CN111093164A - Method for rapidly collecting important data based on increasing codes - Google Patents
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Abstract
The invention discloses a method for quickly collecting important data based on an increasing code, which is characterized by comprising the following steps: firstly, randomly scattering sensing nodes to a detection area, and placing sink nodes at the edge position of the monitoring area; then, each node is divided into layers; then, the sensing node starts working, and detects the surrounding environment to generate monitoring data xi; then the sensing node calculates the weight of the code word and selects the best code word according to the betting round; then selecting the most suitable neighbor for code word exchange according to the calculated forwarding probability table and the energy consumption condition of the neighbor node; finally, the sink node receives the code word and performs decoding operation on the received code word; the invention divides the network into layers and performs special treatment aiming at the coding and the exchange of the important code words, so that the important data can be quickly recovered by the sink node, and meanwhile, the invention can play a good role in protecting the data in the network.
Description
Technical Field
The invention relates to the field of wireless sensor networks, in particular to a method for quickly collecting important data generated by key nodes.
Background
The wireless sensor network is a distributed self-organizing network which has data sensing, storage and transmission and takes data as a center, and in some disaster scenes or war scenes, data monitored and collected by the sensor network plays an important role, such as monitoring and early warning of forest fires, tsunamis and landslides and battlefield situation monitoring in war periods. The environment in which the nodes of the sensor network are located is relatively harsh, and the resources contained in the nodes are relatively limited, such as computing and storage resources, and the nodes may be damaged at any time for various reasons, so that the data perceived and collected by the network is lost. Important data in the sensor network, such as fire point information, needs to be immediately transmitted to the hands of the decision maker, and thus, it is very important for the important data in the sensor network to be transmitted quickly.
The technology of network coding can significantly improve the data persistence of the sensor network and increase the throughput of the network, and data collection protocols based on the network coding technology can be generally divided into two types: a delayed data collection protocol and a fast data collection protocol. The aggregation node in the former is movable, and after the sensing node senses data, the aggregation node does not collect the data immediately, but the sensing node encodes the data and stores the encoded data in the network, and the aggregation node is moved to the network to collect the data at a certain moment. The aggregation node of the latter is relatively fixed, and after the sensing node finishes deployment and senses the environment data, the data collection work of the aggregation node is started. The delayed data collection protocol focuses on the secure storage and recovery of data and represents an encoding protocol with LT Codes. The fast data collection protocol focuses on the secure collection and recovery of data and represents a coding protocol with Growth Codes. The research focus of the current data collection protocol is on the recovery efficiency of data and node energy consumption, and the research on rapid collection of specific data is very little.
The main focus of a data collection protocol in a traditional wireless sensor network is on the recovery efficiency of data, and different strategies are not adopted for the importance of the data, however, in a production environment, the data generated by sensor nodes have different importance, so that it is more meaningful for important data to preferentially arrive at a sink node and be decoded in time by a decision maker.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for rapidly collecting important data based on an incremental code, which combines network layering and node coding data probability selection and data propagation probability among nodes optimized by a BP algorithm and a genetic algorithm, and can accelerate the efficiency of receiving and decoding the important data by a sink node.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention relates to a method for quickly collecting important data based on an increasing code, which comprises the following steps:
1) randomly scattering a plurality of sensing nodes Ni in a monitoring area, and placing a sink node at the edge position of the monitoring area;
2) according to the distance from the sink node, each node is divided into layers by using a layering algorithm;
3) each sensing node Ni detects the surrounding environment to generate monitoring data xi, and the maximum MaxMerge of the sensing node Ni is set to be 1;
4) the sensing node fills the monitoring data xi of the sensing node into the cache medium C of the sensing node, so that all the code words S in the cache medium C are xi, and the monitoring data xi of the sensing node is stored as source data of the node code;
5) selecting one centralized sensing node to perform a round of calculation, selecting a code word S of the sensing node from a buffer medium C, and selecting whether the code word S per se is subjected to encoding operation or not by the sensing node according to the maximum degree MaxMerge of the sensing node Ni, the degree of the code word S and (S) and whether the code word S contains detection data xi or not;
6) calculating the probability of forwarding the data packet of the sink node to each neighbor and forming a probability forwarding table, sensing the neighbor around the sensing node by the sensing node, establishing an energy consumption estimation table related to the neighbor, and selecting one neighbor node to exchange code words according to the probability forwarding table and the energy consumption estimation table, wherein the exchanged code words are the code words generated in the step 5;
7) the sink node initializes a simple code word set X and a complex code word set Y, wherein the simple code word set X is used for storing decoded original data, and the complex code word set Y is used for storing un-decoded code words;
8) the aggregation node decodes the code words in the received complex code word set Y;
9) and (5) judging whether all the code words S are solved or not or whether the maximum round set by people is reached, if not, selecting another sensing node to perform the next round of calculation, returning to the step 5, and if so, finishing.
Preferably, the layering algorithm in step 2 comprises the following steps:
2.1) the aggregation node generates a hello data packet, wherein the hello data packet carries incremental hierarchical data L and sends the incremental hierarchical data L to the neighbor node;
2.2) the neighbor node takes out the L in the hello packet, sets the hierarchy of the neighbor node as L, updates the hello data packet, and sets the hierarchy information L in the data packet as L + 1. Broadcasting the data packet to own neighbor nodes;
2.3) if the neighbor node has received the hello data packet, directly discarding the data packet, otherwise, executing the step 2.2;
2.4) repeat steps 2.2 and 2.3 until all nodes have hierarchical information.
Preferably, the algorithm in step 5 is as follows:
5.1) calculating the Weight of each code word S in the current sensing node cache;
5.2) calculating the total weight Sum Sum, calculating the probability of a single code word S, and then calculating the accumulated probability;
5.3) selecting a code word S from the buffer medium C according to the betting round;
5.4) if the degree of the selected codeword S is (S) < MaxDegree and xi is not included in the codeword S, performing exclusive-or calculation S ═ S ⊕ xi, and if xi is already included in the current codeword S or the degree of the selected codeword S is (S) > MaxDegree, not performing encoding;
5.5) if the current round k is larger than the degree conversion round, the maximum degree MaxDegree of the sensing node Ni is automatically increased by 1.
Preferably, the Weight in step 5.1 is calculated as follows:
where p is the number of priority data in the current codeword, n is the number of total priority data in the buffer, c is 60, and K is 10.
Preferably, the weight and Sum calculation method of step 5.1 is as follows:
preferably, the degree conversion round in step 5.5 is represented by KjMaxDgree, wherein,
j is the degree of the current network, N is the number of sensing nodes,
preferably, the steps included in step 6 are as follows:
6.1) fitting the relation between the forwarding probability and the recovery turns by using a BP neural network;
6.2) using GA algorithm to search the optimal forwarding probability to form a probability forwarding table;
6.3) each node senses the neighbor around itself and establishes an energy consumption estimation table about the neighbor, and the energy consumption of each neighbor is initially EiWhen the induction node selects to exchange data with a certain neighbor, updating the energy consumption value of the neighbor in the energy consumption estimation table of the induction node, and if data transmission is carried out, calculating the energy consumption value of the corresponding neighbor Ei＝Ei+0.03, if data reception is performed, the corresponding neighbor energy consumption value is calculated Ei＝Ei+0.01, and updating the energy consumption estimation table in the neighbor node;
6.4) according to the code word value of the code word S, searching the probability A corresponding to the code word value closest to the code word value in a probability forwarding table as a primary probability forwarded to the neighbor of the type with the lowest hierarchy;
6.5) estimating the energy consumption of the neighbor node according to the energy consumption estimation model, and distributing a secondary probability according to the energy consumption;
6.6) the rest 1-A first-level probability is averagely distributed to the rest hierarchical classes, and the inside of each hierarchical class is also distributed to the first-level hierarchical level according to energy consumption;
6.7) organizing all secondary levels, and selecting one neighbor for data forwarding according to a betting round algorithm.
Compared with the prior art, the invention has the following beneficial effects:
the invention divides the sensor network into layers, selects code word codes by using a betting round probability algorithm, fits the probability of forwarding data packets to the lowest-layer-level class node by using a BP algorithm, uses a genetic algorithm to solve the optimal solution of the probability and considers the energy consumption condition of the neighbor nodes, so that important data can be quickly transmitted to a sink node and can be quickly solved, and meanwhile, the total energy consumption of the network is reduced to the minimum.
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FIG. 1 is a block diagram of a general implementation of the method of the present invention.
Fig. 2 is a network diagram after layering.
Detailed Description
For further understanding of the present invention, the present invention will be described in detail with reference to examples, which are provided for illustration of the present invention but are not intended to limit the scope of the present invention.
With reference to fig. 1, the method for rapidly collecting important data based on an incremental code according to the present invention includes the following steps:
1) with reference to fig. 2, randomly scattering a plurality of sensing nodes Ni in a monitoring area, and placing a sink node at an edge position of the monitoring area;
2) in order to enable the sensing node to roughly know the position of the sink node and enable transmission to have certain directivity, each node is divided into levels by using a hierarchical algorithm according to the distance from the sink node, and the level of the nodes is lower when the nodes are closer to the sink node, the method comprises the following specific steps:
2.1) the aggregation node generates a hello data packet, wherein the hello data packet carries incremental hierarchical data L and sends the incremental hierarchical data L to the neighbor node;
2.2) the neighbor node takes out the L in the hello packet, sets the hierarchy of the neighbor node as L, updates the hello data packet, and sets the hierarchy information L in the data packet as L + 1. Broadcasting the data packet to own neighbor nodes;
2.3) if the neighbor node has received the hello data packet, directly discarding the data packet, otherwise, executing the step 2.2;
2.4) repeating the steps 2.2 and 2.3 until all nodes have the hierarchical information;
3) each sensing node Ni detects the surrounding environment to generate monitoring data xi, and the maximum MaxMerge of the sensing node Ni is set to be 1;
4) the sensing node fills the monitoring data xi of the sensing node into the cache medium C of the sensing node, so that all the code words S in the cache medium C are xi, and the monitoring data xi of the sensing node is stored as source data of the node code;
5) selecting one centralized sensing node to perform a round of calculation, selecting a code word S of the sensing node from a buffer medium C, selecting whether the code word S of the sensing node per se is subjected to encoding operation by the sensing node according to the maximum degree MaxMerge of the sensing node Ni, the degree of the code word S, degree (S) and whether the code word S contains detection data xi, and the specific steps are as follows:
5.1) calculating the Weight of each code word S in the current sensing node cache,
wherein p is the number of priority data in the current codeword, n is the number of total priority data in the buffer, c is 60, and K is 10;
5.2) calculate the Sum of total weights Sum Sum,
calculating the probability of a single code word S, and then calculating the accumulated probability;
5.3) selecting a code word S from the buffer medium C according to a gambling wheel, wherein the larger the weight of the code word is, the higher the probability of selection is;
5.4) if the degree of the selected codeword S is (S) < MaxDegree and xi is not included in the codeword S, performing exclusive-or calculation S ═ S ⊕ xi, and if xi is already included in the current codeword S or the degree of the selected codeword S is (S) > MaxDegree, not performing encoding;
5.5) if the current round K is greater than the degree conversion round, the maximum degree MaxMerge of the sensing node Ni is increased by 1 and expressed by MaxMerge + +, if the current round K is less than the degree conversion round, no action is executed, and the degree conversion round uses KjMaxDgree, wherein,
j is the degree of the current network, N is the number of sensing nodes,
6) calculating the probability of forwarding the data packet of the sink node to each neighbor and forming a probability forwarding table, sensing the neighbor around the sensing node by the sensing node, establishing an energy consumption estimation table related to the neighbor, selecting one neighbor node to exchange code words according to the probability forwarding table and the energy consumption estimation table, wherein the exchanged code words are the code words generated in the step 5, and the specific steps are as follows:
6.1) fitting the relation between the forwarding probability and the recovery turns by using a BP neural network;
6.2) using GA algorithm to search the optimal forwarding probability to form a probability forwarding table;
6.3) each node senses the neighbor around itself and establishes an energy consumption estimation table about the neighbor, and the energy consumption of each neighbor is initially EiWhen the induction node selects to exchange data with a neighbor, updating the energy consumption value of the neighbor in the energy consumption estimation table of the induction node, and if data transmission is carried outThe energy consumption value of the corresponding neighbor is calculated Ei＝Ei+0.03, if data reception is performed, the corresponding neighbor energy consumption value is calculated Ei＝Ei+0.01, and updating the energy consumption estimation table in the neighbor node;
6.4) according to the code word value of the code word S, searching the probability A corresponding to the code word value closest to the code word value in a probability forwarding table as a primary probability forwarded to the neighbor of the type with the lowest hierarchy;
6.5) estimating the energy consumption of the neighbor nodes according to the energy consumption estimation model, and distributing secondary probability according to the energy consumption, wherein the higher the energy consumption is, the less the distributed secondary probability is;
6.6) the rest 1-A first-level probability is averagely distributed to the rest hierarchical classes, and the inside of each hierarchical class is also distributed to the first-level hierarchical level according to energy consumption;
6.7) organizing all secondary levels, and selecting a neighbor for data forwarding according to a betting round algorithm;
7) the sink node initializes a simple code word set X and a complex code word set Y, wherein the simple code word set X is used for storing decoded original data, and the complex code word set Y is used for storing un-decoded code words;
8) the aggregation node decodes the code words in the received complex code word set Y;
9) and (5) judging whether all the code words S are solved or not or whether the maximum round set by people is reached, if not, selecting another sensing node to perform the next round of calculation, returning to the step 5, and if so, finishing.
The sensing node can sense whether the data generated by the sensing node is important data, and when the code word S is generated, the sensing node can also check the content of the important data in the code word (a field in the code word S is used for indicating how many important data in the current code word)
The present invention has been described in detail with reference to the embodiments, but the description is only for the preferred embodiments of the present invention and should not be construed as limiting the scope of the present invention. All equivalent changes and modifications made within the scope of the present invention shall fall within the scope of the present invention.
Claims (7)
1. An important data rapid collection method based on an increasing code is characterized in that: which comprises the following steps:
1) randomly scattering a plurality of sensing nodes Ni in a monitoring area, and placing a sink node at the edge position of the monitoring area;
2) according to the distance from the sink node, each node is divided into layers by using a layering algorithm;
3) each sensing node Ni detects the surrounding environment to generate monitoring data xi, and the maximum MaxMerge of the sensing node Ni is set to be 1;
4) the sensing node fills the monitoring data xi of the sensing node into the cache medium C of the sensing node, so that all the code words S in the cache medium C are xi, and the monitoring data xi of the sensing node is stored as source data of the node code;
5) selecting one centralized sensing node to perform a round of calculation, selecting a code word S of the sensing node from a buffer medium C, and selecting whether the code word S per se is subjected to encoding operation or not by the sensing node according to the maximum degree MaxMerge of the sensing node Ni, the degree of the code word S and (S) and whether the code word S contains detection data xi or not;
6) calculating the probability of forwarding the data packet of the sink node to each neighbor and forming a probability forwarding table, sensing the neighbor around the sensing node by the sensing node, establishing an energy consumption estimation table related to the neighbor, and selecting one neighbor node to exchange code words according to the probability forwarding table and the energy consumption estimation table, wherein the exchanged code words are the code words generated in the step 5;
7) the sink node initializes a simple code word set X and a complex code word set Y, wherein the simple code word set X is used for storing decoded original data, and the complex code word set Y is used for storing un-decoded code words;
8) the aggregation node decodes the code words in the received complex code word set Y;
9) and (5) judging whether all the code words S are solved or not or whether the maximum round set by people is reached, if not, selecting another sensing node to perform the next round of calculation, returning to the step 5, and if so, finishing.
2. The method for rapidly collecting important data based on the extended code as claimed in claim 1, wherein: the layering algorithm in the step 2 comprises the following steps:
2.1) the aggregation node generates a hello data packet, wherein the hello data packet carries incremental hierarchical data L and sends the incremental hierarchical data L to the neighbor node;
2.2) the neighbor node takes out the L in the hello packet, sets the hierarchy of the neighbor node as L, updates the hello data packet, and sets the hierarchy information L in the data packet as L + 1. Broadcasting the data packet to own neighbor nodes;
2.3) if the neighbor node has received the hello data packet, directly discarding the data packet, otherwise, executing the step 2.2;
2.4) repeat steps 2.2 and 2.3 until all nodes have hierarchical information.
3. The method for rapidly collecting important data based on the extended code as claimed in claim 1, wherein: the algorithm in step 5 comprises the following steps:
5.1) calculating the Weight of each code word S in the current sensing node cache;
5.2) calculating the total weight Sum Sum, calculating the probability of a single code word S, and then calculating the accumulated probability;
5.3) selecting a code word S from the buffer medium C according to the betting round;
5.4) if the degree of the selected codeword S is (S) < MaxDegree and xi is not included in the codeword S, performing exclusive-or calculation S ═ S ⊕ xi, and if xi is already included in the current codeword S or the degree of the selected codeword S is (S) > MaxDegree, not performing encoding;
5.5) if the current round k is larger than the degree conversion round, the maximum degree MaxDegree of the sensing node Ni is automatically increased by 1.
4. The method for rapidly collecting important data based on the extended code as claimed in claim 3, wherein: the calculation method of the Weight in the step 5.1 is as follows:
where p is the number of priority data in the current codeword, n is the number of total priority data in the buffer, c is 60, and K is 10.
5. The method for rapidly collecting important data based on the extended code as claimed in claim 3, wherein: the weight and Sum calculation mode of the step 5.1 is as follows:
6. the method for rapidly collecting important data based on the extended code as claimed in claim 3, wherein: the degree conversion round K in the step 5.5jMaxDgree, wherein,
j is the degree of the current network, N is the number of sensing nodes,
7. the method for rapidly collecting important data based on the extended code as claimed in claim 1, wherein: the steps included in step 6 are as follows:
6.1) fitting the relation between the forwarding probability and the recovery turns by using a BP neural network;
6.2) using GA algorithm to search the optimal forwarding probability to form a probability forwarding table;
6.3) each node senses the neighbor around itself and establishes an energy consumption estimation table about the neighbor, and the energy consumption of each neighbor is initially EiAt the induction node of 0Updating the energy consumption value of a neighbor in the energy consumption estimation table of the induction node when the point selects to exchange data with the neighbor, and calculating the energy consumption value of the corresponding neighbor if the point selects to exchange data with the neighbori＝Ei+0.03, if data reception is performed, the corresponding neighbor energy consumption value is calculated Ei＝Ei+0.01, and updating the energy consumption estimation table in the neighbor node;
6.4) according to the code word value of the code word S, searching the probability A corresponding to the code word value closest to the code word value in a probability forwarding table as a primary probability forwarded to the neighbor of the type with the lowest hierarchy;
6.5) estimating the energy consumption of the neighbor node according to the energy consumption estimation model, and distributing a secondary probability according to the energy consumption;
6.6) the rest 1-A first-level probability is averagely distributed to the rest hierarchical classes, and the inside of each hierarchical class is also distributed to the first-level hierarchical level according to energy consumption;
6.7) organizing all secondary levels, and selecting one neighbor for data forwarding according to a betting round algorithm.
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