CN110784843A - A cluster formation method for large-scale wireless sensor networks - Google Patents
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Abstract
本发明公开一种面向大规模无线传感器网络的簇形成方法，包括以下步骤：1)网络初始化；2)候选簇头选举，网络中各个节点都会产生一个随机数，之后与预设的门限值比较来决定是否成为候选簇头；3)非均匀分簇，对候选簇头节点三个因素经模糊系统处理，计算不等簇的半径；4)根据候选簇头节点的综合信任值和设定的信任阈值比较，完成安全簇头筛选；5)根据节点信任值、节点密度和距离计算候选簇头节点的成簇因子，完成最终簇头选择。本发明采用非均匀分簇结构，簇头节点根据自身状况动态调整簇的半径；采用信任机制得到的信任值引入簇头选举和节点入簇。本方法在均衡网络能耗、保障路由安全、延长网络生命周期等性能方面有显著的提高。
The invention discloses a cluster formation method for a large-scale wireless sensor network, which includes the following steps: 1) network initialization; 2) candidate cluster head election, each node in the network will generate a random number, and then compare with a preset threshold value Compare to determine whether to become a candidate cluster head; 3) Non-uniform clustering, the three factors of the candidate cluster head node are processed by a fuzzy system, and the radius of the unequal cluster is calculated; 4) According to the comprehensive trust value and setting of the candidate cluster head node 5) Calculate the clustering factor of candidate cluster head nodes according to the node trust value, node density and distance, and complete the final cluster head selection. The invention adopts a non-uniform clustering structure, and the cluster head node dynamically adjusts the radius of the cluster according to its own conditions; the trust value obtained by using the trust mechanism is introduced into the cluster head election and the node into the cluster. The method significantly improves performance in balancing network energy consumption, ensuring routing security, and extending network life cycle.
Description
技术领域technical field
本发明涉及通信技术领域，尤其涉及无线传感器网络的分簇路由方法。The present invention relates to the technical field of communication, in particular to a clustering routing method of a wireless sensor network.
背景技术Background technique
由于无线传感器网络经常部署在条件恶劣的外部环境中而且无线信道是完全开放的，因此更容易遭受各种攻击。传统的加密和认证技术只能抵御外部攻击，一旦节点突破这一道防线，就会成为网络中的恶意节点，再对网络发起攻击，会对网络造成严重的伤害。因此，如何实现内部数据安全可靠的传输，保证数据在路由中的安全是十分必要的。而能量受限是无线传感器网络的另一个重要特点，因为无线传感器网络中节点往往通过电池进行能量供给，无法对其进行及时充电，一旦耗电完毕将无法参与到网络中。因而，如何节约节点能耗，以延长网络整体寿命是路由协议设计过程中要考虑的另一个问题。而本发明能够很好地解决上面的问题。Since wireless sensor networks are often deployed in harsh external environments and the wireless channels are completely open, they are more vulnerable to various attacks. Traditional encryption and authentication technologies can only resist external attacks. Once a node breaks through this line of defense, it will become a malicious node in the network, and then launch an attack on the network, which will cause serious damage to the network. Therefore, it is very necessary to realize the safe and reliable transmission of internal data and ensure the safety of data in routing. Energy limitation is another important feature of wireless sensor networks, because nodes in wireless sensor networks often supply energy through batteries, which cannot be charged in time, and will not be able to participate in the network once the power is consumed. Therefore, how to save the energy consumption of nodes to prolong the overall life of the network is another problem to be considered in the design process of the routing protocol. The present invention can solve the above problems well.
发明内容SUMMARY OF THE INVENTION
本发明目的在于解决了上述现有技术的不足，提出了一种面向大规模无线传感器网络的簇形成方法，该方法在分簇阶段采用非均匀分簇结构，簇的半径由簇头到基站的距离、节点度和剩余能量通过模糊逻辑系统计算得出，簇头节点根据自身状况动态调整簇的半径，节省自身能量；在可信簇建立阶段，采用信任机制得到的信任值引入簇头选举和节点入簇阶段，提高网络安全性。The purpose of the present invention is to solve the above-mentioned shortcomings of the prior art, and propose a cluster formation method for large-scale wireless sensor networks. The method adopts a non-uniform clustering structure in the clustering stage. The distance, node degree and remaining energy are calculated by the fuzzy logic system. The cluster head node dynamically adjusts the radius of the cluster according to its own conditions to save its own energy; in the stage of trusted cluster establishment, the trust value obtained by the trust mechanism is introduced into the cluster head election and Nodes enter the cluster stage to improve network security.
本发明解决其技术问题所采取的技术方案是：一种面向大规模无线传感器网络的簇形成方法，该方法包括如下步骤：The technical scheme adopted by the present invention to solve the technical problem is: a cluster formation method for large-scale wireless sensor networks, the method comprises the following steps:
步骤1：网络初始化；Step 1: Network initialization;
在初始化阶段，BS以可以覆盖整个监测区域的发射功率广播，向网络中的传感器节点发送“start”数据包。每个传感器节点在接收到此数据包后会通过接收信号的强弱估算自己到BS的距离。之后每个传感器节点都会在自己的广播距离内广播“initial”数据包，包括自身ID和到BS的距离。节点收到“initial”数据包后，通过解析该数据包构建本地邻居节点表，包括ID号、到BS距离、节点间距离以及节点初始信任值。In the initialization phase, the BS broadcasts with a transmit power that can cover the entire monitoring area, and sends a "start" packet to the sensor nodes in the network. After each sensor node receives this data packet, it will estimate the distance from itself to the BS through the strength of the received signal. Each sensor node will then broadcast an "initial" packet within its own broadcast distance, including its own ID and distance to the BS. After the node receives the "initial" data packet, it constructs a local neighbor node table by parsing the data packet, including the ID number, the distance to the BS, the distance between nodes, and the initial trust value of the node.
步骤2：候选簇头选举，网络中各个节点都会产生一个随机数，之后与预设的门限值比较来决定是否成为候选簇头；Step 2: Candidate cluster head election, each node in the network will generate a random number, and then compare it with the preset threshold to decide whether to become a candidate cluster head;
步骤3：非均匀分簇，对候选簇头节点三个因素经模糊系统处理，计算不等簇的半径Rc；Step 3: Non-uniform clustering, the three factors of the candidate cluster head node are processed by the fuzzy system, and the radius R c of the unequal cluster is calculated;
通过节点的剩余能量、节点度和到BS的距离，经过模糊推理系统得到候选簇头的竞争半径。模糊推理系统包含模糊化、模糊推理以及去模糊处理三部分：Through the residual energy of the node, the node degree and the distance to the BS, the competition radius of the candidate cluster head is obtained through the fuzzy inference system. The fuzzy inference system includes three parts: fuzzification, fuzzy inference and defuzzification:
(1)模糊化是将精确值的输入变量转化为对应的模糊集合；(1) Fuzzification is to convert the input variables of exact values into corresponding fuzzy sets;
(2)模糊推理是根据模糊规则将输入变量模糊集映射到半径的模糊集；(2) Fuzzy reasoning is to map input variable fuzzy set to radius fuzzy set according to fuzzy rules;
(3)去模糊是将半径模糊集转化为具体的半径精确值。(3) Deblurring is to transform the radius fuzzy set into a specific radius precise value.
步骤4：根据候选簇头节点的综合信任值和设定的信任阈值比较，完成安全簇头筛选；Step 4: According to the comparison between the comprehensive trust value of the candidate cluster head node and the set trust threshold value, complete the security cluster head screening;
步骤5：根据节点信任值、节点密度和距离计算候选簇头节点的成簇因子，完成最终簇头选择。Step 5: Calculate the clustering factor of the candidate cluster head node according to the node trust value, node density and distance, and complete the final cluster head selection.
与现有技术相比，本发明具有以下有益的技术效果：Compared with the prior art, the present invention has the following beneficial technical effects:
本发明分析在分簇阶段采用非均匀分簇结构，簇的半径由簇头到基站的距离、节点度和剩余能量通过模糊逻辑系统计算得出，簇头节点根据自身状况动态调整簇的半径，节省自身能量；在可信簇建立阶段，采用信任机制得到的信任值引入簇头选举、节点入簇和簇间路由阶段，提高网络安全性。The invention analyzes that the non-uniform clustering structure is adopted in the clustering stage, the radius of the cluster is calculated by the distance from the cluster head to the base station, the node degree and the residual energy through the fuzzy logic system, and the cluster head node dynamically adjusts the radius of the cluster according to its own conditions, Save its own energy; in the stage of trusted cluster establishment, the trust value obtained by using the trust mechanism is introduced into the stage of cluster head election, node entry into cluster and inter-cluster routing to improve network security.
附图说明Description of drawings
图1为本发明方法流程图。Fig. 1 is the flow chart of the method of the present invention.
图2为模糊推理系统图。Figure 2 is a diagram of the fuzzy inference system.
图3为输入变量模糊集隶属度函数图。Figure 3 is a graph of the membership function of the fuzzy set of input variables.
图4为簇头节点的簇半径隶属度函数图。Figure 4 is a graph of the cluster radius membership function of the cluster head node.
表1为模糊规则表。Table 1 is a fuzzy rule table.
具体实施方式Detailed ways
下面结合附图和实施例对本发明进行详细说明。The present invention will be described in detail below with reference to the accompanying drawings and embodiments.
如图1所示，本发明提供了一种面向大规模无线传感器网络的簇形成方法，该方法面向大规模无线传感器网络，针对内部数据安全以及最大化网络寿命的簇形成方法，包括如下步骤：As shown in FIG. 1 , the present invention provides a cluster formation method for large-scale wireless sensor networks. The method is oriented towards large-scale wireless sensor networks, aiming at internal data security and maximizing network life. The cluster formation method includes the following steps:
步骤1：网络初始化；Step 1: Network initialization;
在初始化阶段，BS以可以覆盖整个监测区域的发射功率广播，向网络中的传感器节点发送“start”数据包。每个传感器节点在接收到此数据包后会通过接收信号的强弱估算自己到BS的距离。之后每个传感器节点都会在自己的广播距离内广播“initial”数据包，包括自身ID和到BS的距离。节点收到“initial”数据包后，通过解析该数据包构建本地邻居节点表，包括ID号、到BS距离、节点间距离以及节点初始信任值。In the initialization phase, the BS broadcasts with a transmit power that can cover the entire monitoring area, and sends a "start" packet to the sensor nodes in the network. After each sensor node receives this data packet, it will estimate the distance from itself to the BS through the strength of the received signal. Each sensor node will then broadcast an "initial" packet within its own broadcast distance, including its own ID and distance to the BS. After the node receives the "initial" data packet, it constructs a local neighbor node table by parsing the data packet, including the ID number, the distance to the BS, the distance between nodes, and the initial trust value of the node.
步骤2：候选簇头选举，网络中各个节点都会产生一个随机数，之后与预设的门限值比较来决定是否成为候选簇头；Step 2: Candidate cluster head election, each node in the network will generate a random number, and then compare it with the preset threshold to decide whether to become a candidate cluster head;
在每一轮开始时，网络中各个节点都会产生一个[0,1]的随机数，如果随机数小于预设的门限值，则成为候选簇头节点。与之相反，该节点成为网络中的普通节点。本协议门限值T(n)的计算过程中考虑了节点的能量因素，预设的门限值T(n)如下：At the beginning of each round, each node in the network will generate a random number of [0,1]. If the random number is less than the preset threshold, it will become a candidate cluster head node. Instead, the node becomes an ordinary node in the network. The energy factor of the node is considered in the calculation of the threshold value T(n) of this protocol. The preset threshold value T(n) is as follows:
其中，r表示当前轮次，G表示本轮次未成为为簇头的节点集，p为设定的网络中簇头比例，Eresidual为节点的剩余能量，Eoriginal为节点的初始能量。Among them, r represents the current round, G represents the node set that has not become a cluster head in this round, p is the set proportion of cluster heads in the network, E residual is the residual energy of the node, and E original is the initial energy of the node.
网络中的节点只在初始时刻具有相同的初始能量，随着网络的不断运行，簇头节点不仅要接受和融合簇内数据还要转发其他簇头数据，因此会消耗更多的能量，使得簇头节点剩余能量较少，造成网络中节点剩余能量不均。因此，在计算门限值T(n)时，将剩余能量与初始能量的比值纳入计算之中，剩余能量越多成为候选簇头的机会越大，反之，当选候选簇头节点的几率越小，均衡节点能耗，延长网络生存周期。The nodes in the network only have the same initial energy at the initial moment. With the continuous operation of the network, the cluster head node not only needs to accept and fuse the data in the cluster but also forward the data of other cluster heads, so it will consume more energy and make the cluster head. The head node has less residual energy, resulting in uneven residual energy of nodes in the network. Therefore, when calculating the threshold value T(n), the ratio of the remaining energy to the initial energy is included in the calculation. The more remaining energy is, the greater the chance of becoming a candidate cluster head. On the contrary, the probability of being elected as a candidate cluster head node is smaller. , balance the energy consumption of nodes and prolong the network life cycle.
步骤3：非均匀分簇，对候选簇头节点三个因素经模糊系统处理，计算不等簇的半径Rc；Step 3: Non-uniform clustering, the three factors of the candidate cluster head node are processed by the fuzzy system, and the radius R c of the unequal cluster is calculated;
通过节点的剩余能量、节点度和到BS的距离，经过模糊推理系统得到候选簇头的竞争半径。如图2所示模糊推理系统包含模糊化、模糊推理以及去模糊处理三部分：Through the residual energy of the node, the node degree and the distance to the BS, the competition radius of the candidate cluster head is obtained through the fuzzy inference system. As shown in Figure 2, the fuzzy inference system includes three parts: fuzzification, fuzzy inference and defuzzification:
(1)模糊化是将精确值的输入变量转化为对应的模糊集合；(1) Fuzzification is to convert the input variables of exact values into corresponding fuzzy sets;
是将精确值的三个输入变量到BS的距离、剩余能量和密度转化为口语化的模糊集，以便后续的模糊处理。对本方法所用变量应采用等价的模糊语言变量。簇头节点到BS距离的模糊语言描述为“远”，“中”，“近”；簇头节点的剩余能量的模糊语言变量为“高”，“中”，“低”；节点度的模糊语言为“高”，“中”，“低”三种，各个输入变量的模糊隶属度函数如图2所示。is to convert the exact-valued distance, residual energy and density of the three input variables to the BS into a colloquial fuzzy set for subsequent fuzzy processing. Equivalent fuzzy linguistic variables should be used for the variables used in this method. The fuzzy language description of the distance from the cluster head node to the BS is "far", "middle", "near"; the fuzzy language variable of the residual energy of the cluster head node is "high", "medium", "low"; the fuzzy language of the node degree The language is "high", "medium" and "low", and the fuzzy membership function of each input variable is shown in Figure 2.
(2)模糊推理是根据模糊规则将输入变量模糊集映射到半径的模糊集；(2) Fuzzy reasoning is to map input variable fuzzy set to radius fuzzy set according to fuzzy rules;
簇头节点的簇半径对应的模糊集合隶属度函数如图4所示。模糊推理系统的输出变量是簇头节点的竞争半径，其模糊语言有如下7种“很小”，“小”，“中等小”，“中等”，“大”，“中等大”，“很大”。模糊规则具体定义如表1所示。The fuzzy set membership function corresponding to the cluster radius of the cluster head node is shown in Figure 4. The output variable of the fuzzy inference system is the competition radius of the cluster head node, and its fuzzy language has the following 7 kinds of "small", "small", "medium small", "medium", "large", "medium large", "very large". Big". The specific definitions of fuzzy rules are shown in Table 1.
(3)去模糊是将半径模糊集转化为具体的半径精确值。(3) Deblurring is to transform the radius fuzzy set into a specific radius precise value.
利用重心法进行解模糊处理，得到最终的精确值。计算公式如下：Defuzzification is carried out using the centroid method to obtain the final accurate value. Calculated as follows:
其中，Z*是解模糊的输出，Ci(Z)是融合隶属度函数，Z是输出变量，就是最终的精确值Rc。Among them, Z* is the output of defuzzification, Ci(Z) is the fusion membership function, and Z is the output variable, which is the final precise value R c .
步骤4：根据候选簇头节点的综合信任值和设定的信任阈值比较，完成安全簇头筛选；Step 4: According to the comparison between the comprehensive trust value of the candidate cluster head node and the set trust threshold value, complete the security cluster head screening;
本方法会计算每个节点的综合信任值，根据节点的信任值，来合理地判断节点是否安全，从而保证网络的安全性。设定信任阈值CTTH进行安全簇头筛选，通过这种方式保证簇头的安全性和可靠性。设某一簇头节点i的综合信任值为CTi，节点成为候选簇头节点后会向周围的邻居节点广播自己是簇头的CH_MSG消息，消息内容包括节点自身ID，节点剩余能量Eresidual。普通节点收到候选簇头发送的CH_MSG消息后，首先在邻居节点的信任值表中查看候选簇头节点i的综合信任值CTi，当综合信任值CTi小于信任阈值CTTH时，拒绝其作为簇头的请求。否则，将节点i存入候选簇头集合PrelimCH中。This method will calculate the comprehensive trust value of each node, and reasonably judge whether the node is safe according to the trust value of the node, so as to ensure the security of the network. The trust threshold CT TH is set to screen the security cluster heads, in this way, the security and reliability of the cluster heads are guaranteed. Assuming that the comprehensive trust value of a cluster head node i is CT i , after the node becomes a candidate cluster head node, it will broadcast the CH_MSG message that it is the cluster head to the surrounding neighbor nodes. The message content includes the node's own ID and the node residual energy E residual . After receiving the CH_MSG message sent by the candidate cluster head, the common node first checks the comprehensive trust value CT i of the candidate cluster head node i in the trust value table of the neighbor node, and rejects it when the comprehensive trust value CT i is less than the trust threshold CT TH . as a request for a cluster head. Otherwise, the node i is stored in the candidate cluster head set PrelimCH.
步骤5：根据节点信任值、节点密度和距离计算候选簇头节点的成簇因子，完成最终簇头选择。Step 5: Calculate the clustering factor of the candidate cluster head node according to the node trust value, node density and distance, and complete the final cluster head selection.
根据节点信任值、节点密度和距离完成最终簇头选择。若普通节点信任表中存储的簇头节点信任值越高，则说明该节点越可信，则加入该簇较安全。本方法定义节点密度为在其竞争半径Rc内存活的节点数。如果簇头节点密度较大，则其邻居节点就会较多，因此簇内节点向簇头节点发送数据的能耗就会降低，那么整个簇的能耗就会降低。根据无线传输模型，簇内节点能耗与簇内节点到簇头节点之间的距离有很重要的关系，在通信范围内，能耗以距离的二次方增长。考虑普通节点到簇头节点的距离，定义距离因子，距离因子与普通节点到簇头节点的距离成反比，与竞争半径成正比。因此，本方法设计成簇因子CFi如公式(3)所示：The final cluster head selection is done according to the node trust value, node density and distance. If the trust value of the cluster head node stored in the common node trust table is higher, it means that the node is more trustworthy, and it is safer to join the cluster. This method defines node density as the number of nodes surviving within its competition radius Rc. If the density of cluster head nodes is high, there will be more neighbor nodes, so the energy consumption of nodes in the cluster to send data to the cluster head node will be reduced, and the energy consumption of the entire cluster will be reduced. According to the wireless transmission model, the energy consumption of the nodes in the cluster has a very important relationship with the distance between the nodes in the cluster and the cluster head node. In the communication range, the energy consumption increases with the square of the distance. Considering the distance from the common node to the cluster head node, a distance factor is defined. The distance factor is inversely proportional to the distance from the common node to the cluster head node, and proportional to the competition radius. Therefore, this method is designed into a cluster factor CF i as shown in formula (3):
CFi＝γ1CTi+γ2NDi+γ3MDi 式(3)CF i =γ 1 CT i +γ 2 ND i +γ 3 MD i Formula (3)
其中，CTi表示信任因子，NDi表示密度因子，MDi表示距离因子，γ1,γ2,γ3为信任因子、密度因子和距离因子的权重，γ1,γ2,γ3＞0且γ1+γ2+γ3＝1。Among them, CT i represents the trust factor, ND i represents the density factor, MD i represents the distance factor, γ 1 , γ 2 , γ 3 are the weights of the trust factor, density factor and distance factor, γ 1 , γ 2 , γ 3 > 0 And γ 1 +γ 2 +γ 3 =1.
本方法定义候选簇头节点i的密度因子NDi如公式(4)所示，可以发现其与自身周围的邻居节点个数NodeDensityi成正比。This method defines the density factor ND i of the candidate cluster head node i as shown in formula (4), and it can be found that it is proportional to the number of neighbor nodes NodeDensity i around itself.
其中，NodeDensityi表示候选簇头节点i的邻居节点个数。Among them, NodeDensity i represents the number of neighbor nodes of the candidate cluster head node i.
本方法定义候选簇头节点的距离因子如公式(5)所示，可以发现其与簇内节点的距离成反比，与簇头节点的竞争半径Rc成正比。This method defines the distance factor of the candidate cluster head node as shown in formula (5). It can be found that it is inversely proportional to the distance of the nodes in the cluster and proportional to the competition radius R c of the cluster head node.
其中，Di,j表示候选簇头节点i与普通节点j的距离，Rc表示候选簇头节点i的竞争半径。Among them, D i,j represents the distance between the candidate cluster head node i and the common node j, and R c represents the competition radius of the candidate cluster head node i.
普通节点会从候选簇头集合PrelimCH中选取最大的成簇因子CFi，并向其发送如簇请求JoinCH_MSG消息，选择加入该簇。同时，簇头节点收到普通节点的入簇请求JoinCH_MSG消息，会查询本地的信任表，判断是否为恶意节点，若是恶意节点则拒绝入簇。反之，则同意该节点的入簇请求，向普通节点回复同意入簇消息RelyCH_MSG。至此完成可信节点入簇。The common node will select the largest clustering factor CF i from the candidate cluster head set PrelimCH, and send a JoinCH_MSG message such as a cluster request to it, and choose to join the cluster. At the same time, when the cluster head node receives the JoinCH_MSG message of the clustering request from the ordinary node, it will query the local trust table to determine whether it is a malicious node, and if it is a malicious node, it will refuse to join the cluster. On the contrary, it agrees to the clustering request of the node, and replies to the ordinary node with a message RelyCH_MSG that agrees to join the clustering. At this point, the trusted node clustering is completed.
本发明依据大规模无线传感器网络分布式结构的特点，建立一种面向大规模无线传感器网络的簇形成方法，根据自身的剩余能量、节点度和到BS的距离通过模糊逻辑系统计算簇头节点的竞争半径，动态调整竞争半径大小。将决策信任值引入簇头选举、节点入簇阶段，从节点安全、节点能耗和均衡负载能耗出发设计成簇因子。本方法能够保障网络内部的路由安全，均衡网络能耗，延长网络寿命。According to the characteristics of the distributed structure of the large-scale wireless sensor network, the invention establishes a cluster formation method oriented to the large-scale wireless sensor network, and calculates the cluster head node's cluster head node according to its own residual energy, node degree and distance to the BS through a fuzzy logic system. Competition radius, dynamically adjust the size of the competition radius. The decision trust value is introduced into the cluster head election and node clustering stage, and the clustering factor is designed from the perspective of node security, node energy consumption and balanced load energy consumption. The method can ensure the routing security inside the network, balance the energy consumption of the network, and prolong the life of the network.
表1模糊规则Table 1 Fuzzy rules
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