An information transmission optimal control method, system and device for the Internet of Things

Abstract

The application provides an information transmission optimal control method, system and device for Internet of Things, comprising the following steps: S1, constructing an information transmission model based on Internet of Things nodes, wherein the model is configured to transmit information from a transmission node to a destination node with a transmission probability; S2, based on the number of transmission nodes carrying information and the number of destination nodes at a certain moment or in a time interval, analyzing the energy consumption change in the information transmission process, and establishing a performance measurement index of the transmission; S3, optimizing the information transmission model based on the energy consumption in the information transmission process, and obtaining a balance point of energy consumption and information transmission efficiency in the node transmission process; S4, using the Pontryagin maximum value theorem to analyze and obtain the optimal transmission strategy of information in the time limit. The application comprehensively considers the energy consumption of the transmission node and the information transmission performance and other targets, proposes an information transmission model based on a Markov process, and obtains an optimal information transmission strategy by using the maximum value theorem.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to an optimal information control method, system, and device for the Internet of Things. Background Technology

[0002] With the rapid development of network and sensor technologies, the Internet of Things (IoT) has gradually become known and integrated into people's daily lives. On November 17, 2005, the ITU (International Telecommunication Union) released the "ITU Internet Report 2005: The Internet of Things," formally proposing the concept of "Internet of Things." Simply put, the Internet of Things means the interconnection of everything; it is a new concept extended from the Internet. Its core is the interconnection, interoperability, and interoperability of everything in the world based on different types of network models, thus forming a vast and complex system. The Internet of Things quickly attracted the attention of researchers and industries worldwide upon its introduction and is now reflected in all aspects of people's daily lives, such as smart homes and intelligent transportation. Recently, with the proposal of the metaverse concept, the value of the Internet of Things will be further highlighted. The metaverse forms a new space that blends the virtual and real worlds by integrating various cutting-edge technologies. Its key point is the link between the real and virtual worlds, and the Internet of Things is one of the important supporting technologies for achieving this goal. Conversely, the metaverse will inevitably drive the large-scale development of the components of the Internet of Things, making the Internet of Things system more complex. The premise for the interconnection and interoperability of the various components of the Internet of Things is to ensure that each node has timely access to relevant information; therefore, the effectiveness of information transmission is crucial. However, in large-scale IoT systems, the sheer number of nodes and the complexity of the network architecture make information transmission even more challenging. Furthermore, with the widespread adoption of IoT systems, their application scenarios are becoming increasingly diverse, including harsh environments such as battlefields and disaster relief. In these situations, communication infrastructure becomes more unpredictable, and information transmission interruptions are frequent. To improve information transmission efficiency in such environments, researchers have proposed the concept of opportunistic networks. By adding a bundle layer, a store-and-carry-and-forward information transmission model is implemented, mitigating network interruption and segmentation problems as much as possible. Therefore, opportunistic networks undoubtedly have broad application prospects in large-scale IoT systems and serve as a powerful support for achieving ubiquitous interconnectivity in the IoT.

[0003] In the store-and-carry-forward transmission mode of opportunistic networks, nodes do not need to maintain routing information about other nodes as in traditional mobile ad hoc network routing strategies. Instead, they only need to temporarily store the information to be transmitted on the current node and carry it with them as they move. Once a suitable communication opportunity arises, the information is copied or forwarded, thus achieving relay-style information transmission. However, in practical applications, the information forwarding process consumes a certain amount of energy, and there are many wireless devices in IoT systems that are small in size and have limited energy capacity. In this context, unlimited flooding of information forwarding could lead to rapid energy depletion in some nodes. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an optimal control method, system and device for information transmission for the Internet of Things, so as to solve at least one of the above-mentioned problems in the prior art.

[0005] To achieve the above objectives, one or more embodiments of this application provide an optimal control method for information transmission in the Internet of Things (IoT), comprising the following steps:

[0006] S1. Construct an information transmission model based on IoT nodes, in which the transmission node is set to transmit information to the destination node with a transmission probability.

[0007] S2. Based on the number of information-carrying transmission nodes and the number of destination nodes within a certain time or time interval, analyze the changes in energy consumption during information transmission and establish performance metrics for node transmission.

[0008] S3. Optimize the information transmission model based on the energy consumption during the information transmission process, obtain the balance point between energy consumption and information transmission efficiency during node transmission, and determine the conditions for continuing information transmission.

[0009] S4. Using Pontryagin's maximum theorem, we can analyze and obtain the optimal transmission strategy for information within the time limit and the necessary conditions that it needs to satisfy.

[0010] Based on the above technical solution of the present invention, the following improvements can also be made:

[0011] Optionally, in step S1, the number of transmission nodes is set to N, the number of destination nodes to M, the timeliness of node transmission to T, the encounter rate between transmission nodes to α, the encounter rate between a transmission node and a destination node to β, and the probability that two transmission nodes meet within the time interval Δt is 1-e -αΔtp(t) represents the probability that a transmission node sends information to other transmission nodes at time t, q represents the probability of successful transmission, and the value of q is (0,1]. Let X(t) represent the number of transmission nodes carrying information at time t, and X(0) = 1. Let Y(t) represent the number of destination nodes carrying information at time t, and satisfy X(0) = 0.

[0012] Then, for the variable X(t), its change follows a Markov distribution, that is, it satisfies the following:

[0013] X(t+Δt)=X(t)+∑ j∈Ω(t) ω j (t,t+Δt) (1);

[0014] Where Ω(t) represents the set of transmission nodes that do not carry information at time t, ω j (t, t+Δt) represents the probability that node j obtains information within the time interval [t, t+Δt]. Therefore:

[0015] ω j (t, t + Δt) = 1 - e -αΔtX(t) p(t)q (2);

[0016] Therefore, we get:

[0017]

[0018] Based on formula (3), we can obtain:

[0019]

[0020] in, The derivative of E(Y(t)) is represented. It represents the derivative of E(X(t)).

[0021] Optionally, in step S2, since the energy transmitted by a node is proportional to the number of transmissions, let F(t) represent the number of transmissions at time t, then we get:

[0022]

[0023] The resulting performance metrics are as follows:

[0024] E(U(T))=E(Y(T))-δE(F(T)) (6);

[0025] Where E(Y(T)) represents the number of destination nodes for obtaining information, which reflects the final information transmission performance; δ represents the energy consumption weighting factor, used to balance performance and energy consumption ratio.

[0026] Optionally, in step S3, the information transmission model is optimized based on performance metrics.

[0027] max E(U(T))

[0028] stp(t)∈[0,1],t∈[0,T](7);

[0029] Where max represents the maximum value and st represents the constraint condition; according to formula (6), we know that:

[0030]

[0031] If q≤δ, then E(U(t)) will always be in a non-increasing state, that is, the utility value will never increase, and the transmission node does not need to transmit information in this case; if q>δ, then information transmission can continue.

[0032] Optionally, step S4 includes:

[0033] Based on the optimized information transmission model, the Hamiltonian equation is established:

[0034]

[0035] in, and Consistent, and Consistent, and Consistent;

[0036] λ X and λ Y For the adjoint state variables, their respective derivatives are shown in formula (10). According to the Ponte-Lyakin maximum theorem, the adjoint state equations are obtained as follows:

[0037]

[0038] The corresponding terminal conditions are met:

[0039] λ X (T)=λ Y (T)=0 (11);

[0040] According to Pontryagin's maximum theorem, it is clear that there exist continuous or piecewise continuous differentiable states and their adjoint state functions that satisfy:

[0041]

[0042] In the above formula, arg represents the set of all possible policies, and p * Represents the optimal strategy;

[0043] Based on formula (12), assuming that at a given time t, the parameters are in a known state, the expression for the control parameters is:

[0044]

[0045] The optimal information transmission strategy and the conditions that need to be met in the Internet of Things are determined by expression (13).

[0046] Optionally, the optimal information transmission strategy satisfies one of the following conditions: A, B, or C:

[0047] A: p(t) = 1, 0 ≤ t ≤ T;

[0048] B: p(t) = 0, 0 ≤ t ≤ T;

[0049] C: There exists a time s such that p(t) = 1, 0 ≤ t <s;p(t)=0,s<t≤T;

[0050] And satisfying that at any time t, there exists v(t)=(1+λY)q-δ>0.

[0051] According to a second aspect of the present invention, an optimal control system for information transmission for the Internet of Things (IoT) is provided, the control system comprising a computer program that, when executed by a processor, implements any of the above-described optimal control methods for information transmission for the IoT.

[0052] According to a third aspect of the present invention, an optimal control device for information transmission for the Internet of Things (IoT) is provided. The control device includes a memory, a processor, and a communication circuit. The memory and the communication circuit are respectively coupled to the processor. The communication circuit is connected to the processor and interacts with an external terminal device under the control of the processor. The memory includes local storage and stores a computer program. The processor is used to run the computer program to execute any of the above-described optimal control methods for information transmission for the IoT.

[0053] The beneficial effects of this invention are that it provides an optimal control method, system, and device for information transmission in the Internet of Things (IoT). Addressing the high dynamics and complex operating environment of large-scale IoT systems, it utilizes opportunistic networks for information transmission. Taking into account objectives such as energy consumption of transmission nodes and information transmission performance, an information transmission model based on Markov processes is proposed. Based on this model, the optimal information transmission strategy is obtained using the maximum value theorem. Attached Figure Description

[0054] Figure 1This is a schematic diagram illustrating the calculation and simulation results analysis of a Poisson-contact model for an optimal control method, system, and device for information transmission in the Internet of Things according to an embodiment of the present invention.

[0055] Figure 2 This is a schematic diagram illustrating the calculation and simulation results analysis of the actual motion trajectory model of an optimal control method, system, and device for information transmission in the Internet of Things according to an embodiment of the present invention.

[0056] Figure 3 This is a schematic diagram illustrating the calculation and simulation results analysis of an optimal control method, system, and device for information transmission in the Internet of Things according to an embodiment of the present invention, compared with three static strategies. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0058] It should be noted that, unless otherwise defined, the technical or scientific terms used in one or more embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in one or more embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0059] One or more embodiments of this application provide an optimal control method for information transmission in the Internet of Things (IoT), which includes the following steps:

[0060] S1. Construct an information transmission model based on IoT nodes, in which the transmission node is set to transmit information to the destination node with a transmission probability.

[0061] IoT nodes are divided into two categories: transmission nodes and destination nodes. Transmission nodes are nodes with information transmission capabilities, assuming a number of N; destination nodes are nodes that need to acquire information, assuming a number of M. At the initial time 0, only one transmission node carries information and needs to send it to as many destination nodes as possible within the information's validity period. The information validity period is set to T. Let X(t) represent the number of transmission nodes carrying information at time t, obviously X(0) = 1. Similarly, let Y(t) represent the number of destination nodes carrying information at time t, satisfying X(0) = 0. Based on some current research results, the node motion model can be described by an exponential model, that is, the number of node encounters follows a Poisson distribution. In this embodiment, it is assumed that the encounter rate between transmission nodes is α, and the encounter rate between them and the destination node is β. At this time, the probability of two transmission nodes meeting within the time interval Δt is 1 - e^(-αΔt). In addition, let p(t) represent the probability that a transmission node sends information to other transmission nodes at time t. Since the destination node is the information demander, it is assumed that the transmission node always sends information to it with a probability of 1. q represents the probability of successful transmission, and its value is (0,1).

[0062] Let X(t) represent the number of transmission nodes carrying information at time t, and X(0) = 1; let Y(t) represent the number of destination nodes carrying information at time t, and satisfy X(0) = 0.

[0063] Then, for the variable X(t), its change follows a Markov distribution, that is, it satisfies the following:

[0064] X(t+Δt)=X(t)+∑ j∈Ω(t) ω j (t,t+Δt) (1);

[0065] Where Ω(t) represents the set of transmission nodes that do not carry information at time t, ω j (t, t+Δt) represents the probability that node j obtains information within the time interval [t, t+Δt]. Therefore:

[0066] ω j (t, t + Δt) = 1 - e -αΔtX(t) p(t)q (2);

[0067] Therefore, we get:

[0068]

[0069] Based on formula (3), we can obtain:

[0070]

[0071] in, The derivative of E(Y(t)) is represented. It represents the derivative of E(X(t)).

[0072] S2. Based on the number of information-carrying transmission nodes and the number of destination nodes within a certain time or time interval, analyze the changes in energy consumption during information transmission and establish performance metrics for node transmission.

[0073] In step S2, the information transmission process consumes energy. Even if the transmission fails, energy is still generated. Therefore, energy consumption is proportional to the number of transmissions. Let F(t) represent the number of transmissions at time t, then we get:

[0074]

[0075] The resulting performance metrics are as follows:

[0076] E(U(T))=E(Y(T))-δE(F(T)) (6);

[0077] Where E(Y(T)) represents the number of destination nodes for obtaining information, which reflects the final information transmission performance; δ represents the energy consumption weighting factor, used to balance performance and energy consumption ratio.

[0078] S3. Optimize the information transmission model based on the energy consumption during the information transmission process, obtain the balance point between energy consumption and information transmission efficiency during node transmission, and determine the conditions for continuing information transmission.

[0079] In step S3, the information transmission model is optimized based on performance metrics.

[0080] max E(U(T))

[0081] stp(t)∈[0,1],t∈[0,T](7);

[0082] Where max represents the maximum value and st represents the constraint condition; according to formula (6), we know that:

[0083]

[0084] If q≤δ, then E(U(t)) will always be in a non-increasing state, that is, the utility value will never increase, and the transmission node does not need to transmit information in this case; if q>δ, then information transmission can continue.

[0085] S4. Using Pontryagin's maximum theorem, we can analyze and obtain the optimal transmission strategy for information within the time limit and the necessary conditions that it needs to satisfy.

[0086] The optimal solution is a curve that varies with time, ranging from [0,1], which is a typical functional extremum problem. This embodiment uses Pontryagin's maximum theorem to solve it. First, based on the optimized information transmission model, the Hamiltonian equation is established:

[0087]

[0088] in, and Consistent, and Consistent, and Consistent;

[0089] λ X and λ Y For the adjoint state variables, their respective derivatives are shown in formula (10). According to the Ponte-Lyakin maximum theorem, the adjoint state equations are obtained as follows:

[0090]

[0091] The corresponding terminal conditions are met:

[0092] λ X (T)=λ Y (T)=0 (11);

[0093] According to Pontryagin's maximum theorem, it is clear that there exist continuous or piecewise continuous differentiable states and their adjoint state functions that satisfy:

[0094]

[0095] In the above formula, arg represents the set of all possible policies, and p * Represents the optimal strategy;

[0096] Based on formula (12), formula (12) transforms the optimal control problem shown in formula (7) into a problem of maximizing the Hamiltonian function H. In formula (12), apart from the control parameter p, we can assume that the other parameters are known at a given time t, and the expression for the control parameters is:

[0097]

[0098] The optimal information transmission strategy and the conditions that need to be met in the Internet of Things are determined by expression (13).

[0099] Optionally, the optimal information transmission strategy satisfies one of the following conditions: A, B, or C:

[0100] A: p(t) = 1, 0 ≤ t ≤ T;

[0101] B: p(t) = 0, for 0 ≤ t ≤ T;

[0102] C: There exists a time s such that p(t) = 1, for 0 ≤ t < s; p(t) = 0, for s < t ≤ T;

[0103] And for any time t, there exists v(t) = (1 + λY)q - δ > 0.

[0104] Demonstrate the above conditions:

[0105] Theorem 1: The optimal strategy p satisfies one of the following structures: 1) p(t) = 1, for 0 ≤ t ≤ T; 2) p(t) = 0, for 0 ≤ t ≤ T; 3) There exists a time s such that p(t) = 1, for 0 ≤ t < s; p(t) = 0, for s < t ≤ T.

[0106] Proof: First

[0107]

[0108] Since both N - X and X are greater than 0 (when N = X, all transmission nodes have already obtained the information and p = 0), f can be converted into the function g,

[0109] g = λ X q - δ

[0110]

[0111] Assume that there exists a time s such that f(s) = g(s) = 0,

[0112]

[0113] According to Theorem 2, it can be known that That is, at time s, the function g is in a decreasing state. Obviously, for the next time h, g(h) < 0. At this time, p(h) = 0 can be obtained. According to formula (16), it can be known that Therefore, if s exists, then g(t) < 0 for s < t ≤ T. If s does not exist, then g is always greater than 0 or less than 0, and p follows the form of 1) or 2). When s exists, before time s, g must be greater than 0. Therefore, the function g satisfies g(t) > 0 for 0 ≤ t < s; g(s) < 0 = 0; g(t) < 0 for s < t ≤ T. At this time, the optimal transmission strategy satisfies p(t) = 1 for 0 ≤ t < s; p(t) = 0 for s < t ≤ T, that is, it follows the form of 3).

[0114] Theorem 2: At any time t, v(t) = (1 + λY)q - δ > 0.

[0115] Proof: First, take the derivative of it to get

[0116]

[0117] Suppose there exists a time s such that v(s) ≤ 0. Then we know that from time s onwards, v always satisfies v ≤ 0, that is, λY ≤ δ / q-1. Since q > δ, we know that λY ≤ δ / q-1 < 0, and thus λY(T) < 0, which contradicts the terminal condition shown in formula (11). Obviously, the assumption is not valid, and v(t) is always greater than 0.

[0118] experiment

[0119] First, the accuracy of the model was verified. The experimental environment used was the Opportunistic Network Environment Simulator (ONE). Two different motion models were considered: the Poisson-contact model and the actual motion trajectory. These two models simulate the motion patterns of vehicles and people, respectively, and have been widely used in existing research. For the Poisson-contact model, node encounters follow a negative exponential distribution, and the encounter rate between the transmitting node and the destination node was set to 3.71 × 10⁻⁶. -6s-1 Its value is derived from the movement trajectory of Shanghai taxis, assuming it includes 100 transmission nodes and 10 destination nodes. The encounter rate within the transmission nodes is set to 2 × 3.71 × 10⁻⁶. -6s-1 For the second model, this invention uses the Infocom'05 dataset, which contains the movement trajectories of 41 people. First, a negative exponential meeting model is used to fit the dataset. Then, the calculated average meeting time interval is used as a parameter to generate 200 nodes, including 150 transmission nodes and 50 destination nodes. Other parameter settings are as follows: q = 0.5, δ = 0.01, T = 100000. Three static transmission strategies are considered: Case 1, p(t) = 0, 0 ≤ t ≤ T; Case 2, p(t) = 0.5, 0 ≤ t ≤ T; Case 3, p(t) = 1, 0 ≤ t ≤ T. Each scenario is run 50 times in simulation experiments, and the calculation and simulation results are as follows: Figure 1 and Figure 2 As shown.

[0120] from Figure 1 and Figure 2 It can be seen that the difference between the calculated results of this model and the actual simulation results is small, with an average error within 3.9%. The following section focuses on the Poisson-contact model as an example to analyze the performance of the optimal transmission strategy obtained in this paper. By comparing it with the three static strategies mentioned earlier, we obtain... Figure 3 .

[0121] from Figure 3It can be seen that the performance of the optimal strategy proposed in the embodiment is better than that of the other static strategies. However, at intermediate points, the performance of the strategy proposed in the embodiment is slightly lower than that of the static strategies (p=1). In fact, when p=1, it is the flooding strategy (Epidemic Routing), which has the fastest information propagation speed, but its energy consumption is huge, causing its performance to fall below that of the optimal control strategy later on. That is, within the validity period of the information, its final performance is lower than that of the optimal control strategy proposed in the embodiment.

[0122] In another possible embodiment, an optimal control system for information transmission for the Internet of Things (IoT) is provided, the control system including a computer program that, when executed by a processor, implements any of the above-described optimal control methods for information transmission for the IoT.

[0123] In another possible embodiment, an optimal control device for information transmission for the Internet of Things (IoT) is provided. The control device includes a memory, a processor, and a communication circuit. The memory and the communication circuit are respectively coupled to the processor. The communication circuit is connected to the processor and interacts with an external terminal device under the control of the processor. The memory includes local storage and stores a computer program. The processor is used to run the computer program to execute any of the above-described optimal control methods for information transmission for the IoT.

[0124] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0125] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0129] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An optimal control method for information transmission in the Internet of Things (IoT), characterized in that, It includes the following steps: S1. Construct an information transmission model based on IoT nodes, in which the transmission node is set to transmit information to the destination node with a transmission probability. This includes setting the number of transmission nodes to N, the number of destination nodes to M, the timeliness of node transmission to T, the encounter rate between transmission nodes to α, the encounter rate between a transmission node and a destination node to β, and the probability that two transmission nodes meet within a time interval Δt to 1-e -αΔt p(t) represents the probability that a transmission node sends information to other transmission nodes at time t, q represents the probability of successful transmission, and the value of q is (0, 1]. X(t) represents the number of transmission nodes carrying information at time t, and X(0) = 1. Y(t) represents the number of destination nodes carrying information at time t, and X(0) = 0. Then, for the variable X(t), its change follows a Markov distribution, that is, it satisfies the following: (1)； Where Ω(t) represents the set of transmission nodes that do not carry information at time t, ω j (t, t+Δt) represents the probability that node j obtains information within the time interval [t, t+Δt]. Therefore: (2)； Therefore, we get: (3)； Based on formula (3), we can obtain: (4)； in, The derivative of E(Y(t)) is represented. represent The derivative; S2. Based on the number of information-carrying transmission nodes and the number of destination nodes within a certain time or time interval, analyze the changes in energy consumption during information transmission and establish performance metrics for node transmission. S3. Optimize the information transmission model based on the energy consumption during the information transmission process, obtain the balance point between energy consumption and information transmission efficiency during node transmission, and determine the conditions for continuing information transmission. S4. Using Pontryagin's maximum theorem, we can analyze and obtain the optimal transmission strategy for information within the time limit and the necessary conditions that it needs to satisfy.

2. The optimal control method for information transmission in the Internet of Things as described in claim 1, characterized in that, In step S2, since the energy transmitted by a node is proportional to the number of transmissions, and F(t) represents the number of transmissions at time t, we obtain: (5)； The resulting performance metrics are as follows: (6)； Where E(Y(T)) represents the number of destination nodes for obtaining information, which reflects the final information transmission performance; δ represents the energy consumption weighting factor, used to balance performance and energy consumption ratio.

3. The optimal control method for information transmission for the Internet of Things as described in claim 2, characterized in that, In step S3, the information transmission model is optimized based on performance metrics. (7)； Where max represents the maximum value and st represents the constraint condition; According to formula (6), we know that: (8)； If q≤δ, then E(U(t)) will always be in a non-increasing state, that is, the utility value will never increase, and the transmission node does not need to transmit information in this case; if q>δ, then information transmission can continue.

4. The optimal control method for information transmission in the Internet of Things as described in claim 3, characterized in that, Step S4 includes: Based on the optimized information transmission model, the Hamiltonian equation is established: (9)； in, and Consistent, and Consistent, and Consistent; and For the adjoint state variables, their respective derivatives are shown in formula (10). According to the Ponte-Lyakin maximum theorem, the adjoint state equations are obtained as follows: (10)； symbol Represents partial derivatives; The corresponding terminal conditions are met: (11)； According to Pontryagin's maximum theorem, it is clear that there exist continuous or piecewise continuous differentiable states and their adjoint state functions that satisfy: (12)； In the above formula, arg represents the set of all possible policies, and p * Represents the optimal strategy; Based on formula (12), assuming that at a given time t, all parameters are in a known state, the expression for the control parameters is: (13)； The optimal information transmission strategy and the conditions that need to be met in the Internet of Things are determined by expression (13).

5. The optimal control method for information transmission for the Internet of Things as described in claim 4, characterized in that, The optimal information transmission strategy satisfies one of the following conditions: A, B, or C: A: p(t) = 1, 0 ≤ t ≤ T; B: p(t) = 0, 0 ≤ t ≤ T; C: There exists a time s such that p(t) = 1, 0 ≤ t <s;p(t)=0,s<t≤T; And satisfying that at any time t, there exists v(t)=(1+λY)q-δ>0.

6. An optimal control system for information transmission for the Internet of Things, characterized in that, The control system includes a computer program that, when executed by a processor, implements the optimal control method for information transmission for the Internet of Things as described in any one of claims 1-5.

7. An optimal control device for information transmission for the Internet of Things, characterized in that, The control device includes a memory, a processor, and a communication circuit. The memory and the communication circuit are respectively coupled to the processor. The communication circuit is connected to the processor and interacts with an external terminal device under the control of the processor. The memory includes local storage and stores a computer program. The processor is used to run the computer program to execute the optimal control method for information transmission for the Internet of Things as described in any one of claims 1-5.

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