CN113423129A - Internet of things IM-D-SMART method based on negative fraction - Google Patents
Internet of things IM-D-SMART method based on negative fraction Download PDFInfo
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- CN113423129A CN113423129A CN202110569355.2A CN202110569355A CN113423129A CN 113423129 A CN113423129 A CN 113423129A CN 202110569355 A CN202110569355 A CN 202110569355A CN 113423129 A CN113423129 A CN 113423129A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
An Internet of things IM-D-SMART method based on negative fraction relates to an Internet of things IM-D-SMART method, and the method comprises five steps: clustering stage, fragmentation stage, intra-cluster data serial connection recombination stage, inter-cluster data aggregation stage and aggregation result uploading stage; the method comprises a dynamic fragmentation method and also comprises the following steps of sending negative number fragments: and (3) a cluster data reorganization process: after the fragmentation work is finished, the nodes encrypt the data of the fragments and then transmit the data to the target node, the data of the fragments are decrypted and recombined, each node encrypts and transmits the finally recombined data to the cluster head node, and the cluster head decrypts and assembles the recombined data. The method can improve the privacy of the data and save the data energy. The method also utilizes the negative number fragment sending to balance the loss caused by data collision, improves the accuracy of the data, has lower calculated amount and communication traffic, and can prolong the life cycle of the whole network.
Description
Technical Field
The invention relates to an Internet of things IM-D-SMART method, in particular to an Internet of things IM-D-SMART method based on negative fraction.
Background
The development of the internet of things is promoted by the development of information technology, and the wireless Sensor network wsn (wireless Sensor networks) is also paid more and more attention by people, and the wireless Sensor network is composed of a large number of cheap small-sized wireless Sensor networks and has wide application in the aspects of industry, military, medical treatment and the like.
WenBo He et al propose a classical data fusion fragmentation algorithm SMART (slice Mixing aggregation). Jun Wang et al propose an algorithm D-SMART (data Aggregation Protocol Based on SMART) for performing fragmentation according to data importance degree on the basis of SMART algorithm, but the algorithm does not consider factors of node residual energy and relative density, and if the node residual energy is small, more fragmentation cannot be performed.
A plurality of scholars provide a wireless sensor network data fusion algorithm, but the existing algorithm has the problem that fragmentation is not reasonable enough, data collision is easy to occur in the data communication process, and the data fusion precision is reduced.
Disclosure of Invention
The invention aims to provide an Internet of things IM-D-SMART method based on negative number fragments. Through performance analysis, the method has lower communication traffic, can protect data privacy and prolong the service life of the network.
The purpose of the invention is realized by the following technical scheme:
an Internet of things IM-D-SMART method based on negative fraction, the method comprises five steps: clustering stage, fragmentation stage, intra-cluster data serial connection recombination stage, inter-cluster data aggregation stage and aggregation result uploading stage; the dynamic fragmentation method comprises the following specific fragmentation rules: assuming a residual energy ofE s The ratio of the remaining energy of the nodes,P c is the relative density of the nodes and is calculated in the manner ofP c =N c /N，WhereinN c For the number of nodes in a cluster,Nthe number of nodes for the entire network;
further comprising sending negative number fragments: the cluster is provided with 6 nodes,Ais a cluster head node, and the other nodes are member nodesBAnd nodeCThe data collected is general databAndcso the data slice is represented as (b 1 ,b 2 ) And (a)c 1 ，c 2 ) Node ofDAnd nodeEThe collected data is important datadAndetherefore, data is divided into (d 1 ，d 2 ，-d 3 ，d 4 ) And (a)e 1 ，e 2 ，-e 3 ，e 4 ) And a nodeFThe collected data is confidential data, so that the nodes are connectedFCollected datafIs cut intof 1 ，f 2 ，-f 3 ，f 4 ，-f 5 ，f 6 ). After the fragmentation is finished, the node sends the fragmented data to the target node;
and (3) a cluster data reorganization process: after the fragmentation work is finished, the nodes encrypt the fragmented data and then transmit the fragmented data to the target node, the target node waits for a period of time to ensure that all the received nodes receive the fragmented data, the fragmented data are decrypted and recombined after all the fragmented data are received, each node encrypts and transmits the finally recombined data to the cluster head node, and the cluster head decrypts and assembles the recombined data.
The Internet of things IM-D-SMART method based on negative fraction, whereinE s AndP c the value of (2) and the importance degree of the node dynamically segment the node, wherein the segment rule is as follows:
when in useE s <At 30% of time orP c When the content is more than or equal to 30 percent:
when in useE s Not less than 30 percent ofP c <When the content is 30 percent:
the invention has the advantages and effects that:
the method carries out dynamic fragmentation on the nodes according to the residual energy, the relative density and the importance degree of the nodes, and simultaneously sends the negative fragments in the data communication process, and the negative fragments can compensate the loss caused by the collision of the node data. Through performance analysis, the algorithm has lower communication traffic, can protect data privacy and prolong the service life of the network.
Drawings
FIG. 1 is a diagram of a data collusion scenario in accordance with the present invention;
FIG. 2 is a diagram of the intra-cluster data fragmentation process of the present invention;
FIG. 3 is a diagram of the process of data reorganization within a cluster according to the present invention;
FIG. 4 is a flow chart of the IM-D-SMART method of the invention.
Detailed Description
The present invention will be described in detail with reference to the embodiments shown in the drawings.
The IM-D-SMART (Improved D-SMART) based on the negative fraction method is proposed on the basis of a D-SMART algorithm. According to the importance degree of the nodes, the residual energy and the relative density of the nodes, the nodes are dynamically partitioned, the number of the partitioned nodes is more reasonable, excessive energy consumption is avoided, and meanwhile the privacy of data can be protected. In order to reduce the loss of the nodes caused by data collision in the collusion process, a method for sending negative number fragments is provided, and the negative number fragments can compensate the loss of the nodes caused by the data collision.
The IM-D-SMART method comprises a total of five steps: the cluster-based data clustering method comprises a clustering stage, a fragmentation stage, an intra-cluster data serial connection recombination stage, an inter-cluster data aggregation stage and an aggregation result uploading stage. The dynamic fragmentation method and the negative number transmission fragmentation method of the method are mainly carried out in a fragmentation stage and a cluster data serial communication recombination stage.
The symbols used in the present invention are shown in the following table:
TABLE 1 legends
Dynamic fragmentation method
When the IM-D-SMART method is used for fragmentation, two factors of node residual energy and node relative density are also considered: in the initial stage, the energy of the node is sufficient, and more fragmentation can be performed, but in the later stage, the energy of the node is insufficient, and only less fragmentation can be performed; meanwhile, if the relative density of one node is high, the number of neighbor nodes of the node is large, the number of fragments of the node can be reduced properly, and if the relative density of one node is low, the number of neighbor nodes is small, the number of fragments of the node can be reduced properly. The rule for the particular fragmentation of the IM-D-SMART method is as follows: assuming a residual energy ofE s The ratio of the remaining energy of the nodes,P c is the relative density of the nodes and is calculated in the manner ofP c =N c /N，WhereinN c For the number of nodes in a cluster,Nis the number of nodes of the entire network. According toE s AndP c the value of (2) and the importance degree of the node dynamically segment the node, wherein the segment rule is as follows:
when in useE s Not less than 30 percent ofP c <When the content is 30 percent:
the fragmentation mode can be used in many scenes, such as a school information management system. Information of students is divided into three categories: general data such as height, gender; important data such as name, confidential data such as student's home address. And then, the data is fragmented according to the fragmentation rule, the more important data fragments are, the more the data fragments are, and the less the general data fragments are.
Negative number fragment sending method
(1) Principle of method
In the collusion stage, data collision is likely to occur, the fusion precision of data is affected finally, and the negative number fragment is sent, so that the precision loss caused by collision can be compensated. Three cases of data collusion are listed below: assuming that 4 nodes exist in the cluster, each node is divided into three pieces, one piece of the node is reserved for the node, and the remaining two pieces of the node are sent to neighbor nodes;
as shown in case 1 of FIG. 1, the data collusion accuracy at this time is Pcase1= 12-4 × 4/12 × 4 ≈ 0.67, in case 2 of fig. 1, assuming that data also collide, the accuracy of data collusion is Pcase2= 12+2-3 × 4\12 × 4 ≈ 0.92, in case 3 of fig. 1, the fusion precision of collusion is Pcase3=（12+3-2）×4\12×4>1. Through comparison, the accuracy of the node sending the negative number fragment is higher than that of the node sending the data with all positive numbers, but in the case 3, the accuracy of the node is a number larger than 1, distortion occurs, in order to avoid the situation, the fragments are sent in a mode of sending the positive number fragment first and then sending the negative number fragment, the condition that the sum of the sent positive numbers is larger than the sum of the negative numbers is ensured, and the number of the fragments at the node is larger than that of the positive numbersJAnd when the number of the fragments is more than or equal to 3, the fragments are sent alternately by positive and negative numbers.
(2) Examples of the methods
The cluster is provided with 6 nodes,Ais a cluster head node, and the other nodes are member nodesBAnd nodeCThe data collected is general databAndcso the data slice is represented as (b 1 ,b 2 ) And (a)c 1 ，c 2 ) Node ofDAnd nodeEThe collected data is important datadAndetherefore, data is divided into (d 1 ，d 2 ，-d 3 ，d 4 ) And (a)e 1 ，e 2 ，-e 3 ，e 4 ) And a nodeFThe collected data is confidential data, so that the nodes are connectedFCollected datafIs cut intof 1 ，f 2 ，-f 3 ，f 4 ，-f 5 ，f 6 ). After the fragmentation is completed, the node sends the fragmented data to the target node, and the specific data fragmentation process in the cluster is shown in fig. 2.
After the fragmentation work is finished, the nodes encrypt the fragmented data and then transmit the data to the target node, the target node waits for a period of time to ensure that all the received nodes receive the fragmented data, the fragmented data are decrypted and recombined after all the fragmented data are received, each node encrypts and transmits the finally recombined data to the cluster head node, and the cluster head decrypts and assembles the recombined data. By usingM i Representing the recombined data values of each node after slice transmission.
The process of data reassembly within a cluster is shown in FIG. 3. The flow chart of the IM-D-SMART method of the invention is shown in figure 4.
The IM-D-SMART method of the invention comprehensively considers several factors of node density, data importance degree and residual energy to dynamically segment the acquired data, thus improving the privacy of the data and saving the energy of the data. The method also utilizes the negative number fragment sending to balance the loss caused by data collision, improves the accuracy of the data, has lower calculated amount and communication traffic, and can prolong the life cycle of the whole network.
Claims (2)
1. An Internet of things IM-D-SMART method based on negative fraction, which is characterized by comprising five steps: become intoThe cluster-based data clustering method comprises a cluster stage, a fragmentation stage, an intra-cluster data serial connection recombination stage, an inter-cluster data aggregation stage and an aggregation result uploading stage; the dynamic fragmentation method comprises the following specific fragmentation rules: assuming a residual energy ofE s The ratio of the remaining energy of the nodes,P c is the relative density of the nodes and is calculated in the manner ofP c =N c /N，WhereinN c For the number of nodes in a cluster,Nthe number of nodes for the entire network;
further comprising sending negative number fragments: the cluster is provided with 6 nodes,Ais a cluster head node, and the other nodes are member nodesBAnd nodeCThe data collected is general databAndcso the data slice is represented as (b 1 ,b 2 ) And (a)c 1 ，c 2 ) Node ofDAnd nodeEThe collected data is important datadAndetherefore, data is divided into (d 1 ，d 2 ，-d 3 ，d 4 ) And (a)e 1 ，e 2 ，-e 3 ，e 4 ) And a nodeFThe collected data is confidential data, so that the nodes are connectedFCollected datafIs cut intof 1 ，f 2 ，-f 3 ，f 4 ，-f 5 ，f 6 ）；
After the fragmentation is finished, the node sends the fragmented data to the target node;
and (3) a cluster data reorganization process: after the fragmentation work is finished, the nodes encrypt the fragmented data and then transmit the fragmented data to the target node, the target node waits for a period of time to ensure that all the received nodes receive the fragmented data, the fragmented data are decrypted and recombined after all the fragmented data are received, each node encrypts and transmits the finally recombined data to the cluster head node, and the cluster head decrypts and assembles the recombined data.
2. The negative score slice-based internet of things IM-D-SMART method according to claim 1, wherein the method is characterized in thatE s AndP c the value of (2) and the importance degree of the node dynamically segment the node, wherein the segment rule is as follows:
when in useE s <At 30% of time orP c When the content is more than or equal to 30 percent:
when in useE s Not less than 30 percent ofP c <When the content is 30 percent:
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