile base station calculates:
then, the mobile base station calculates:
finally, the mobile base station calculates G (s, a) = (λ) 1 ，λ 2 ，λ 3 ，λ 4 )。
It is clear that G (s, A) is consistent, so according to the general Chinese remainder theorem, it is understood that
So the mobile base station gets the perception data +.>
Obviously (I)>
Is a reasonable approximation of X.
According to the coding rate ER, there are:
wherein ,
the scheme provided by the embodiment of the invention mainly has the following beneficial effects:
1) Saving storage resources: each source node stores only compressed data, i.e. fragments of perceived data.
2) Reliability: segment erasure problems due to failure or wireless network unreliability can be tolerated.
3) Lightweight encryption: the compression of the perceived data by the source node is essentially a lightweight encryption, and if the corresponding modulus is not known, the perceived data cannot be obtained even if the segment is obtained.
4) Higher coding rate: the coding rate is increased and the length of the segment is further shortened compared to the scheme of Chessa et al.
5) Tolerance measurement errors exist: compared with the solution of Yatskiv et al, the method can tolerate small measurement errors and better meets the real requirement of a wireless sensor network.
From the description of the above embodiments, it will be apparent to those skilled in the art that the above embodiments may be implemented in software, or may be implemented by means of software plus a necessary general hardware platform. With such understanding, the technical solutions of the foregoing embodiments may be embodied in a software product, where the software product may be stored in a nonvolatile storage medium (may be a CD-ROM, a U-disk, a mobile hard disk, etc.), and include several instructions for causing a computer device (may be a personal computer, a server, or a network device, etc.) to perform the methods of the embodiments of the present invention.
Example two
The invention also provides a distributed storage system based on RCRT, which is realized mainly based on the method provided by the previous embodiment, as shown in FIG. 2, and the system mainly comprises:
a modulus selecting part for selecting prime numbers of corresponding numbers according to the number of the source nodes to form a modulus corresponding to each source node;
the sensing data compression storage part senses the physical data to be measured by each source node, compresses the sensing data by combining with the self modulus, and stores and transmits the compressed sensing data to the mobile base station;
and the perceived data reconstruction part is used for selecting the compressed perceived data of any source node as a reference after the mobile base station receives the compressed perceived data of a part of source nodes, adjusting the received compressed perceived data based on RCRT, reconstructing the perceived data by utilizing the adjusted data, wherein RCRT represents the robust Chinese remainder theorem.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the system is divided into different functional modules to perform all or part of the functions described above.
Example III
The present invention also provides a processing apparatus, as shown in fig. 3, which mainly includes: one or more processors; a memory for storing one or more programs; wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods provided by the foregoing embodiments.
Further, the processing device further comprises at least one input device and at least one output device; in the processing device, the processor, the memory, the input device and the output device are connected through buses.
In the embodiment of the invention, the specific types of the memory, the input device and the output device are not limited; for example:
the input device can be a touch screen, an image acquisition device, a physical key or a mouse and the like;
the output device may be a display terminal;
the memory may be random access memory (Random Access Memory, RAM) or non-volatile memory (non-volatile memory), such as disk memory.
Example IV
The invention also provides a readable storage medium storing a computer program which, when executed by a processor, implements the method provided by the foregoing embodiments.
The readable storage medium according to the embodiment of the present invention may be provided as a computer readable storage medium in the aforementioned processing apparatus, for example, as a memory in the processing apparatus. The readable storage medium may be any of various media capable of storing a program code, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, and an optical disk.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (10)
1. A distributed storage method based on RCRT, comprising:
selecting prime numbers with corresponding numbers according to the number of the source nodes to form a corresponding modulus of each source node;
each source node perceives the detected physical data, compresses the perceived data by combining with the self modulus, and stores and transmits the compressed perceived data to the mobile base station;
after receiving compressed sensing data of a part of source nodes, the mobile base station selects the compressed sensing data of any source node as a reference, adjusts the received compressed sensing data based on RCRT, reconstructs sensing data by utilizing the adjusted data, and RCRT represents the robust Chinese remainder theorem.
2. The RCRT-based distributed storage method according to claim 1, wherein the selecting the prime numbers of the corresponding numbers according to the number of the source nodes, and forming the corresponding modulus of each source node comprises:
the number of source nodes is n, and n prime numbers gamma different from each other are selected i I is more than or equal to 1 and less than or equal to n, and the modulus M corresponding to the ith source node i ＝mΓ i Where m > 4 μ, μ is a known positive integer.
3. The RCRT-based distributed storage method according to claim 1, wherein each source node senses the measured physical quantity and compresses sensing data in combination with its own modulus, comprising:
the measured physical data is recorded as X, and for the ith source node, the perceived data is X i ＝X+e i ，e i Measurement error for the ith source node;
the i-th source node compresses the sensing data by combining the self modulus:
wherein ,
representing the compressed sensing data of the ith source node, mod is a residual function, and Mi is a modulus corresponding to the ith source node.
4. A distributed storage method based on RCRT according to claim 1 or 3, wherein said measured physical data is marked as X, satisfying X e [ μ, B), where μ is a known positive integer, parameter quantity
lcm (-) represents a function of the least common multiple, T is the set of moduli corresponding to all source nodes,
n is the number of source nodes, n-f represents compressed sensing data of n-f source nodes received by the mobile base station, S represents a set formed by moduli of the n-f source nodes, and S represents the number of elements in the set S.
5. The distributed storage method according to claim 1, wherein after the mobile base station receives compressed sensing data of a part of source nodes, selecting compressed sensing data of any source node as a reference, adjusting the received compressed sensing data based on the RCRT, and reconstructing sensing data using the adjusted data comprises:
when the number of the source nodes is n and the operation of f source nodes is abnormal, the mobile base station receives compressed sensing data of k source nodes, wherein f is smaller than n, and k is larger than or equal to n-f;
recording compressed sensing data of k source nodes as a set
The modulus of k source nodes is recorded as a set T A ＝(M 1 ，M 2 ，...，M k ) Wherein->
M s Respectively representing the compressed sensing data and the modulus of the s-th source node, wherein s is more than or equal to 1 and less than or equal to k;
compressed sensing data of the s-th source node selected by the mobile base station
As a reference, the adjustment is performed to obtain adjusted data G (s, a) = (λ) 1 ，λ 2 ，...，λ k), wherein ,λs Representing the compressed sensing data of the s-th source node after adjustment;
constructing a congruence equation system by using the adjusted data G (s, A), and solving a solution of the congruence equation system by using the Chinese remainder theorem
I.e. reconstructed perceptual data.
6. The RCRT-based distributed storage method of claim 5, wherein the mobile base station selects compressed sensing data of the s-th source node
As a reference, the adjustment is performed to obtain adjusted data G (s, a) = (λ) 1 ，λ 2 ，…，λ k ) Comprising the following steps:
and (3) calculating:
where m > 4. Mu.m, is a known positive integer, mod is a residual function,
compressed sensing data representing a first source node;
then, calculation:
wherein ,
representing a round up->
ξ ss 、ξ sl Are all intermediate parameters;
and (3) recalculating:
wherein ,λl Compressed sensing data for the adjusted first source node;
finally, the adjusted product is obtainedCompressed sensing data lambda of k source nodes 1 ，λ 2 ，...，λ k Obtaining adjusted data G (s, A) = (lambda) 1 ，λ 2 ，…，λ k )。
7. The distributed storage method according to claim 5, wherein the congruence equation set is constructed by using the adjusted data G (s, A), and the solution of the congruence equation set is solved by using the Chinese remainder theorem
Comprising the following steps:
constructing a congruence equation set by using the adjusted data G (s, A):
solving the solution of the congruence equation system by using the Chinese remainder theorem
Wherein the parameters are
Parameter r c ≡λ l (moa m), parameter->
Γ l To calculate modulus M l Prime number, parameter selected at the time->
Parameter->
μ is a known positive integer and mod is a remainder function.
8. A distributed storage system based on RCRT, implemented based on the method of any one of claims 1-7, the system comprising:
a modulus selecting part for selecting prime numbers of corresponding numbers according to the number of the source nodes to form a modulus corresponding to each source node;
the sensing data compression storage part senses the physical data to be measured by each source node, compresses the sensing data by combining with the self modulus, and stores and transmits the compressed sensing data to the mobile base station;
and the perceived data reconstruction part is used for selecting the compressed perceived data of any source node as a reference after the mobile base station receives the compressed perceived data of a part of source nodes, adjusting the received compressed perceived data based on RCRT, reconstructing the perceived data by utilizing the adjusted data, wherein RCRT represents the robust Chinese remainder theorem.
9. A processing apparatus, comprising: one or more processors; a memory for storing one or more programs;
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-7.
10. A readable storage medium storing a computer program, characterized in that the method according to any one of claims 1-7 is implemented when the computer program is executed by a processor.
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