CN118574150B - Wireless sensor network simulation method based on cellular automaton - Google Patents
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Abstract
The invention discloses a wireless sensor network simulation method based on cellular automata, which comprises the following steps: acquiring wireless sensor network information; processing the wireless sensor network information by using a grid space simulation model to obtain wireless sensor grid information; performing state updating and evaluation processing on the wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information; the wireless sensor network evaluation result information is used for representing the destruction resistance of the wireless sensor network. According to the invention, the functions of network simulation operation, fault scenario simulation, performance test and the like under dynamic conditions are realized by constructing the wireless sensor network model, so that the accurate and rapid evaluation of the network survivability is realized, and a foundation is laid for the wireless network task planning.
Description
Technical Field
The invention relates to the field of computer simulation, in particular to a wireless sensor network simulation method based on cellular automata.
Background
In recent years, with the increasing maturity of sensor technology, wireless communication technology and microprocessor technology and the reduction of related hardware cost and computing cost, a large number of sensor nodes with the capabilities of collecting information, processing data and wireless communication are integrated, so that a wireless sensor network for monitoring a designated field is widely put into use, and the wireless sensor network is gradually an important bottom network for realizing the application of the internet of things. In practical application, because the wireless sensor network has the characteristics of large scale, limited resources, random deployment, self-organization and the like, and the internal factors such as topological structures, transmission paths and the like are dynamically changed, the running state and life cycle of the network in actual work are difficult to predict, and the reliability, availability and other survivability indexes of the collected data are also difficult to evaluate. The existing wireless sensor network model is inaccurate in modeling factors such as network degree distribution, network side weight, node communication radius, geographical space position constraint, energy failure, component failure, network survivability, failure condition of network assumption, incapability of covering real network characteristics and the like, and the survivability performance of the wireless sensor network in the actual situation cannot be truly represented.
Therefore, a feasible and effective survivability evaluation method and device are constructed under the conditions that network degree distribution, network side weight influence, influence of node communication radius on network energy consumption, geographical space position constraint, network assumption failure situation cannot cover real network characteristics, energy failure, component failure, network survivability and other factors are comprehensively considered, and the method and device have important theoretical and practical values for researching the operation mechanism and practical application of the wireless sensor network.
Disclosure of Invention
The invention aims to solve the technical problems of providing a wireless sensor network simulation method and device based on cellular automaton, which comprehensively consider the factors such as network degree distribution, network side weight influence, influence of node communication radius on network energy consumption, geographical space position constraint, incapability of covering real network characteristics due to network assumption failure, energy failure, component failure, network survivability and the like, and improve survivability evaluation accuracy.
In order to solve the technical problems, a first aspect of the embodiment of the present invention discloses a wireless sensor network simulation method based on cellular automata, including:
S1, acquiring wireless sensor network information; the wireless sensor network information comprises network distribution range, node position information, node connection information, node state information and node type information of the wireless sensor network; the wireless sensor network comprises wireless sensor nodes; the node connection information comprises information of adjacent nodes connected with each node; the node type information comprises edge nodes and sink nodes; the sink node is used for receiving the data of the edge node; the node state information comprises normal operation and failure;
S2, processing the wireless sensor network information by using a grid space simulation model to obtain wireless sensor grid information;
S3, carrying out state updating and evaluation processing on the wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information; the wireless sensor network evaluation result information is used for representing the destruction resistance of the wireless sensor network.
The processing the wireless sensor network information by using the grid space simulation model to obtain wireless sensor grid information comprises the following steps:
S21, carrying out regular grid division on the network distribution range to obtain a plurality of cells; the unit cell comprises unit cell range information and network node information; the network node information comprises type information and node state information of wireless sensor nodes contained in a cell range;
S22, determining the network node information of each cell according to the range information of each cell and the position information of the network node;
S23, determining a state value of each cell according to the network node information of the cell;
s24, determining adjacent state values of the cells according to the sum of the state values of adjacent cells of each cell in the network distribution range;
s25, determining a transfer function of each cell according to the state value and the adjacent state value of the cell;
s26, constructing and obtaining wireless sensor grid information by using the state values, the adjacent state values and the transfer function of all the cells.
The step of carrying out state updating and evaluation processing on the wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information comprises the following steps:
S31, acquiring attack information of the wireless sensor network; updating the wireless sensor grid information by utilizing the attack information;
s32, processing the wireless sensor grid information by using a fault model to obtain the fault probability and the energy value of the initialized unit grid;
s33, performing convergence connection judgment processing on the wireless sensor network information and the wireless sensor grid information to obtain a convergence node set and updated wireless sensor grid information;
S34, carrying out state update processing on the aggregation node set, the fault probability and the energy value of the unit cells and the wireless sensor grid information obtained in the S33 to obtain first wireless sensor grid information;
And S35, evaluating the first wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information.
The convergence connection discrimination processing includes:
constructing and obtaining a sink node set by utilizing all sink nodes in the wireless sensor network;
Determining all edge nodes connected with the sink node and all edge nodes not connected with the sink node according to node connection information in the wireless sensor network information;
Adding all edge nodes connected with the sink node into a sink node set; setting the state values of all the corresponding unit cells of the edge nodes connected with the sink node to be 2; and setting the state value of all the corresponding cells of the edge nodes which are not connected with the sink node to be 1.
And performing state update processing on the aggregation node set, the fault probability and the energy value of the unit cells, and the wireless sensor grid information obtained in the step S33 to obtain first wireless sensor grid information, wherein the method comprises the following steps:
s341, numbering all nodes in the aggregation node set to obtain serial number values of all nodes; setting the current sequence number value as 1;
s342, obtaining a node corresponding to the current sequence number value in the aggregation node set;
s343, randomly generating a first discrimination value; the value range of the first discrimination value is (0, 1);
S344, judging whether the failure probability of the corresponding cell of the node is larger than a first judging value, if not, setting the state value of the corresponding cell of the node as 1, setting the node and the adjacent collecting nodes as not connected, and updating the node connection information in the wireless sensor network information;
S345, judging whether the energy value of the node is smaller than or equal to 0, if the energy value of the node is smaller than or equal to 0, setting the state value of a corresponding cell of the node to be 1, setting the node to be unconnected with an adjacent collecting node, and updating the node connection information in the wireless sensor network information;
s346, updating the wireless sensor grid information by using the state values of the cells;
S347, processing the wireless sensor grid information by using a fault model to obtain an updated value of the fault probability and an updated value of the energy value of the cell;
s348, increasing the current sequence number by 1, and judging whether the current sequence number is larger than the total number of nodes in the aggregation node set or not to obtain a first judging result; if the first discrimination result is not greater than the total number of nodes in the aggregation node set, executing S342; if the first discrimination result is greater than the total number of nodes in the aggregation node set, executing S349;
S349, convergence connection judgment processing is carried out on the updated wireless sensor network information and the wireless sensor mesh information, and the obtained wireless sensor mesh information is confirmed to be the first wireless sensor mesh information.
The step of evaluating the first wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information comprises the following steps:
Performing survival rate calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain a survival rate index;
the expression of the survival rate calculation is as follows:
，
wherein, For the total number of nodes in the wireless sensor network,A survival index indicating the time t,Representing the number of cells with a status value of 2 in the first wireless sensor mesh information,A state value of a node corresponding cell indicating that the node position information is (i, j),Representing wireless sensor mesh information;
performing network coverage rate calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain network coverage rate;
The expression of the network coverage rate calculation is as follows:
，
wherein, A state discrimination value indicating a node whose node position information is (i, j), when the sum of the state values of the cells corresponding to all the connected nodes of the node whose node position information is (i, j) is equal to the number of all the connected nodes of the node whose node position information is (i, j),; When the sum of the state values of the cells corresponding to all the connected nodes of the node whose node position information is (i, j) is greater than the number of all the connected nodes of the node whose node position information is (i, j),；Representing the sum of state values of all cells, and n represents the side length of the network distribution range of the wireless sensor network;
Performing network efficiency calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain a network efficiency value;
the expression of the network efficiency calculation is as follows:
，
wherein, Representing the set of nodes that are operating properly at time t,Node representing node position information (i, j) atHop count from moment to adjacent sink node;
And carrying out combination processing on the survival rate index, the network coverage rate and the network efficiency value to obtain the evaluation result information of the wireless sensor network.
The fault model comprises an energy value calculation sub-model and a fault probability calculation sub-model;
The energy value calculation sub-model is used for calculating the energy value of the node according to the message quantity sent by the node; the calculation expression of the energy value calculation sub-model is as follows:
，
，
wherein, To calculate the energy value of the node at time t,Is the initial energy value of the node at time t,For the node direction distance at time tThe external node sends the data with the size ofIs the energy consumed by the message of (a),Is node transmissionThe energy that the data is required to consume,Is a coefficient of transmission loss;
the fault probability calculation sub-model is used for calculating the fault probability of the node;
The calculation expression of the fault probability calculation sub-model is as follows:
，
wherein, Representing the probability of failure of a node whose node position information is (i, j),Is the number of neighboring nodes to the node,Is the failure coefficient.
The second aspect of the embodiment of the invention discloses a wireless sensor network simulation device based on cellular automaton, which comprises:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute the wireless sensor network simulation method based on the cellular automaton.
In a third aspect of the embodiments of the present invention, a computer-readable medium is disclosed, where the computer-readable medium stores computer instructions that, when invoked, are configured to perform the cellular automaton-based wireless sensor network simulation method.
According to a fourth aspect of the embodiment of the invention, an information data processing terminal is used for realizing the wireless sensor network simulation method based on cellular automaton.
The beneficial effects of the invention are as follows:
1. According to the invention, the functions of network simulation operation, fault scenario simulation, performance test and the like under dynamic conditions are realized by constructing the wireless sensor network model, so that the accurate and rapid evaluation of the network survivability is realized, and a foundation is laid for the wireless network task planning.
2. The method simulates a plurality of nodes in the wireless sensor network by using the cells aiming at the running situation of the wireless sensor network, and the state information of each node and the adjacent nodes can be used for updating the state of the next time segment. Meanwhile, the energy exhaustion, software and hardware faults, connectivity failure, malicious attack and other fault conditions are introduced and operation design is carried out, and three survivability measurement indexes of survival rate, network coverage rate and network efficiency are introduced into the model, so that the method can effectively calculate the operation state of the wireless sensor network in a real scene and analyze the network performance change of the wireless sensor network, and further achieve the beneficial effects of guiding the deployment or maintenance of the wireless sensor network in the real scene through the network operation result so as to improve the survivability and prolong the network service life.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
For a better understanding of the present disclosure, two embodiments are presented herein. FIG. 1 is a flow chart of the method of the present invention.
Example 1
The invention discloses a wireless sensor network simulation method based on cellular automata, which is realized based on a computer and comprises the following steps:
S1, acquiring wireless sensor network information; the wireless sensor network information comprises network distribution range, node position information, node connection information, node state information and node type information of the wireless sensor network; the wireless sensor network comprises wireless sensor nodes; the node connection information comprises information of adjacent nodes connected with each node; the node type information comprises edge nodes and sink nodes; the sink node is used for receiving the data of the edge node; the node state information comprises normal operation and failure;
s2, processing the wireless sensor network information by using a grid space simulation model to obtain wireless sensor grid information; the cellular automaton is realized based on the grid space simulation model;
S3, carrying out state updating and evaluation processing on the wireless sensor grid information to obtain wireless sensor network evaluation result information; the wireless sensor network evaluation result information is used for representing the destruction resistance of the wireless sensor network.
The processing the wireless sensor network information by using the grid space simulation model to obtain wireless sensor grid information comprises the following steps:
S21, carrying out regular grid division on the network distribution range to obtain a plurality of cells; the unit cell comprises unit cell range information and network node information; the network node information comprises type information and node state information of wireless sensor nodes contained in a cell range;
S22, determining the network node information of each cell according to the range information of each cell and the position information of the network node;
S23, determining the state value of any cell according to the network node information of the cell;
S24, determining the adjacent state value of any cell according to the sum of the state values of the adjacent cells of the cell in the network distribution range;
S25, determining a transfer function of any cell according to the state value and the adjacent state value of the cell;
s26, constructing and obtaining wireless sensor grid information by using the state value, the adjacent state value and the transfer function of any cell.
The determining the state value of the cell according to the network node information of any cell comprises the following steps:
Consider the deployment state of 4 nodes: undeployed nodes, deployed nodes and working normally, deployed nodes are failed, and sink nodes are deployed;
In the wireless sensor network node, the state value of each cell has four values, which are expressed as 。
"0" Means that no node is deployed in the cell;
"1" means that a sensor node is deployed in the cell, but the sensor node has failed;
"2" means that an edge sensor is disposed in the cell and the sensor node is capable of normal operation;
"3" indicates that the sink node is deployed in the cell, and messages generated by other nodes are ultimately delivered to the sink node.
The transfer function may be characterized by a Markov function.
The step of carrying out state updating and evaluation processing on the wireless sensor grid information to obtain wireless sensor network evaluation result information comprises the following steps:
S31, acquiring attack information of the wireless sensor network; updating the wireless sensor grid information by utilizing the attack information;
s32, processing the wireless sensor grid information by using a fault model to obtain the fault probability and the energy value of the initialized unit grid;
s33, performing convergence connection judgment processing on the wireless sensor network information and the wireless sensor grid information to obtain a convergence node set and updated wireless sensor grid information;
S34, carrying out state update processing on the aggregation node set, the fault probability and the energy value of the unit cells and the wireless sensor grid information obtained in the S33 to obtain first wireless sensor grid information;
And S35, evaluating the first wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information.
The S31 includes:
Acquiring a wireless sensor node under attack in the wireless sensor network; determining a cell where the wireless sensor node is located; and modifying the state value of the cell of the attacked wireless sensor node to be 1.
The convergence connection discrimination processing includes:
constructing and obtaining a sink node set by utilizing all sink nodes in the wireless sensor network;
Determining all edge nodes connected with the sink node and all edge nodes not connected with the sink node according to node connection information in the wireless sensor network information;
Adding all edge nodes connected with the sink node into a sink node set; setting the state values of all the corresponding unit cells of the edge nodes connected with the sink node to be 2; and setting the state value of all the corresponding cells of the edge nodes which are not connected with the sink node to be 1.
And performing state update processing on the aggregation node set, the fault probability and the energy value of the unit cells, and the wireless sensor grid information obtained in the step S33 to obtain first wireless sensor grid information, wherein the method comprises the following steps:
s341, numbering all nodes in the aggregation node set to obtain serial number values of all nodes; setting the current sequence number value as 1;
s342, obtaining a node corresponding to the current sequence number value in the aggregation node set;
s343, randomly generating a first discrimination value; the value range of the first discrimination value is (0, 1);
S344, judging whether the failure probability of the corresponding cell of the node is larger than a first judging value, if not, setting the state value of the corresponding cell of the node as 1, setting the node and the adjacent collecting nodes as not connected, and updating the node connection information in the wireless sensor network information;
S345, judging whether the energy value of the node is smaller than or equal to 0, if the energy value of the node is smaller than or equal to 0, setting the state value of a corresponding cell of the node to be 1, setting the node to be unconnected with an adjacent collecting node, and updating the node connection information in the wireless sensor network information;
s346, updating the wireless sensor grid information by using the state values of the cells;
S347, processing the wireless sensor grid information by using a fault model to obtain the fault probability and the updated value of the energy value of the cell;
s348, increasing the current sequence number by 1, and judging whether the current sequence number is larger than the total number of nodes in the aggregation node set or not to obtain a first judging result; if the first discrimination result is not greater than the total number of nodes in the aggregation node set, executing S342; if the first discrimination result is greater than the total number of nodes in the aggregation node set, executing S349;
S349, convergence connection judgment processing is carried out on the updated wireless sensor network information and the wireless sensor mesh information, and the obtained wireless sensor mesh information is confirmed to be the first wireless sensor mesh information.
The step of evaluating the first wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information comprises the following steps:
Performing survival rate calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain a survival rate index;
the expression of the survival rate calculation is as follows:
，
wherein, For the total number of nodes in the wireless sensor network,A survival index indicating the time t,Representing the number of cells with a status value of 2 in the first wireless sensor mesh information,A state value of a node corresponding cell indicating that the node position information is (i, j),Representing wireless sensor mesh information;
performing network coverage rate calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain network coverage rate;
The expression of the network coverage rate calculation is as follows:
，
wherein, A state discrimination value indicating a node whose node position information is (i, j), when the sum of the state values of the cells corresponding to all the connected nodes of the node whose node position information is (i, j) is equal to the number of all the connected nodes of the node whose node position information is (i, j),; When the sum of the state values of the cells corresponding to all the connected nodes of the node whose node position information is (i, j) is greater than the number of all the connected nodes of the node whose node position information is (i, j),；Representing the sum of state values of all cells, and n represents the side length of the network distribution range of the wireless sensor network;
Performing network efficiency calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain a network efficiency value;
the expression of the network efficiency calculation is as follows:
，
wherein, Representing the set of nodes that are operating properly at time t,Node representing node position information (i, j) atHop count from moment to adjacent sink node;
And carrying out combination processing on the survival rate index, the network coverage rate and the network efficiency value to obtain the evaluation result information of the wireless sensor network.
The fault model comprises an energy value calculation sub-model and a fault probability calculation sub-model;
The energy value calculation sub-model is used for calculating the energy value of the node according to the message quantity sent by the node; the calculation expression of the energy value calculation sub-model is as follows:
，
，
wherein, To calculate the energy value of the node at time t,Is the initial energy value of the node at time t,For the node direction distance at time tThe external node sends the data with the size ofIs the energy consumed by the message of (a),Is node transmissionThe energy that the data is required to consume,Is a coefficient of transmission loss.
The fault probability calculation sub-model is used for calculating the fault probability of the node;
The calculation expression of the fault probability calculation sub-model is as follows:
，
wherein, Representing the probability of failure of a node whose node position information is (i, j),Is the number of neighboring nodes to the node,Is the failure coefficient.
The second aspect of the embodiment of the invention discloses a wireless sensor network simulation device based on cellular automaton, which comprises:
a memory storing executable program code;
a processor coupled to the memory;
and the processor calls the executable program codes stored in the memory to execute the wireless sensor network simulation method based on the cellular automaton.
In a third aspect of the embodiments of the present invention, a computer-readable medium is disclosed, where the computer-readable medium stores computer instructions that, when invoked, are configured to perform the cellular automaton-based wireless sensor network simulation method.
According to a fourth aspect of the embodiment of the invention, an information data processing terminal is used for realizing the wireless sensor network simulation method based on cellular automaton.
Example two
The invention discloses a wireless sensor network simulation method based on cellular automata, which comprises the following steps:
S1, acquiring wireless sensor network information; the wireless sensor network information comprises network distribution range, node position information, node connection information, node state information and node type information of the wireless sensor network; the wireless sensor network comprises wireless sensor nodes; the node connection information comprises information of adjacent nodes connected with each node; the node type information comprises edge nodes and sink nodes; the sink node is used for receiving the data of the edge node; the node state information comprises normal operation and failure;
S2, processing the wireless sensor network information by using a grid space simulation model to obtain wireless sensor grid information;
S3, carrying out state updating and evaluation processing on the wireless sensor grid information to obtain wireless sensor network evaluation result information; the wireless sensor network evaluation result information is used for representing the destruction resistance of the wireless sensor network.
The parameter setting of the wireless sensor network information and the wireless sensor grid information in the invention comprises the following steps: is a regular grid space made up of cells, Is the size of the grid space and,Is a finite set of states owned by a cell,Is the conversion function to be followed and,Is a cellAt the position ofThe state of the moment of time,Is a cellIs selected from the group of adjacent cells,Is with a cellAdjacent unit cells are atThe sum of the states of the moments in time,Is atTime of day and cellThe number of adjacent cells is determined by the number of cells,Is a cellAt the position ofThe energy of the moment of time is,Is a cellAt the position ofThe utility value of the time of day,Is a cellAt the position ofHop count from time to sink node,Is the weight of the routing protocol and,Is a cellIs used for the failure probability of the (c) in the (c),Is a cellIs used for the degree of (3),Is the attack scale.
The specific explanation is as follows:
Represents one Each element that makes up the grid space is called a cell. The grid space boundary is closed so that cells at the boundary have fewer neighbor cells than cells in other regions.
Representing a set of states that a cell has, the states of the cell are limited, and in the simulation of a wireless sensor network node, each cell has four states.。
"0" Means that no node is deployed in the cell;
"1" means that a sensor node is deployed in the cell, but the sensor node has failed;
"2" means that a sensor node is disposed in the cell, and the sensor node is capable of normal operation;
"3" indicates that the sink node is deployed in the cell, and messages generated by other nodes are ultimately delivered to the sink node.
In the model construction of the wireless sensor network node, a molar neighborhood structure is adopted, and each cell can have no more than eight nodes as adjacent cells.
The set of neighboring cells may be defined as:
。
Is assumed to be in Time of day, cellThe state of (2) is expressed asThen the sum of the states of this set of cell neighbor cell nodes can be expressed as。
At the same time can be obtainedTime cellThe number of surrounding neighbor nodes.
Representing the transfer function followed by the change in cell state, in this modeling effort the state of the cell is set to be influenced by the state of its neighboring cells. At a point in timeTime of day, cellThe state transfer function of (2) can be defined as
，
In an actual wireless sensor network, one sensor node needs to satisfy two conditions to work normally. The first condition is that its functional components can function properly, i.e. that it is neither faulty nor attacked nor energy-depleted. The second condition is that it can maintain at least one active path to the sink node. Based on these two conditions, a more specific and accurate transfer function can be obtained. The transfer function may be represented by a Markov chain function.
In a wireless sensor network applied in a real scene, the failure and failure sources of sensor nodes are various, and the failure and failure sources can be caused by external malicious attacks, can be caused by self energy exhaustion or software/hardware failure, or can be caused by self failure, but the connectivity failure caused by disconnection with a sink node. Notably, in wireless sensor networks, the failure process of a node is not irreversible, and if the sink node is mobile, some connectivity-failed nodes may reestablish contact with the sink node. In this section, energy depletion failure, software/hardware failure, and connectivity failure are modeled to obtain a failure model. The fault models include an energy exhaustion fault model, a hardware/software fault model and a connectivity failure fault model.
The energy exhaustion fault is a node failure condition caused by node energy exhaustion. In the simulation of the wireless sensor network, the sink node is considered to be not limited by energy, namely, is considered to have infinite energy. All other sensor nodes have limited energy, and the initial energy of all the sensor nodes is set to be the same. When inThe remaining energy at the moment is insufficient to support the normal operation of the sensor node, and the sensor node is set to fail due to energy exhaustion. In each time interval, the sensor node consumes a certain amount of energy to maintain the self-function, and the energy consumption is mainly considered to be transferred besides the normal energy consumption. Modeling sensor node energy consumption process using a first order radio model in which sensor nodes are facing distanceThe external sensor node sends the data with the size ofWill be consumed by the sensor nodeIs a function of the energy of the (c). The calculation formula is as follows: Wherein, the method comprises the steps of, wherein, Is node transmissionThe energy that the data is required to consume,Is a coefficient of transmission loss, where a free space channel model is used to calculate a specific value of transmission loss. And the sensor node receives a signal of the size ofWill be consumed by the sensor nodeEnergy. The calculation method comprises the following steps:。
Calculating sensor node energy consumption Previously, the data transmission of the wireless sensor node is first determined. Firstly, calculating to obtain the utility value of each wireless sensor node, wherein data transmission occurs in the transmission process from a low utility value node to a high utility value node. And calculating the energy consumption value of the data sending node in the transmission process from the low utility value node to the high utility value node between adjacent nodes, and then calculating to obtain the energy value. The present invention designs a specific routing protocol to support operation for forwarding activity of data by the sensor nodes. In this protocol, each sensor node is assigned a utility value and a message is sent from the low utility value node to the high utility value node. The utility value of each sensor node depends on both its remaining energy and its minimum hop distance to the sink node. Because the remaining energy of each sensor node in the network and its minimum hop distance to the sink node change over time, the utility value also changes. The specific calculation method of the utility value comprises the following steps: . Wherein the method comprises the steps of Refers to the node (i, j) atThe utility value of the time of day,Is a nodeAt the position ofThe minimum hop distance of the moment, i.e. the minimum hop count of the node to the sink node closest to it,Representing energy (i, j represents coordinates); And Is to adjust the weighting coefficient of the routing performance, and the condition should be satisfiedObviously, the utility value of the sensor node is as followsBetween them. It should be noted that when a sensor node fails, its utility will be set to 0. In addition, in order to ensure that the data finally reaches the sink nodes, the utility value of all the sink nodes is set to be 1, and the highest utility value of the sink nodes in the network is ensured.
For hardware/software failure models, in a practical scenario, the load differences between sensor nodes are very significant. The load on the "central" nodes is much higher than other nodes, which makes these central nodes more likely to fail due to buffer overflows and other reasons, while the "edge" nodes are more in environments where natural conditions are severe, with different probabilities of failure. Modeling hardware/software faults asHere, whereIs a sensor nodeProbability of software and hardware failure in each time step (emulation time stamp); Is a sensor node I.e. the number of sensor nodes and sink nodes in its neighboring cell space.Is the failure coefficient. In the present invention, there is provided=5。
For a connectivity failure model, in a wireless sensor network, when some sensor nodes fail, connectivity of other nodes in the network may be affected. In this process, some sensor nodes may be isolated because their paths to the sink node are cut off, so their messages eventually cannot be sent to the sink node. In this simulation model, an isolated fault may be considered to be a fault due to the lack of a properly functioning node in the adjacent cell space. If the sensor node is located in the cellCannot meetAndUnder the two conditions, the node is considered to be an 'island' with connectivity failure in the network, and the connectivity value of the node is judged to be 0; if the two conditions are met, judging that the connectivity value is 1; in S347 and S32, updating and calculating the node connectivity value are also included.
After S344, before S345, determining a connectivity value of a node, and if the connectivity value of the node is equal to 0, setting a state value of a cell corresponding to the node to be 1, setting the node to be disconnected from an adjacent aggregation node, and updating node connection information in the wireless sensor network information;
the attack information of the wireless sensor network is acquired; updating the wireless sensor mesh information by using the attack information, including:
In order to comprehensively evaluate and analyze the survivability of the wireless sensor network under different types of attacks, three attack strategies, namely random attack, maximum attack and maximum-betweenness attack, are designed in operation modeling. In the model, all attacks are considered as synchronous attacks, that is to say, the attacks on the target node are completed at the same time. In the model, the object of the attack does not consider the sink node, because the entire network collapses immediately once the sink node fails, without the value of the comparative analysis.
The random attack means that an attacker randomly selects some nodes to attack, and under the random attack strategy, the attack proportion is randomly selectedAnd sets the node status after the attack to 1.
The maximum degree attack refers to that an attacker selects an attack target as a node with the maximum degree of centrality in the network, namely, a node with the maximum number of directly connected nodes. The goal of this attack is to break the integrity and stability of the network. Under the maximum attack strategy, firstly calculating the degree of all nodes, carrying out descending order and sorting, and attacking proportionThe node whose degree value is large is set to 1.
The maximum-betweenness attack refers to that an attacker selects an attack target as a node with highest betweenness centrality. The betting center refers to the frequency at which the node in the network acts as an intermediary. The goal of the attack mode is to destroy the connectivity of the network, because the medium central node plays an important role of medium in the network, under the maximum medium attack strategy, the medium of all nodes is calculated at first, descending order and sorting are carried out, and the attack proportion is calculatedThe node with the large medium value sets its state to 1. Unlike the general dielectric values of the network, the directional dielectric values designed for the wireless sensor network are used in this attack strategy, and when the shortest path number passing through the nodes is calculated, the starting points of the paths are any other nodes, but the end points must be sink nodes. The betweenness can be calculated by using the reciprocal of the attack cost of the node.
The directional medium value reflects the ability of the sensor node to control the network data flow. From the perspective of the shortest path, the larger the medium value of a node, the more times the node appears on the shortest path between other nodes connected in the network, the more information the node forwards to the sink node in the network, so the node has a great influence on the efficiency of the network as a hub.
In a wireless sensor network, the cost of attacking different nodes depends on the location, functionality, security measures, and vulnerability of the node. Edge nodes are nodes located at the edge of the network that are generally less secure and therefore vulnerable and relatively less costly to attack. Core nodes are nodes that connect the entire network in a central area of the network, are relatively high in security and resources, and are located in relatively secure areas. The cost of attacking these nodes is high, requiring considerable technology and resources.
In the model, for simplicity, the difference of attack costs to sensor nodes is set according to the distance from the sink node, so as to distinguish the attack costs of core nodes from non-core nodes. Sensor nodes with the jump distance within a certain range from the sink node are considered as core nodes, and are deployed in a place with safer task area position space in an actual scene and are provided with certain protective measures, so that the cost of attack is higher for an attacker. The nodes near the edge in the network are non-core nodes, so that the security is weak due to limited resources, spatial position relation and the like, and the cost required for attacking the nodes is low. The specific calculation formula is designed as follows. Calculation formula of cost of each nodeHere, where，Are all the cost coefficients of the two-dimensional model,Is a nodeMinimum number of hops to sink node.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.
Claims (7)
1. The cellular automaton-based wireless sensor network simulation method is characterized by comprising the following steps of:
S1, acquiring wireless sensor network information; the wireless sensor network information comprises network distribution range, node position information, node connection information, node state information and node type information of the wireless sensor network; the wireless sensor network comprises wireless sensor nodes; the node connection information comprises information of adjacent nodes connected with each node; the node type information comprises edge nodes and sink nodes; the sink node is used for receiving the data of the edge node; the node state information comprises normal operation and failure;
S2, processing the wireless sensor network information by using a grid space simulation model to obtain wireless sensor grid information;
S3, carrying out state updating and evaluation processing on the wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information; the wireless sensor network evaluation result information is used for representing the destruction resistance of the wireless sensor network;
the processing the wireless sensor network information by using the grid space simulation model to obtain wireless sensor grid information comprises the following steps:
S21, carrying out regular grid division on the network distribution range to obtain a plurality of cells; the unit cell comprises unit cell range information and network node information; the network node information comprises type information and node state information of wireless sensor nodes contained in a cell range;
S22, determining the network node information of each cell according to the range information of each cell and the position information of the network node;
S23, determining a state value of each cell according to the network node information of the cell;
s24, determining adjacent state values of the cells according to the sum of the state values of adjacent cells of each cell in the network distribution range;
s25, determining a transfer function of each cell according to the state value and the adjacent state value of the cell;
s26, constructing and obtaining wireless sensor grid information by using the state values, the adjacent state values and the transfer function of all the cells.
2. The cellular automaton-based wireless sensor network simulation method of claim 1, wherein the performing state update and evaluation processing on the wireless sensor mesh information and the wireless sensor network information to obtain wireless sensor network evaluation result information includes:
S31, acquiring attack information of the wireless sensor network; updating the wireless sensor grid information by utilizing the attack information;
s32, processing the wireless sensor grid information by using a fault model to obtain the fault probability and the energy value of the initialized unit grid;
s33, performing convergence connection judgment processing on the wireless sensor network information and the wireless sensor grid information to obtain a convergence node set and updated wireless sensor grid information;
S34, carrying out state update processing on the aggregation node set, the fault probability and the energy value of the unit cells and the wireless sensor grid information obtained in the S33 to obtain first wireless sensor grid information;
S35, evaluating the first wireless sensor grid information and the wireless sensor network information to obtain wireless sensor network evaluation result information;
The convergence connection discrimination processing includes:
constructing and obtaining a sink node set by utilizing all sink nodes in the wireless sensor network;
Determining all edge nodes connected with the sink node and all edge nodes not connected with the sink node according to node connection information in the wireless sensor network information;
Adding all edge nodes connected with the sink node into a sink node set; setting the state values of all the corresponding unit cells of the edge nodes connected with the sink node to be 2; and setting the state value of all the corresponding cells of the edge nodes which are not connected with the sink node to be 1.
3. The cellular automaton-based wireless sensor network simulation method according to claim 2, wherein the performing a state update process on the collection of sink nodes, the failure probability and energy value of the cells, and the wireless sensor mesh information obtained in S33 to obtain first wireless sensor mesh information includes:
s341, numbering all nodes in the aggregation node set to obtain serial number values of all nodes; setting the current sequence number value as 1;
s342, obtaining a node corresponding to the current sequence number value in the aggregation node set;
s343, randomly generating a first discrimination value; the value range of the first discrimination value is (0, 1);
S344, judging whether the failure probability of the corresponding cell of the node is larger than a first judging value, if not, setting the state value of the corresponding cell of the node as 1, setting the node and the adjacent collecting nodes as not connected, and updating the node connection information in the wireless sensor network information;
S345, judging whether the energy value of the node is smaller than or equal to 0, if the energy value of the node is smaller than or equal to 0, setting the state value of a corresponding cell of the node to be 1, setting the node to be unconnected with an adjacent collecting node, and updating the node connection information in the wireless sensor network information;
s346, updating the wireless sensor grid information by using the state values of the cells;
S347, processing the wireless sensor grid information by using a fault model to obtain an updated value of the fault probability and an updated value of the energy value of the cell;
s348, increasing the current sequence number by 1, and judging whether the current sequence number is larger than the total number of nodes in the aggregation node set or not to obtain a first judging result; if the first discrimination result is not greater than the total number of nodes in the aggregation node set, executing S342; if the first discrimination result is greater than the total number of nodes in the aggregation node set, executing S349;
S349, convergence connection judgment processing is carried out on the updated wireless sensor network information and the wireless sensor mesh information, and the obtained wireless sensor mesh information is confirmed to be the first wireless sensor mesh information.
4. The cellular automaton-based wireless sensor network simulation method of claim 3, wherein the evaluating the first wireless sensor mesh information and the wireless sensor network information to obtain wireless sensor network evaluation result information includes:
Performing survival rate calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain a survival rate index;
performing network coverage rate calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain network coverage rate;
Performing network efficiency calculation processing on the first wireless sensor grid information and the wireless sensor network information to obtain a network efficiency value;
And carrying out combination processing on the survival rate index, the network coverage rate and the network efficiency value to obtain the evaluation result information of the wireless sensor network.
5. The cellular automaton-based wireless sensor network simulation method of claim 4, wherein the expression of the survival rate calculation is:
，
wherein, For the total number of nodes in the wireless sensor network,A survival index indicating the time t,Representing the number of cells with a status value of 2 in the first wireless sensor mesh information,A state value of a node corresponding cell indicating that the node position information is (i, j),Representing wireless sensor mesh information;
The expression of the network coverage rate calculation is as follows:
，
wherein, A state discrimination value indicating a node whose node position information is (i, j), when the sum of the state values of the cells corresponding to all the connected nodes of the node whose node position information is (i, j) is equal to the number of all the connected nodes of the node whose node position information is (i, j),; When the sum of the state values of the cells corresponding to all the connected nodes of the node whose node position information is (i, j) is greater than the number of all the connected nodes of the node whose node position information is (i, j),；Representing the sum of state values of all cells, and n represents the side length of the network distribution range of the wireless sensor network;
the expression of the network efficiency calculation is as follows:
，
wherein, Representing the set of nodes that are operating properly at time t,Node representing node position information (i, j) atHop count from moment to its neighboring sink node.
6. The cellular automaton-based wireless sensor network simulation method of claim 5, wherein the fault model comprises an energy value calculation sub-model and a fault probability calculation sub-model;
The energy value calculation sub-model is used for calculating the energy value of the node according to the message quantity sent by the node;
The fault probability calculation sub-model is used for calculating the fault probability of the node.
7. The cellular automaton-based wireless sensor network simulation method of claim 6, wherein the energy value calculation submodel has a calculation expression of:
，
，
wherein, To calculate the energy value of the node at time t,Is the initial energy value of the node at time t,For the node direction distance at time tThe external node sends the data with the size ofIs the energy consumed by the message of (a),Is node transmissionThe energy that the data is required to consume,Is a coefficient of transmission loss;
The calculation expression of the fault probability calculation sub-model is as follows:
，
wherein, Representing the probability of failure of a node whose node position information is (i, j),Is the number of neighboring nodes to the node,Is the failure coefficient.
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