An electronic communication control method and system based on the Internet of Things

Abstract

The present invention belongs to the field of communication, and provides an electronic communication control method and system based on the Internet of Things, including: a mobile communication node maintains a neighbor information table of a neighbor node, periodically receives broadcasts from neighbor nodes, and updates the neighbor information table; in the current cycle, the position of the neighbor node in the next cycle is predicted according to the neighbor information table; according to the predicted position of the neighbor node, the residual energy of the neighbor node, the link quality and the position of the fixed station, a node that can transmit data to the fixed station at the minimum energy cost is comprehensively optimized and selected as a relay node; data is transmitted to the relay node; the relay node receives and aggregates data, and uses Reed-Solomon coding for data redundancy; in the next cycle, the relay node transmits the aggregated data to the next relay node or fixed station through the predicted path. The present invention can efficiently and reliably control electronic communications based on the Internet of Things.

Description

Technical Field

[0001] The present invention belongs to the field of communications, and in particular relates to an electronic communication control method and system based on the Internet of Things. Background Art

[0002] The Internet of Things (IoT) refers to connecting objects in the physical world (including equipment, vehicles, buildings, etc.) to the Internet through various information sensing devices (such as RFID, infrared sensors, GPS, laser scanners, etc.) and network communication technologies, enabling information exchange and communication between objects and between objects and people, thereby achieving intelligent identification, positioning, tracking, monitoring and management.

[0003] Electronic communication in the Internet of Things refers to the transmission and exchange of data between IoT devices through electronic and communication technologies. Electronic communication plays a vital role in the Internet of Things because it enables information exchange between devices and is the basis for the operation of the IoT system.

[0004] In the prior art, there are IoT scenarios where the topology changes frequently, such as moving automated guided vehicles (AGVs), drones, industrial robots, etc. In such networks, there are a large number of nodes in the network, and the connection relationships are complex and change frequently. The characteristics of the wireless channel lead to unstable link quality, frequent connection and disconnection. Nodes may dynamically join or leave the network, resulting in constant changes in the network topology.

[0005] This type of IoT requires a dynamic routing protocol that can quickly adapt to topology changes, ensure reliable data transmission in unstable links, and reduce data loss. At the same time, nodes are usually limited by battery power and need to optimize energy consumption. Summary of the invention

[0006] In order to solve the problems in the prior art, the present invention provides an electronic communication control method based on the Internet of Things, comprising the following steps:

[0007] A mobile communication node maintains a neighbor information table of a neighbor node, periodically receives broadcasts from neighbor nodes, and updates the neighbor information table, wherein the neighbor information table includes at least: the ID of the neighbor node, the location of the neighbor node, the speed of the neighbor node, the remaining energy of the neighbor node, and the link quality; in the current cycle, the location of the neighbor node in the next cycle is predicted according to the neighbor information table; based on the predicted location of the neighbor node, the remaining energy of the neighbor node, the link quality and the location of the fixed station, a node that can transmit data to the fixed station at the minimum energy cost is comprehensively optimized and selected as a relay node; the data is transmitted to the relay node; the relay node receives and aggregates the data, and uses Reed-Solomon coding for data redundancy; in the next cycle, the relay node transmits the aggregated data to the next relay node or fixed station through the predicted path.

[0008] Furthermore, an adaptive broadcast frequency is used to dynamically adjust the broadcast period based on node mobility and network density:

[0009]

[0010] Where T is the broadcast period, T base is the basic broadcast period, v and ρ are the current mobile speed and current network density, v max and ρ max are the maximum moving speed and the maximum network density, respectively, and ∈ is a small constant to prevent the denominator from being zero.

[0011] Further, predicting the position of the neighbor node in the next cycle according to the neighbor information table includes:

[0012] Generate N possible positions for each node

[0013]

[0014] in, is the kth position at time t-1;

[0015] According to the state transfer function, the state of each possible position is predicted to obtain the set of predicted possible positions at time t.

[0016]

[0017] in is the process noise, f is the state transition function

[0018] Using Observational Data Calculate the weight of each possible position

[0019]

[0020] in, is the observation model, which represents the possible positions Observed below probability;

[0021] Calculate the sum of the weights of all possible positions and normalize the weight of each possible position:

[0022]

[0023] Calculate the cumulative weight of each possible position, which is used to determine the number of times a possible position is replicated:

[0024]

[0025] in, is the cumulative weight of the kth possible position;

[0026] Generate N uniformly distributed random numbers {u1,u2,…,u N}satisfy

[0027]

[0028] in, is a uniformly distributed random offset;

[0029] By comparing the accumulated weights and the random number, a new set of possible positions is selected

[0030]

[0031] Calculate the weighted average position of possible positions using the normalized weights

[0032]

[0033] in, is the estimated position of the ith neighbor node in the tth time period.

[0034] Furthermore, the process noise is a Gaussian distribution:

[0035]

[0036] where Q is the covariance matrix.

[0037] Furthermore, the state transfer function is:

[0038]

[0039] in, is the velocity of the node at time t-1; △t is the time step; is the process noise.

[0040] Furthermore, the observation model is

[0041]

[0042] in is the observation function, mapping possible positions to the observation space, and R is the observation noise covariance matrix.

[0043] Furthermore, the comprehensive optimization selection of nodes that can transmit data to the fixed station at the minimum energy cost as relay nodes includes:

[0044] At time t, there are multiple neighbor nodes, and the position, energy and link quality of each node are The fixed station position is x f ;

[0045] Transmission cost Defined as:

[0046]

[0047] in, is the distance from node i to the fixed station, expressed as Euclidean distance:

[0048]

[0049] Traverse all neighbor nodes, calculate the transmission cost of each node, and select the node with the smallest cost as the relay node:

[0050]

[0051] Furthermore, the weight factor w is introduced E ,w D ,w Q Represent the weights of energy, distance and link quality respectively;

[0052] The transmission cost function is expressed as:

[0053]

[0054] Furthermore, the relay node may receive data from multiple other nodes in each cycle. In this process, each node may become a relay node for other nodes. After receiving the data, the received data will be aggregated and each piece of data will be marked with its source sending node.

[0055] On the other hand, the present invention also discloses an electronic communication control system based on the Internet of Things, comprising the following modules:

[0056] A neighbor information module is used for the mobile communication node to maintain a neighbor information table of a neighbor node, periodically receive the broadcast of the neighbor node, and update the neighbor information table, wherein the neighbor information table at least includes: the ID of the neighbor node, the location of the neighbor node, the speed of the neighbor node, the remaining energy of the neighbor node, and the link quality; in the current cycle, the location of the neighbor node in the next cycle is predicted according to the neighbor information table;

[0057] A prediction module is used to comprehensively optimize and select a node that can transmit data to the fixed station with the minimum energy cost as a relay node based on the predicted location of the neighbor node, the remaining energy of the neighbor node, the link quality and the location of the fixed station;

[0058] A transmission module is used to transmit data to the relay node; the relay node receives and aggregates the data, and uses Reed-Solomon coding for data redundancy; in the next cycle, the relay node transmits the aggregated data to the next relay node or fixed station through a predicted path.

[0059] The present invention can produce the following beneficial effects through the above technical solution:

[0060] By periodically updating the neighbor node information table and predicting the node location of the next cycle, we can accurately understand the dynamic changes of neighbor nodes and select the optimal relay node, thereby improving the reliability and stability of data transmission.

[0061] Taking into account the remaining energy and link quality of neighboring nodes, selecting the relay node with the minimum energy cost for data transmission helps to balance the energy consumption of each node and extend the service life of the entire network.

[0062] Using a multi-cycle data transmission strategy and predictive path data transmission can reduce conflicts and interference caused by simultaneous data transmission, reduce data loss rate, reduce retransmission requirements, and thus improve transmission efficiency.

[0063] By maintaining the neighbor information table and updating it periodically, we can flexibly respond to changes in network topology, adapt to node movement and environmental changes, and maintain network flexibility and scalability. By comprehensively optimizing the selection of relay nodes and paths, we can ensure the efficiency of data transmission and the optimization of energy consumption, and improve the overall network performance and data transmission efficiency.

[0064] This electronic communication control method based on the Internet of Things significantly improves the reliability, stability and efficiency of data transmission by periodically updating the neighbor information table, predicting node locations, comprehensively optimizing the selection of relay nodes, and using Reed-Solomon coding for data redundancy. It also optimizes energy consumption, enhances the network's adaptability and overall performance, and is an efficient and reliable communication control solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0067] Below, the invention is preferably described in conjunction with the accompanying drawings and specific implementation methods.

[0068] This embodiment solves the above problem through the following steps:

[0069] In one embodiment, reference Figure 1 The present invention provides an electronic communication control method based on the Internet of Things, comprising the following steps:

[0070] The mobile communication node maintains a neighbor information table of a neighbor node, periodically receives the broadcast of the neighbor node, and updates the neighbor information table, which includes at least: the ID of the neighbor node, the location of the neighbor node, the speed of the neighbor node, the remaining energy of the neighbor node, and the link quality.

[0071] Mobile communication nodes refer to IoT devices that can move in the network, such as automated guided vehicles (AGVs), drones, smartphones, etc. In order to perform topology updates, each mobile node maintains a table that records the information of neighbor nodes with which it communicates directly.

[0072] Each node periodically sends broadcast messages containing its own information. These messages can be broadcasted via low-power wireless communication technologies (such as Zigbee, LoRa, BLE, etc.), including node ID, current location, speed, remaining energy, and link quality.

[0073] Before broadcasting, each node can periodically obtain data from the IMU and odometer to calculate the current position and speed. UWB or RSSI technology can be used for position calibration to improve positioning accuracy. The battery voltage or power management chip data is read regularly to update the remaining energy information. When receiving each data packet, RSSI and LQI are recorded to update the link quality information.

[0074] The mobile node periodically receives broadcast information sent by neighbor nodes to update its neighbor information table. When the node receives a broadcast message from a neighbor node, it parses the message content and updates the corresponding neighbor information table. If the neighbor node already exists in the information table, its position, speed, remaining energy and link quality information are updated. If it is a new neighbor node, the node's information is added to the information table.

[0075] Since fixed broadcast frequency may lead to unnecessary energy consumption or untimely information update, an adaptive broadcast frequency is adopted to dynamically adjust the broadcast period based on node mobility and network density.

[0076]

[0077] Where T base is the basic broadcast period, v and ρ are the current mobile speed and current network density, vmax and ρ max are the maximum moving speed and the maximum network density, respectively, and ∈ is a small constant to prevent the denominator from being zero.

[0078] In the current cycle, the location of the neighbor node in the next cycle is predicted according to the neighbor information table.

[0079] Generate N possible positions for each node

[0080]

[0081] in, is the kth position at time t-1

[0082] According to the state transfer function, the state of each possible position is predicted to obtain the set of predicted possible positions at time t.

[0083]

[0084] in is the process noise, which is set as Gaussian distribution in this implementation

[0085]

[0086] f is a state transfer function. In this embodiment, it is assumed that each node runs at a constant speed. The state transfer function is expressed as:

[0087]

[0088] in, is the velocity of the node at time t-1.

[0089] △t is the time step.

[0090] is the process noise, which represents the uncertainty of the model and is set to Gaussian distribution in this implementation.

[0091] Using Observational Data (Information about the location of neighbor nodes collected from the environment, obtained through the neighbor information table) Calculate the weight of each possible location

[0092]

[0093] in, is the observation model, which represents the possible positions Observed below probability.

[0094] In this embodiment, the observation noise is Gaussian distributed, and the observation model can be expressed as:

[0095]

[0096] in is the observation function, mapping possible positions to the observation space, and R is the observation noise covariance matrix.

[0097] Calculate the sum of the weights of all possible positions and normalize the weight of each possible position:

[0098]

[0099] Calculate the cumulative weight of each possible position, which is used to determine the number of times a possible position is replicated:

[0100]

[0101] in, is the cumulative weight of the kth possible position.

[0102] Generate N uniformly distributed random numbers {u1,u2,…,u N}satisfy

[0103]

[0104] in, is a uniformly distributed random offset;

[0105] By comparing the accumulated weights and the random number, a new set of possible positions is selected

[0106]

[0107] In this way, possible positions with large weights and high cumulative weights will be selected many times, while possible positions with small weights will be selected less frequently or not at all.

[0108] Calculate the weighted average position of possible positions using the normalized weights

[0109]

[0110] in, is the estimated position of the ith neighbor node in the tth time period.

[0111] The above method can handle complex nonlinear and non-Gaussian noise systems and provide more accurate state estimation. The resampling step ensures that the possible position distribution is concentrated in the high probability area, which improves the robustness of the system to noise and uncertainty. It can be effectively applied in real-time systems to adapt to dynamically changing environments and states.

[0112] According to the predicted location of neighbor nodes, the remaining energy of neighbor nodes, link quality and the location of fixed stations, the node that can transmit data to the fixed station with the minimum energy cost is selected as the relay node through comprehensive optimization.

[0113] In order to select a neighbor node as a relay node, the energy consumption of transmitting data to the fixed station is minimized. Assume that there are multiple neighbor nodes at time t, and the position, energy and link quality of each node are The fixed station position is x f .

[0114] Transmission cost Defined as:

[0115]

[0116] in, is the distance from node i to the fixed station, expressed as Euclidean distance:

[0117]

[0118] Traverse all neighbor nodes, calculate the transmission cost of each node, and select the node with the smallest cost as the relay node:

[0119]

[0120] In wireless sensor networks and other wireless communication-based systems, nodes are usually powered by batteries. By selecting relay nodes with the lowest energy consumption, the service life of the entire network can be extended. Considering link quality can reduce errors and packet loss during data transmission and improve data transmission reliability. By considering the remaining energy, some nodes are prevented from running out of energy too early, thus balancing the load distribution of the entire network.

[0121] Furthermore, for different application scenarios, the weights of energy, distance, and link quality can be adjusted to optimize the overall performance. E ,w D ,w Q Represent the weights of energy, distance and link quality respectively.

[0122] The new transmission cost function can be expressed as:

[0123]

[0124] The data is transmitted to the relay node; the relay node receives and aggregates the data, and uses Reed-Solomon coding for data redundancy.

[0125] Each node collects data that needs to be transmitted to the server during operation, such as cargo delivery data collected by AGV carts, etc. The node transmits the data to the relay node through the wireless communication protocol.

[0126] After receiving the data from each sensor node, the relay node aggregates the data. The relay node may receive data from multiple other nodes in each cycle. In this process, each node may become a relay node for other nodes. After receiving the data, it will aggregate the received data and mark each piece of data with its source sending node. Each piece of data is marked with the source node, and the server can trace the source of the data for more accurate analysis and processing.

[0127] Furthermore, since the topology of IoT transmission links changes rapidly, in order to improve the reliability of data transmission, redundant data is added through Reed-Solomon coding, which can correct a certain number of erroneous data blocks during the transmission process and improve transmission reliability.

[0128] In the next cycle, the relay node transmits the aggregated data to the next relay node or fixed station via the predicted path.

[0129] After receiving the data from each sensor node, the relay node needs a certain amount of time to aggregate and encode the data. Delaying the transmission task to the next cycle can ensure the completion of data processing and improve the reliability and integrity of data transmission. At the same time, performing data processing and transmission in cycles can make more reasonable use of the node's computing and communication resources and improve the overall efficiency of the system.

[0130] In the above scheme, by periodically updating the neighbor node information table and predicting the node position of the next cycle, the dynamic changes of neighbor nodes can be accurately understood, and the optimal relay node can be selected, thereby improving the reliability and stability of data transmission.

[0131] Taking into account the remaining energy and link quality of neighboring nodes, selecting the relay node with the minimum energy cost for data transmission helps to balance the energy consumption of each node and extend the service life of the entire network.

[0132] Using a multi-cycle data transmission strategy and predictive path data transmission can reduce conflicts and interference caused by simultaneous data transmission, reduce data loss rate, reduce retransmission requirements, and thus improve transmission efficiency.

[0133] By maintaining the neighbor information table and updating it periodically, we can flexibly respond to changes in network topology, adapt to node movement and environmental changes, and maintain network flexibility and scalability. By comprehensively optimizing the selection of relay nodes and paths, we can ensure the efficiency of data transmission and the optimization of energy consumption, and improve the overall network performance and data transmission efficiency.

[0134] This electronic communication control method based on the Internet of Things significantly improves the reliability, stability and efficiency of data transmission by periodically updating the neighbor information table, predicting node locations, comprehensively optimizing the selection of relay nodes, and using Reed-Solomon coding for data redundancy. It also optimizes energy consumption, enhances the network's adaptability and overall performance, and is an efficient and reliable communication control solution.

[0135] This solution has significant advantages in improving imaging accuracy, computational efficiency, and model robustness. It can effectively assist doctors in performing visualized acupuncture operations and improve the safety and success rate of surgery.

[0136] On the other hand, the present invention also provides an electronic communication control system based on the Internet of Things.

[0137] A neighbor information module is used for the mobile communication node to maintain a neighbor information table of a neighbor node, periodically receive the broadcast of the neighbor node, and update the neighbor information table, wherein the neighbor information table includes at least: the ID of the neighbor node, the location of the neighbor node, the speed of the neighbor node, the remaining energy of the neighbor node, and the link quality;

[0138] In the current cycle, predicting the location of the neighbor node in the next cycle according to the neighbor information table;

[0139] A prediction module is used to comprehensively optimize and select a node that can transmit data to the fixed station with the minimum energy cost as a relay node based on the predicted location of the neighbor node, the remaining energy of the neighbor node, the link quality and the location of the fixed station;

[0140] A transmission module is used to transmit data to the relay node; the relay node receives and aggregates the data, and uses Reed-Solomon coding for data redundancy; in the next cycle, the relay node transmits the aggregated data to the next relay node or fixed station through a predicted path.

[0141] Furthermore, the specific implementation methods of the above-mentioned electronic communication control system based on the Internet of Things are the same as an electronic communication control method based on the Internet of Things, and all further technical solutions in an electronic communication control method based on the Internet of Things are fully introduced into an electronic communication control system based on the Internet of Things.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

[0143] The present invention does not particularly specify the structure of some modules, which shall be subject to the contents recorded in the prior art. The prior art mentioned in the above background technology section and the specific embodiment section of the present invention can be used as part of the present invention to understand the meaning of some technical features or parameters. The scope of protection of the present invention shall be subject to the contents actually recorded in the claims.

Claims

1. An electronic communication control method based on the Internet of Things, characterized in that The method comprises the following steps: The mobile communication node maintains a neighbor information table of a neighbor node, periodically receives broadcasts from neighbor nodes, and updates the neighbor information table, wherein the neighbor information table includes at least: an ID of the neighbor node, a location of the neighbor node, a speed of the neighbor node, a remaining energy of the neighbor node, and a link quality; In the current cycle, the position of the neighbor node in the next cycle is predicted according to the neighbor information table; according to the predicted position of the neighbor node, the remaining energy of the neighbor node, the link quality and the position of the fixed station, a node that can transmit data to the fixed station at the minimum energy cost is comprehensively optimized and selected as a relay node; Transmitting data to the relay node; the relay node receives and aggregates the data, and uses Reed-So lomon coding for data redundancy; In the next cycle, the relay node transmits the aggregated data to the next relay node or fixed station via the predicted path; Use adaptive broadcast frequency to dynamically adjust the broadcast period based on node mobility and network density: Where T is the broadcast period, T base is the basic broadcast period, v and ρ are the current mobile speed and current network density, v max and ρ max are the maximum mobile speed and the maximum network density, respectively, and ∈ is a small constant to prevent the denominator from being zero; Predicting the position of the neighbor node in the next cycle according to the neighbor information table includes: Generate N possible positions for each node in, is the kth position at time t-1; According to the state transfer function, the state of each possible position is predicted to obtain the set of predicted possible positions at time t. in is the process noise, f is the state transition function Using Observational Data Calculate the weight of each possible position in, is the observation model, which represents the possible positions Observed below probability; Calculate the sum of the weights of all possible positions and normalize the weight of each possible position: Calculate the cumulative weight of each possible position, which is used to determine the number of times a possible position is replicated: in, is the cumulative weight of the kth possible position; Generate N uniformly distributed random numbers {u1,u2,…,u N }satisfy in, is a uniformly distributed random offset; By comparing the accumulated weights and the random number, a new set of possible positions is selected Calculate the weighted average position of possible positions using the normalized weights in, is the estimated position of the i-th neighbor node in the t-th time period; The process noise is a Gaussian distribution: Where Q is the covariance matrix; The state transfer function is: in, is the velocity of the node at time t-1; Δt is the time step; is the process noise; The observation model is in is the observation function, which maps possible positions to the observation space, and R is the observation noise covariance matrix; The comprehensive optimization selection of nodes that can transmit data to fixed stations at the minimum energy cost as relay nodes includes: At time t, there are multiple neighbor nodes, and the position, energy and link quality of each node are The fixed station position is x f ; Transmission cost Defined as: in, is the distance from node i to the fixed station, expressed as Euclidean distance: Traverse all neighbor nodes, calculate the transmission cost of each node, and select the node with the smallest cost as the relay node: Introducing the weight factor w E ,w D ,w Q represent the weights of energy, distance and link quality respectively; the transmission cost function is expressed as: The relay node may receive data from multiple other nodes in each cycle. In this process, each node may become a relay node for other nodes. After receiving the data, the received data will be aggregated and each piece of data will be marked with its source sending node.

2. An electronic communication control system based on the Internet of Things, using the method as claimed in claim 1 for communication control, characterized in that The system includes the following modules: A neighbor information module is used for the mobile communication node to maintain a neighbor information table of a neighbor node, periodically receive the broadcast of the neighbor node, and update the neighbor information table, wherein the neighbor information table includes at least: the ID of the neighbor node, the location of the neighbor node, the speed of the neighbor node, the remaining energy of the neighbor node, and the link quality; In the current cycle, the position of the neighbor node in the next cycle is predicted according to the neighbor information table; the prediction module is used to comprehensively optimize and select the node that can transmit data to the fixed station with the minimum energy cost as the relay node according to the predicted position of the neighbor node, the remaining energy of the neighbor node, the link quality and the position of the fixed station; A transmission module is used to transmit data to the relay node; the relay node receives and aggregates the data, and uses Reed-Sol omon coding for data redundancy; in the next cycle, the relay node transmits the aggregated data to the next relay node or fixed station through a predicted path.

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