Energy adaptive communication method and system based on sensing acquisition system

Abstract

The invention relates to an energy self-adaptive communication method and system based on a sensing acquisition system, which are applied to the technical field of sensor networks, and the method comprises the following steps: arranging sensors at preset node positions, and carrying out network initialization; detecting the energy state of the sensor at the node position in real time, wherein the energy state at least comprises the residual electric quantity and the voltage level; dynamically adjusting communication parameters according to the energy state, and selecting an optimal communication protocol; sharing the information of the energy state among the nodes in a low-power-consumption broadcast mode; and performing node data relay and energy transfer based on the shared energy state. Through energy detection and communication parameter adjustment between sensor nodes, energy state information can be shared between the nodes, and the communication mode is cooperatively adjusted based on the information, so that optimal utilization of energy is realized.

Description

Technical Field

[0001] The present application relates to the field of sensor network technology, and in particular to an energy adaptive communication method and system based on a sensor acquisition system. Background Art

[0002] In the Internet of Things (IoT) and Wireless Sensor Networks (WSN), sensor nodes are often deployed in locations where it is difficult to access or replace batteries, so energy consumption is a key consideration in the design of these systems. Low-power wireless communication technologies, such as Zigbee, LoRa, and BLE (Bluetooth Low Energy), are widely used in these systems to extend the working life of sensor nodes.

[0003] Existing sensor acquisition systems usually use fixed communication parameters and fixed communication protocols for communication transmission, and cannot be dynamically adjusted according to the real-time energy status of sensor nodes, resulting in low energy utilization efficiency.

[0004] Therefore, how to optimize the energy utilization of the sensor system becomes an issue to be solved. Summary of the invention

[0005] In order to improve the problem of inability to dynamically adjust according to the real-time energy status of sensor nodes, resulting in low energy utilization efficiency, the present application provides an energy adaptive communication method and system based on a sensor acquisition system.

[0006] In a first aspect, the present application provides an energy adaptive communication method based on a sensor collection system, which adopts the following technical solutions: including: Deploy sensors at preset node locations and initialize the network; Real-time detection of the energy state of the sensor at the node location, the energy state including at least: remaining power and voltage level; Dynamically adjusting communication parameters according to the energy state and selecting an optimal communication protocol; Sharing the energy status information between nodes by low-power broadcasting; Node data relay and energy transfer are performed based on the shared energy state.

[0007] Optionally, the step of placing sensors at preset node locations and performing network initialization includes: Deploy sensors at the node locations and perform initial configuration so that each node is equipped with an energy state detection module, a communication parameter adjustment module, and a related software-defined communication protocol; The sensor nodes are started and the network is initialized so as to detect the energy status of the sensor nodes in real time.

[0008] Optionally, dynamically adjusting communication parameters according to the energy state and selecting an optimal communication protocol includes: Dynamically adjust the communication parameters according to the energy state, the communication parameters at least including: communication frequency, transmission power and data transmission rate; According to the current energy state of the node, select the best communication protocol according to the preset communication strategy; By using software-defined communication, flexible switching and optimization of communication protocols can be achieved. Each node regularly evaluates the effect of the current communication protocol to adjust the communication strategy.

[0009] Optionally, after dynamically adjusting the communication parameters according to the energy state and selecting the best communication protocol, the method further includes: Predict the energy consumption of nodes based on machine learning algorithms and obtain prediction results; The communication parameters and communication protocols are adjusted in advance according to the prediction results.

[0010] Optionally, in each time slot, based on the instantaneous queue state and channel state, the compression ratio and the number of packets served are adaptively adjusted; In wireless communications over fast fading channels, the average queue delay is minimized with average power and compression distortion as constraints.

[0011] Optionally, the step of adaptively adjusting the compression ratio and the number of data packets served in each time slot based on the instantaneous queue state and channel state includes: Quantifying and framing the data collected by the sensor to obtain data packets; Based on a preset Markov decision process architecture and instantaneous queue status and channel status, the compression ratio of the data packet and the number of data packets transmitted are adjusted.

[0012] In a second aspect, the present application provides an energy adaptive communication device based on a sensor collection system, which adopts the following technical solutions, including: The node placement module is used to place sensors at preset node locations and perform network initialization; An energy detection module, used for detecting the energy state of the sensor at the node location in real time, wherein the energy state includes at least: remaining power and voltage level; A communication adjustment module, used to dynamically adjust communication parameters according to the energy state and select an optimal communication protocol; The energy sharing module is used to share the energy status information between nodes through low-power broadcasting, and to perform node data relay and energy transfer based on the shared energy status.

[0013] In a third aspect, the present application further provides a control device, the device comprising: The system comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and executes the energy adaptive communication method based on the sensor collection system.

[0014] In a fourth aspect, the present application also provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as the above-mentioned energy adaptive communication method based on the sensor collection system.

[0015] In summary, the system in the present application deploys sensors at preset node positions, performs network initialization, detects the energy state of the sensors at the node positions in real time, and dynamically adjusts communication parameters and selects the best communication protocol according to the energy state. The energy state information is shared between nodes through low-power broadcasting, and node data relay and energy transfer are performed based on the shared energy state, so that the nodes can share energy state information and collaboratively adjust the communication mode based on this information to achieve optimal energy utilization. At the same time, the system adaptively schedules the compression ratio and the number of data packets served based on the instantaneous queue state and channel state in each time slot, so that in wireless communications on fast fading channels, the average queue delay is minimized with average power and compression distortion as constraints, so as to achieve the effect of a cross-layer scheduling strategy that simultaneously schedules application layer lossy compression and physical layer wireless transmission. At the same time, a constrained Markov decision process (CMDP) and a dynamic programming algorithm are used to ensure the efficiency and feasibility of the cross-layer scheduling strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of an energy adaptive communication method based on a sensor collection system.

[0017] Figure 2 It is a flowchart of dynamically adjusting communication parameters.

[0018] Figure 3 It is a structural diagram of a cross-layer scheduling strategy that can simultaneously schedule application layer lossy compression and physical layer wireless transmission.

[0019] Figure 4 The structure block diagram of an energy adaptive communication device based on a sensor collection system.

[0020] Explanation of reference numerals: 310, node arrangement module; 320, energy detection module; 330, communication adjustment module; 340, energy sharing module. DETAILED DESCRIPTION

[0021] The following combination Figure 1 - Figure 4 This application is described in further detail.

[0022] The sensor acquisition system in wireless communication is a distributed network architecture used to collect, process and transmit environmental or equipment data monitored by multiple sensor nodes. This type of system is widely used in many fields such as industrial monitoring, environmental monitoring, smart home, health monitoring, etc. The following is a basic description of the sensor acquisition system in wireless communication: Sensor node: The core component of the system, responsible for collecting various types of physical or chemical parameters, such as temperature, humidity, pressure, light intensity, gas concentration, etc. A sensor node usually includes one or more sensors, a microprocessor, a wireless transceiver, and a power module.

[0023] Wireless communication: Sensor nodes communicate with each other through wireless technologies, such as Zigbee, Wi-Fi, Bluetooth, LoRa or NBIoT, to transmit data to a gateway or aggregation node. Wireless communication allows sensor nodes to exchange data without physical connection, enhancing the flexibility and scalability of the system.

[0024] Energy management: Sensor nodes are often deployed in inaccessible locations and rely on batteries for power, so energy management is critical. Energy-saving techniques such as sleep modes, data compression, and smart scheduling are used to extend the life cycle of nodes.

[0025] However, existing sensor collection systems have the following major deficiencies in terms of energy management and communication efficiency: Static energy management: Existing systems usually use fixed communication parameters and cannot be dynamically adjusted according to the real-time energy status of the nodes, resulting in inefficient energy utilization.

[0026] Single communication protocol: Most systems use a fixed communication protocol and lack the flexibility to choose from a variety of communication methods, making it difficult to balance energy efficiency and communication performance in different application scenarios.

[0027] Lack of collaborative sensing mechanism: There is a lack of effective energy state sharing and collaborative communication strategies between sensor nodes, resulting in non-optimal overall energy management.

[0028] High latency problem: In some latency-sensitive applications, existing systems cannot provide low-latency communication guarantees, affecting the real-time and reliability of the system.

[0029] Based on this, the embodiment of the present application discloses an energy adaptive communication method based on a sensor collection system. The execution subject is a sensor system. The communication parameters are dynamically adjusted and the optimal communication protocol is selected according to the energy state, the energy state information is shared between nodes through a low-power broadcast method, and node data is relayed and energy is transferred based on the shared energy state, so that the energy state information can be shared between nodes, and the communication method is collaboratively adjusted based on this information to achieve optimal energy utilization.

[0030] Reference Figure 1 , the embodiment of the present application at least includes step S10 to step S40.

[0031] S10, placing sensors at preset node locations and initializing the network.

[0032] S20, detecting the energy status of the sensor at the node location in real time.

[0033] S30, dynamically adjusting communication parameters according to the energy state and selecting the best communication protocol.

[0034] S40, sharing energy status information between nodes in a low-power broadcast manner, and performing node data relay and energy transfer based on the shared energy status.

[0035] The energy status includes at least remaining power and voltage level.

[0036] Specifically, the system deploys sensor nodes at predetermined locations and performs initial configuration to ensure that each node is equipped with an energy state detection module, a communication parameter adjustment module, and related software-defined communication protocols. The system starts the sensor nodes and performs network initialization, including preliminary synchronization and link establishment between nodes. Each node begins to detect its own energy state in real time. At the same time, the system dynamically adjusts communication parameters and selects the best communication protocol according to the energy state, shares energy state information between nodes through low-power broadcasting, and performs node data relay and energy transfer based on the shared energy state, so that nodes can share energy state information and collaboratively adjust communication methods based on this information to achieve optimal energy utilization. Encryption technology and identity authentication mechanisms are used to ensure the security of data and communications and prevent data leakage and communication attacks.

[0037] At the same time, in this embodiment, the data of each sensor is integrated through data fusion technology, which can improve the accuracy and robustness of the overall system, thereby increasing the energy detection accuracy of the sensor; through edge computing, data preprocessing and preliminary analysis are performed close to the data source, reducing the need to transmit large amounts of raw data to the central server, and at the same time, it can also respond more quickly, ensuring that the detection results can reflect the energy status of the node in a timely manner.

[0038] In some embodiments, step S20 specifically includes the following steps: deploying sensors at node locations and performing initial configuration so that each node is equipped with an energy status detection module, a communication parameter adjustment module, and related software-defined communication protocols; starting the sensor nodes and performing network initialization to detect the energy status of the sensor nodes in real time.

[0039] Specifically, during the system initialization phase, each sensor node is deployed at a preset location and initially configured. Each node is equipped with the following modules: energy status detection module, communication parameter adjustment module, and software-defined communication protocol module. Each node monitors its energy status in real time, including the remaining power and voltage level. Assume that a high-precision ADC (Analog-to-Digital Converter) is used for voltage sampling, and the remaining power is calculated using the following formula: ; in, is the remaining power (Wh), is the battery voltage (V), is the battery capacity (mAh), and η is the energy conversion efficiency (usually 0.9 to 0.95).

[0040] Furthermore, step S30 specifically includes the following steps: dynamically adjusting communication parameters according to the energy state, the communication parameters including at least: communication frequency, transmission power and data transmission rate; selecting the best communication protocol according to the preset communication strategy based on the current energy state of the node; utilizing software-defined communication to achieve flexible switching and optimization of communication protocols, and each node regularly evaluates the effectiveness of the current communication protocol to adjust the communication strategy.

[0041] Specifically, each node monitors its own energy state in real time through the energy state detection module, including the remaining power and voltage level. The energy state information is regularly updated and stored in the node memory. According to the detected energy state, the communication parameter adjustment module dynamically adjusts the communication parameters, including communication frequency, transmission power and data transmission rate. According to the current energy state of the node, the most suitable communication protocol is automatically selected. In the high energy state, the communication protocol with high efficiency but high energy consumption is preferred; in the low energy state, the communication protocol with low power consumption is selected. The software-defined communication method is used to achieve flexible switching and optimization of the protocol. Each node regularly evaluates the effect of the current communication protocol and adjusts it when necessary. Based on the shared energy state information, data relay and energy transfer are carried out between nodes to optimize the overall energy utilization. When the energy of a node is low, other nodes with sufficient energy can relay its data to ensure the stability of the communication link. At the same time, the system configuration and communication strategy can be flexibly adjusted according to different application scenarios to meet specific needs.

[0042] It should be understood that the specific data of the words such as high energy, low energy, high consumption, and low consumption in this embodiment need to be set according to the specific sensor information, so no specific data description is given in this embodiment.

[0043] In practice, communication parameters, including communication frequency, transmission power, and data transmission rate, are dynamically adjusted according to the detected energy state. Figure 2 , the system adjusts the communication parameters and communication protocol according to the following steps: 1. Energy status update: S201, the node updates the energy state every time interval t, which is achieved by the following formula: ; in, is the updated energy state, is the power consumption of the node.

[0044] 2. Calculate the energy threshold: S202, setting energy threshold ,When it is lower than this threshold, the adjustment mechanism is triggered.

[0045] 3. Adjust communication parameters: S203, when When the communication parameters are adjusted, the adjustment formula is as follows: ; in, , , They are basic communication frequency, transmission power and data transmission rate, , , They are the adjusted communication frequency, transmission power and data transmission rate respectively.

[0046] 4. Select the communication protocol: S204, selecting the best communication protocol according to the adjusted communication parameters: ; in: is the power consumption of the communication protocol, For the efficiency of the communication protocol 5. Broadcast energy status: S205, nodes share energy status information through low-power broadcasting. The energy status information is sent to neighbor nodes through broadcast , the formula is as follows: ; in, For Node To Node The transmission power of the broadcast, is the minimum transmission power, is the transmission power adjustment factor.

[0047] The specific broadcast communication steps are as follows: Initialization: When each node is initialized, the broadcast parameters and communication module are configured.

[0048] Energy status detection: Real-time detection of node energy status.

[0049] Energy status broadcast: The energy status is sent to neighboring nodes via broadcast.

[0050] Signal confirmation: The receiving node sends a confirmation signal to ensure that the broadcast information is successfully received.

[0051] Redundant transmission: Repeated broadcasting within a specific time interval to increase the reliability of information transmission.

[0052] Adaptive adjustment: Dynamically adjust the broadcast power and frequency according to the reception of the broadcast signal.

[0053] To ensure the stability of broadcast communication, the corresponding processing steps are as follows: Redundant transmission: Repeated broadcasting at a specific time interval to ensure that the broadcast information can be received by all nodes. Repeatedly send energy status information every second: ; Signal confirmation mechanism: The receiving node sends a confirmation signal to ensure that the broadcast information is successfully received. Each node sends a confirmation signal (ACK) after receiving the broadcast information, and the sending node rebroadcasts if it does not receive the confirmation signal.

[0054] Adaptive adjustment of broadcast power: Dynamically adjust the broadcast power according to the reception of the broadcast signal. If the confirmation signal is not received for many times, increase the broadcast power. ; in, For the new broadcast power, is the basic broadcast power, is the number of times the confirmation signal was not received, The maximum number of retries.

[0055] Frequency adjustment: If the channel is congested or interfered with severely, the broadcast frequency will be adjusted dynamically ; in, For the new broadcast frequency, is the basic broadcast frequency, is the current channel interference strength, is the maximum permissible interference intensity.

[0056] S206: Based on the shared energy status information, node data relay and energy transfer are performed. When , other nodes relay their data through the following formula: ; in, is the amount of relay data, is the total data volume.

[0057] 6. Real-time feedback and adjustment: The system evaluates the adjustment effect at every time interval t, and provides real-time feedback and further adjustments.

[0058] In some embodiments, considering the problem that energy consumption can be predicted, the corresponding processing steps are as follows: predict the energy consumption of the node based on the machine learning algorithm and obtain the prediction result; adjust the communication parameters and communication protocol in advance according to the prediction result.

[0059] Specifically, a machine learning algorithm is used to predict the energy consumption of nodes. The prediction model is trained based on the historical energy consumption data of the nodes and the current sensor data. According to the prediction results, the communication strategy is adjusted in advance to avoid excessive energy consumption, including adjusting the transmission frequency, power and selecting the optimal communication path.

[0060] In practice, machine learning algorithms are used to predict node energy consumption. For example, linear regression models are used to predict energy consumption: ; in, To predict the energy consumption of the next cycle, , , , are model parameters, is the current communication time, The number of packets currently being transmitted.

[0061] At the same time, according to the prediction results, the communication parameters and protocols are adjusted in advance to ensure efficient use of energy: ; in, is the adjusted communication time, The basic communication time.

[0062] Through the above scheme, the system can achieve high-precision and real-time energy status detection to ensure the optimization of energy utilization. This method not only improves the overall performance of the system, but also extends the working life of the sensor nodes.

[0063] Furthermore, the following method is used to optimize low-latency communication: Low-latency communication protocol: Design a communication protocol optimized for low-latency applications, including a fast confirmation mechanism and priority queues, to ensure that critical data can be transmitted first and reduce overall communication latency.

[0064] Fast link establishment and maintenance: Use pre-negotiated link information and fast synchronization technology to reduce communication establishment time, regularly maintain and update link information, and ensure stable and efficient communication links.

[0065] Low-latency scheduling algorithm: Design a low-latency scheduling algorithm to ensure that key data and events can be transmitted in the shortest time, dynamically adjust resource allocation according to real-time requirements, and optimize transmission efficiency.

[0066] In some embodiments, considering the problem of cross-layer scheduling strategy for simultaneously scheduling application layer lossy compression and physical layer wireless transmission, the corresponding processing steps are as follows: in each time slot, based on the instantaneous queue status and channel status, the compression ratio and the number of data packets served are adaptively adjusted, and in wireless communications in fast fading channels, the average queue delay is minimized with average power and compression distortion as constraints.

[0067] Furthermore, the data collected by the sensor is quantized and framed to obtain data packets; based on a preset Markov decision process architecture and instantaneous queue status and channel status, the compression ratio of the data packets and the number of data packets transmitted are adjusted.

[0068] Reference Figure 3 , the functions of each module are described as follows: 1. Industrial Internet of Things Destination Node Monitor: Responsible for acquiring data from sensor nodes and performing preliminary processing and analysis. This node is the final receiving point of the data and is responsible for summarizing and displaying the monitoring results.

[0069] 2. Industrial Internet of Things Source Node 1. Quantization and framing: The analog signals collected by the sensor are quantized and framed, and converted into digital signals for transmission.

[0070] ; in, represents the quantized signal, Represents the original analog signal collected by the sensor.

[0071] 2. Adaptive lossy compression: Adaptively adjust the data compression ratio based on queue status information to reduce data volume and optimize transmission efficiency.

[0072] ; in, represents the compressed signal, represents the remaining energy, For maximum energy.

[0073] 3. Adaptive modulation and coding: Based on channel state information, adaptively select the best modulation and coding scheme to improve communication reliability and efficiency.

[0074] ; in, represents the modulated signal, Indicates the signal-to-noise ratio of the channel.

[0075] 4. Source coding and channel coding: responsible for data compression and error correction coding to ensure the integrity and reliability of data during transmission.

[0076] ; in, Represents the encoded signal.

[0077] 3. Cross-layer Scheduling Layer 1. Cross-layer scheduler: The core module is responsible for coordinating and managing the resources and parameter adjustments of each layer. Based on the energy status, queue status and channel status information, it dynamically adjusts the communication parameters to optimize energy utilization and transmission efficiency. This is achieved using the following formula: ; 2. Subordinate Markov Decision: Through the Markov decision process, the cross-layer scheduling strategy is optimized to achieve the best communication performance.

[0078] ; in, is the optimal strategy, is the discount factor, For the status Take Action of returns.

[0079] 3. Adaptive modulation control coding: Combine queue status and channel status to dynamically adjust modulation and coding strategies to ensure system stability and efficiency under various conditions.

[0080] ; in, is the bit error rate, is the energy per bit, is the noise power spectral density.

[0081] 4. Application Layer Source decoding and information reconstruction: The data received after source encoding is decoded and reconstructed to restore the original information and provide it to the monitor for further analysis.

[0082] 5. Physical Layer Uniform quantization: Quantize the data to facilitate subsequent transmission and processing.

[0083] Channel estimation: Evaluate the channel status and provide it to the cross-layer scheduler for adjusting the communication strategy.

[0084] Send / Receive: Responsible for the actual sending and receiving of data, ensuring that data can be transmitted reliably between nodes.

[0085] 6. Adjust and optimize processes Real-time monitoring and adjustment: The system dynamically adjusts communication parameters and strategies by monitoring the energy status, queue status and channel status of sensor nodes in real time.

[0086] Lossy compression and modulation coding: Adaptively adjust the data compression ratio and modulation coding scheme according to the specific application scenario and transmission requirements to minimize transmission delay and energy consumption.

[0087] Cross-layer coordination: The cross-layer scheduler coordinates the work of each layer to ensure the efficient operation of the entire system and optimizes the cross-layer scheduling strategy through the subsidiary Markov decision process.

[0088] This diagram shows the application of the system in the industrial Internet of Things. Through the collaborative work of various modules, efficient, stable and low-power data collection and transmission are achieved.

[0089] Specifically, a constrained Markov decision process (CMDP) architecture is first established to characterize the joint scheduling strategy for compression ratio and number of served packets. The application scenario is environmental monitoring in the Industrial Internet. In a wireless link in the Industrial Internet, the transmitter of the link has the functions of adaptive lossy compression and power allocation. A sensor monitors a random process in real time and sends the data to a remote server to reconstruct the entire random process.

[0090] The data collected by the sensor is quantized and framed, and the resulting data packets are temporarily stored in the buffer. All data packets can be losslessly compressed, and all data packets need to be transmitted under certain delay constraints to ensure that the receiving end can effectively reconstruct the source information. The size of each data packet is L bits. The sensor has its own source encoder, and the redundant data in the data packet has been removed before the data packet enters the buffer. In other words, the data entering the buffer has been fully losslessly compressed and no longer contains any redundancy. Before each transmission, the sender needs to use a cross-layer scheduler to determine the lossy compression ratio and transmission rate. In this way, the compression ratio and the number of data packets served can be adaptively adjusted in each time slot based on the instantaneous queue state and channel state, so as to minimize the average queue delay in wireless communications over fast fading channels with average power and compression distortion as constraints through cross-layer scheduling strategies, combined with lossy compression at the application layer and wireless transmission at the physical layer.

[0091] The implementation principle of an energy adaptive communication method based on a sensor acquisition system in an embodiment of the present application is as follows: the system deploys sensors at preset node positions and performs network initialization, detects the energy status of the sensors at the node positions in real time, dynamically adjusts communication parameters and selects the best communication protocol according to the energy status, shares energy status information between nodes through low-power broadcasting, relays node data and transfers energy based on the shared energy status, so that nodes can share energy status information and collaboratively adjust communication methods based on this information to achieve optimal energy utilization. At the same time, the system adaptively schedules the compression ratio and the number of data packets served in each time slot based on the instantaneous queue status and channel status, so that in wireless communications in fast fading channels, the average queue delay is minimized with average power and compression distortion as constraints, so as to achieve the effect of a cross-layer scheduling strategy that simultaneously schedules application layer lossy compression and physical layer wireless transmission.

[0092] In different scenarios, traditional methods are difficult to balance energy efficiency and communication performance. The following examples illustrate: Example 1: In an industrial monitoring environment, the environment is complex, with much interference, long-term continuous monitoring is required, and the node energy consumption is large. The adjustment method is as follows: 1. Energy optimization: Reduce energy consumption by dynamically adjusting communication parameters, using the following formula: ; 2. Multi-hop communication: Use neighboring nodes to relay data and reduce node burden.

[0093] ; 3. Anti-interference adjustment: When interference is severe, adjust the communication frequency and power.

[0094] .

[0095] Example 2: In the environmental monitoring process, the nodes are widely distributed, the environmental conditions are changeable, and the data transmission volume is large, so an efficient communication method is required. The adjustment method is as follows: 1. Adaptive data compression: Share energy status information between nodes and adaptively adjust the data compression ratio.

[0096] ; 2. Frequency hopping: Avoid fixed frequency communication and use frequency hopping technology to reduce interference.

[0097] ; in, Comment jump step length, is the number of jumps.

[0098] 3. Redundant broadcast: Use redundant broadcast to increase reliability when transmitting important data.

[0099] .

[0100] Example 3: Smart home. Its nodes are dense, communications are frequent, and fast response and low latency are required.

[0101] 1. Fast confirmation mechanism: Use low-latency protocols to ensure that critical data is transmitted first.

[0102] ; 2. Priority queue: Implement data priority queue inside the node, and transmit key data first.

[0103] ; 3. Low power mode: When frequent communication is not required, the node enters low power sleep mode to save energy.

[0104] .

[0105] Example 4: Health monitoring. Its data transmission involves personal health, requires high reliability, requires 24 / 7 monitoring, and has high node energy requirements. The adjustment method is as follows: 1. Energy prediction and optimization: Use machine learning algorithms to predict energy consumption and adjust communication parameters in advance.

[0106] ; 2. Data fusion: Integrate data from multiple sensors to reduce the amount of data transmitted by a single sensor.

[0107] ; 3. Secure transmission: Encryption technology and identity authentication mechanism are used to ensure the security of data transmission.

[0108] .

[0109] Figure 1 FIG. 1 is a flow chart of an energy adaptive communication method based on a sensor collection system in one embodiment. It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but the steps are not necessarily executed in the order indicated by the arrows; unless otherwise specified in this document, there is no strict order restriction for the execution of the steps, and the steps may be executed in other orders; and Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0110] Based on the same technical concept, Figure 4 The embodiment of the present application also provides an energy adaptive communication device based on a sensor collection system, which adopts the following technical solution, and the device includes: The node arrangement module 310 is used to arrange sensors at preset node positions and perform network initialization; An energy detection module 320 is used to detect the energy state of the sensor at the node location in real time, and the energy state includes at least: remaining power and voltage level; A communication adjustment module 330, for dynamically adjusting communication parameters according to energy status and selecting an optimal communication protocol; The energy sharing module 340 is used to share energy status information between nodes through low-power broadcasting, and to perform node data relay and energy transfer based on the shared energy status.

[0111] In some embodiments, the node placement module 310 is specifically used to deploy sensors at node locations and perform initial configuration so that each node is equipped with an energy state detection module, a communication parameter adjustment module, and a related software-defined communication protocol; Start the sensor nodes and initialize the network to detect the energy status of the sensor nodes in real time.

[0112] In some embodiments, the communication adjustment module 330 is specifically used to dynamically adjust communication parameters according to the energy state, and the communication parameters include at least: communication frequency, transmission power and data transmission rate; According to the current energy state of the node, the best communication protocol is selected according to the preset communication strategy; By using software-defined communication, flexible switching and optimization of communication protocols can be achieved. Each node regularly evaluates the effectiveness of the current communication protocol to adjust the communication strategy.

[0113] In some embodiments, the communication adjustment module 330 is further used to predict the energy consumption of the node based on a machine learning algorithm and obtain a prediction result; Adjust communication parameters and protocols in advance based on prediction results.

[0114] In some embodiments, the communication adjustment module 330 is further used to adaptively adjust the compression ratio and the number of packets served in each time slot based on the instantaneous queue state and channel state; In wireless communications over fast fading channels, the average queue delay is minimized with average power and compression distortion as constraints.

[0115] In some embodiments, the communication adjustment module 330 is further used to quantize and frame the data collected by the sensor to obtain a data packet; Based on the preset Markov decision process architecture and the instantaneous queue state and channel state, the compression ratio of the data packet and the number of data packets transmitted are adjusted.

[0116] The embodiment of the present application also discloses a control device.

[0117] Specifically, the control device includes a memory and a processor, and the memory stores a computer program that can be loaded by the processor and execute the above-mentioned energy adaptive communication method based on the sensor collection system.

[0118] The embodiment of the present application also discloses a computer-readable storage medium.

[0119] Specifically, the computer-readable storage medium stores a computer program that can be loaded by a processor and executes the energy adaptive communication method based on the sensor acquisition system as described above. The computer-readable storage medium includes, for example: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0120] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. An energy adaptive communication method based on a sensor collection system, characterized in that: include: Deploy sensors at preset node locations and initialize the network; Real-time detection of the energy state of the sensor at the node location, the energy state including at least: remaining power and voltage level; Dynamically adjusting communication parameters according to the energy state and selecting an optimal communication protocol; Sharing the energy status information between nodes by low-power broadcasting; Node data relay and energy transfer are performed based on the shared energy state.

2. The energy adaptive communication method based on the sensor collection system according to claim 1, characterized in that: The step of placing sensors at preset node locations and initializing the network includes: Deploy sensors at the node locations and perform initial configuration so that each node is equipped with an energy state detection module, a communication parameter adjustment module, and a software-defined communication protocol; The sensor nodes are started and the network is initialized, and the energy states of the sensor nodes are detected in real time.

3. The energy adaptive communication method based on the sensor collection system according to claim 1, characterized in that: The dynamically adjusting communication parameters according to the energy state and selecting the best communication protocol comprises: Dynamically adjust the communication parameters according to the energy state, the communication parameters at least including: communication frequency, transmission power and data transmission rate; According to the current energy state of the node, select the best communication protocol according to the preset communication strategy; The communication protocol is switched and optimized by using software-defined communication. Each node periodically evaluates the effect of the current communication protocol to adjust the communication strategy.

4. The energy adaptive communication method based on the sensor collection system according to claim 3 is characterized in that: After dynamically adjusting the communication parameters according to the energy state and selecting the best communication protocol, the method further includes: Predict the energy consumption of nodes based on machine learning algorithms and obtain prediction results; The communication parameters and communication protocols are adjusted in advance according to the prediction results.

5. The energy adaptive communication method based on the sensor collection system according to claim 4 is characterized in that: The method further comprises: In each time slot, the compression ratio and the number of packets served are adaptively adjusted based on the instantaneous queue status and channel status; In wireless communications over fast fading channels, the average queue delay is minimized with average power and compression distortion as constraints.

6. The energy adaptive communication method based on the sensor collection system according to claim 5, characterized in that: The method of adaptively adjusting the compression ratio and the number of data packets served in each time slot based on the instantaneous queue state and channel state includes: Quantifying and framing the data collected by the sensor to obtain data packets; Based on a preset Markov decision process architecture and instantaneous queue status and channel status, the compression ratio of the data packet and the number of data packets transmitted are adjusted.

7. An energy adaptive communication device based on a sensor collection system, characterized in that: The device comprises: The node placement module is used to place sensors at preset node locations and perform network initialization; An energy detection module, used for detecting the energy state of the sensor at the node location in real time, wherein the energy state includes at least: remaining power and voltage level; A communication adjustment module, used to dynamically adjust communication parameters according to the energy state and select an optimal communication protocol; The energy sharing module is used to share the energy status information between nodes through low-power broadcasting, and to perform node data relay and energy transfer based on the shared energy status.

8. A control device, characterized in that: The device comprises: The method comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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