A low-energy wireless sensor network routing control method based on grey decision making

Abstract

The present invention discloses a low-energy wireless sensor network routing control method based on grey decision-making. The method incorporates a trust evaluation mechanism, wherein a convergence node evaluates trust evaluation nodes in different regions, and the trust evaluation nodes evaluate nodes within the region and trust evaluation nodes in adjacent partitions, thereby fully utilizing the trust management mechanism to achieve security supervision of the entire network. In terms of routing selection, the method preliminarily screens out qualified candidate nodes by establishing polar coordinates, and then selects the next hop of the node by combining grey decision-making with comprehensive trust value, energy, and the number of hops between base stations. Experiments show that the method proposed by the present invention has good performance in terms of security and energy consumption.

Description

Technical Field

[0001] The present invention belongs to the field of wireless sensor network routing security, in particular to a low-power wireless sensor network routing control method based on grey decision-making. Background Art

[0002] Wireless sensor networks have now been applied in various fields. In the research of wireless sensor networks, due to the expansion of network scale and increase in complexity, problems such as network security and energy consumption have restricted the development of wireless sensor networks.

[0003] Research on the security of wireless sensor network routing protocols is largely based on trust assessment mechanisms. By designing trust assessment mechanisms, node security is analyzed and assessed to select secure and efficient nodes that meet network requirements and participate in normal network operation. Wu Yinfeng et al. proposed an innovative WSN secure routing protocol, SRBNT, for calculating node trust values. The protocol defines multiple reference methods and uses a weighted average approach to calculate the trust values of given data. During the cluster head election phase, the protocol first requires ordinary nodes within the cluster to perform a trust rating test on candidate nodes. A node with a high trust score is selected from all candidate nodes as the cluster head. Simultaneously, ordinary sensor nodes select a node with a high trust score as a shadow cluster head. The protocol then assesses the trust values of nodes within the cluster. If a node's trust score falls below a threshold, it is marked as malicious and excluded from network operation. This approach ensures that only nodes with the highest trust scores are elected as cluster heads, preventing malicious nodes from significantly impacting the network after becoming cluster heads. However, the protocol requires a trust assessment mechanism for each cluster head generated. This energy-intensive mechanism is not suitable for energy-constrained WSNs. Therefore, while improving reliability, energy consumption should be appropriately reduced.

[0004] The TSRF protocol takes into account a node's current and historical behavior, defines direct trust values based on the specifications of the first two, and provides specific trust calculation and trust derivation methods based on the recommended trust values of neighboring nodes. It also constructs secure routes based on the network's packet loss rate and latency. Building on TSRF, the TSSRM protocol introduces node energy and location coordinate attributes into the trust calculation process, enabling the network to effectively respond to highly concealed switch attacks. The TRPM protocol adds data-specific trust evaluation to the trust calculation process and provides an evaluation mechanism based on multiple attributes. However, the protocol stipulates that nodes will be in information mixing mode when analyzing data packet content. Eavesdropping on the data of the main node will increase the energy consumption of the evaluation node.

[0005] In summary, secure routing protocols for wireless sensor networks have become a research hotspot. Based on the energy consumption and security deficiencies of the aforementioned routing protocols, this paper proposes a low-power secure routing protocol (GDERP) based on grey decision-making. Grey theory, a concept proposed by Professor Deng Julong in 1982, is a new mathematical theory derived from grey sets. Its purpose is to address the uncertainty issues of discrete data and incomplete information. Summary of the Invention

[0006] In order to solve the technical problems mentioned in the above background technology, the present invention proposes a low-power wireless sensor network routing control method based on grey decision-making.

[0007] In order to achieve the above technical objectives, the technical solution of the present invention is:

[0008] A low-power wireless sensor network routing control method based on grey decision-making includes the following steps:

[0009] (1) The monitoring area is divided into several sub-areas. The base station is responsible for selecting a trust assessment node from each sub-area to monitor the security status of each area, thus realizing the deployment of trust assessment nodes.

[0010] (2) Enabling the trust evaluation mechanism: first, the trust evaluation node needs to perform trust evaluation on the nodes in the partition, then the trust evaluation node evaluates the neighboring trust evaluation nodes, and then forwards the data of each trust evaluation node to the aggregation node for processing, so as to decide whether to replace the evaluation node and improve the network's anti-attack capability;

[0011] (3) Based on the grey decision method and combined with the trust evaluation mechanism, the trust value of the candidate node pair, the remaining energy and the number of hops between base stations are input into the grey decision model, and the candidate node with better security and energy is comprehensively selected as the next hop.

[0012] Furthermore, in step (1), the deployment method of the trust evaluation node is as follows:

[0013] (101) The side length is uniformly determined as d0, and the monitoring area is equally divided into m*m sub-areas.

[0014] The base station is responsible for selecting a trust evaluation node from each sub-area to monitor the security status of each area, where m = [600 / d0];

[0015] (102) In each d0*d0 area, obtain the horizontal and vertical coordinates of each node within the area.

[0016] Generate a list of x-coordinates and a list of y-coordinates. Let the number of nodes in the region be n, and calculate the coordinates of the region's center point. The calculation formula is as follows:

[0017]

[0018]

[0019] (103) According to the distance between the node and the center point, the position relationship of each node is determined.

[0020] The calculation formula is:

[0021]

[0022] The calculation result of formula (1-3) is expressed as the position attribute of the node;

[0023] A node's trust value is a criterion for determining whether it is secure. A node with a higher trust value is more likely to be selected as a routing relay. It is determined by both current and past behavior. During the evaluation process, the evaluating node uses the evaluated node as the target node, transmits n1 packets to it, and then estimates the evaluated node based on the number of packets received, n2, using the classical Beta distribution to calculate the current trust value. The calculation method is as follows:

[0024]

[0025] Among them, the calculation result of formula (1-4) is the trust value of the node, E res Indicates the remaining energy value of the node, represents the mean value of the residual energy of nodes in the cluster;

[0026] (104) Consider the location attribute, trust value and residual energy as observation attributes, and design the “excellent” and “medium”

[0027] The three types of "poor" are used as gray classes to indicate whether the node is capable of serving as a trust evaluation node, and the possibility function of each attribute is set;

[0028] (105) Calculate the weight cost of each observation attribute according to (104), and obtain the clustering coefficient of the node belonging to the gray class "excellent" based on the location attribute, trust value and residual energy of each node based on the gray clustering evaluation algorithm;

[0029] (106) Sort the clustering coefficients within the region and select the node with the highest coefficient as the evaluation node.

[0030] Furthermore, in step (2), the method of evaluating the sink node is as follows:

[0031] (201) Calculate the average trust value of nodes in the statistical partition and construct a trust mean list;

[0032] (202) Select the median by comparing the constructed trust mean list, subtract each data in the list from the median, calculate the absolute difference, and construct a median difference list;

[0033] (203) Obtain the median of the median difference list and multiply it by the coefficient 1.4826 to generate the absolute median;

[0034] (204) Calculate the trust thresholds low and high according to the following formula:

[0035] low=MID-3*MAD (2-1)

[0036] high=MID+3*MAD (2-2)

[0037] Where MID is the median of the trust mean list, and MAD is the absolute median;

[0038] (205) Select the nodes in the trust mean list whose values are less than the threshold low or greater than the threshold high and regard them as suspect nodes. The partition marked by the suspect node executes (206), and the remaining nodes jump to (207);

[0039] (206) The aggregation node replaces the trust evaluation node of the current partition according to the trust evaluation node selection algorithm in step (1), and then proceeds to (208);

[0040] (207) The current trust value of the aggregation node is determined by the average trust value, and the comprehensive trust value is obtained based on the saved historical trust values of the nodes;

[0041] (208) After executing the above steps, the aggregation node records the trust values of all nodes.

[0042] Furthermore, in step (3), the specific method of grey decision making is as follows:

[0043] Based on the grey decision-making method and combined with the trust evaluation mechanism, the input parameters are set. Consider inputting the candidate node trust value, residual energy, and the number of hops between base stations into the decision model. Since the input of grey decision-making is linguistic variables, the grey decision-making model is used to make decisions by converting the actual numerical value into a grey number.

[0044] Assume S={S1,S2,...,S n} are m independent candidate nodes, Q={Q1,Q2,...,Q n} is a set of n attributes of candidate nodes, which are independent of addition operation. n} is the attribute weight vector. The attribute weights and attribute ratings of candidate nodes are considered as linguistic variables. Attribute weights are expressed as decimals between 0 and 1, and attribute ratings range from 0 to 10.

[0045] (301) Clearly define the attribute weights of candidate nodes. Assuming there are k decision makers, then Q j The attribute weights can be described as:

[0046]

[0047] w j k (j=1,2,3,...,n) is the attribute weight of the k-th decision maker, which can be described by gray numbers.

[0048] (302) Generate attribute rating value using language variables. Attribute rating value G j The calculation process is:

[0049]

[0050] G ij k (i=1,2,...,m,j=1,2,...,n) is the attribute rating value of the k-th decision maker and can be represented by the grey number express;

[0051] (303) Establishing a grey decision matrix

[0052]

[0053] (304) Normalized grey decision matrix

[0054]

[0055] Among them, the benefit attributes It can be expressed as:

[0056]

[0057]

[0058] The cost attribute It can be expressed as:

[0059]

[0060]

[0061] Normalize the data so that the normalized gray number is in the range of [0,1];

[0062] (305) Create a grey weighted normalized decision matrix. Since various attributes have different degrees of importance, the weighted normalized decision matrix can be described by the following matrix:

[0063]

[0064] in

[0065] (306) Take the ideal solution as an alternative, and for m candidate nodes S = {S1, S2, ..., S m}, ideal candidate node

[0066]

[0067] (307) Calculate the ideal reference node S max Grey possibility degree between the comparison candidate node S:

[0068]

[0069] (308) Sort the candidate nodes. When P{S i ≤S max The smaller the value, the higher the candidate node ranking, and vice versa.

[0070] According to the above steps, the ranking order of all candidate nodes can be determined, and the candidate node with the highest ranking can be selected as the next hop.

[0071] The trust value represents the sink node's objective assessment of the current node's security. Higher values indicate greater security and trustworthiness. Therefore, it serves as a benefit attribute in the model's input parameters. The trust interval is determined based on the trust value T and its variance V1, and can be expressed as [T-V1, T+V1]. Residual energy is also fed into the decision model as a benefit attribute. The energy interval is determined based on the network energy mean E and its variance V2, and can be expressed as [E-V2, E+V2]. The number of hops between a node and the base station objectively reflects the dynamic changes in the network topology. The hop value A between a node and the base station, as a cost attribute in the model, can be expressed as [A-1, A+1]. After the model parameters are determined, candidate nodes are selected according to the following rules. First, a polar coordinate system is established with the base station as the center. All nodes are distributed in two quadrants, and each quadrant is divided into three sectors. Second, the selection of candidate nodes should consider both the polar radius and the polar angle. The polar radius controls the direction of the next hop, ensuring that the transmission is to a node closer to the base station. The polar angle controls the degree of detour of the next hop, minimizing route tortuosity. Within the transmission power radius of the node, the following formula is used to filter out qualified candidate nodes:

[0072]

[0073] where R cur Represents the polar radius of the current node, θ cur Indicates the current node polar angle, R i and θ i They represent the polar radius and polar angle of the candidate node respectively. A node can be considered a candidate node only if both conditions are met.

[0074] The beneficial effects brought about by adopting the above technical solution are:

[0075] (1) The present invention incorporates a trust evaluation mechanism. The aggregation node evaluates the trust evaluation nodes in different regions. The trust evaluation nodes evaluate the nodes within the region and the trust evaluation nodes in adjacent partitions. This fully utilizes the trust management mechanism to achieve security supervision of the entire network and improve the network's anti-attack capability.

[0076] (2) In terms of routing selection, the present invention initially screens out qualified candidate nodes by establishing polar coordinates, and then selects the next hop of the node by combining the gray decision-making comprehensive trust value, energy, and the number of hops between base stations. This ensures security while reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a schematic diagram of the deployment distribution of trust assessment nodes of the present invention;

[0078] Figure 2 It is a schematic diagram of a trust evaluation table of a sink node of the present invention;

[0079] Figure 3 Schematic diagram of candidate cluster head selection of the present invention;

[0080] Figure 4 It is a line graph of the membership function of the node position attribute of the present invention;

[0081] Figure 5 It is a line graph of the membership function of the node energy of the present invention;

[0082] Figure 6 It is a line graph of the membership function of the node trust value of the present invention. DETAILED DESCRIPTION

[0083] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0084] A low-power wireless sensor network routing control method based on grey decision-making includes the following steps:

[0085] Step 1: Divide the monitoring area into several sub-areas. The base station is responsible for selecting a trust evaluation node from each sub-area to monitor the security status of each area and implement the deployment of trust evaluation nodes. Figure 1 As shown;

[0086] Step 2: Enable the trust evaluation mechanism. First, the trust evaluation node needs to perform trust evaluation on the nodes in the partition. Then, the trust evaluation node evaluates the neighboring trust evaluation nodes. Then, the data of each trust evaluation node is forwarded to the sink node for processing. The trust evaluation table of the sink node is as follows: Figure 2 As shown;

[0087] Step 3: Based on the grey decision method and combined with the trust evaluation mechanism, the trust value of the candidate node pair, the remaining energy and the number of hops between base stations are input into the grey decision model, and the candidate node with better security and energy is comprehensively selected as the next hop. The schematic diagram of candidate cluster head selection is shown in the figure below. Figure 3 shown.

[0088] In this embodiment, the above step 1 can be implemented by adopting the following preferred solution:

[0089] 101. The side length is uniformly determined as d0, and the monitoring area is evenly divided into m*m sub-areas. The base station is responsible for selecting a trust evaluation node from each sub-area to monitor the security status of each area, where m = [600 / d0];

[0090] 102. In each d0*d0 area, obtain the horizontal and vertical coordinates of each node within the area.

[0091] Generate a list of x-coordinates and a list of y-coordinates. Let the number of nodes in the region be n, and calculate the coordinates of the region's center point. The calculation formula is as follows:

[0092]

[0093]

[0094] 103. According to the distance between the node and the center point, determine the position relationship of each node and calculate

[0095] The calculation formula is:

[0096]

[0097] The calculation result of formula (1-3) is expressed as the position attribute of the node;

[0098] Calculate the trust value of the node. The calculation formula is as follows:

[0099]

[0100] Among them, the calculation result of formula (1-4) is the trust value of the node, E res represents the residual energy value of the node, E represents the mean residual energy of the nodes in the cluster; n1 represents the number of data packets transmitted by the base station to the node, and n2 represents the number of data packets received by the node;

[0101] 104. Consider location attributes, trust value and residual energy as observation attributes and design “excellent” and “medium”

[0102] The three types of "bad" are used as gray classes to indicate whether the node is capable of serving as a trust evaluation node. The possibility function of each attribute is set. The possibility function is as follows: Figure 4 、 Figure 5 、 Figure 6 As shown;

[0103] 105. Calculate the weight cost of each observation attribute according to 103, and calculate the weight cost of each node according to

[0104] The location attribute, trust value and residual energy are compared with the possibility function of each attribute. The clustering coefficient of the node belonging to the gray class "excellent" is obtained based on the gray clustering evaluation algorithm;

[0105] 106. Sort the clustering coefficients in the region and select the node with the highest coefficient as the evaluation node.

[0106] In this embodiment, the following preferred solution can be used to implement the above step 2:

[0107] 201. Calculate the average trust value of nodes in the statistical partition and build a trust mean list;

[0108] 202. Compare and select the median based on the constructed trust mean list, subtract each data in the list from the median, calculate the absolute difference, and construct a median difference list;

[0109] 203. Find the median in the list of median differences and multiply it by the coefficient 1.4826 to generate the absolute median.

[0110] 204. Calculate the trust thresholds low and high according to the following formula:

[0111] low=MID-3*MAD

[0112] high=MID+3*MAD

[0113] Where MID is the median of the trust mean list, and MAD is the absolute median;

[0114] 205. Select nodes in the trust mean list whose values are less than the threshold low or greater than the threshold high and regard them as suspect nodes. The partition marked by the suspect node executes 206, and the remaining nodes jump to 207.

[0115] 206. The aggregation node replaces the trust evaluation node of the current partition according to the trust evaluation node selection algorithm in step (1), and then proceeds to 208;

[0116] 207. The current trust value of the sink node is determined by the average trust value, and the comprehensive trust value is obtained based on the saved historical trust values of the nodes;

[0117] 208. After executing the above steps, the aggregation node records the trust values of all nodes.

[0118] In this embodiment, the following preferred solution can be used to implement the above step 3:

[0119] 301. According to a known trust evaluation analysis algorithm, the remaining energy and trust value of the current node are obtained, and the number of hops to the base station is counted according to the routing status;

[0120] 302. Select candidate nodes S1, S2, ..., S according to the polar angle and polar radius of the neighboring nodes. M , exclude nodes with a larger polar radius than itself, and exclude nodes that are not in the same sector as itself based on the polar angle;

[0121] 303. Based on the nodes selected in 302, grayscale decision calculation is performed, where the attributes of the nodes Q1, Q2, ..., Q M The gray value sorting results S1, S2, ..., S are respectively the trust value, the residual energy and the hop count, and the gray value sorting results S1, S2, ..., S M, select the node with the highest ranking as the next hop node, the candidate node selection diagram is as follows Figure 3 As shown in Figure 2, the steps of grey decision making are as follows:

[0122] Clarify the attribute weights of candidate nodes. Assuming there are k decision makers, then Q j The attribute weights can be described as:

[0123]

[0124] w j k (j=1,2,3,...,n) is the attribute weight of the k-th decision maker, which can be described by gray numbers.

[0125] Generate attribute rating value using language variables. Attribute rating value G j The calculation process is:

[0126]

[0127] G ij k (i=1,2,...,m,j=1,2,...,n) is the attribute rating value of the k-th decision maker and can be represented by the grey number express;

[0128] Establishing a grey decision matrix

[0129]

[0130] Normalized grey decision matrix

[0131]

[0132] Among them, the benefit attributes It can be expressed as:

[0133]

[0134]

[0135] The cost attribute It can be expressed as:

[0136]

[0137]

[0138] Normalize the data so that the normalized gray number is in the range of [0,1];

[0139] Create a grey weighted normalized decision matrix. Since various attributes have different importance, the weighted normalized decision matrix can be described by the following matrix:

[0140]

[0141] in

[0142] Take the ideal solution as an alternative, for m candidate nodes S={S1,S2,...,S m}, ideal candidate node

[0143]

[0144] Calculate the ideal reference node S max Grey possibility degree between the comparison candidate node S:

[0145]

[0146] Sort the candidate nodes. When P{S i ≤S max The smaller the value, the higher the candidate node ranking, and vice versa;

[0147] 304. The data is transmitted to the next hop node.

Claims

1. A low-power wireless sensor network routing control method based on grey decision making, characterized in that: The following steps are involved: (1) The monitoring area is divided into several sub-areas. The base station is responsible for selecting a trust assessment node from each sub-area to monitor the security status of each area, thus realizing the deployment of trust assessment nodes. (2) Enabling the trust evaluation mechanism: first, the trust evaluation node needs to perform trust evaluation on the nodes in the partition, then the trust evaluation node evaluates the neighboring trust evaluation nodes, and then forwards the data of each trust evaluation node to the aggregation node for processing, so as to decide whether to replace the evaluation node and improve the network's anti-attack capability; (3) Based on the grey decision method and combined with the trust evaluation mechanism, the trust value, residual energy and hop count of the candidate node pair are input into the grey decision model, and the candidate node with better security and energy is comprehensively selected as the next hop. The specific process is as follows: First, according to the known trust evaluation analysis algorithm, the residual energy and trust value of the current node are obtained, and the number of hops to the base station is counted according to the routing status; then, the candidate nodes S1, S2, ..., S are selected according to the polar angle and polar radius of the neighboring nodes. M , exclude nodes with a larger polar radius than itself, and exclude nodes that are not in the same sector as itself according to the polar angle; perform grayscale decision calculation based on the selected nodes, where the node attributes Q1, Q2, ..., Q M The gray value sorting results S1, S2, ..., S are respectively the trust value, the residual energy and the hop count, and the gray value sorting results S1, S2, ..., S M , select the node with the highest ranking as the next hop node.

2. The low-power wireless sensor network routing control method based on grey decision-making according to claim 1, characterized in that: The specific process of step (1) is as follows: (101) The side length is uniformly determined as d0, and the monitoring area is equally divided into m*m sub-areas. The base station is responsible for selecting a trust evaluation node from each sub-area to monitor the security status of each area, where m = [600 / d0]; (102) In each d0*d0 area, obtain the horizontal and vertical coordinates of each node within the area to form an x-coordinate list and a y-coordinate list; let the number of nodes in the area be n, and calculate the coordinates of the center point of the area; the calculation formula is as follows: (103) According to the distance between the node and the center point, the position relationship of each node is determined. The calculation formula is: The calculation result of formula (2-3) is expressed as the position attribute of the node; (104) The location attribute, trust value and residual energy are regarded as observation attributes, and three types of "excellent", "medium" and "poor" are designed as gray classes to indicate whether the node is capable of serving as a trust evaluation node, and the possibility function of each attribute is set; (105) According to (104), the weight cost of each observation attribute is calculated, and the clustering coefficient of the node belonging to the gray class "excellent" is obtained based on the gray clustering evaluation algorithm according to the location attribute, trust value and residual energy of each node; (106) Sort the clustering coefficients within the region and select the node with the highest coefficient as the evaluation node.

3. The low-power wireless sensor network routing control method based on grey decision-making according to claim 1, characterized in that: The specific steps of the aggregation node evaluation in step (2) are as follows: (201) Calculate the average trust value of nodes in the statistical partition and construct a trust mean list; (202) Select the median by comparing the constructed trust mean list, subtract each data in the list from the median, calculate the absolute difference, and construct a median difference list; (203) Obtain the median of the median difference list and multiply it by the coefficient 1.4826 to generate the absolute median; (204) Calculate the trust thresholds low and high according to the following formula: low=MID-3*MAD (3-1) high=MID+3*MAD (3-2) Where MID is the median of the trust mean list, and MAD is the absolute median; (205) Select the nodes in the trust mean list whose values are less than the threshold low or greater than the threshold high and regard them as suspect nodes. The partition marked by the suspect node executes (206), and the remaining nodes jump to (207); (206) The aggregation node replaces the trust evaluation node of the current partition according to the trust evaluation node selection algorithm in step (1), and then proceeds to (208); (207) The current trust value of the aggregation node is determined by the average trust value, and the comprehensive trust value is obtained based on the saved historical trust values of the nodes; (208) After executing the above steps, the aggregation node records the trust values of all nodes.

4. The low-power wireless sensor network routing control method based on grey decision-making according to claim 1, characterized in that: The specific steps of grey decision-making in step (3) are as follows: S={S1,S2,...,S n } are m independent candidate nodes, Q={Q1,Q2,...,Q n } is a set of n attributes of the candidate node; (301) Clearly define the attribute weights of candidate nodes; assuming there are k decision makers, then Q j The attribute weights can be described as: w j k (j=1,2,3,...,n) is the attribute weight of the k-th decision maker, which can be described by gray numbers. (302) Generate attribute rating value using language variables; attribute rating value G j The calculation process is: G ij k (i=1,2,...,m,j=1,2,...,n) is the attribute rating value of the k-th decision maker and can be represented by the grey number express; (303) Establishing a grey decision matrix (304) Normalized grey decision matrix Among them, the benefit attributes It can be expressed as: The cost attribute It can be expressed as: Normalize the data so that the normalized gray number is in the range of [0,1]; (305) Create a grey weighted normalized decision matrix. Since various attributes have different degrees of importance, the weighted normalized decision matrix can be described by the following matrix: in (306) Take the ideal solution as an alternative, and for m candidate nodes S = {S1, S2, ..., S m }, ideal candidate node (307) Calculate the ideal reference node S max Grey possibility degree between the comparison candidate node S: (308) Sort the candidate nodes. When P{S i ≤S max The smaller the value, the higher the candidate node ranking, and vice versa.

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