Mobile D2D network information distribution scale prediction method and system based on trust

Abstract

The invention relates to a mobile D2D network information distribution scale prediction method and system based on trust, and the method comprises the steps: respectively constructing a geometric network and a trusted network according to the node transmission distance in a D2D network, the position information of the nodes along with the time change, and the trust relationship between the nodes, and carrying out the coupling to obtain a safety network, augmenting a processing security network based on a trust-based transfer relationship, and constructing a trust-based mobile D2D network; the method comprises the following steps: defining events related to information distribution in a network, modeling an information distribution process, and capturing a change condition of a trust relationship and a dynamic process of information distribution through a time point process theory; the information distribution process obtained through modeling is predicted, the distribution scale is counted, and information distribution scale prediction is achieved. The mobility of the equipment and the trust relationship of dynamic change between the equipment are considered, the mobile D2D network based on trust is constructed, and the authenticity and dynamic adaptability of mobile D2D network modeling are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of information distribution prediction, and in particular to a trust-based mobile D2D network information distribution scale prediction method and system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As one of the key technologies for implementing 5G cellular networks, D2D (Device-to-Device) technology enables direct information exchange between nearby devices, reducing data pressure on the core network of the communication system. Therefore, D2D networks enable direct information exchange between physical devices without relying on core network infrastructure or intermediaries, supporting large-scale mobile terminal access and large data transmission.

[0004] However, current predictions of information distribution in D2D networks ignore the impact of device mobility and the dynamically changing trust relationships between devices, resulting in low accuracy in predicting the scale of information distribution. Summary of the Invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a trust-based mobile D2D network information distribution scale prediction method and system, which takes into account the mobility of devices and the dynamically changing trust relationship between devices, constructs a trust-based mobile D2D network, and simultaneously constructs a geometric network and a trust network. A secure network is generated through a coupling mechanism, and trust transitivity is introduced for augmentation processing, thereby improving the authenticity and dynamic adaptability of mobile D2D network modeling.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: A first aspect of the present invention provides a trust-based mobile D2D network information distribution scale prediction method, comprising the following steps: Based on the node transmission distance, the location information of the nodes changing over time, and the trust relationship between the nodes in the D2D network, a geometric network and a trust network are constructed respectively. After coupling, a secure network is obtained. The secure network is augmented based on the trust transfer relationship to build a trust-based mobile D2D network. Define events involving information distribution in trust-based mobile D2D networks; model the information distribution process in trust-based mobile D2D networks, and capture the changes in trust relationships and the dynamic process of information distribution through time point process theory; The information distribution process obtained by modeling is predicted. Specifically, the geometric network is updated according to the position of the node after movement by predicting the intensity of the mobile event; the current trust strength is predicted on the updated network, and the intensity of the security link establishment event and deletion event is predicted based on the real-time trust strength, and the security network is updated; the intensity of the information distribution event is predicted based on the current network structure, the distribution scale is calculated, and the information distribution scale is predicted.

[0007] Furthermore, the geometric network is represented as , the trust network is represented as , the safety network is expressed as ; Among them, as the node moves, the node position at time t Constantly changing, geometric D2D link at time t The trust relationship between nodes is represented by the trust D2D link set at time t. When there are both geometric D2D links and trust D2D links between nodes, a secure D2D link set at time t is formed. .

[0008] Furthermore, events involving information distribution in a trust-based mobile D2D network are defined, specifically: the source nodes, target nodes, and event types involved during mobility events, link establishment events, link deletion events, and information distribution events are represented by a four-tuple equation.

[0009] Furthermore, the information distribution process of the trust-based mobile D2D network is modeled. The time point process theory is used to capture the changes in trust relationships and the dynamic process of information distribution. Specifically, the events occurring in the network at time t are described through a counting process, the number of times an event occurs per unit time is characterized through event intensity, and the expected number of events in the model is calculated by defining an intensity function for each event.

[0010] Furthermore, the information distribution process obtained by modeling is predicted, including setting the initial trust strength between two nodes, updating the security network according to the degree to which the link quality changes with the distance between nodes, the Euclidean distance between nodes, and the establishment time of node-related events.

[0011] Furthermore, the information distribution process obtained by the model is predicted, which also includes determining the expected number of various types of events based on the obtained event intensity, and realizing the information distribution scale prediction through statistics.

[0012] A second aspect of the present invention provides a trust-based mobile D2D network information distribution scale prediction system, comprising: The network construction unit is configured to: construct a geometric network and a trust network based on the transmission distance of nodes in the D2D network, the location information of the nodes changing over time, and the trust relationship between the nodes, respectively; obtain a secure network through coupling; augment the secure network based on the trust transfer relationship, and construct a trust-based mobile D2D network; The distribution process modeling unit is configured to: define events related to information distribution in the trust-based mobile D2D network; model the information distribution process of the trust-based mobile D2D network, and capture the changes in trust relationships and the dynamic process of information distribution through time point process theory; The distribution scale prediction unit is configured to: predict the information distribution process obtained by modeling, specifically: update the geometric network according to the position of the node after movement by predicting the intensity of the mobile event; predict the current trust strength on the updated network, predict the intensity of the security link establishment event and the deletion event based on the real-time trust strength, and update the security network; predict the intensity of the information distribution event based on the current network structure, calculate the distribution scale, and realize the information distribution scale prediction.

[0013] A third aspect of the present invention provides a computer program product comprising computer-readable instructions, which, when executed on an electronic device, enables the electronic device to implement the above-mentioned trust-based mobile D2D network information distribution scale prediction method.

[0014] The fourth aspect of the present invention provides an electronic device, comprising at least one processor and a memory connected to the processor, the memory being used to store a computer program; the processor being used to execute the computer program, so that the electronic device can implement the above-mentioned trust-based mobile D2D network information distribution scale prediction method.

[0015] A fifth aspect of the present invention provides a computer storage medium carrying one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the above-mentioned trust-based mobile D2D network information distribution scale prediction method.

[0016] Compared with the existing technology, one or more of the above technical solutions have the following beneficial effects: 1. Considering the mobility of devices and the dynamically changing trust relationships between devices, a trust-based mobile D2D network is constructed. At the same time, a geometric network and a trust network are constructed. A secure network is generated through a coupling mechanism, and trust transitivity is introduced for augmentation processing, which improves the authenticity and dynamic adaptability of mobile D2D network modeling.

[0017] 2. Based on TPP, we modeled the information distribution process in mobile D2D networks, breaking through the resolution limitations of traditional discrete-time models and enabling millisecond-level event time prediction. We also proposed a trust strength prediction method for mobile environments, which is used to model the real-time changes in trust strength in mobile D2D networks caused by factors such as link quality, historical behavior, and neighbor feedback. Based on this trust strength, we predict the scale of information distribution, improving the realism of modeling information distribution mechanisms in mobile D2D networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0019] Figure 1 is a schematic diagram of the overall process of information distribution scale prediction provided by one or more embodiments of the present invention; Figure 2 This is a schematic diagram of a trust-based D2D network information distribution process in a mobile environment provided by one or more embodiments of the present invention; Figure 3 is a schematic diagram of the information distribution scale prediction process provided by one or more embodiments of the present invention; Figure 4 is a geometric network diagram provided by one or more embodiments of the present invention; Figure 5 is a schematic diagram of a trust network provided by one or more embodiments of the present invention; Figure 6 is a schematic diagram of the SNet establishment process provided by one or more embodiments of the present invention; Figure 7 is a schematic diagram of constructing a TMDN by augmenting SNet according to one or more embodiments of the present invention; Figure 8 This is a schematic diagram of a mobile D2D network provided by one or more embodiments of the present invention. DETAILED DESCRIPTION

[0020] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0021] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0022] As described in the background, the high mobility of nodes and frequent changes in network topology in mobile D2D networks introduce numerous uncertainties and security risks to the information distribution process. Therefore, trust relationships can be used as a key indicator of the degree of mutual trust between network nodes. Highly trusted nodes are more likely to gain support from other nodes, promoting rapid and accurate information dissemination. Low-trust or malicious nodes, on the other hand, can become bottlenecks to information distribution and even pose security risks. For example, in mobile social networks, mobile devices rely on D2D communication to share social information with other devices.

[0023] Factors influencing trust relationships, such as link quality, historical behavior, and neighbor feedback, play a key role in determining contact opportunities and information distribution efficiency. The stochastic dynamics of trust-based information distribution can provide valuable insights into the mechanisms governing these processes, offering guidance for optimizing interventions and improving distribution predictions in mobile D2D environments. Therefore, constructing effective trust models and analyzing their impact on the information distribution process have become important research directions for information distribution mechanisms in mobile D2D networks.

[0024] Traditional research methods for information dissemination in mobile D2D networks typically model inter-node trust relationships as fixed trust values, which cannot accurately reflect how trust relationships evolve over time. However, in reality, trust relationships between nodes are not static. Over time, trust relationships can change due to various factors, such as link quality, historical behavior, and neighbor feedback. Nodes can alter their link behavior due to changes in trust, and research has shown that these structural dynamics significantly influence the information dissemination process. Therefore, modeling the impact of trust relationship changes on the network structure in mobile D2D networks is of great significance.

[0025] Example 1: In a mobile D2D network, each physical device can only establish communication links and share information with other trusted devices within a certain range. Some mobile D2D network devices, such as temperature and humidity sensors or smart switches, are typically stationary and maintain a fixed network connection. However, other devices, such as smartphones, drones, and smart cars, are constantly in motion. The variability in link quality caused by this mobility affects trust relationships, leading to changes in the fundamental network structure that influences information distribution. This historical behavior and network information, in turn, influence trust relationships.

[0026] Therefore, modeling the information dissemination process in mobile D2D networks through trust relationships in a mobile environment can help understand the impact of dynamic trust relationships on random information dissemination. This approach can also help more accurately predict the scale of dissemination. Current research on modeling the impact of network structure on information dissemination focuses on temporal networks, which use discrete time units to represent the time intervals between events. However, due to the asynchronous nature of event data, choosing a uniform time scale for modeling purposes is challenging. This approach cannot effectively solve fine-grained, time-sensitive queries, such as predicting when a device will disseminate information.

[0027] While predictions are constrained by the resolution of the chosen time interval, existing techniques achieve time-sensitive predictions by capturing the co-evolution of network structure and information distribution in continuous time. Alternatively, discrete-time micro-Markov chain methods (MMCA) are used to simulate the interactive diffusion of multiple messages within complex network structures. Dynamic equations constructed using the susceptible-infected-susceptible (SIS) model can simulate the state transitions of nodes in multilayer networks. Recent work has extended the SIS model by incorporating stochastic information reproduction during the distribution process, leading to the development of a state transition model, namely, the Susceptible Uncertain-Infected-Recovered-Susceptible (SUIRS). However, this model does not fully consider the impact of trust relationships on network structure in mobile D2D networks.

[0028] How changes in network structure caused by trust relationships over time affect the information distribution process is difficult to grasp. Considering that the randomness of device movement makes it difficult to accurately capture changes in trust relationships, this makes it even more challenging to understand how these changes affect information distribution.

[0029] To address the above issues, this solution proposes a trust-based D2D network information distribution model in a mobile environment. By introducing the Time Point Process (TPP) theory, it captures the changes in trust relationships and the dynamics of information distribution in mobile D2D networks.

[0030] TPP is a stochastic process with a natural advantage in describing and modeling event sequences in the continuous time domain, making it an ideal framework for modeling random events. This approach considers the dynamically changing trust relationships between nodes as a key factor determining their connectivity. Therefore, leveraging changes in network topology, we investigate the impact of changing trust relationships on information dissemination in mobile D2D networks. This approach captures the dynamics of information dissemination in mobile D2D networks, specifically the impact of inter-node trust on this process.

[0031] like Figure 1 As shown, the trust-based mobile D2D network information distribution scale prediction method includes the following steps: Based on the node transmission distance, the location information of the nodes changing over time, and the trust relationship between the nodes in the D2D network, a geometric network and a trust network are constructed respectively. After coupling, a secure network is obtained. The secure network is augmented based on the trust transfer relationship to build a trust-based mobile D2D network. Define events involving information distribution in trust-based mobile D2D networks; model the information distribution process in trust-based mobile D2D networks, and capture the changes in trust relationships and the dynamic process of information distribution through time point process theory; The information distribution process obtained by modeling is predicted. Specifically, the geometric network is updated according to the position of the node after movement by predicting the intensity of the mobile event; the current trust strength is predicted on the updated network, and the intensity of the security link establishment event and deletion event is predicted based on the real-time trust strength, and the security network is updated; the intensity of the information distribution event is predicted based on the current network structure, the distribution scale is calculated, and the information distribution scale is predicted.

[0032] like Figure 2 As shown, this plan consists of three parts: S1: Trust-based mobile D2D network construction; S2: Modeling of information distribution process in mobile D2D networks based on TPP; S3: Trust strength prediction in mobile environments.

[0033] In S1, a multi-layer network mechanism was introduced to construct a trust-based mobile D2D network. In S2, the information distribution process in a mobile D2D network based on the Transmission Propagation Protocol (TPP) was modeled. Key elements of a mobile D2D network and events occurring within the network, such as mobility events, secure link establishment events, and information distribution events, were defined. In S3, a method for predicting trust strength was proposed, taking into account the trust relationships between devices in the mobile D2D network and predicting the scale of information distribution based on computational intensity.

[0034] like Figure 3 As shown in FIG, the trust-based mobile D2D network information distribution scale prediction method is mainly divided into two parts: network construction and distribution scale prediction.

[0035] In the network construction part, the initial geometric network and trust network are input, and a secure network is constructed by coupling the two networks. Then, the secure network is augmented based on the trust transitive relationship to build a trust-based mobile D2D network.

[0036] In the distribution scale prediction part, first, the intensity of mobile events is predicted based on the constructed network, and the geometric network is updated according to the position of the node after movement. Secondly, the current trust strength is predicted on the new network. Based on the real-time trust strength, the intensity of S-D2D link establishment and deletion events is predicted, and the security network is updated based on this. Finally, the intensity of information distribution events is predicted based on the current network structure, and the distribution scale is calculated.

[0037] S1, for building a trust-based mobile D2D network, includes: S101, defining a geometric network and a trust network, where the links in these two networks are independent of each other; S102, coupling these two networks to build a secure network; S103, augmenting the secure network to obtain a trust-based mobile D2D network.

[0038] S101: Define a mutually independent geometric network and trust network. Specifically, without considering trust, as long as the geometric distance between two nodes in the geometric network is not greater than their D2D transmission distance, data can be transmitted between the nodes.

[0039] This scheme models the geometric network as a dynamic random graph ,in represents the total number of n node locations at time t, and r represents the transmission distance of the node. In the initial state, assume The node positions in are randomly generated and follow a uniform distribution. The following definitions are introduced to describe the network topology of the geometric network.

[0040] Definition 1: Geometry D2D link (G-D2D). and (0≤ i , j ≤ n ) exists between G-D2D if and only if the Euclidean distance between them is less than r, i.e. ,in and Represent user node i and user node j respectively, and Respectively indicate their positions, Denotes the Euclidean norm. Represents the set of geometric D2D links at time t.

[0041] Definition 2: Geometry Network (GNet). It consists of a set of vertices and link collections Constructing GNet, which can be expressed as The geometric network evolves continuously over time t.

[0042] In a mobile D2D network, as the node moves, the node position Links that are constantly changing and limited by the transmission distance r In order to simplify the calculation, the random walk model is used to model the random movement of the node. Each step of the node can be represented by a two-dimensional vector To indicate that and is the random step length of the node in the x and y directions, and its value follows uniform distribution, where Indicates the maximum distance a node moves each time. Given the position of a node at time t , we can get the position of the node at time t' after the next movement: ; Therefore, the distance between node i and node j is: .

[0043] There are heterogeneous trust relationships between nodes in the network, which are independent of the location of the devices. Two devices that trust each other tend to cooperate with each other, which helps reduce the risk of data leakage and improve network security. The trust relationship between nodes in the trust network changes in real time. Establish trust links. The following definitions are introduced to describe the network topology of the trust network.

[0044] Definition 3: Trust D2D (T-D2D). and (0≤ i , j ≤ n ) if and only if there is a trust relationship between them. .use represents the set of trusted D2D links at time t.

[0045] Definition 4: Trust Network (TNet). It consists of a set of vertices and link collections Constructing TNet, which can be expressed as .

[0046] In mobile D2D networks, there are many factors that affect the trust relationship between nodes, such as link quality and historical behavior. As the trust relationship changes, T-D2D is constantly updated and TNet is constantly evolving. In mobile D2D networks, GNet and TNet are independent of each other and do not affect each other, but in the process of secure information distribution, GNet and TNet are indispensable. Figure 4 and Figure 5Shown is a schematic diagram of GNet (geometric network) and TNet (trust network) at a certain moment.

[0047] S102: A secure network is constructed by coupling the geometric network and the trust network. The construction and evolution of the secure network depends on the synergy between the geometric network and the trust network. To ensure the security of information distribution in the network, the following definition is introduced to describe the secure network.

[0048] Definition 5: Secure D2D (S-D2D). and (0≤ i , j ≤ n ) exists between S-D2D, if and only if there is both G-D2D and T-D2D between them, use represents the set of secure D2D links at time t.

[0049] Definition 6: Secure Network (SNet). It consists of a set of vertices and link collections Constructing SNet, which can be expressed as .

[0050] The topology of SNet is completely determined by GNet and TNet at the same time, such as Figure 6 The establishment process of SNet is shown in Figure 2. Nodes A and B, C and F, and D and E have both G-D2D and T-D2D, so There are only three links in the network, and information can only be distributed among these three pairs of nodes, and cannot be well propagated to most nodes.

[0051] This shows that the strict conditions for establishing S-D2D make it difficult to form an effective information path in a secure network. Even if the nodes in the network have the physical conditions for information distribution and most neighboring nodes clearly trust each other, they cannot establish a secure link. This is obviously inconsistent with the situation in real-world mobile D2D networks, because in reality, trust relationships can be transmitted through neighboring nodes, such as Figure 7 If nodes A and C in the network trust node F at the same time, then A and C also trust each other. Therefore, in order to better simulate the real-world security network situation and enhance the connectivity of the network, it is necessary to augment S-D2D by leveraging the transferability of trust relationships between nodes.

[0052] S103: Augment the secure network to obtain a trust-based mobile D2D network.

[0053] Definition 7: Trust-based Mobile D2D Network (TMDN): Two nodes with G-D2D can establish S-D2D with the help of a third node if T-D2D does not exist.

[0054] The augmented SNet is called TMDN, which can better describe the trust-based network structure in real environments. Figure 7 The process of expanding SNet to TMDN is demonstrated.

[0055] First, nodes A and C find the same trusted node F, and nodes B and D find the same trusted node E.

[0056] Then, there is G-D2D between nodes A and C, and G-D2D between nodes B and D.

[0057] Therefore, in this SNet, the two S-D2Ds that can be augmented are (A, C) and (B, D).

[0058] Compared with the SNet before augmentation, the number of S-D2D in TMDN has increased, which greatly enhances network connectivity and expands the coverage of information distribution while ensuring security to the greatest extent. At the same time, it is closer to the mobile D2D network structure in real situations and can more realistically model the changes in trust relationships in mobile environments.

[0059] S2 models the information distribution process in mobile D2D networks based on TPP. Trust relationships between devices in mobile D2D networks are influenced by numerous unobservable factors and exhibit a high degree of randomness. Based on this, we introduce the stochastic process theory of TPP to model various events in the information distribution process, capturing the dynamics of random events in the network. This work is divided into three parts: event definition, TPP-based information distribution, and the mobile D2D network information distribution process.

[0060] S201, Event Definition. In TMDN, the initial location of devices and the average degree of the network are specified. Some devices are fixed, while others are free to move. Whether a device carries information is considered a device attribute in TMDN. Through S-D2D, each device can distribute information to other devices, and the receiving device decides whether to continue distributing information. The topology of TMDN changes dynamically as trust relationships evolve. Figure 8A TMDN is shown, where mesh nodes represent devices carrying information and gray nodes represent devices without information. S-D2D is established only when the devices are within a certain range and trust each other. At time t=1, mobile node A carrying information approaches node B and initiates S-D2D creation. The information is shared directly with B and then distributed to the entire cluster connected to B through the inter-node connections within the group. For simplicity, this embodiment may use "node" and "device" interchangeably, as shown in Figure 8 shown.

[0061] The mobile D2D network information distribution model includes four types of events: mobility events, link establishment events, link deletion events, and information distribution events. These events are defined as follows.

[0062] Definition 8: Each event Use a quad to represent it. ; Where t records the time when the event occurs, s is the source node of the event, d is the target node of the event, and type indicates the type of event. Here, type = 0 indicates a mobility event, type = 1 indicates an S-D2D establishment event, type = 2 indicates an S-D2D deletion event, and type = 3 indicates an information distribution event.

[0063] As shown in Table 1, the Figure 8 All event quadruples from t=0 to t=2 in .

[0064] Table 1 Figure 8 Specific events in the example

[0065] For mobile events , where s is the node that moves, which means the node moves at time t. For example, the event in Table 1 Indicates that node a experienced a movement event at t=0.1.

[0066] For S-D2D establishment events , which means that node d establishes S-D2D with node s from time t. In order to more accurately describe the changes in network structure, S-D2D can be established multiple times after deletion, but self-linking of nodes is not allowed. For example, the event in Table 1 Indicates that nodes A and B are S-D2D was established at the same time.

[0067] For S-D2D deletion events , which indicates that the S-D2D connection between node d and node s was deleted at time t. An S-D2D deletion event occurs when two connected nodes move out of communication range or their trust strength is too low. To more accurately describe changes in network structure, a deleted S-D2D connection between a pair of nodes cannot be repeatedly deleted until the pair reestablishes S-D2D.

[0068] For information distribution events , which means that two connected nodes d and s perform information distribution at time t. Information is distributed from node s to node d. Once node d receives the information, it can continue to distribute it to other nodes. For example, the event in Table 1 Indicates that node A is When an information distribution event is initiated to node B.

[0069] S202, TPP-based information distribution. This paper introduces the Time Point Process (TPP) theory to capture the dynamics of trust relationships and information distribution in mobile D2D networks, and constructs a trust-based mobile D2D network information distribution model. This model is applicable to a variety of mobile D2D network scenarios, such as post-disaster emergency communications, smart city dynamic updates, industrial IoT device diagnostics, and military reconnaissance and communication.

[0070] In this type of network, a group of devices, acting as mobile base stations (MBSs), are deployed to transmit information to devices throughout the scene. Each MBS selects an initial location and then begins moving, transmitting information through dynamically changing trusted D2D connections. Fixed-location devices initially carry no information but can participate in distribution once they receive information through these links. Information distribution is initially driven by the movement of the MBS, which distributes information randomly across the network. This process ceases when all devices hold the information or when the maximum run time is reached. The events in this process are interconnected. As an MBS moves, if it enters the communication range of another device and T-D2D communication is present, an S-D2D establishment event is triggered. If it moves out of range or T-D2D communication is disconnected from a previously connected device, an S-D2D removal event occurs. When an S-D2D establishment event occurs, if a device holds information, an information distribution event is triggered, enabling it to transfer data to the newly connected device. Conversely, an S-D2D removal event prevents distribution between disconnected devices. Once a device receives information, it can further distribute it to its connected trusted devices.

[0071] S203, Mobile D2D Network Information Distribution. Node mobility and trust relationship changes in mobile D2D networks are fraught with unpredictable random factors. Therefore, the trust-based information distribution process in TSMN is modeled using TPP theory. This comprehensively considers factors such as link quality, historical behavior, and neighbor feedback to accurately simulate the impact of various factors on S-D2D. In mobile D2D networks, the inherent randomness of node behavior requires the use of stochastic processes to accurately model information distribution.

[0072] As a stochastic process, the Transient Proportional Process (TPP) model is specifically designed to describe the random distribution of events occurring in the continuous time domain, making it well-suited for this purpose. Data generated in mobile D2D networks, such as the number of mobility events, S-D2D establishment events, and information distribution events, can be effectively represented by the TPP, accounting for the uncertainty in the frequency and timing of these events. These seemingly random events often conceal complex information distribution mechanisms. For example, node movement in the network leads to the establishment of new S-D2D connections, which in turn trigger new information distribution events. Extensive literature supports the effectiveness of the TPP in modeling random events and capturing complex network dynamics, particularly in environments with frequently changing topologies. Furthermore, the importance of the TPP lies in its ability to reveal the dependencies between ongoing events in mobile D2D networks, such as node movement, S-D2D establishment, S-D2D deletion, and information distribution. By analyzing historical data, the TPP predicts the intensity and precise timing of these events.

[0073] Based on these key features, TPP addresses challenges that traditional methods cannot. First, by modeling the dynamic relationship between device mobility events and related events such as S-D2D establishment and information distribution, TPP improves the accuracy of predicting the scale of information distribution, even in the presence of dynamically changing trust relationships. Second, TPP captures the changes in network structure caused by dynamic trust relationships and uses a strength function to predict their impact on the distribution process. Finally, through fine-grained event time prediction and event-driven simulation algorithms, TPP achieves higher accuracy in predicting the scale of information distribution than traditional methods.

[0074] A sequential point process consists of a series of discrete events that are localized in time. Composition, of which A sequential point process It is represented as a counting process that records the number of times an event occurs up to but not including time t. The total number of nodes in a TMDN is n, and four sets of counting processes are used to describe the events that occur in the network. Specifically: 1) Use an n*1 matrix Records the movement event that occurs at time t, the sth item of the matrix Records whether the node moves at time t. Records historical movement events in the matrix middle; 2) Use the n*n matrix S(t) to record the evolution of SNet at time t. The (d, s) item in the matrix Record whether there is S-D2D between node s and node d at time t. Record the historical S-D2D establishment event in middle. , where G(t) denotes GNet and T(t) denotes TNet is given by the following counting process; a) Use the n*n matrix G(t) to record the evolution of GNet at time t. The (d, s) item in the matrix is It records whether there is G-D2D between node s and node d at time t; b) Use the n*n matrix T(t) to record the evolution of TNet at time t. The (d, s) item in the matrix It records whether there is T-D2D between node s and node d at time t. , where the matrix The items in the matrix represent the number of times the corresponding T-D2D is established. The items in represent the number of corresponding T-D2D deletions; 3) Using an n*n matrix Record the information distribution event that occurs at time t, the (d, s)th item in the matrix Records whether node s distributes information to node d at time t. Recorded in the matrix.

[0075] Assumptions is an arbitrarily small time interval, in time At most one event can occur in Indicates The number of mobility events, S-D2D establishment events, S-D2D deletion events, and information distribution events can be expressed in the same way. During a time point, the number of events occurring per unit time can be characterized by event intensity. Therefore, the expected number of events in the model is calculated by defining an intensity function for each event.

[0076] S3, Trust strength prediction in mobile environment.

[0077] S301, Trust Prediction Based on TPP in a Mobile Environment. Specifically: In TMDN, various types of devices are distributed across the network. They can move and establish D2D connections with other devices. G-D2D is automatically updated based on node location, while T-D2D is randomly established based on the trust relationship between devices that changes over time. When both G-D2D and T-D2D exist between two devices, S-D2D is established, and they can forward information via S-D2D. At t = 0, a group of MBSs appear at a predetermined initial location, carrying the same information with the goal of distributing it to as many devices in the network as possible. When MBSs establish S-D2D through trust relationships, they are affected by various factors. A characteristic of D2D connections is that as the distance between nodes increases, the quality of the link deteriorates, and the likelihood of information loss increases, which significantly reduces the level of trust between them. Therefore, S-D2D is easier to establish between two nodes that are closer together.

[0078] Furthermore, nodes that frequently establish S-D2D with other nodes may have better reputations, which increases the likelihood of establishing S-D2D. Furthermore, the impact of this historical behavior on current events decays over time. Furthermore, neighbor feedback also influences trust relationships. For example, two nodes with G-D2D can establish S-D2D with the help of a common neighbor. Due to the influence of these factors, trust relationships between nodes constantly change over time. Based on these changing trust relationships, the TMDN network topology continuously evolves, and information is randomly distributed to new devices on the network.

[0079] In order to accurately describe the probability of events in TMDN, the trust strength is predicted by comprehensively considering the link strength, historical behavior and neighbor feedback. Assume that the initial trust strength between two nodes is , then the trust strength between node d and node s at time t It can be obtained by the following formula: ; in, represents the trust strength at t=0; It indicates that the link quality decreases as the distance between nodes increases, resulting in a decrease in the trust between nodes. dist(d, s) represents the Euclidean distance between node d and node s, which can be obtained from the relevant formula of "Definition 2" in S101; It indicates that the credibility of node s increases with the increase of historical S-D2D establishment, where t i represents the time of the S-D2D establishment event associated with node s; represents the augmentation of S-D2D by neighbor feedback information. When d and s have common neighbors in TNet, they can establish S-D2D with the help of common neighbors. It represents the number of nodes that have T-D2D with both node d and node s at time t.

[0080] S302, Prediction of the intensity of information distribution events in mobile D2D networks based on trust. The prediction of the intensity of information distribution events in mobile D2D networks based on trust can be derived by taking the trust intensity as the medium. First, assuming that the probability of node movement and information distribution is a uniform random number, the mobile intensity and information distribution intensity It can be given by the strength of the Poisson process: ; ; in, is the trust strength between node d and node s at time t, The movement intensity of node s at time t, Is whether there is S-D2D between node s and node d at time t.

[0081] Secondly, the strength of the S-D2D establishment event is affected by the trust strength. At time t, the state of G-D2D between node d and node s is , which is only affected by the distance between the two nodes; the establishment of T-D2D between node d and node s is related to the trust strength affected by link quality, historical behavior, and neighbor feedback. Therefore, the strength of the S-D2D establishment event is defined as: ; in, It ensures that only one S-D2D can exist at a time; Represents the trust strength between node d and node s at time t.

[0082] Finally, unlike the intensity of S-D2D establishment events, the intensity of S-D2D deletion events is only affected by the link quality. As the distance between the two nodes increases, the link quality continues to decrease, and the intensity of S-D2D deletion increases. It is defined as follows: ; in, represents the base strength of the S-D2D deletion, This indicates that the intensity of S-D2D deletion increases rapidly as the link quality decreases.

[0083] It is important to note that establishing links is not just about adding paths or allowing information sources to take shortcuts in the distribution process. The establishment of S-D2D affects the underlying structure of the network, which fundamentally changes the distribution dynamics and steady-state distribution of the information distribution process. In other words, given the network structure at two moments and , the structures of the two networks differ due to a few edges, which have a more than additive effect on information distribution. The modifications to the characteristic structure of , their impact on the dynamics of information distribution can be very significant.

[0084] S303, strength-based information distribution scale prediction. The trust model in a mobile environment can capture the randomness of trust-based information distribution in mobile D2D networks, predict the trust strength between nodes in real time, and accurately infer the information distribution scale through fine-grained time prediction. Based on the obtained event strength, the expected number of various events in the TMDN can be expressed as: ; ; ; ; in, 、 、 and They represent the expected number of mobile events, GNet evolution, TNet evolution and information distribution events at time t in S203 respectively; It indicates the foundation strength established by G-D2D; 、 、 and They represent the mobility event intensity, S-D2D establishment event intensity, S-D2D deletion event intensity and information distribution event intensity in S302 respectively.

[0085] Based on this intensity prediction method conditioned by historical events, this solution accounts for the interplay between events during information distribution in mobile D2D networks. Specifically, when a mobility event occurs, trust relationships change, triggering S-D2D establishment and deletion events. These two events, in turn, trigger information distribution events, which in turn impact trust relationships. While trust relationships don't directly affect information distribution, their influence is transmitted through network evolution. This process alters the underlying network structure, resulting in complex impacts on information distribution within the network.

[0086] This solution constructs a trust-based mobile D2D network, simultaneously building a geometric network and a trust network. A secure network is generated through a coupling mechanism, and trust transitivity is introduced for augmentation processing, improving the realism and dynamic adaptability of mobile D2D network modeling. The information distribution process in mobile D2D networks is modeled based on TPP, breaking through the resolution limitations of traditional discrete-time models and supporting millisecond-level event time prediction. A trust strength prediction method for mobile environments is proposed to model the real-time changes in trust strength in mobile D2D networks caused by link quality, historical behavior, and neighbor feedback, and to predict the scale of information distribution based on this strength. This solution improves the realism of modeling information distribution mechanisms in mobile D2D networks and reveals the key role of trust relationships in improving the prediction of information distribution scale.

[0087] Example 2: The trust-based mobile D2D network information distribution scale prediction system includes: The network construction unit is configured to: construct a geometric network and a trust network based on the transmission distance of nodes in the D2D network, the location information of the nodes changing over time, and the trust relationship between the nodes, respectively; obtain a secure network through coupling; augment the secure network based on the trust transfer relationship, and construct a trust-based mobile D2D network; The distribution process modeling unit is configured to: define events related to information distribution in the trust-based mobile D2D network; model the information distribution process of the trust-based mobile D2D network, and capture the changes in trust relationships and the dynamic process of information distribution through time point process theory; The distribution scale prediction unit is configured to: predict the information distribution process obtained by modeling, specifically: update the geometric network according to the position of the node after movement by predicting the intensity of the mobile event; predict the current trust strength on the updated network, predict the intensity of the security link establishment event and the deletion event based on the real-time trust strength, and update the security network; predict the intensity of the information distribution event based on the current network structure, calculate the distribution scale, and realize the information distribution scale prediction.

[0088] This solution constructs a trust-based mobile D2D network, taking into account both geometric and trust network structural variations to describe real-world mobile D2D networks. It also models the information dissemination process in mobile D2D networks based on the Transmission Propagation Protocol (TPP) to describe the dynamic information dissemination process in mobile D2D networks driven by changes in trust relationships over a continuous time domain. A trust strength prediction method is proposed in a mobile environment to model the changes in trust strength in mobile D2D networks driven by link quality, historical behavior, and neighbor feedback, and to predict the scale of information dissemination based on the trust strength.

[0089] Example 3: A computer program product includes computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the above-mentioned trust-based mobile D2D network information distribution scale prediction method.

[0090] Example 4: An electronic device includes at least one processor and a memory connected to the processor, the memory being used to store a computer program; the processor being used to execute the computer program, so that the electronic device can implement the above-mentioned trust-based mobile D2D network information distribution scale prediction method.

[0091] Embodiment 5: A computer storage medium carrying one or more computer programs, which, when executed by an electronic device, enables the electronic device to implement the above-mentioned trust-based mobile D2D network information distribution scale prediction method.

[0092] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A trust-based mobile D2D network information distribution scale prediction method, characterized by: The following steps are involved: Based on the node transmission distance, the location information of the nodes changing over time, and the trust relationship between the nodes in the D2D network, a geometric network and a trust network are constructed respectively. After coupling, a secure network is obtained. The secure network is augmented based on the trust transfer relationship to build a trust-based mobile D2D network. Define events involving information distribution in trust-based mobile D2D networks; Model the information distribution process of trust-based mobile D2D networks and capture the changes in trust relationships and the dynamic process of information distribution through time point process theory. The information distribution process obtained by modeling is predicted. Specifically, the geometric network is updated according to the position of the node after movement by predicting the intensity of the mobile event; the current trust strength is predicted on the updated network, and the intensity of the security link establishment event and deletion event is predicted based on the real-time trust strength, and the security network is updated; the intensity of the information distribution event is predicted based on the current network structure, the distribution scale is calculated, and the information distribution scale is predicted.

2. The trust-based mobile D2D network information distribution scale prediction method according to claim 1, characterized in that: The geometric network is represented as , the trust network is represented as , the safety network is expressed as ; Among them, as the node moves, the node position at time t Constantly changing, geometric D2D link at time t The trust relationship between nodes is represented by the trust D2D link set at time t. When there are both geometric D2D links and trust D2D links between nodes, a secure D2D link set at time t is formed. .

3. The trust-based mobile D2D network information distribution scale prediction method according to claim 1, characterized in that: The events involved in information distribution in a trust-based mobile D2D network are defined as follows: the source node, target node, and event type involved in a mobility event, a link establishment event, a link deletion event, and an information distribution event are represented by a four-tuple equation.

4. The trust-based mobile D2D network information distribution scale prediction method according to claim 1, characterized in that: The information distribution process of trust-based mobile D2D networks is modeled. The time point process theory is used to capture the changes in trust relationships and the dynamic process of information distribution. Specifically, a counting process is used to describe the events occurring in the network at time t. The event intensity is used to characterize the number of times an event occurs per unit time. The expected number of events in the model is calculated by defining an intensity function for each event.

5. The trust-based mobile D2D network information distribution scale prediction method according to claim 1, characterized in that: Predict the information distribution process obtained by modeling, This includes setting the initial trust strength between two nodes, updating the security network based on the degree to which link quality changes with node distance, the Euclidean distance between nodes, and the establishment time of node-related events.

6. The trust-based mobile D2D network information distribution scale prediction method according to claim 1, characterized in that: The information distribution process obtained by modeling is predicted, which also includes determining the expected number of various types of events based on the obtained event intensity, and realizing the prediction of the scale of information distribution through statistics.

7. A trust-based mobile D2D network information distribution scale prediction system, characterized by: include: The network construction unit is configured to: construct a geometric network and a trust network based on the transmission distance of nodes in the D2D network, the location information of the nodes changing over time, and the trust relationship between the nodes, respectively; obtain a secure network through coupling; augment the secure network based on the trust transfer relationship, and construct a trust-based mobile D2D network; The distribution process modeling unit is configured to: define events involving information distribution in a trust-based mobile D2D network; Model the information distribution process of trust-based mobile D2D networks and capture the changes in trust relationships and the dynamic process of information distribution through time point process theory. The distribution scale prediction unit is configured to: predict the information distribution process obtained by modeling, specifically: update the geometric network according to the position of the node after movement by predicting the intensity of the mobile event; predict the current trust strength on the updated network, predict the intensity of the security link establishment event and the deletion event based on the real-time trust strength, and update the security network; predict the intensity of the information distribution event based on the current network structure, calculate the distribution scale, and realize the information distribution scale prediction.

8. A computer program product, characterized in that The method comprises computer-readable instructions, which, when executed on an electronic device, enable the electronic device to implement the steps in the trust-based mobile D2D network information distribution scale prediction method according to any one of claims 1 to 6.

9. An electronic device, characterized in that: The electronic device comprises at least one processor and a memory connected to the processor, the memory being used to store a computer program; the processor being used to execute the computer program, so that the electronic device can implement the steps in the trust-based mobile D2D network information distribution scale prediction method as described in any one of claims 1 to 6.

10. A computer storage medium, characterized in that The storage medium carries one or more computer programs, which, when executed by an electronic device, enable the electronic device to implement the steps in the trust-based mobile D2D network information distribution scale prediction method as described in any one of claims 1 to 6.

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