Improved cluster head election method and improved leach routing protocol method

Abstract

The cluster head election method for improving LEACH algorithm and the improved LEACH routing protocol method belong to the technical field of routing soft security of underwater wireless sensor network, and particularly relate to a cluster head election process of underwater wireless sensor network; solve the problem that the existing cluster-based routing protocol ignores the security problem and the security of the cluster head election process is low; the method comprises the following steps: each node updates its own mse according to the trust evaluation result of the sink node on itself; each node uses an improved cluster head election threshold function T imp (n) to calculate the cluster head election threshold; each node compares the random number generated by itself with the cluster head election threshold; if it is less than the cluster head election threshold, the node becomes the cluster head node in the current large period. The improved cluster head election method for improving LEACH algorithm and the improved LEACH routing protocol method are suitable for providing a cluster-based routing protocol.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of routing soft security of underwater wireless sensor networks, and particularly relates to a cluster head election process of underwater wireless sensor networks. BACKGROUND

[0002] Underwater wireless sensor networks (UWSNs) as an important part of underwater information networks, have important significance for expanding the monitoring range of underwater information networks and perfecting the sea, land, air and sky three-dimensional information network, and have been widely applied in pollution monitoring, oil and gas exploration, navigation control, tsunami warning and military and security fields.

[0003] UWSNs is a distributed wireless communication network formed by self-organizing using underwater acoustic wireless communication mode by deploying a large number of communication nodes formed by low-cost, carrying micro-sized acoustic, magnetic, marine biochemical and other sensors to the designated sea area. The nodes in the network cooperate with each other to complete environmental monitoring, data collection and other work, and the collected data is transmitted to the underwater sink node or the water surface base station through hop-by-hop forwarding, and then transmitted to the user through the radio frequency communication mode or the wired communication mode.

[0004] Security problem is an important problem in the field of UWSNs. The security requirements of UWSNs include node authentication, data confidentiality, data integrity and the like. Many researchers use cryptographic methods to ensure the above security requirements.

[0005] However, sensor nodes are disposable and low-cost, so most of these nodes are not implemented with tamper-proofing technology, and at the same time, sensor nodes are often deployed in unattended or even hostile areas, and a malicious attacker may make a sensor node become a compromised node that can integrate into the network to launch malicious attacks (such as tampering attacks, Sybil attacks and selfish attacks, etc.) by physical intrusion and redeployment of the sensor node. Since the compromised node is physically attacked, its memory is parsed and rewritten, and it already has the encryption information required by the legitimate node, so the cryptographic technology cannot provide sufficient protection for the network in the case of a compromised node in the network.

[0006] The existence of compromised nodes in the network will cause new security risks. A node may pass the traditional encryption hard security check (authorization and access), but still report false measurement results to gain an advantage over other nodes. This security risk is called "soft security threat".

[0007] Trust management mechanism is considered as an effective complement to traditional cryptographic mechanism, which is composed of a series of trust-based security technologies to protect the network from compromised nodes (i.e. to deal with soft security threats). Since Marsh introduced the trust research into the computer field, many trust models have been proposed in distributed networks, ubiquitous computing, peer-to-peer computing, self-organizing networks and other aspects to improve the security, reliability and fairness of the system.

[0008] There are mainly three topologies in UWSNs: centralized, distributed and cluster-based. The existing trust models for cluster-based network topology have the following problems:

[0009] (1) The traditional trust model ignores the fuzziness and uncertainty of trust when using precise values to represent trust, which will affect the trust evaluation effect.

[0010] (2) The method of identifying malicious nodes in the traditional trust model relies on the threshold and standard trust cloud set by humans, and the strong subjectivity will limit the detection rate of malicious nodes in the model.

[0011] (3) Due to the lack of relevant field data and simulation data sets, it is difficult to apply machine learning methods in the trust model.

[0012] In addition, in the cluster-based network topology, underwater nodes are dynamically clustered, each cluster has a cluster head node and multiple intra-cluster member nodes, intra-cluster member nodes send data packets to the cluster head node, and the cluster head node forwards to the surface sink node after performing data processing functions. The energy of UWSNs nodes is limited, and the cluster-based routing protocol can balance the node energy consumption and prolong the network lifetime by designing the method of selecting cluster heads, which is a common routing protocol in the cluster-based network topology of UWSNs.

[0013] However, the existing cluster-based routing protocol mainly considers factors such as the distance of nodes from the surface sink node, residual energy, node density, etc. in the cluster head selection process, while ignoring security issues. When malicious nodes in the network act as intra-cluster member nodes, selfish attacks will reduce their own energy consumption, which will disrupt the energy balance between nodes and lead to unfair cluster head election; when launching tampering attacks, it will tamper with its own sensor data, which will damage the authenticity of the data; when launching a Sybil attack, it will increase the possibility of malicious nodes becoming cluster head nodes by forging identity. In addition, the cluster-based network topology of UWSNs determines that once the cluster head node is compromised, the communication security of the entire network cannot be guaranteed, and when malicious nodes become cluster head nodes, the malicious attacks they launch will be more harmful to the network.

[0014] In summary, there is an urgent need for a security mechanism to ensure the security of the cluster head election process in the cluster-based routing protocol. SUMMARY

[0015] The application provides a cluster head election method for improving the LEACH algorithm, and solves the problem that the existing cluster-based routing protocol ignores security and the security of the cluster head election process is low.

[0016] The cluster head election method for improving the LEACH algorithm comprises the following steps:

[0017] A mean square error updating step: each node obtains an updated mean square error mse of itself by using a centralized trust evaluation method based on a cloud theory;

[0018] Wherein, mse [0, +∞);P is a priori determined proportion of cluster head nodes in the network;r is the current round;G is a set of nodes that have not become cluster head nodes in the last 1 / P rounds;

[0019] A cluster head election threshold calculating step: each node calculates a cluster head election threshold by using an improved cluster head election threshold function T imp (n) based on the number of times of serving as a cluster head node of itself, the a priori determined proportion of cluster head nodes and the mse;

[0020] The improved cluster head election threshold function T imp (n) is as follows:

[0021]

[0022] F(mse)=1.02^(24^(10*mse));

[0023] A random number generating step: each node generates a random number by itself;The random number is generated by a random variable in [0, 1];The random number is subject to a uniform distribution;

[0024] A threshold value comparing step: each node compares the random number generated by itself with the cluster head election threshold:

[0025] If the random number is less than the cluster head election threshold, the node becomes a cluster head node in the current large period;

[0026] Otherwise, the node does not become a cluster head node.

[0027] Further, a preferred embodiment is provided, and the centralized trust evaluation method based on the cloud theory is applied to an underwater wireless sensor network with a cluster type network topology.

[0028] The centralized trust evaluation method based on the cloud theory comprises the following steps:

[0029] Trust evidence collection step: the member nodes in the cluster collect packet sending trust evidence, data trust evidence and energy trust evidence, and send the three types of trust evidence to the cluster head node;

[0030] Trust calculation step: the cluster head node calculates the packet sending trust value, data trust value and energy trust value of the member nodes in the cluster based on the three types of trust evidence collected by the member nodes in the cluster, and sends the three types of trust values to the sink node;

[0031] Trust updating step: the sink node calculates and updates the packet sending cloud model, data cloud model and energy cloud model of all nodes in the network using the reverse cloud algorithm according to the trust values of all nodes in the network;

[0032] Malicious node step: the sink node combines the three types of cloud models of the evaluated node into a 1*9 tensor as an output sample input into the VAE model, obtains the output of the VAE model as an output sample, calculates the mean square error between the input sample and the output sample as a reconstruction error, and inputs the reconstruction error into the SVM model for node binary classification to realize malicious node identification.

[0033] The application also proposes a cluster head election device for improving the LEACH algorithm, which comprises the following modules:

[0034] Mean square error updating module: each node obtains its updated mean square error mse by using a centralized trust evaluation method based on cloud theory;

[0035] Wherein, mse [0, +∞);P is a priori determined proportion of cluster head nodes in the network;R is the current round;G is a set of nodes that have not become cluster head nodes in the last 1 / P rounds;

[0036] Calculate the election threshold module: each node calculates the cluster head election threshold based on the number of times it has served as a cluster head node, the a priori determined proportion of cluster head nodes and mse, using an improved cluster head election threshold function T imp (n) to calculate the cluster head election threshold;

[0037] The improved cluster head election threshold function T imp (n) is as follows:

[0038]

[0039] F(mse)=1.02^(24^(10*mse));

[0040] Random number generation module: each node generates a random number itself;The random number is generated by a random variable in [0, 1];The random number is uniformly distributed;

[0041] Threshold comparison module: each node compares the random number generated by itself with the cluster head election threshold:

[0042] If less than the cluster head election threshold, the node becomes the cluster head node of the current large period;

[0043] Otherwise, the node does not become the cluster head node.

[0044] The application also proposes an improved LEACH routing protocol method, which comprises a clustering step and a data transmission step.

[0045] The clustering step: the nodes periodically re-elect cluster head nodes for clustering, and this period is called a large period, denoted as T1; wherein the cluster head nodes are elected by using the cluster head election method of any one of the improved LEACH algorithms described above.

[0046] The data transmission step: the cluster head nodes periodically forward the integrated data packets of the cluster member nodes to the sink node, and this period is called a small period, denoted as T2.

[0047] Further, a preferred embodiment is provided, in which, in the data transmission step, the cluster member nodes send their packet sending trust evidence, energy trust evidence, and energy trust evidence to the cluster head nodes along with the data packets.

[0048] Further, a preferred embodiment is provided, in which, in the data transmission step, after receiving the data packets of the cluster member nodes, the cluster head nodes first store and integrate the data packets; then periodically send the integrated data packets to the sink node at a period of T2.

[0049] The cluster head nodes calculate the trust values of the cluster member nodes based on the collected trust evidence at a period of T2, and send the calculation results of the trust values to the sink node along with the integrated data packets.

[0050] The application also proposes an improved LEACH routing protocol device, which comprises a clustering module and a data transmission module.

[0051] The clustering module: the nodes periodically re-elect cluster head nodes for clustering, and this period is called a large period, denoted as T1; wherein the cluster head nodes are elected by using the cluster head election method of any one of the improved LEACH algorithms described above.

[0052] The data transmission module: the cluster head nodes periodically forward the integrated data packets of the cluster member nodes to the sink node, and this period is called a small period, denoted as T2.

[0053] The application also proposes a computer device, which comprises a processor and a memory, wherein the memory is used to store executable instructions of the processor, and the processor is configured to execute the improved LEACH routing protocol method of any one of the above-described embodiments by executing the executable instructions.

[0054] The application further provides a computer storage medium, wherein the computer storage medium stores a computer program.

[0055] The application further provides a computer program product, comprising computer programs / instructions, which, when executed by a processor, implement the steps of the improved LEACH routing protocol method.

[0056] The application has the following beneficial effects:

[0057] The cluster head election method of the improved LEACH algorithm effectively reduces the proportion of malicious nodes in the cluster head nodes in the improved LEACH protocol based on the routing soft security mechanism of the trust model, and the proportion is finally basically stabilized below 1.4%, thereby improving the security of the cluster head election process.

[0058] The cluster head election method of the improved LEACH algorithm and the improved LEACH routing protocol method are suitable for providing a cluster-based routing protocol. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0060] Figure 1 For an embodiment of the application, the flowchart of the cluster head election method of the improved LEACH algorithm;

[0061] Figure 2 For an embodiment of the application, the flowchart of the centralized trust evaluation method based on the cloud theory;

[0062] Figure 3 For an embodiment of the application, the structure diagram of the VAE model;

[0063] Figure 4 For an embodiment of the application, the vector diagram of the cloud model of various attack nodes; wherein, Figure 4 (a) is the vector diagram of the cloud model of the selfish attack node; Figure 4 (b) is the vector diagram of the cloud model of the tampering attack node; Figure 4 (c) is the vector diagram of the cloud model of the Sybil attack node;

[0064] Figure 5For an embodiment of the present application, the SVM model parameter grid optimization result graph;

[0065] Figure 6 For an embodiment of the present application, the F(mse) function curve graph; it can be seen that when mse∈[0, 0.15], F(mse) increases with the increase of mse, and the increasing amplitude gradually increases, in fact, when mse∈[0, +∞), F(mse) is monotonically increasing, and F(mse)∈[1, +∞);

[0066] Figure 7 For an embodiment of the present application, the trust evaluation method detection rate and false detection rate change with simulation time graph;

[0067] Figure 8 For an embodiment of the present application, the node cloud model mean square error MSE changes with simulation time graph;

[0068] Figure 9 For an embodiment of the present application, the malicious node proportion in cluster head node changes with simulation time graph;

[0069] Figure 10 For an embodiment of the present application, the UWSNs network topology structure graph;

[0070] Figure 11 For an embodiment of the present application, the underwater routing protocol classification graph

[0071] Figure 12 For an embodiment of the present application, the centralized reputation system general framework graph; wherein, Figure 12 (a) shows the interaction between the subjects has ever been, after each interaction, the interaction parties will provide evaluation on each other's performance, these evaluations will be uploaded to the reputation center; the reputation center collects all the evaluations, and continuously updates the reputation score of each subject according to the received evaluations; Figure 12 (b) shows that the subject A and B are currently considering the interaction between them about to happen, the updated reputation score will be provided to all subjects to view, and can be used by the subject to decide whether to interact with a particular subject. DETAILED DESCRIPTION

[0072] In order to make the technical solutions and advantages of the present application clearer, the specific embodiments of the present application will be described in further detail and completely below with reference to the drawings. The various embodiments described below are only some preferred solutions of the present application, but not all embodiments; the various embodiments described below are intended to explain the present application, and cannot be understood as a limitation of the present application; the reasonable combinations of the technical features defined in the various embodiments of the present application, and all other embodiments obtained by those skilled in the art without making any creative efforts based on the embodiments of the present application, all fall within the scope of protection of the present application.

[0073] For better parameter specific embodiments, some concepts of the design are described as follows:

[0074] Regarding the topology structure of UWSNs:

[0075] It should be noted that the communication between the nodes of UWSNs mainly has three topological structures: centralized, distributed and clustered. Among them, the coverage range of the centralized network topological structure is generally small, so from the perspective of expanding the network monitoring range, the commonly used are the distributed network topological structure and the clustered network topological structure.

[0076] In the UWSNs network of the clustered network topological structure, the identity of the underwater node is divided into cluster member (CM) node and cluster header (CH) node, and the data is aggregated from the cluster member node to the cluster header node, and then sent to the sink node on the water surface by the cluster header node.

[0077] The clustered network has good expansibility, and the network size is not limited, which is suitable for UWSNs with large deployment range, sparse nodes and requirement for scalability.

[0078] However, compared with the cluster member node, the cluster header node which performs high-energy-consuming tasks such as data processing and forwarding will consume energy faster. At the same time, since the cluster header node has a special role, a malicious node may compete to become a cluster header node by improper means and further perform more effective attacks, which makes this type of network face serious single-point failure risk.

[0079] In the present embodiment, the trust evaluation method (or called trust model) is applied to the clustered network topological structure.

[0080] In the clustered network topological structure:

[0081] The underwater nodes are homogeneous and uniformly distributed in the cubic water area, and a sink node is located at the center of the water surface;

[0082] All nodes have no mobility and each has a globally unique node ID to determine identity;

[0083] The number of data packets generated by the underwater node per second as a random variable follows a Poisson distribution;

[0084] The member nodes in the cluster send packets to the cluster head node as source nodes;

[0085] The cluster head node collects the data packets in the cluster and performs information processing, and sends the data packets to the surface sink node;

[0086] The data packet is considered to be successfully delivered after reaching the sink node.

[0087] Routing protocols for UWSNs:

[0088] It should be noted that the design of routing protocols is an important research content in UWSNs. The role of the routing protocol is to find a path from the source node to the destination node and correctly forward the data packet along the path. The main goal of the design of the routing protocol in UWSNs is to establish an efficient energy path, to prolong the life of the network as much as possible, to improve the fault tolerance of the routing, and to form a reliable data forwarding mechanism.

[0089] According to the characteristics of underwater routing protocols, the protocols can be divided into three categories: energy-based routing protocols, data-based routing protocols, and geographic information-based routing protocols.

[0090] The nodes of UWSNs are powered by batteries and it is difficult to supplement the power, so the energy of the nodes is limited. The energy-based routing protocol can improve the energy efficiency of the network, which is very important to prolong the life of the network. The energy-based routing protocol selects the most suitable path from the source node to the destination node based on the residual energy of the sensor node to forward the data packet. The energy-based routing protocol can be further divided into energy-aware routing protocol and cluster-based routing protocol. Among them, the cluster-based routing protocol divides the entire network into dynamic clusters, each cluster has a cluster head node and multiple cluster member nodes, the cluster member nodes transmit data to the cluster head node, and the cluster head node performs data processing tasks and transmits the processed data to the surface sink node. Since the energy consumption of the cluster head node is large, therefore, the cluster-based underwater routing protocol balances the energy consumption among the nodes in the network by designing a method to select the cluster head, prolonging the life of the network.

[0091] It should be noted that the clustering routing protocol in the traditional wireless sensor network clusters the network, which can balance the energy consumption of the network and prolong the life cycle of the network. However, in the traditional clustering algorithm, the cluster head is pre-selected and fixed throughout the running period of the system, which will make the nodes selected as the cluster head lose the working ability due to energy depletion soon, resulting in all nodes belonging to these clusters cannot work normally. In order to solve this problem, the person skilled in the art proposes the LEACH (routing) protocol (or LEACH algorithm).

[0092] LEACH is a self-organizing and adaptive clustering protocol that uses randomization to evenly distribute the energy load of sensors in the network. In LEACH, sensor nodes self-organize into local clusters, with one node acting as a cluster head node and other nodes becoming member nodes within the cluster. The location of the cluster head node is randomly rotated among sensor nodes to avoid rapid depletion of the energy of a single sensor node.

[0093] The LEACH protocol is divided into four phases, namely the advertisement phase, the cluster set-up phase, the schedule creation phase, and the data transmission phase. The protocol content of each phase is as follows:

[0094] In the advertisement phase, each node decides whether to become the cluster head of the current round based on the number of times it has served as a cluster head and the prior-determined proportion of cluster head nodes.

[0095]

[0096] where P is the prior-determined proportion of cluster head nodes in the network, r is the current round, and G is the set of nodes that have not become cluster heads in the last 1 / P rounds. Through simple calculation, it can be found that the design of the cluster head election probability formula T(n) aims to maintain the proportion of cluster head nodes selected in each round in the total number of nodes, and to prevent nodes that have recently become cluster heads from being re-elected as cluster heads in the short term.

[0097] Each node that elects itself as a cluster head will consume the same transmission energy to broadcast advertisement information to the remaining non-cluster head nodes, and the non-cluster head nodes will remain in a listening state and decide the cluster head to which they belong in the current round based on the received signal strength. In order to enter the cluster, the non-cluster head node will select the cluster head corresponding to the packet with the strongest signal strength it has listened to and enter the cluster.

[0098] In the cluster formation phase, after each non-cluster head node decides which cluster it belongs to, it must notify the cluster head node that it will become a member node of the cluster.

[0099] In the scheduling phase, the cluster head node creates a TDMA schedule according to the number of nodes in the cluster based on the cluster entry information of non-cluster head nodes, and arranges the data transmission time of each member node in the cluster.

[0100] In the data transmission phase, the nodes in the cluster transmit data to the cluster head node using as little energy as possible according to the needs and arrangements, and the cluster head node first organizes and fuses the data packets after collecting the data transmitted by the nodes in the cluster, and then transmits the fused data packets to the sink node.

[0101] The existing LEACH algorithm does not consider the security problem when there are malicious nodes in the network, and how to appropriately elect a cluster head (i.e., cluster head) is an important problem to be solved when researching such a clustering routing protocol.

[0102] In the embodiment, the LEACH, a cluster-based routing protocol, is selected in the network scenario.

[0103] In the simulation, a common propagation model is used, and the nodes are used as variables in distance, and the signal transmission power is adaptively controlled under the condition of ensuring the signal-to-noise ratio at the receiving node. When calculating the signal transmission power, the distance between nodes, signal frequency, receiver noise bandwidth, noise intensity and other parameters are used as parameters, and the minimum power available can be calculated while ensuring that the signal-to-noise ratio at the receiver is higher than a certain threshold.

[0104] For two nodes far apart, due to the serious sound signal attenuation in water, the source node will use a larger transmission power to ensure that the acoustic signal received by the destination node has a higher signal-to-noise ratio, and vice versa. For two nodes close to each other, the source node will choose to use a smaller transmission power.

[0105] Regarding the security problem of UWSNs, that is, common malicious attacks:

[0106] It should be noted that the security problem is an important problem in the field of UWSNs, and UWSNs proposes many requirements in the security aspect, including data confidentiality, data integrity, data freshness, and network availability. Malicious attacks launched by nodes within the network will destroy these security requirements.

[0107] Common malicious attacks can be divided into active attacks and passive attacks according to the interaction mode between the malicious nodes and the attacked nodes. Passive attacks refer to the affected devices attempting to detect activities and collect data transmitted in the network without interfering with the network functions. Active attacks aim to inject, change, destroy or delete data carried on the network, and can capture network data and attempt to change or destroy data packets to interfere with network communication and operation. Both internal attackers and external attackers can launch active attacks. If the attack is made through a node that does not belong to the network, it is an external attack. If the attack comes from a node inside the network, it is classified as an internal attack.

[0108] Although passive attacks pose a non-negligible threat to the network, it is difficult to determine passive attackers based on trust model methods because the network functions are not affected. Therefore, the present embodiment does not consider passive attacks. In addition, according to previous research results, internal attacks are more difficult to track than external attacks and can cause greater danger, so the malicious attacks considered in the present embodiment are all internal attacks.

[0109] It should be noted that in the cluster network topology, the three common malicious attacks are tampering attacks, Sybil attacks and selfish attacks, whose principles and effects are as follows:

[0110] Tampering attack: A node launching a tampering attack will maliciously tamper with data in its own sensor or in a data packet to be forwarded, and then re-inject it into the network, aiming to destroy the integrity and correctness (authenticity) of the data.

[0111] Sybil attack: Sybil attack is a network layer malicious attack. Sybil nodes pretend to be in multiple locations at the same time and have multiple identities (i.e. fake identities), thereby misleading the routing protocol, increasing the possibility of becoming a hot node and further launching other attacks.

[0112] Selfish attack: A selfish attack node will reduce the frequency of data packet transmission in order to prolong its own life, which will destroy the energy balance between nodes in a cluster-based routing protocol and may lead to unfair cluster head election.

[0113] In view of the harm caused by tampering attacks, Sybil attacks and selfish attacks to the network and the routing protocol, the present embodiment studies the design of a trust model (trust evaluation method) for these malicious attacks. The trust model can be used to design a routing soft security mechanism.

[0114] Regarding the trust model (or trust evaluation method), trust system and reputation system:

[0115] It should be noted that the trust model is an abstract model describing the trust relationship between individuals, which is used to describe the trust relationship between individuals based on some trust indicators or trust metrics, and the trust system is a software tool or system that specifically implements and supports the trust model, and the main goal is to create a trusted environment. The trust system is an important part of the soft security mechanism, and the trust model is the core content of the trust system.

[0116] It should be noted that the traditional mechanisms such as identity authentication and access control based on cryptographic technology belong to hard security mechanisms, and trust and reputation systems belong to soft security mechanisms. On the one hand, encryption and identity authentication mechanisms provide identity trust, which is a measure of identity correctness. However, hard security mechanisms lack robustness and cannot protect the network from internal attacks after password leakage. On the other hand, users are also interested in the reliability of the authentication party or the quality of the goods and services provided, which is called provision trust, and only trust and reputation systems (i.e. soft security mechanisms) are useful tools to obtain provision trust. Therefore, in the face of internal attacks on the network, the trust model has irreplaceable advantages over traditional cryptographic technology.

[0117] It should be noted that the trust system and the reputation system are different but closely related.

[0118] Based on UWSNs, the main differences between trust systems and reputation systems are described as follows: the trust system of node A generates a score reflecting the subjective view of the trustworthiness of node A to node B, while the reputation score generated by the reputation system of node B can be seen by all nodes in the entire network. Secondly, the trust path and the transitivity of the network are an explicit component in the trust system, which usually appears in the form of recommended trust in UWSNs, while the reputation system usually does not consider transitivity or only considers it in an implicit way. Finally, the input of the trust system is the subjective expression of trust for other nodes, while the input of the reputation system is the evaluation of objective events. The trust system may contain elements of the reputation system, and vice versa, so it is not always clear how to classify a given system. Due to the mutual inclusion of trust systems and reputation systems and the unclear classification boundaries, it is feasible to study and distinguish centralized and distributed trust systems in UWSNs based on the classification of reputation system architecture by predecessors.

[0119] The network architecture determines how evaluations and reputation scores are passed between agents in a reputation system. There are two main architectures for reputation models, centralized reputation systems and decentralized reputation systems.

[0120] In a centralized reputation system, information about a particular agent's behavior is collected from evaluations by other agents in the system who have had direct experience with the particular agent. The collected evaluations are centralized at a reputation center, which computes a reputation score for each agent in the system based on the information and makes the scores public. Agents in the system can use the scores to make judgments and decisions before interacting with other agents.

[0121] The two basic aspects of a centralized reputation system are:

[0122] a. A centralized communication protocol that allows agents to submit evaluations of other agents to the reputation center and to retrieve reputation scores of other agents from the reputation center.

[0123] b. The reputation center is responsible for reputation computation, computing reputation scores for each agent in the system based on the evaluations and other information received.

[0124] Decentralized reputation systems: In some environments, decentralized reputation systems are more appropriate than centralized systems. In a decentralized system, there is no reputation center designed to collect information and centralize computation, instead, storage and computation are distributed locally among the agents, and evaluations and other information can be recommended to other agents for use in evaluating a particular agent.

[0125] The two basic aspects of a decentralized reputation system are:

[0126] a. A decentralized communication protocol that allows agents to retrieve evaluations from other agents in the system.

[0127] b. Each agent computes a reputation score for a target agent based on the evaluations received and possibly other information.

[0128] In this embodiment, the trust evaluation method (or called trust model) is similar to a centralized reputation system, so it can be called a centralized trust evaluation method (or called a centralized trust model).

[0129] The centralized trust model is suitable for the clustered network. In the centralized trust model, the cluster member nodes, the cluster head nodes and the surface sink nodes undertake the relevant work, the information related to trust is sent to the surface sink nodes for processing, and the trust evaluation results are visible in the whole network. Since the surface sink nodes usually have stronger computing capacity and larger storage space, and the batteries are easy to replace or charge, most of the trust calculation work and storage requirements in the model are implemented on the surface sink nodes, which reduces the occupation of the trust evaluation work on the underwater node computing and storage resources. Since the complete trust information of the whole network can be obtained continuously, the centralized trust model is more accurate in evaluating the trustworthiness of the nodes. In addition, the cluster-based routing protocol can also alleviate the excessive occupation of energy resources of the nodes when transmitting information related to trust.

[0130] It should be noted that there are many studies on the trust model of the roadbed wireless sensor network (TWSNs) in the prior art, however, the environmental characteristics of TWSNs and UWSNs are different, and the existing TWSNs trust model cannot be directly applied to UWSNs. The nodes in TWSNs are densely deployed on a two-dimensional plane, while the nodes in UWSNs are located in a three-dimensional underwater environment considering depth. Unlike the radio frequency radio signals used for communication by land-based sensor nodes, underwater nodes usually use acoustic signals for communication. The transmission rate of underwater acoustic signals is five orders of magnitude lower than that of radio signals, resulting in serious time and space uncertainty of underwater acoustic communication. The complex underwater environment noise can increase the bit error rate and packet loss rate, and the wind and current can cause the node position to change dramatically, also increasing the probability of packet loss due to data packet collision, which makes the underwater link very fragile. At the same time, the serious Doppler effect and multipath effect of the underwater acoustic channel result in high transmission loss.

[0131] Regarding the cloud model and the reverse cloud algorithm:

[0132] It should be noted that the cloud model is a model used to describe concepts in nature, which can reflect the uncertainty in natural language through its three parameters, and realize the mutual conversion between qualitative and quantitative. The three parameters of cloud theory are expectation ex, entropy en and hyper entropy he. ex is the expectation of the attribute, reflecting the central trust value. en is the entropy of the attribute, reflecting the trust fuzziness of ex. he is the hyper entropy of the attribute, reflecting the uncertainty of en. Through the forward cloud algorithm, the overall characteristics of the qualitative concept can be transformed into quantitative numerical representation, realizing the conversion from concept space to numerical space; through the reverse cloud algorithm, the conversion from quantitative value to qualitative concept can be realized, and a group of quantitative data can be converted into a qualitative concept represented by numerical characteristics.

[0133] For any trust attribute x, x∈X, where X is the trust evaluation domain, let x be the exact value, if There is always a mapping μ that satisfies

[0134] μ: X→[0, 1], x→μ(x)∈[0, 1];

[0135] Then, the distribution of x in the domain X is called a trust cloud. μ(x) is called a membership function, and the formula is as follows.

[0136]

[0137] The node selects N data newly collected by the sensor of the node itself as cloud droplets, and uses a reverse cloud algorithm to locally calculate a cloud model related to the sensor data of the node itself;

[0138] When the cloud model is constructed, three numerical characteristic values (i.e., three parameters of the cloud theory) ex, en, and he of the cloud are calculated according to the reverse cloud algorithm to estimate values, so as to realize qualitative analysis of the trust degree of the node.

[0139] Let the qth data in the N data newly collected by the sensor of the node itself be x q The steps of the reverse cloud algorithm are as follows:

[0140] Step 1: Calculate the mean value q and the variance S 2 of x

[0141]

[0142] Step 2: Calculate the estimation value of ex

[0143]

[0144] Step 3: Calculate the estimation value of en

[0145]

[0146] Step 4: Calculate the estimation value of he

[0147]

[0148] In an embodiment, a centralized trust evaluation method based on the cloud theory is provided, the method is applied to an underwater wireless sensor network with a clustered network topology, and the method comprises the following steps:

[0149] A trust evidence collection step: member nodes in a cluster collect packet trust evidence, data trust evidence, and energy trust evidence, and send the three types of trust evidence to a cluster head node;

[0150] ​Trust calculation step: The cluster head node calculates the packet trust value, data trust value and energy trust value of the cluster member node based on the three types of trust evidence collected by the cluster member node, and sends the three types of trust values to the sink node;

[0151] Trust updating step: The sink node calculates and updates the packet cloud model, data cloud model and energy cloud model of all network nodes using the reverse cloud algorithm according to the trust values of all network nodes.

[0152] Malicious node step: The sink node combines the three types of cloud models of the evaluated node into a 1*9 tensor as an input sample to input the VAE model, and obtains the output of the VAE model as an output sample; calculates the mean square error between the input sample and the output sample as the reconstruction error; inputs the reconstruction error into the SVM model for node binary classification to realize malicious node identification.

[0153] In this embodiment, the trust evaluation method (or trust model) is periodically repeated. In order to realize the trust evaluation of the centralized trust evaluation method for the nodes in the network, the cluster member nodes, the cluster head nodes and the water surface sink nodes (i.e. sink nodes, because the sink nodes are located on the water surface, they are also called water surface sink nodes) undertake the tasks in the trust evaluation method.

[0154] Specifically:

[0155] According to the attack characteristics of selfish attack, tampering attack and Sybil attack, the cluster member node collects trust evidence related to the number of node packets, sensor data and energy consumption of itself (i.e. packet trust evidence, data trust evidence and energy trust evidence) to reflect abnormal situations of packet frequency, sensor data and energy consumption, and reports the above information to the cluster head node.

[0156] The cluster head node calculates the packet trust value, data trust value and energy trust value of the cluster member node based on the three types of trust evidence of the cluster member node, and reports the three types of trust value information to the water surface sink node.

[0157] After the sink node obtains the trust values of all network nodes, it calculates and updates the packet cloud model, data cloud model and energy cloud model of all network nodes using the reverse cloud algorithm.

[0158] Then, the sink node combines the three types of cloud models of the evaluated node into a 1*9 (i.e. 1 times 9) tensor as an input sample to input the VAE model (Variational Autoencoder, i.e. variational autoencoder), calculates the mean square error (Mean Squared Error, MSE) between the input sample and the output sample as the reconstruction error, inputs the reconstruction error into the SVM model for node binary classification, and realizes malicious node identification.

[0159] Finally, the sink node informs all the nodes in the network of the malicious node identification result and other related information in the form of broadcast.

[0160] The malicious node identification result and other related information can be used to guide routing decisions, achieve logical isolation of the network from malicious nodes, and further achieve physical isolation.

[0161] In addition, in an embodiment, the cluster head node calculates the packet sending trust value of the member nodes in the cluster based on the packet sending trust evidence collected by the member nodes in the cluster:

[0162]

[0163] wherein T packet is the packet sending trust value; PN is the packet sending trust evidence; PN average is the average number of packets sent by the member nodes in the cluster within a given time period, which is counted by the cluster head node.

[0164] It should be noted that, compared with the radio frequency communication mode in the land-based wireless sensor network, the underwater acoustic communication of the nodes in the UWSN consumes more energy, and therefore most of the energy of the nodes is used for communication, especially for sending data packets. In order to protect the energy consumption of the malicious nodes, the malicious nodes will adopt the hidden attack strategy of selfish attack, and the nodes launching the selfish attack will refuse to comply with the arrangement of the protocol and refuse to generate and send data packets, so as to avoid energy consumption caused by sending data packets, which will destroy data integrity.

[0165] In the embodiment, the number of data packets generated and sent by the nodes within a period of time PN is selected as the packet sending trust evidence according to the characteristics of the nodes launching the selfish attack. The member nodes in the cluster record the number of data packets generated and sent to the cluster head node within a period of time as the packet sending trust evidence, and send the packet sending trust evidence to the cluster head node with the packet.

[0166] In the embodiment, the packet sending trust values of the member nodes in the cluster and the cluster head node are sent to the surface sink node by the cluster head node with the packet.

[0167] In the embodiment, the packet sending trust value is used to quantify the normality of the number of data packets generated and sent by the nodes.

[0168] In addition, in an embodiment, the cluster head node calculates the data trust value of the member nodes in the cluster based on the data trust evidence collected by the member nodes in the cluster:

[0169] The data trust evidence of each member node in the cluster is converted into a cloud vector using a reverse cloud algorithm; the cloud vector includes three parameters: expectation ex, entropy en, and hyper entropy he;

[0170] The estimated values of the three parameters are:

[0171]

[0172] wherein the mean and the variance S 2 is expressed as:

[0173]

[0174] wherein x q represents the data trust evidence of the member node q in the cluster, q = 1, 2, …, M-1;

[0175] The cluster head node uses a cloud similarity algorithm to quantify the similarity between each member node in the cluster and any other member node in the cluster.

[0176] The similarity between two cloud vectors is represented by the cosine value of the angle between the two cloud vectors, and the similarity between the two cloud vectors is:

[0177]

[0178] wherein and represent the vectors composed of the numerical features of the two cloud vectors C i and C j , and i and j represent the cloud vectors C i and C j The corresponding member nodes before conversion are converted using a reverse cloud algorithm, wherein C i = (ex i , en i , he i ), C j = (ex j , en j , he j );

[0179] The relative trust RT ij of the data of the member node i and the member node j in the cluster in this period is expressed as:

[0180]

[0181] The cluster head node calculates the data trust value of each member node in the cluster:

[0182] The data trust value T data of the member node i is the average value of the relative trust of the member node i by all other member nodes in the cluster except the member node i:

[0183]

[0184] Wherein, M is the total number of cluster member nodes; k is any other cluster member node except the cluster member node i, k = 1, 2,..., M, k ≠ i; T data ∈ [0, 1].

[0185] In this embodiment, the data trust value is calculated by collecting data trust evidence related to sensor data based on the characteristics that the node will tamper with the data collected by its own sensor when launching tampering attack.

[0186] For simplicity, it is assumed that the sensing data is digital information, on the basis of which, the data trust value T data ∈ [0, 1].

[0187] The node selects the N data newly collected by its own sensor as the data trust evidence {u1, u2,..., u N};

[0188] The data trust evidence is taken as a cloud drop, and the cloud vector of the data trust evidence related to the sensor data of each cluster member node is calculated locally using the reverse cloud algorithm, that is, the data trust evidence of each cluster member node is converted into a cloud vector using the reverse cloud algorithm.

[0189] The cluster head node sends the calculated cloud vector to the cluster head node with the packet.

[0190] The cluster head node quantifies the similarity between the cloud vectors of each cluster member node and any other cluster member node using the cloud similarity algorithm, and then quantifies the relative trust degree of the data of the two nodes at the same period (i.e., relative trust) based on the cloud similarity, and then obtains the data trust value of each cluster member node according to the average value of the relative trust.

[0191] Finally, the data trust values of the cluster member nodes and the cluster head node are sent to the water surface sink node by the cluster head node with the packet.

[0192] In addition, in an embodiment, the cluster head node calculates the energy trust value of the cluster member nodes based on the energy trust evidence collected by the cluster member nodes:

[0193] The energy trust evidence is modified as follows:

[0194]

[0195] Wherein, E original is the energy consumption of the cluster member node in a given time period, which is taken as the energy trust evidence; E consumeE is the corrected energy consumption of the cluster head node in the current round; d is the straight-line distance between the cluster head node and the cluster member node; f is the center frequency used in acoustic communication (i.e., underwater acoustic communication mode); A(d, f) is an attenuation function;

[0196] Based on the corrected energy consumption E consume , the energy trust value T energy is calculated using a normal distribution probability density function:

[0197]

[0198]

[0199] where f(x) is the normal distribution probability density function of the corrected energy consumption E consume ; x is a random variable subject to normal distribution; E average is the mean value of the normal distribution, which is the mean value of the corrected energy consumption of all cluster member nodes calculated by the cluster head node; σ is the standard deviation of the normal distribution; e evaluated = E consume .

[0200] It should be noted that the present embodiment studies underwater communication related problems. In underwater communication, the radio propagation distance is limited, so the acoustic mode is used for communication, i.e., acoustic communication.

[0201] In the present embodiment, by analyzing the characteristics of the witch attack with multiple identities, it is found that the node launching the witch attack will imitate the behavior of normal nodes, such as cooperating with the clustering protocol and generating and sending data packets at similar frequencies, in order to hide the false identity among normal nodes, which will inevitably lead to additional energy consumption. Therefore, in the case that other characteristics of the node are normal, the potential witch node can be identified by the abnormal energy consumption of the node.

[0202] In the present embodiment, the energy consumption E original of the cluster member node in a given time period is used as energy trust evidence.

[0203] In the present embodiment, since the cluster head node undertakes high-energy-consuming tasks such as receiving data packets of cluster member nodes, information processing, and packet sending to the surface sink node, the calculation and evaluation of the energy trust value only include the cluster member nodes, but not the cluster head node in the current round.

[0204] In the present embodiment, when the energy trust evidence is counted, although the number of packet sending per second of the cluster member node as a random variable follows the Poisson distribution with the same parameters, unlike the land-based wireless sensor network, the underwater acoustic signal in UWSNs attenuates severely, and the energy consumption of the communication between nodes at different distances differs greatly, making it difficult to directly identify abnormal energy consumption.

[0205] To solve this problem, the energy trust evidence is modified by taking distance as a parameter, and more accurate trust evaluation results are obtained by reducing the impact of distance on the energy consumption of underwater node communication:

[0206] Through the analysis of the underwater acoustic signal attenuation function formula, the modification of the energy trust evidence is as follows:

[0207]

[0208] wherein, E original is the energy consumption of the cluster member node within a given time period, that is, the energy trust evidence; E consume is the modified energy consumption, which is the modified energy trust evidence; d (unit is m) is the straight-line distance between the cluster member node and the cluster head node; f (unit is Hz) is the center frequency used for acoustic communication; A(d,f) is the attenuation function.

[0209] The cluster member node periodically records its own energy consumption and sends the modified energy trust evidence to the cluster head node with the packet.

[0210] In this embodiment, the cluster head node calculates the energy trust value based on the modified energy trust evidence, and the energy trust value is used to quantify the normal degree of energy consumption of the node within a given time period.

[0211] In this embodiment, the energy trust value of the cluster member node is sent to the surface sink node by the cluster head node.

[0212] In this embodiment, the standard deviation σ of the normal distribution can be set according to the specific scene.

[0213] In addition, in an embodiment, the sink node calculates and updates the packet sending cloud model, data cloud model and energy cloud model of all network nodes using the reverse cloud algorithm according to the trust values of all network nodes, including:

[0214] Let the packet sending trust value of any node be {T packet1 ,T packet2 ,......,T packetn}, wherein n is the number of packet sending trust value data of any node saved at the sink node; using the packet sending trust value of any node as a cloud drop, the reverse cloud algorithm is used to calculate the packet sending cloud model as (Ex packet ,En packet ,He packet );

[0215] Based on the data trust value, the data cloud model (Ex data ,En data ,He data ) is obtained;

[0216] Based on the energy trust value, an energy cloud model (Ex energy , En energy , He energy ) is obtained.

[0217] The three types of cloud models, the package cloud model, the data cloud model and the energy cloud model, are periodically calculated and updated with the accumulation of the three types of trust values, the package trust value, the data trust value and the energy trust value.

[0218] In the embodiment, the cloud model refers to the cloud model related to the node trust value, that is, the trust cloud. After the water surface aggregation node completes the collection of the trust values of all network nodes, the trust values are sorted and saved locally to calculate and update the trust cloud (cloud model) related to the trust value.

[0219] In the embodiment, Ex packet is the expectation of the package cloud model; En packet is the entropy of the package cloud model; and He packet is the hyper-entropy of the package cloud model.

[0220] In the embodiment, Ex data is the expectation of the data cloud model; En data is the entropy of the data cloud model; and He data is the hyper-entropy of the data cloud model.

[0221] In the embodiment, Ex energy is the expectation of the energy cloud model; En energy is the entropy of the energy cloud model; and He energy is the hyper-entropy of the energy cloud model.

[0222] In the embodiment, the three types of trust clouds (cloud models) constructed can qualitatively represent the trust degree of the evaluated node in the aspects of package, data and energy,

[0223] In addition, in an embodiment, the VAE model is obtained after the initial VAE model is trained by using a VAE data set.

[0224] The VAE data set is cloud model data generated by nodes when attack node samples exist, which is collected by using network simulation simulation technology.

[0225] In the embodiment, the network simulation simulation technology refers to simulation based on NS-3 software to collect cloud model data generated by nodes when attack node samples exist.

[0226] It should be noted that NS-3 software is an open source project written in C++ language, and the main operating platform is GNU / Linux, such as CentOS, Ubuntu, Fedora, etc. Although Windows users can also use Cygwin or Visual Studio to run NS-3, some functions may not be available, such as interaction with physical networks. The functions and characteristics of NS-3 software mainly include:

[0227] Network simulation: NS-3 is mainly used for simulating computer networks, which can simulate various types and sizes of network structures in the physical world on a computer.

[0228] Discrete event driven: NS-3 uses discrete event simulation technology to abstract a continuous process in the physical world into a series of discrete events in the virtual world, which can very realistically simulate various network protocols in the physical world.

[0229] Script support: NS-3 simulation scripts can support both C++ and Python programming languages, and users can call various application programming interfaces (APIs) provided by NS-3 for network simulation in simulation scripts to build their own virtual network structures.

[0230] NS-3 also has the following auxiliary functions:

[0231] trace generation: allows users to directly analyze the data generated by NS-3 through third-party software such as Wireshark and tcpdump.

[0232] Mobile module: can automatically assign starting positions and movement trajectories to nodes.

[0233] Interaction with physical networks: the virtual network built by NS-3 can be highly integrated with the physical network environment, and virtual nodes can use physical networks to transmit data, and physical nodes can also use virtual channels built by NS-3 to transmit data packets.

[0234] As for the alternative software of NS-3, NS-2 is undoubtedly a choice. NS-2 is also a mainstream software in the field of network simulation, but it is the predecessor of NS-3, part of which is written in C++ and the other part is written in OTcl. Compared with NS-3, NS-2 has stopped updating, but in some specific fields, such as Reina D.G. Network probability broadcast scheme simulation, wireless sensor network group intelligent routing protocol (FANETs) routing protocol simulation, etc., NS-2 may still have advantages.

[0235] In this embodiment, the simulation parameters based on NS-3 software simulation are shown in the following table:

[0236] Parameter name Parameter setting Deployment range 5000m*5000m*5000m Number of packet sending nodes 100 Data packet size 400Bytes Total number of malicious nodes 15 Number of selfish attack nodes 5 Number of tampering attack nodes 5 Number of Sybil attack nodes 5 Movement model Fixed Simulation time 3000s Attack start time 1000s Data collection time 2000s

[0237] In this embodiment, the process is simulated based on the NS-3 software:

[0238] The cluster member nodes collect and report their own trust evidence to the cluster head node, and the cluster head node calculates the trust value and reports it to the water surface sink node;

[0239] In the case of malicious node attack, change the random seed and run the simulation multiple times, collect 2100 pieces of three types of trust cloud data of normal nodes for modeling normal node cloud model;

[0240] At the same time, collect 300 pieces of three types of trust cloud data of malicious nodes for testing model training effect.

[0241] In this embodiment, the essence of the VAE model (Variational Autoencoder) is to extract the hidden features of the data and construct a neural network model from the hidden features to the generated target. It has two structures of encoder (Encoder) and decoder (Decoder) in the design architecture, wherein the encoder converts the input data into a smaller and more compact encoding expression in the hidden space, and the decoder restores the encoding to the original input data.

[0242] In this embodiment, since the VAE model can fit the prior distribution of the hidden variables of the existing samples and then sample them to obtain new samples, the VAE can be used to extract the features of the normal node cloud model and realize the modeling of the normal node cloud model. Then, according to the feature that the corresponding cloud model of the malicious node will change after launching an attack, the abnormal cloud model is detected by identifying the large fluctuation of the VAE model.

[0243] In this embodiment, the network structure of the VAE model:

[0244] The VAE model includes an encoder (Encoder) and a decoder (Decoder);

[0245] The encoder includes, in sequence:

[0246] Input layer: input size is 9;

[0247] Self-attention layer: input size is 9, and output size is 9;

[0248] Fully connected layer 1: input size is 9, and output size is 128;

[0249] Mean layer: input size is 128, and output size is 2;

[0250] Variance layer: input size is 128, and output size is 2;

[0251] obtaining the latent variable according to the variance layer and the mean layer;

[0252] The decoder comprises, in sequence:

[0253] Full connection layer 2: input size is 2, output size is 128; the latent variable inputs the full connection layer 2;

[0254] Output layer: input size is 128, output size is 9.

[0255] In order to enable the encoder to adaptively focus on the importance of different parts in the input when extracting features, improve the expression ability and feature extraction ability of the encoder, an attention mechanism is introduced into the VAE model: a self-attention layer with an input size of 9 and an output size of 9 is added to the VAE model structure.

[0256] In the embodiment, as Figure 4 described, the training effect of the test VAE model is tested:

[0257] A 1*3 vector (Ex, En, He) composed of the expectation, entropy and hyperentropy of the cloud model is named as the feature vector of the cloud model.

[0258] Firstly, the three-class cloud models of normal nodes and malicious nodes in the VAE data set are shown as vectors in a three-dimensional space, and the changes before and after passing through the VAE model are shown.

[0259] Among them, the three-dimensional coordinates are the expectation, entropy and hyperentropy of the cloud model, and the feature vectors corresponding to the cloud models are represented by arrows of different colors in the three-dimensional graph. From left to right, the three graphs respectively represent the cases when the selective forwarding attack, tampering attack and witch attack nodes are launched.

[0260] Among them, the light red arrow represents the feature vector of the cloud model after the malicious node launches an attack, the dark red arrow represents the feature vector of the output cloud model after the input cloud model of the malicious node launches an attack is input into the VAE model, the light green arrow represents the feature vector of the normal node cloud model, and the dark green arrow represents the feature vector of the output cloud model after the input cloud model of the normal node is input into the VAE model.

[0261] As Figure 4 shown, Figure 4 (a), Figure 4 (b), Figure 4 (c) are the vector graphs of the cloud models of the selfish attack, tampering attack and witch attack nodes respectively.

[0262] In the figure, the light red arrows are obviously deviated from the light green arrows, which shows that the attack behavior of the node causes the abnormality of the cloud model of the node, which is obviously different from the cloud model of the normal node. Moreover, the deep red arrow and the deep green arrow are different, which shows that the error generated by the VAE model when the input data is abnormal has a fluctuation compared with the conventional error.

[0263] In the following, the mean square error (MSE) of the generated vector and the input vector is taken as an index, and the training set, the test set in the normal node data set and the malicious node data set are input respectively to test the training effect, and the results are as follows:

[0264] Data set type MSE Normal node data set 0.0011 Malicious node data set 0.0164

[0265] It can be seen that the MSE obtained by inputting the malicious node data set is about 15 times that of the normal node data set, and the difference is obvious, which shows that the VAE model can effectively extract the data features of the cloud model of the normal node in the network, and reconstruct and generate on this basis, and when the malicious node cloud model data passes through the VAE model, the MSE result has a significant fluctuation.

[0266] In addition, in an embodiment, the SVM model is obtained by training an initial SVM model using an SVM data set;

[0267] The SVM data set is obtained by collecting using network simulation technology (i.e., based on NS-3 software simulation) on the basis of completing the initial VAE model training. The simulation parameters are the same as above.

[0268] In the embodiment, the SVM data set includes three types of cloud vectors (i.e., cloud models) of normal nodes and malicious nodes, and the MSE calculated by the VAE model at 2000s.

[0269] In the embodiment, the SVM data set is labeled, and label 1 represents a normal node and label -1 represents a malicious node.

[0270] In the embodiment, in the process of simulation based on NS-3 software, the simulation is run multiple times by changing the random seed in the presence of malicious node attack, and 3240 data are collected, of which 2000 are used as a training set and 1240 are used as a test set. In the SVM data set, the number of malicious node data accounts for 15% of the total number of data.

[0271] In the embodiment, the SVM model (i.e., support vector machine) has higher accuracy in small sample and non-linear classification than other models based on artificial intelligence technology.

[0272] Due to the sparse deployment of underwater network nodes, the trust evidence collected by the nodes is less, and compared with other supervised learning algorithms, SVM can effectively solve the learning problem of relatively small samples, so the SVM method is used to realize node classification.

[0273] It should be noted that the training resources required by the SVM model are large; in the traditional trust evaluation method, due to the lack of data sets in the relevant scene, it is difficult to apply the SVM model, and the present embodiment solves this technical problem by using network simulation technology (i.e., simulation based on NS-3 software).

[0274] In the present embodiment, before training the initial SVM model, the type and parameters of the SVM model need to be determined first.

[0275] The type of SVM model is selected as C-SVM type;

[0276] Due to the small number of sample features, a radial basis kernel function is used;

[0277] The grid traversal method is used to find the optimal parameters c and γ with the smallest error, and then the optimal parameters are selected to train the initial SVM model.

[0278] In the present embodiment, in order to determine the optimal parameters, the parameters c and γ of C-SVC are first optimized based on the grid traversal principle; the grid optimization results of the parameters c and γ of C-SVM are as shown in Figure 5 Since the model reaches 100% classification accuracy on the training set when c=8.0 and γ=0.5, the optimal parameters are selected.

[0279] In the present embodiment, the hyperparameters set when training the initial SVM model are as shown in the following table:

[0280] Attribute name Attribute value SVM type C-SVM Kernel function type RBF kernel Set the order of kernel function 3 Gamma parameter in kernel function 0.5 Coef0 parameter in kernel function 0 Penalty coefficient C 8 Cache memory size 40MB Tolerable deviation in termination principle 0.001 Whether to use heuristic Yes Weight the penalty coefficient C of each type of sample 1

[0281] After training the initial SVM model, the parameters of the obtained SVM model are as shown in the following table:

[0282]

[0283]

[0284] In addition, in an embodiment, the aggregation node combines the three types of cloud models of the evaluated nodes into a 1*9 tensor as the output sample input into the VAE model, and obtains the output of the VAE model as the output sample; the mean square error between the input sample and the output sample is calculated as the reconstruction error, which is:

[0285] Let the input sample be X=(X1, X2, X3, L X9);

[0286] The reconstruction result output by the VAE model, i.e., the output sample is

[0287] The reconstruction error MSE is:

[0288]

[0289] In addition, in an embodiment, the reconstruction error is input into an SVM model for node binary classification to realize malicious node identification, which is:

[0290] The reconstruction error is input into the SVM model for node binary classification:

[0291] If the classification result is -1, it indicates that the reconstruction error of the cloud model of the evaluated node exceeds the given threshold, the cloud model is abnormal, and the evaluated node is identified as a malicious node.

[0292] On the contrary, if the classification result is 1, the evaluated node is identified as a normal node.

[0293] In the embodiment, in the malicious node identification process, the VAE model generates a certain error when decoding and restoring the data after the encoder. If there is an anomaly in the original input data, the generated error has a greater fluctuation compared with the conventional error. Based on the SVM model classification of the errors generated by different nodes, the normal nodes and malicious nodes can be distinguished, and the trust evaluation of the evaluated node is completed.

[0294] In an embodiment, a centralized trust evaluation device based on cloud theory is provided, which is applied to an underwater wireless sensor network in a clustered network topology structure. The device comprises the following modules:

[0295] A trust evidence collection module: the in-cluster member nodes collect packet trust evidence, data trust evidence, and energy trust evidence, and send the three types of trust evidence to the cluster head node.

[0296] A trust calculation module: the cluster head node calculates the packet trust value, data trust value, and energy trust value of the in-cluster member nodes based on the three types of trust evidence collected by the in-cluster member nodes, and sends the three types of trust values to the sink node.

[0297] A trust updating module: the sink node calculates and updates the packet cloud model, data cloud model, and energy cloud model of all network nodes using the reverse cloud algorithm according to the trust values of all network nodes.

[0298] Malicious node module: the sink node combines the three types of cloud models of the evaluated nodes into a 1*9 tensor as an output sample input into the VAE model, and obtains the output of the VAE model as an output sample; the mean square error between the input sample and the output sample is calculated as a reconstruction error; the reconstruction error is input into the SVM model for node binary classification, so as to realize malicious node identification.

[0299] In an embodiment, a cluster head election method for improving the LEACH algorithm is provided, and the method comprises the following steps:

[0300] Mean square error updating step: each node obtains its updated mean square error mse by using any one of the centralized trust evaluation methods based on the cloud theory described above;

[0301] Wherein, mse∈[0, +∞); P is a prior-determined proportion of cluster head nodes in the network; r is the current round; G is a set of nodes that have not become cluster head nodes in the last 1 / P rounds;

[0302] Cluster head election threshold calculation step: each node uses an improved cluster head election threshold function T imp (n) to calculate the cluster head election threshold based on the number of times it has served as a cluster head node, the prior-determined proportion of cluster head nodes, and mse.

[0303] The improved cluster head election threshold function T imp (n) is as follows:

[0304]

[0305] F(mse)=1.02^(24^(10*mse));

[0306] Random number generation step: each node generates a random number by itself; the random number is generated by a random variable in [0, 1]; the random number is subject to a uniform distribution;

[0307] Threshold comparison step: each node compares the random number it generates with the cluster head election threshold:

[0308] If it is less than the cluster head election threshold, the node becomes a cluster head node in the current large period.

[0309] Otherwise, the node does not become a cluster head node.

[0310] In this embodiment, each node obtains its updated mean square error mse by using any one of the centralized trust evaluation methods based on the cloud theory described above:

[0311] In the cloud theory-based centralized trust evaluation method described in any of the above, each node is taken as an evaluated node; the sink node combines the three types of cloud models of the evaluated node (i.e., each node) into a 1*9 tensor as an output sample input into the VAE model, and the output of the obtained VAE model is taken as an output sample; and the mean square error between the input sample and the output sample is calculated as the updated mean square error mse of each node itself.

[0312] It should be noted that the traditional LEACH algorithm does not consider the security problem when there are malicious nodes in the network, and how to appropriately elect a cluster head is an important problem to be solved when researching such a clustering routing protocol. Therefore, the present embodiment designs a routing soft security mechanism based on a trust model to obtain a cluster head election method of the improved LEACH algorithm.

[0313] In the present embodiment, the F(mse) function curve is as shown in Figure 6

[0314] It can be seen that when mse∈[0, 0.15], F(mse) increases with the increase of mse, and the increasing amplitude gradually increases. In fact, when mse∈[0, +∞), F(mse) is monotonically increasing, and F(mse)∈[1, +∞). In the improved LEACH algorithm, the larger the mse of a node is, the larger the F(mse) of the node is, and the smaller the T imp (n) is. When T imp (n) decreases, the probability of the node becoming a cluster head node also decreases. Therefore, the design of the cluster head election threshold function in the improved LEACH protocol can reduce the possibility of a node with a higher mse being elected as a cluster head node, while having little effect on the cluster head election process of a node with a lower mse.

[0315] In an embodiment, a cluster head election device of the improved LEACH algorithm is provided, and the device comprises the following modules:

[0316] A mean square error updating module: each node obtains an updated mean square error mse of itself by using a cloud theory-based centralized trust evaluation method;

[0317] Wherein, mse∈[0, +∞); P is a priori determined proportion of cluster head nodes in the network; r is the current round; and G is a set of nodes that have not become cluster head nodes in the last 1 / P rounds;

[0318] A calculation of an election threshold module: each node calculates a cluster head election threshold by using an improved cluster head election threshold function T imp (n) based on the number of times the node has served as a cluster head node, the a priori determined proportion of cluster head nodes, and the mse;

[0319] The improved cluster head election threshold function T imp ​(n) as follows:

[0320]

[0321] F(mse) = 1.02^(24^(10*mse));

[0322] A random number generating module: each node generates a random number by itself; the random number is generated by a random variable within [0, 1]; the random number is subject to a uniform distribution;

[0323] A threshold comparison module: each node compares the random number generated by itself with a cluster head election threshold:

[0324] If the random number is less than the cluster head election threshold, the node becomes a cluster head node in a current large period;

[0325] Otherwise, the node does not become a cluster head node.

[0326] In an embodiment, an improved LEACH routing protocol method is provided, which includes a clustering step and a data transmission step;

[0327] The clustering step: nodes periodically re-elect cluster head nodes for clustering, and this period is referred to as a large period, denoted as T1; wherein the cluster head nodes are elected by using the cluster head election method of the improved LEACH algorithm described above;

[0328] The data transmission step: the cluster head nodes periodically forward the integrated data packets of the in-cluster member nodes to the sink node, and this period is referred to as a small period, denoted as T2.

[0329] In the data transmission step in the embodiment, the number of packets sent by the in-cluster member nodes per second is subject to a Poisson distribution as a random variable, and the in-cluster member nodes immediately attempt to send data packets to the cluster head nodes whenever data is generated.

[0330] In the data transmission step in the embodiment, the in-cluster member nodes also send their packet sending trust evidence, energy trust evidence, and energy trust evidence to the cluster head nodes along with the data packets.

[0331] In the data transmission step in the embodiment, after receiving the data packets of the in-cluster member nodes, the cluster head nodes first store and integrate the data packets; then periodically send the integrated data packets to the surface sink node at a period of T2.

[0332] In the data transmission step in the embodiment, the cluster head nodes periodically calculate the trust values of the in-cluster member nodes based on the collected trust evidence at a period of T2, and send the trust value calculation results to the surface sink node along with the integrated data packets.

[0333] In an embodiment, an improved LEACH routing protocol device is provided, which includes a cluster building module and a data transmission module;

[0334] The cluster building module: the cluster head nodes are periodically re-elected by the nodes, and the cluster is built. This period is called a large period, denoted by T1. The cluster head nodes are elected by using the cluster head election method of the improved LEACH algorithm described above.

[0335] The data transmission module: the cluster head nodes periodically integrate the received data packets of the cluster member nodes and forward them to the sink node. This period is called a small period, denoted by T2.

[0336] In addition, in an embodiment, a simulation experiment is provided to verify the performance of the centralized trust evaluation method based on the cloud theory and the cluster head election method of the improved LEACH algorithm.

[0337] Simulation environment setting:

[0338] The simulation is based on the NS-3 network simulation software and the Aqua-Sim-NG underwater acoustic communication simulation module.

[0339] The simulation includes simulation experiments related to the trust evaluation method and simulation experiments related to the improved LEACH algorithm. The former mainly considers the ability of the trust evaluation method to accurately identify malicious nodes, and the latter mainly considers the security of the cluster head election process in the improved routing protocol.

[0340] Simulation parameter setting:

[0341] The underwater nodes are randomly deployed in a cubic water area of 5000m*5000m*5000m, and the surface sink node is located at the center of the water surface. In the simulation, it is assumed that there is no malicious attack during the initial deployment of the network, and the malicious attack starts in the middle and continues until the end of the simulation. The signal transmission power of the nodes is adaptively adjusted according to the distance, and the minimum power is selected to transmit the signal under the premise of ensuring the signal-to-noise ratio at the receiving end. In order to facilitate the collection of energy trust evidence, the lower limit of the transmission power is additionally specified in the simulation.

[0342] In addition, the medium access control (MAC) protocol used in the simulation is a modified version of the broadcast MAC protocol studied by those skilled in the art. In the broadcast MAC protocol, a node senses the channel before sending a data packet, and if the channel is idle, the packet is sent, otherwise a backoff operation is performed, a backoff interval is randomly determined and the attempt is made again until the data packet is successfully sent or the number of attempts reaches an upper limit, at which time the data packet will be discarded. The MAC protocol used in the simulation is modified based on the broadcast MAC protocol. In order to facilitate the collection of trust evidence, an ACK confirmation mechanism is additionally added in the MAC protocol. In the ACK confirmation mechanism, when a node receives a data packet sent to itself, it will immediately return an ACK frame to notify the previous hop node that it has successfully received the data packet.

[0343] In the simulation, the detection rate and the false detection rate are mainly used as indicators to test the effectiveness of the designed trust model (i.e., the centralized trust evaluation method based on cloud theory). When testing the security of the improved routing protocol (i.e., the improved LEACH algorithm), the proportion of malicious nodes in the current cluster head node after each clustering stage is mainly concerned.

[0344] The detailed simulation parameters are shown in the following table:

[0345] Parameter name Parameter setting Total number of underwater nodes 100 Number of surface sink nodes 1 Node deployment range 5000m*5000m*5000m Number of packets sent by each node per second As a random variable, follow the Poisson distribution with mean 1 / 10 Proportion of cluster head nodes 5％ Proportion of malicious nodes among nodes 15％ Number of selfish attack nodes 5 Number of tampering attack nodes 5 Number of Sybil nodes 5 Total simulation time 3000s Attack start time 1000s Parameter σ 28 Cluster head rotation period (large period T1) 100s Cluster head packet period (small period T2) 10s Receiving power 1.5W Idle power 0.003W

[0346] Trust model (trust evaluation method) simulation experiment:

[0347] In order to study the effectiveness of the trust evaluation method in identifying malicious nodes, Figure 7 The detection rate and the false detection rate of the trust evaluation method with respect to the simulation time are shown.

[0348] As Figure 7To avoid the problem that the cloud model is inaccurate due to too few cloud droplets, the condition for calculating the cloud model is set in the simulation, i.e., the cloud model is not calculated and the trust evaluation is not performed when the number of cloud droplets is too small, so the data from 0s to 400s is meaningless. Between 500s and 600s, the malicious node does not launch an attack, and the detection rate and false detection rate of the trust evaluation method first increase and then gradually decrease, because the trust evaluation method has fewer cloud droplets in the early stage of the network, and the cloud model calculated each time changes greatly, resulting in a high false detection rate, and the detection rate also increases. Between 700s and 1000s, the detection rate and false detection rate gradually decrease, because as the number of cloud droplets increases, the calculated cloud model can more accurately describe the trust degree of the node, which reduces the false detection rate. After 1000s, the malicious node starts to launch an attack, and the malicious node detection rate first increases slowly with time, and then rises rapidly, and the simulation data shows that the detection rate eventually gradually stabilizes at a high level of more than 99.3%. This trend is because it takes a process for the malicious node to launch an attack to be recognized by the trust evaluation method, during which the trust evidence of the malicious node first appears abnormal, and the trust value also decreases, and as the low trust value accumulates as cloud droplets, the cloud model gradually becomes abnormal. After a period of time, the abnormal cloud model will cause the calculated MSE to fluctuate greatly when passing through the VAE, and the SVM will only recognize the corresponding node as a malicious node after detecting such fluctuations. After 1000s, it is noted that the false detection rate gradually decreases with time and stabilizes at a low level of less than 0.24%, because as the number of cloud droplets increases, the cloud model calculation result of the normal node gradually stabilizes, which reduces the possibility of the trust model detecting the normal node as a malicious node.

[0349] In the trust evaluation method, MSE (Mean Square Error) as an important indicator can quantitatively evaluate the degree of deviation of the node cloud model from the normal cloud model, the higher the MSE, the farther the deviation from the normal cloud model, the cloud model is abnormal, and vice versa, the lower the MSE, the closer to the normal cloud model. The following studies the changes of the MSE of the cloud model of the attack node and the normal node with the simulation time.

[0350] As shown in Figure 8 , the MSE of the normal node cloud model is always in a low area of about 0.02, while the MSE of the attack node cloud model gradually increases after the attack at 1000s and rises to about 0.15 at 1900s, which is obviously different from the MSE of the normal node cloud model. This is because after the malicious node launches an attack, the attack behavior is reflected in the trust evidence, resulting in a decrease in the trust value and an abnormal cloud model, and when the abnormal cloud model passes through the VAE, the MSE will fluctuate, while the normal node does not have obvious changes in behavior, and the trust evidence, trust value and cloud model are normal, and the MSE of the normal node cloud model does not change much when it passes through the VAE.

[0351] The MSE corresponding to different attack types in the figure is not strictly monotonically increasing with simulation time, and the fluctuations can be explained by the randomness in the simulation. The packet sending frequency of the node obeys the Poisson distribution as a random variable, so the number of packets sent by the node in a period of time is not fixed, which also causes the energy consumption level of the node in different time periods to be different. In addition, the malicious tampering of data by the node is also affected by the random number. The above reasons will cause the fluctuation of the trust value, and then affect the updating result of the cloud model and the calculation result of the MSE.

[0352] Improved LEACH protocol simulation experiment:

[0353] In order to study the security of the cluster head election link in the improved LEACH protocol, the proportion of malicious nodes in the cluster head nodes in each round of the LEACH protocol and the improved LEACH protocol (algorithm) is tested. A high proportion means that more malicious nodes are selected as cluster heads, and vice versa.

[0354] Figure 9 The proportion of malicious nodes in the cluster head nodes is shown as a function of simulation time, where the malicious nodes start attacking at 1000s. The red circle line represents the performance of the original LEACH protocol. Since the original LEACH protocol does not consider security in the cluster head election process, when there are malicious nodes in the network, it is possible to select malicious nodes as cluster head nodes. After 1000s, the proportion of malicious nodes in the cluster head nodes fluctuates around 15%, which is consistent with the 15% malicious node proportion in the simulation conditions. The blue triangle line represents the performance of the improved LEACH protocol (i.e. the improved LEACH routing protocol method). Since the improved LEACH protocol adds a soft security mechanism based on the trust model, the model's evaluation of the node's trustworthiness affects the cluster head election process (i.e. the cluster head election method of the improved LEACH algorithm), reducing the likelihood of malicious nodes being elected as cluster heads. After 1000s, the proportion of malicious nodes first increases and then gradually decreases, eventually stabilizing below 1.4%. The performance of the improved LEACH protocol can be explained by the running characteristics of the trust model. The identification of malicious nodes by the centralized trust model based on cloud theory (i.e. the centralized trust evaluation method based on cloud theory) is a process. After the attack starts, with the accumulation of trust evidence, the abnormal situation of the cloud model of the malicious node becomes more and more serious, and the reconstruction error gradually increases, resulting in a threshold value generated by the threshold function of the malicious node's cluster head election becoming smaller and smaller, which greatly reduces the likelihood of the malicious node becoming a cluster head. Compared with the original LEACH protocol, the improved LEACH protocol is less likely to elect malicious nodes as cluster head nodes in the cluster head election process, which demonstrates the ability of the routing soft security mechanism based on the trust model to improve the security of the cluster head election process in the clustering routing protocol.

[0355] The malicious node ratio of the two protocol cluster head nodes fluctuates with the change of simulation time, because the original LEACH protocol and the improved LEACH protocol both use the cluster head election method based on probability, and the statistical results in each round will have normal fluctuation phenomenon under the limited simulation times. The fluctuation of the improved LEACH protocol between 3500 seconds and 4000 seconds can also be explained by the characteristics of the cluster head election process. Although the improved LEACH protocol can effectively reduce the possibility of malicious nodes becoming cluster heads, it is not absolute, and the cluster head election threshold function of the nodes that have never been selected as cluster heads will tend to give a higher threshold in the next cluster head election process, which will increase the possibility of the nodes being selected as cluster heads. Therefore, the malicious nodes that have never been selected as cluster heads still have a small possibility of becoming cluster head nodes, but compared with the original LEACH protocol, the possibility of malicious nodes becoming cluster head nodes has been significantly reduced as a whole.

[0356] The above further describes the technical solutions provided by the present application through several specific embodiments, in order to highlight the advantages and benefits of the technical solutions provided by the present application. However, the above several specific embodiments are not used as a limitation of the present application, and any reasonable changes and improvements, reasonable combinations and equivalent replacements of the embodiments, etc. based on the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A cluster head election method for improving LEACH algorithm, characterized in that, The method comprises the following steps: Mean square error updating step: each node obtains its updated mean square error by using a centralized trust evaluation method based on cloud theory ; wherein, ; is a priori determined proportion of cluster head nodes in the network; is the current round; is the set of nodes that have not been cluster head nodes in the last rounds. a step of calculating an election threshold: each node calculates an election threshold based on the number of times it has served as a cluster head node, a cluster head node proportion determined in advance, and , using an improved cluster head election threshold function calculating a cluster head election threshold; Improved cluster head election threshold function As follows: A random number generating step: each node generates a random number by itself; the random number is generated by a random variable within [0, 1]; the random number is subject to a uniform distribution; A threshold comparison step: each node compares the random number generated by itself with a cluster head election threshold value: If less than the cluster head election threshold value, the node becomes a cluster head node in the current large period; Otherwise, the node does not become a cluster head node; Each node is taken as an evaluated node; the converged node combines the three types of cloud models of the evaluated node into the tensor as an output sample, and the output of the VAE model obtained as an output sample; the mean square error between the input sample and the output sample is calculated as the mean square error of each node after self-updating .

2. The cluster head election method of improving LEACH algorithm according to claim 1, characterized in that, The centralized trust evaluation method based on the cloud theory is applied to an underwater wireless sensor network with a clustered network topology; The centralized trust evaluation method based on the cloud theory comprises the following steps: A trust evidence collection step: the member nodes in a cluster collect packet sending trust evidence, data trust evidence and energy trust evidence, and send the three types of trust evidence to the cluster head node; A trust calculation step: the cluster head node calculates the packet sending trust value, data trust value and energy trust value of the member nodes in the cluster based on the three types of trust evidence collected by the member nodes, and sends the three types of trust values to the sink node; A trust updating step: the sink node calculates and updates the packet sending cloud model, data cloud model and energy cloud model of all the nodes in the network using the reverse cloud algorithm according to the trust values of all the nodes in the network; A malicious node step: the sink node combines the three types of cloud models of the node to be evaluated into a 1*9 tensor as an input sample of the VAE model, obtains the output of the VAE model as an output sample, calculates the mean square error between the input sample and the output sample as a reconstruction error, and inputs the reconstruction error into the SVM model for node binary classification to realize malicious node identification.

3. An improved cluster head election apparatus of LEACH algorithm, characterized in that, The device comprises the following modules: Mean square error updating module: each node adopts a centralized trust evaluation method based on cloud theory to obtain its own updated mean square error ; wherein, ; is a proportion of cluster head nodes in the network determined a priori; is the current round; is the set of nodes that have not been a cluster head node in the last rounds. a computing election threshold module: each node computes a cluster head election threshold based on the number of times it has served as a cluster head node, a cluster head node ratio determined a priori, and , using an improved cluster head election threshold function computes a cluster head election threshold; Improved cluster head election threshold function As follows: A random number generating module: each node generates a random number by itself; the random number is generated by a random variable within [0, 1]; the random number is subject to a uniform distribution; A threshold comparison module: each node compares the random number generated by itself with a cluster head election threshold value: If less than the cluster head election threshold value, the node becomes a cluster head node in the current large period; Otherwise, the node does not become a cluster head node; Each node is taken as an evaluated node; the converged node combines the three types of cloud models of the evaluated node into the tensor as an output sample, and the output of the VAE model obtained as an output sample; the mean square error between the input sample and the output sample is calculated as the mean square error of each node after self-updating .

4. An improved LEACH routing protocol method, characterized in that, The method comprises a cluster building step and a data transmission step; Cluster building step: the nodes periodically re-elect cluster head nodes, build clusters, and this period is called a large period, denoted by ; wherein the cluster head nodes are elected by using the cluster head election method of the improved LEACH algorithm according to any one of claims 1 or 2; Data transmission step: the cluster head node periodically integrates the received data packets of the cluster member nodes and forwards them to the sink node, and this period is called a small cycle, denoted as Tsmall. ​ 5. The method for improving LEACH routing protocol according to claim 4, wherein, In the data transmission step, the member nodes in a cluster send their packet sending trust evidence, energy trust evidence and energy trust evidence to the cluster head node together with data packets.

6. The method for improving LEACH routing protocol according to claim 4, wherein, In the data transmission step, the cluster head node first stores and integrates the data packets received from the member nodes in the cluster; then periodically sends the integrated data packets to the sink node at a period of T. The cluster head node calculates the trust value of the member nodes in the cluster based on the collected trust evidence with a period of and sends the trust value calculation result to the sink node with the integrated data packet.

7. An apparatus for improving LEACH routing protocol, the apparatus comprising: The device comprises a cluster building module and a data transmission module; Cluster building module: the nodes periodically re-elect cluster head nodes, and cluster building is carried out, and the period is called a large period, which is represented by ; wherein the cluster head nodes are elected by using the cluster head election method of the improved LEACH algorithm according to any one of claims 1 or 2. Data transmission module: cluster head node periodically integrates the received cluster member node data packet and forwards it to the sink node, this period is called small cycle, denoted by Tsmall. Data transmission module: cluster head node periodically integrates the received cluster member node data packet and forwards it to the sink node, this period is called small cycle, denoted by Tsmall.

8. A computer device comprising: The processor and the memory, characterized in that the memory is used to store executable instructions of the processor, and the processor is configured to execute the executable instructions to perform the method of any one of claims 4-6.

9. A computer storage medium, characterized in that The storage medium stores a computer program, and the computer program runs to perform the method of any one of claims 4-6.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer program / instructions are executed by the processor to implement the steps of the method of any one of claims 4-6.

Images