CN120343682A - An intelligent sleep control system for IoT devices based on environmental self-perception - Google Patents
An intelligent sleep control system for IoT devices based on environmental self-perceptionInfo
- Publication number
- CN120343682A CN120343682A CN202510536568.3A CN202510536568A CN120343682A CN 120343682 A CN120343682 A CN 120343682A CN 202510536568 A CN202510536568 A CN 202510536568A CN 120343682 A CN120343682 A CN 120343682A
- Authority
- CN
- China
- Prior art keywords
- environment
- dormancy
- module
- signal
- internet
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
- H04W52/0287—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level changing the clock frequency of a controller in the equipment
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. Transmission Power Control [TPC] or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
- H04W52/0274—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level by switching on or off the equipment or parts thereof
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
本发明涉及物联网设备智能休眠控制技术领域，公开了一种基于环境自感知的物联网设备智能休眠控制系统，包括环境特征提取层模块、环境动态模型构建模块、自适应休眠决策引擎模块、轻量化状态缓存模块及电源协同控制模块。通过实时获取NB‑IoT协议栈的物理层信号特征和网络层连接状态信息，能够基于环境信号波动构建动态模型，精确评估设备所处环境的稳定性，动态调整休眠触发条件，避免固定阈值引起的误唤醒，不仅减少了对传统传感器的依赖，提升了设备的能效与适应性，同时提高了在干扰环境下的抗干扰能力，确保设备在复杂工业环境中长期稳定运行。
The present invention relates to the technical field of intelligent sleep control of IoT devices, and discloses an intelligent sleep control system for IoT devices based on environmental self-perception, including an environmental feature extraction layer module, an environmental dynamic model construction module, an adaptive sleep decision engine module, a lightweight state cache module, and a power supply collaborative control module. By acquiring the physical layer signal characteristics and network layer connection status information of the NB‑IoT protocol stack in real time, a dynamic model can be constructed based on environmental signal fluctuations, the stability of the environment in which the device is located can be accurately evaluated, the sleep trigger conditions can be dynamically adjusted, and false awakening caused by fixed thresholds can be avoided. This not only reduces the dependence on traditional sensors and improves the energy efficiency and adaptability of the device, but also improves the anti-interference ability in an interference environment, ensuring the long-term stable operation of the device in a complex industrial environment.
Description
Technical Field
The invention relates to an intelligent dormancy control system of internet of things equipment based on environment self-perception, and belongs to the technical field of intelligent dormancy control of internet of things equipment.
Background
With the continuous development of internet of things (IoT) technology, internet of things devices are widely applied to multiple scenes such as intelligent home, intelligent warehouse, industrial monitoring and the like. In order to ensure that devices operate stably even in the presence of changing environments, these devices need to have environmental awareness capabilities. Conventional environmental sensing approaches rely on a variety of sensors, such as temperature and humidity sensors, illumination sensors, motion sensors, and the like. These sensors are capable of capturing environmental changes in real time, providing the necessary data support. However, this approach presents significant energy consumption problems in practical applications.
The existing internet of things equipment generally adopts a continuous power supply mode to keep the running state of the sensor, which leads to the remarkable shortening of the service life of the battery of the equipment and increases the maintenance cost. For example, many sensors need to operate continuously to acquire environmental data, resulting in a device not being able to effectively enter a sleep mode with low power consumption. In order to reduce the energy consumption, measures are taken in the industry, such as introducing a timed sleep mechanism, partial shut down of the sensor, etc. However, these methods do not fully address the energy consumption problem and often introduce new challenges.
Traditional internet of things equipment needs to acquire data of various sensors in real time while keeping environment perception. However, continuous operation of the sensor may cause the device to consume a large amount of power in a normal operation state, and the sleep period is fragmented, making it difficult to ensure long-term use of the battery. This not only increases the energy consumption burden of the device, but also causes the device to not effectively enter a sleep state, thereby affecting the stability and lifetime of the device.
Most of the existing internet of things devices adopt a fixed sleep threshold condition when being designed, for example, sleep or wake-up is triggered based on a fixed environment variation, however, the fixed threshold cannot cope with the dynamic environment variation, and particularly in some complex industrial scenes, sudden environment interference can cause frequent false wake-up or response delay of the devices, so that data loss or device damage is caused. The operation stability of the equipment cannot be effectively guaranteed in some high-requirement industrial applications, and the state of a protocol stack is generally understood and coupled with a power supply pipe when the power supply management is processed by the existing Internet of things equipment. When the equipment enters a dormant state, the protocol stack is always disconnected and network connection needs to be reestablished, so that the communication delay of the system is increased, a large amount of electric energy is consumed, and particularly, when the equipment is frequently awakened, the energy consumption problem is more remarkable.
To avoid these problems, various solutions are adopted in the industry. For example, some devices introduce low power communication protocols or mechanisms to dynamically adjust sleep time in an attempt to balance energy efficiency and context aware requirements. Although the method can reduce the energy consumption to a certain extent, the method still cannot fundamentally solve the stability problem of the equipment in changeable environments, and cannot effectively reduce the energy waste caused by frequent awakening of the equipment, so that how to reduce the energy consumption while ensuring the environmental perception and improve the adaptability of the equipment in dynamic environments becomes a difficult problem in the current technical development.
Disclosure of Invention
The invention provides an intelligent dormancy control system of Internet of things equipment based on environment self-awareness, which mainly aims to solve the problems of high energy consumption, poor environment adaptability and splitting of a protocol stack and power management.
In order to achieve the above object, the present invention provides an intelligent dormancy control system for an internet of things device based on environment self-awareness, comprising:
the environment characteristic extraction layer module is used for acquiring physical layer signal characteristics and network layer connection state information in real time through an NB-IoT protocol stack of the Internet of things device, wherein the physical layer signal characteristics at least comprise signal strength RSSI and bit error rate BER, and the network layer connection state information at least comprises an attachment maintaining state and a channel resource reservation state;
The environment dynamic model construction module is in communication connection with the environment characteristic extraction layer module and is used for constructing a dynamic model reflecting the physical environment stability of the equipment based on the acquired physical layer signal characteristics and network layer connection state information, wherein the signal strength fluctuation is mapped to the environment interference degree, the error rate change is mapped to the channel quality, and the two are combined to infer the stability of the physical environment To quantify, the calculation formula is as follows:
,
wherein, the Indicating that the signal strength or the bit error rate is in the first time windowProbability of individual states;
The adaptive dormancy decision engine module is in communication connection with the environment dynamic model building module and is used for adaptively adjusting dormancy trigger conditions by adopting a dynamic threshold algorithm based on the environment dynamic model, the dynamic threshold algorithm dynamically adjusts the dormancy trigger conditions based on historical environment data so as to avoid excessive awakening caused by a fixed threshold, and the adaptive dormancy decision engine module sends a dormancy request carrying a pseudo-dormancy state code to a core network through the NB-IoT protocol stack before judging that dormancy is allowed so as to maintain an attachment relation and reserve channel resources;
The light-weight state caching module is in communication connection with the self-adaptive dormancy decision engine module and is used for storing key protocol stack parameters in a nonvolatile memory before the Internet of things equipment enters a dormancy state and directly recovering the key protocol stack parameters from the nonvolatile memory when the Internet of things equipment wakes up so as to skip a system information broadcast receiving flow;
The power cooperative control module is in communication connection with the self-adaptive dormancy decision engine module, and is used for controlling a power management unit of the Internet of things equipment to enter or exit a dormancy state according to a decision result of the self-adaptive dormancy decision engine module and dynamically adjusting dormancy time according to an evaluation result of an environment dynamic model.
Preferably, the environment dynamic model construction module further comprises a time sequence feature analysis unit, which is used for analyzing the time sequence fluctuation mode of the physical layer signal features and distinguishing instantaneous interference from continuous environment change by matching with a predefined signal mutation feature library.
Preferably, the environmental dynamic model building module further comprises a dynamic weight adjuster for automatically reducing the weight of the instantaneous signal characteristic in the current environmental stability assessment when the time sequence characteristic analysis unit detects the sudden interference characteristic conforming to the signal sudden change characteristic libraryAnd increasing the weight of the historical environmental dataTo make a sleep decision, wherein,And when bursty interference is detected, the signal processing device,Dynamically adjusted to a value of less than 0.5.
Preferably, the device further comprises an interference immune wake-up mechanism unit, which is used for preventing wake-up operation triggered by transient signal fluctuation and maintaining the current sleep state in a preset time window period after the sudden interference is identified.
Preferably, the historical environmental data includes signal stability data over twenty-four hours.
Preferably, the key protocol stack parameters include at least a Discontinuous Reception (DRX) cycle and channel configuration information.
Preferably, the signal mutation feature library comprises a signal fluctuation mode for representing motor starting, electromagnetic noise or metal shielding interference sources.
Preferably, the matching degree threshold of the preset interference template is more than or equal to eighty percent.
Preferably, the preset time window period is ten seconds.
Preferably, when the environmental feature extraction layer module cannot effectively acquire the physical layer signal feature, the adaptive sleep decision engine module switches to a sleep control strategy based on a preset timer.
Compared with the problems in the background art, the invention has the following beneficial effects:
1. By utilizing physical layer signal characteristics (such as signal strength and bit error rate) of the NB-IoT protocol stack and network layer connection state information, the system can realize the reconstruction of the environment sensing function without depending on a traditional sensor, and the mechanism not only effectively reduces the number of hardware, reduces the complexity of the system, but also improves the energy efficiency, particularly when the equipment is in a dormant state, the energy consumption of the hardware is obviously controlled, thereby prolonging the service life of the equipment and avoiding redundant hardware consumption and power consumption waste.
2. Based on signal intensity fluctuation and error rate change, the environment dynamic model constructed by the system can evaluate environment stability efficiently and accurately, and adopts a self-adaptive dynamic threshold algorithm to adjust dormancy triggering conditions, so that flexible response can be realized under different environment changes, frequent false awakening caused by fixed thresholds in the traditional method is avoided, and the occurrence of false awakening is further reduced by sending a quasi-dormancy state code before dormancy and maintaining an attachment relation with a core network. When the sudden interference such as electromagnetic interference occurs, the instantaneous fluctuation of the signal and the real environment change can be rapidly distinguished through a matching mechanism of the time sequence characteristic analysis unit and the sudden interference characteristic library, and the dynamic weight regulator of the system automatically reduces the weight of the instantaneous signal characteristic and preferentially uses the historical environment data, so that false wake-up is avoided under the condition that the interference is serious, the interference immunity of the system is effectively enhanced, unnecessary wake-up caused by electromagnetic noise in the traditional scheme is avoided, and the ineffective wake-up event of equipment is greatly reduced.
3. The lightweight state buffer module in the system saves necessary protocol stack parameters before the equipment enters the dormant state, so that core data can be directly restored when the equipment is awakened, a redundant flow which needs to re-receive system information broadcast in the traditional system is skipped, the energy consumption during awakening is greatly reduced, the response speed of the system is improved, and the equipment is ensured to be capable of quickly restoring the working state.
Drawings
FIG. 1 is a flow chart of the processing of the core module of the present invention.
FIG. 2 is a flow chart of the system operating state of the present invention.
FIG. 3 is a flow chart of the environment dynamic model building module of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an intelligent dormancy control system of an internet of things device based on environment self-perception, which comprises an environment characteristic extraction layer module, a network layer connection state information and an environment characteristic detection layer module, wherein the environment characteristic extraction layer module is used for acquiring physical layer signal characteristics and network layer connection state information in real time through an NB-IoT protocol stack of the internet of things device, the physical layer signal characteristics at least comprise signal strength RSSI and bit error rate BER, and the network layer connection state information at least comprises an attachment maintaining state and a channel resource maintaining state;
The environment dynamic model construction module is in communication connection with the environment characteristic extraction layer module and is used for constructing a dynamic model reflecting the physical environment stability of the equipment based on the acquired physical layer signal characteristics and network layer connection state information, wherein the signal strength fluctuation is mapped to the environment interference degree, the error rate change is mapped to the channel quality, and the two are combined to infer the stability of the physical environment To quantify, the calculation formula is as follows:
,
wherein, the Indicating that the signal strength or the bit error rate is in the first time windowProbability of individual states;
The adaptive dormancy decision engine module is in communication connection with the environment dynamic model building module and is used for adaptively adjusting dormancy trigger conditions by adopting a dynamic threshold algorithm based on the environment dynamic model, the dynamic threshold algorithm dynamically adjusts the dormancy trigger conditions based on historical environment data so as to avoid excessive awakening caused by a fixed threshold, and the adaptive dormancy decision engine module sends a dormancy request carrying a pseudo-dormancy state code to a core network through the NB-IoT protocol stack before judging that dormancy is allowed so as to maintain an attachment relation and reserve channel resources;
The light-weight state caching module is in communication connection with the self-adaptive dormancy decision engine module and is used for storing key protocol stack parameters in a nonvolatile memory before the Internet of things equipment enters a dormancy state and directly recovering the key protocol stack parameters from the nonvolatile memory when the Internet of things equipment wakes up so as to skip a system information broadcast receiving flow;
The power cooperative control module is in communication connection with the self-adaptive dormancy decision engine module, and is used for controlling a power management unit of the Internet of things equipment to enter or exit a dormancy state according to a decision result of the self-adaptive dormancy decision engine module and dynamically adjusting dormancy time according to an evaluation result of an environment dynamic model.
Preferably, the environment dynamic model construction module further comprises a time sequence feature analysis unit, which is used for analyzing the time sequence fluctuation mode of the physical layer signal features and distinguishing instantaneous interference from continuous environment change by matching with a predefined signal mutation feature library.
Preferably, the environmental dynamic model building module further comprises a dynamic weight adjuster for automatically reducing the weight of the instantaneous signal characteristic in the current environmental stability assessment when the time sequence characteristic analysis unit detects the sudden interference characteristic conforming to the signal sudden change characteristic libraryAnd increasing the weight of the historical environmental dataTo make a sleep decision, wherein,And when bursty interference is detected, the signal processing device,Dynamically adjusted to a value of less than 0.5.
Preferably, the device further comprises an interference immune wake-up mechanism unit, which is used for preventing wake-up operation triggered by transient signal fluctuation and maintaining the current sleep state in a preset time window period after the sudden interference is identified.
Preferably, the historical environmental data includes signal stability data over twenty-four hours.
Preferably, the key protocol stack parameters include at least a Discontinuous Reception (DRX) cycle and channel configuration information.
Preferably, the signal mutation feature library comprises a signal fluctuation mode for representing motor starting, electromagnetic noise or metal shielding interference sources.
Preferably, the matching degree threshold of the preset interference template is more than or equal to eighty percent.
Preferably, the preset time window period is ten seconds.
Preferably, when the environmental feature extraction layer module cannot effectively acquire the physical layer signal feature, the adaptive sleep decision engine module switches to a sleep control strategy based on a preset timer.
Fig. 1 is a processing flow chart of a core module of the present invention, which is mainly divided into two major parts of core processing flow and status and power management. In the core processing flow, firstly, the environmental characteristic extraction layer module acquires physical layer signal characteristics and network layer connection state information in real time and transmits the information to the environmental dynamic model construction module, the environmental dynamic model construction module carries out environmental stability assessment based on the received information to generate dynamic model parameters and sends the parameters to the self-adaptive dormancy decision engine module, and the self-adaptive dormancy decision engine module carries out dormancy decision according to the received dynamic model parameters and combines historical data and outputs dormancy control instructions to the state and power management part. In the state and power management part, the self-adaptive dormancy decision engine module also sends a protocol stack parameter restoration instruction to the lightweight state buffer module for quickly restoring protocol stack parameters when equipment wakes up, and meanwhile, the self-adaptive dormancy decision engine module sends a power control instruction to the power cooperative control module to control the power state of the equipment, and the power cooperative control module feeds back power state feedback to the self-adaptive dormancy decision engine module. FIG. 2 is a flow chart of the system operating state of the present invention, showing the overall operating state of the system. The method comprises the steps of starting an initial running state, periodically triggering equipment to enter an environment monitoring state (every 5 minutes), entering an anti-interference evaluation state if signal mutation is detected in the environment monitoring state, judging whether a preset interference template is matched by more than or equal to 80%, entering an interference immunity state if the matching degree is met, firstly entering the state, starting a time window (10 seconds), blocking a wake-up signal during the period, maintaining a dormant state, returning to the environment monitoring state if the window period is finished (10 seconds), entering a dormant decision state and confirming channel reservation to a core network if the stability is up to standard (entropy value < threshold value), entering a dormant preparation state, buffering protocol stack parameters, finally entering a deep dormant state, entering an awake recovery state through timer triggering or event triggering, directly recovering a DRX period in the deep dormant state, and returning to the running state if the signal mutation is not detected in the environment monitoring state and the stability is not up to standard (entropy value is more than or equal to the threshold value), and continuing to be kept in the environment monitoring state. FIG. 3 is a flow chart of the environment dynamic model building module of the present invention, showing the workflow of the module in detail. The method comprises the steps of firstly, collecting real-time signals, obtaining physical layer signal characteristics and network layer connection state information, then, carrying out signal fluctuation analysis, analyzing fluctuation conditions of signal intensity and bit error rate, then, calculating fluctuation entropy value H, quantifying uncertainty of the environment through the entropy value, evaluating stability of the environment, then, carrying out environment stability judgment, reducing current weight if the environment is stable, and improving historical weight if the environment is unstable, finally, generating dynamic model parameters by combining data after weight adjustment, and outputting the parameters to a decision engine for subsequent dormancy decision.
Embodiment 1 in this embodiment, the role of the environmental feature extraction layer module is to obtain, in real time, physical layer signal features and network layer connection state information through the NB-IoT protocol stack, where the signal features include, but are not limited to, signal strength RSSI and bit error rate BER, and the network layer information includes an attachment hold state and a channel resource reservation state. To ensure that the dynamic changes of the signal strength RSSI and bit error rate BER reflect the stability of the physical environment, we define the RSSI to be the signal strength between the device and the base station in dBm. The change can be used for predicting the interference condition of the environment where the equipment is located, and the BER is the error rate and reflects the channel quality, and the unit is the bit error rate. The quality of the channel and its stability can be evaluated by fluctuations in BER. These physical layer signal characteristics and network layer information will be used in subsequent steps to dynamically model and further determine the dormancy decision of the device.
The core task of the module is to construct a dynamic environment model based on the acquired signal strength and bit error rate data. Environmental stability is quantified by calculating the entropy H of the signal fluctuations. The calculation formula of the entropy value H is as follows:
,
wherein, the Indicating that the signal strength or the bit error rate is in the first time windowProbability of individual states. The formula is used to describe the uncertainty in the environment and thus evaluate the stability of the environment. For the change of the signal strength RSSI and the bit error rate BER, the model comprehensively considers the fluctuation condition of the signal strength RSSI and the bit error rate BER so as to distinguish the sudden interference and the long-term stable change of the environment; And H is an entropy value reflecting the unpredictability of signal fluctuation, and higher entropy values represent unstable environments.
In this embodiment, the calculation of the entropy value dynamically reflects the intensity of the environmental interference, and provides a basis for the subsequent sleep decision. Based on the environmental stability assessment provided by the environmental dynamic model construction module, the dormancy decision engine module adjusts dormancy trigger conditions in a self-adaptive manner through a dynamic threshold algorithm. The engine has the function of deferring dormancy triggering when the signal fluctuation is large or the environment is unstable, and avoiding frequent awakening of equipment due to the environment interference. Specifically, when the device detects a large environmental fluctuation, the historical data weight is increased by reducing the current signal characteristic weight, so that false wake-up caused by short-term burst interference is avoided. The process is realized by the following formula:
,
wherein, the AndRespectively representing the weights of the current signal characteristics and the historical environmental data. Depending on the interference situation of the environment,The value of (2) is automatically adjusted to less than 0.5 to prioritize the historical data.
The lightweight state cache module saves key parameters of the protocol stack (such as discontinuous reception DRX cycle and channel configuration information) in the non-volatile memory before the device enters sleep state. When the equipment wakes up, the equipment can quickly recover the key parameters, so that redundant broadcast receiving steps in the traditional system are skipped, the energy consumption is reduced, and the response speed of the equipment after the equipment wakes up is improved; the discontinuous receiving period is the periodic receiving capability of the device and is used for adjusting the time interval between dormancy and awakening, and the channel configuration information is the communication channel state information between the device and the base station and ensures that the device can quickly recover to the previous communication state when awakening. Meanwhile, the power cooperative control module is tightly matched with the self-adaptive dormancy decision engine, and the power management strategy of the equipment is dynamically adjusted according to the evaluation result of the environment dynamic model. Specifically, the module adjusts the sleep time period under different environmental conditions according to the environmental state of the device, thereby optimizing the use of the power supply. The system can flexibly control the equipment to enter and exit from the sleep mode, and ensure the maximization of the service life of the battery.
Embodiment 2. In this embodiment, the environmental feature extraction module obtains physical layer signal features (such as signal strength RSSI and bit error rate BER) and network layer connection status information in real time through the NB-IoT protocol stack. The signal strength RSSI is used to evaluate the signal quality between the device and the base station, and the bit error rate BER is used to measure the channel quality. When the equipment collects environment data through the physical layer signal and network layer connection state information, the data are transmitted to an environment dynamic model construction module, and according to the received environment characteristics, the environment dynamic model construction module evaluates the environment stability according to the following two characteristics, namely signal strength fluctuation (RSSI change condition) which is used for presuming the interference condition in the environment, bit error rate change (BER fluctuation) which reflects the quality of a channel and helps evaluate the stability of the communication channel. The uncertainty of the environment is quantified by calculating the entropy H of the signal fluctuations. The formula is as follows:
,
wherein, the Indicating that the signal strength or the bit error rate is in the first time windowProbability of individual states. The entropy value is used for measuring the stability of the environment, the higher the entropy value is, the more unstable the environment is, and in this way, the model can effectively distinguish whether the environment where the equipment is located is stable or has burst interference. And meanwhile, the adaptive dormancy decision engine module decides whether the equipment enters the dormancy state or not through a dynamic threshold algorithm based on a dynamic model evaluation result. In order to avoid the problem of false wake-up caused by the traditional fixed threshold method, the system dynamically adjusts the sleep trigger condition according to the fluctuation condition of the signal strength and the error rate. For example, when bursty interference is detected, a dynamic threshold algorithm automatically reduces the weight of the current signal fluctuation signatureAt the same time increase the weight of the historical environment dataThe formula is:
,
wherein, the AndRespectively representing the weights of the current signal characteristics and the historical data. When the environmental stability is poor (the entropy value is higher), the system can give priority to historical data to avoid frequent false wake-up caused by short-term interference, and before the equipment enters the dormant state, the lightweight state buffer module can store key parameters (such as discontinuous reception cycle (DRX) period and channel configuration information) in a protocol stack in a nonvolatile memory. In this way, when the device wakes up, the key parameters can be recovered directly from the non-volatile memory without re-receiving the system information broadcast, thereby saving energy and improving response speed.
The power cooperative control module cooperates with the self-adaptive sleep decision engine to dynamically adjust the power management strategy of the equipment according to the evaluation result of the environment model. By dynamically adjusting the sleep time and the power consumption during wake-up, the minimum energy consumption of the device is ensured to be maintained all the time under the environment change, and the service life of the battery is optimized.
The signal fluctuation entropy value is a key index for evaluating environmental stability. By calculating the fluctuation conditions of RSSI and BER, the system can identify the change trend of the environment and further determine whether to enter a sleep state. In practical applications, when the device is in a strong interference environment, the system delays dormancy to avoid false wake-up caused by environmental interference. The dynamic threshold adjustment mechanism is based on dynamic weight adjustment, and the self-adaptive dormancy decision engine can flexibly adjust dormancy trigger conditions according to the combination mode of historical data and real-time environment data. The mechanism can effectively reduce invalid awakening caused by sudden environment interference and ensure long-time stable operation of equipment.
In embodiment 3, in the present embodiment, when the environment dynamic model is constructed, the method for evaluating the environmental stability is further optimized by collecting the physical layer signal characteristics (such as signal strength RSSI and bit error rate BER) and the network layer connection state information. Specifically, fluctuations in signal strength RSSI and bit error rate BER are used to construct an environmental stability model, and the fluctuation entropy value H of the environment is calculated by the following formula:
,
wherein, the Representing the probability that the signal strength or bit error rate is in the ith state within a preset time window, n being the total number of signal states. The formula quantifies the uncertainty of the environment by calculating the entropy H of the signal fluctuations. The higher the entropy value, the greater the disturbance and instability representing the environment, thereby affecting the sleep decision of the device. Based on the environmental stability assessment, the adaptive sleep decision engine of the system can dynamically adjust the sleep trigger conditions to avoid excessive wake-up due to bursty interference. In specific implementation, by means of a dynamic threshold algorithm, the sleep threshold is adjusted by combining historical environment data with real-time signal characteristics. To further optimize the decision mechanism we define weights for the current signal characteristics and the historical environmental data:
,
wherein, the Weights representing the characteristics of the current signal,Representing the weight of the historical environmental data. When the device detects a large environmental fluctuation, the weight of the current signal featureThe dynamic adjustment is smaller than 0.5 so as to give priority to the historical data, and false awakening caused by short-term interference can be effectively avoided. The lightweight state cache module saves key parameters of the protocol stack, such as Discontinuous Reception (DRX) cycle and channel configuration information, in non-volatile memory before entering sleep state. When the device wakes up, the system directly retrieves these parameters from nonvolatile memory, thereby bypassing the redundant process of re-receiving the system information broadcast in a conventional system. The mechanism can effectively reduce the energy consumption during the wake-up and improve the wake-up response speed of the equipment. Meanwhile, the power supply cooperative control module is tightly cooperated with the self-adaptive dormancy decision engine, and dynamically adjusts the power supply management strategy of the equipment according to the evaluation result of the environment dynamic model. Specifically, the power management unit will adjust the timing of the device entering or exiting the sleep state based on signal strength and environmental stability to further optimize battery life.
To cope with bursty interference, we have introduced an interference immune wake-up mechanism after bursty interference is identified. During a preset time window period, when the device detects the sudden interference, the system prevents the wake-up operation triggered by the transient signal fluctuation, and ensures that the device maintains the current sleep state. This mechanism helps to reduce unnecessary false wake-up events, ensures that the device is able to operate stably for a long period of time in an interfering environment, and enables the system to better cope with environmental changes, especially in complex and dynamic industrial environments. In the implementation process, the equipment firstly collects signal characteristics and connection state information of a physical layer and a network layer in real time, and builds a dynamic environmental stability assessment model. Based on the model, the system can flexibly adjust sleep trigger conditions and save key parameters before the equipment enters a sleep state, so that the stability and energy efficiency of the equipment in long-time operation are improved, and the system belongs to an extension implementation mode known by a person of ordinary skill in the art.
In embodiment 4, the environmental feature extraction layer module first obtains physical layer signal features and network layer connection state information through an NB-IoT protocol stack of the internet of things device. Specifically, the physical layer signal characteristics include signal strength (RSSI) and Bit Error Rate (BER), and the network layer connection state information includes an attach hold state and a channel resource reservation state. These signal characteristics and status information provide reliable data support during real-time environmental monitoring.
For the acquisition of physical layer signals, the RSSI is used to evaluate the signal strength between the device and the base station, while the BER is used as an error rate indicator, reflecting the channel quality. These data are quantified by the following formula:
,
wherein, the Representing that the signal strength or the bit error rate is in the first time windowProbability of individual states, n is the total number of states. The fluctuating entropy value H of the signal is used to represent the uncertainty of the environment and the stability of the physical environment is inferred by calculating the entropy value.
In order to further improve the adaptability of the sleep control system, the embodiment optimizes the weight adjustment mechanism in the environment dynamic model building module. When the sudden interference occurs in the environment, the system automatically adjusts the weights of the current signal characteristics and the historical environment data according to the dynamic weight adjustment strategy of the real-time signal characteristics and the historical data. Specifically, in the dynamic model construction module, when the time sequence feature analysis unit detects signal fluctuation conforming to the predefined bursty interference feature library, the system automatically reduces the current signal feature [ ], and the system automatically reduces the current signal feature [, and the system automatically reduces the current signal feature [, and the current signal feature) And increasing the weight of historical data [ ]) The formula is as follows:
,
Under the circumstances of this mechanism, the control unit, The weight value of (2) is dynamically adjusted to be less than 0.5 in the case of bursty interference. The mechanism effectively avoids false wake-up due to short-term environmental interference. In addition, the system introduces an interference immune wake-up mechanism, and after the sudden interference is detected, the equipment can prevent wake-up operation caused by instantaneous signal fluctuation within a preset time window, so that the equipment is ensured to be kept in a dormant state, and unnecessary wake-up power consumption is avoided.
In this embodiment, the lightweight state caching module further optimizes the caching manner of the protocol stack parameters. When the internet of things device enters a dormant state, key protocol stack parameters (such as discontinuous reception cycle DRX and channel configuration information) are saved in the nonvolatile memory. When the system wakes up, the system directly restores the parameters, so that the process of re-receiving the system information broadcast in the traditional scheme is avoided, and a large amount of energy is saved. The power cooperative control module further dynamically adjusts the power management strategy of the internet of things equipment according to the decision result of the self-adaptive sleep decision engine, and belongs to an extension implementation mode known to one of ordinary skill in the art.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. An intelligent dormancy control system of an internet of things device based on environment self-awareness, which is characterized by comprising:
the environment characteristic extraction layer module is used for acquiring physical layer signal characteristics and network layer connection state information in real time through an NB-IoT protocol stack of the Internet of things device, wherein the physical layer signal characteristics at least comprise signal strength RSSI and bit error rate BER, and the network layer connection state information at least comprises an attachment maintaining state and a channel resource reservation state;
The environment dynamic model construction module is in communication connection with the environment characteristic extraction layer module and is used for constructing a dynamic model reflecting the stability of the physical environment where the equipment is located based on the acquired physical layer signal characteristics and network layer connection state information, wherein the signal intensity fluctuation is mapped to the environment interference degree, the error rate change is mapped to the channel quality, and the environment fluctuation and the channel quality are combined to infer the stability of the physical environment To quantify, the calculation formula is as follows:
,
wherein, the Indicating that the signal strength or the bit error rate is in the first time windowProbability of individual states;
The adaptive dormancy decision engine module is in communication connection with the environment dynamic model building module and is used for adaptively adjusting dormancy trigger conditions by adopting a dynamic threshold algorithm based on the environment dynamic model, the dynamic threshold algorithm dynamically adjusts the dormancy trigger conditions based on historical environment data so as to avoid excessive awakening caused by a fixed threshold, and the adaptive dormancy decision engine module sends a dormancy request carrying a dormancy-planned state code to a core network through the NB-IoT protocol stack before judging that dormancy is allowed;
The light-weight state caching module is in communication connection with the self-adaptive dormancy decision engine module and is used for storing key protocol stack parameters in a nonvolatile memory before the Internet of things equipment enters a dormancy state and directly recovering the key protocol stack parameters from the nonvolatile memory when the Internet of things equipment wakes up so as to skip a system information broadcast receiving flow;
The power cooperative control module is in communication connection with the self-adaptive dormancy decision engine module, and is used for controlling a power management unit of the Internet of things equipment to enter or exit a dormancy state according to a decision result of the self-adaptive dormancy decision engine module and dynamically adjusting dormancy time according to an evaluation result of an environment dynamic model.
2. The intelligent dormancy control system of the internet of things equipment based on the environment self-awareness according to claim 1, wherein the environment dynamic model construction module further comprises a time sequence feature analysis unit for analyzing a time sequence fluctuation mode of the physical layer signal features and distinguishing instantaneous interference from continuous environment change by matching with a predefined signal mutation feature library.
3. The intelligent dormancy control system of the Internet of things equipment based on environment self-awareness according to claim 2, wherein the environment dynamic model building module further comprises a dynamic weight adjuster for automatically reducing the weight of the instantaneous signal characteristic in the current environment stability assessment when the time sequence characteristic analysis unit detects the sudden interference characteristic conforming to the signal sudden change characteristic libraryAnd increasing the weight of the historical environmental dataTo make a sleep decision, wherein,And when bursty interference is detected, the signal processing device,Dynamically adjusted to a value of less than 0.5.
4. The intelligent sleep control system for an internet of things device based on environment self-awareness as set forth in claim 2 or 3, further comprising an interference immune wake-up mechanism unit configured to prevent wake-up operations triggered by transient signal fluctuations during a preset time window after a sudden interference is identified, and maintain a current sleep state.
5. The ambient self-aware based internet of things device intelligent sleep control system of claim 1, wherein the historical ambient data comprises signal stability data over twenty-four hours.
6. The intelligent sleep control system for an internet of things device based on environment self-awareness according to claim 1, wherein the key protocol stack parameters comprise at least a discontinuous reception DRX cycle and channel configuration information.
7. The intelligent sleep control system for an internet of things device based on environment self-awareness according to claim 2, wherein the signal mutation feature library comprises a signal fluctuation mode representing motor start-up, electromagnetic noise or metal shielding interference sources.
8. The intelligent dormancy control system of the internet of things device based on the environment self-awareness according to claim 3, wherein the matching degree threshold of the preset interference template is more than or equal to eighty percent.
9. The intelligent sleep control system for an internet of things device based on environment self-awareness of claim 4, wherein the preset time window period is ten seconds.
10. The intelligent sleep control system of an internet of things device based on environment self-awareness according to claim 1, wherein the adaptive sleep decision engine module switches to a sleep control strategy based on a preset timer when the environment feature extraction layer module cannot effectively acquire physical layer signal features.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510536568.3A CN120343682A (en) | 2025-04-27 | 2025-04-27 | An intelligent sleep control system for IoT devices based on environmental self-perception |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510536568.3A CN120343682A (en) | 2025-04-27 | 2025-04-27 | An intelligent sleep control system for IoT devices based on environmental self-perception |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN120343682A true CN120343682A (en) | 2025-07-18 |
Family
ID=96365828
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510536568.3A Pending CN120343682A (en) | 2025-04-27 | 2025-04-27 | An intelligent sleep control system for IoT devices based on environmental self-perception |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN120343682A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120730447A (en) * | 2025-08-18 | 2025-09-30 | 浙江省通信产业服务有限公司 | Energy-saving edge service call sleep and wake-up control method and system |
| CN120751468A (en) * | 2025-07-24 | 2025-10-03 | 山东圣阳电源股份有限公司 | Power supply energy saving system and method based on load prediction intelligent dormancy strategy |
| CN121552444A (en) * | 2025-12-29 | 2026-02-24 | 华戎技术有限公司 | Humanoid robot based on bionic joint and multi-mode perception |
-
2025
- 2025-04-27 CN CN202510536568.3A patent/CN120343682A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120751468A (en) * | 2025-07-24 | 2025-10-03 | 山东圣阳电源股份有限公司 | Power supply energy saving system and method based on load prediction intelligent dormancy strategy |
| CN120730447A (en) * | 2025-08-18 | 2025-09-30 | 浙江省通信产业服务有限公司 | Energy-saving edge service call sleep and wake-up control method and system |
| CN121552444A (en) * | 2025-12-29 | 2026-02-24 | 华戎技术有限公司 | Humanoid robot based on bionic joint and multi-mode perception |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN120343682A (en) | An intelligent sleep control system for IoT devices based on environmental self-perception | |
| CN101325604B (en) | Energy-saving method for distributed self-adaption industry wireless network | |
| US12219478B1 (en) | Low-power long range wireless network communication protocol with rate switching and scheduled messaging | |
| US8023443B2 (en) | Wireless sensor system | |
| US20210026437A1 (en) | Techniques for adjusting computing device sleep states | |
| CN110113777B (en) | Data acquisition method | |
| CN111970654A (en) | Sensor node dynamic energy-saving sampling method based on data characteristics | |
| CN110958676A (en) | Low-power consumption dormancy equipment awakening method and system | |
| CN119676667B (en) | Sensor dynamic sampling method based on environmental fluctuation perception and CARAFE optimization | |
| CN112601272B (en) | Method for realizing radio wakeup of intelligent sensing terminal of Internet of things | |
| CN105657804A (en) | Self-adaptive monitor matching anti-interference method of wireless sensor network | |
| Miao et al. | IoT adaptive threshold energy management algorithm based on energy harvesting | |
| CN119362470A (en) | Energy management optimization method, device and storage medium for IoT device | |
| CN108449765A (en) | The energy-saving monitoring method of automatic adjusument | |
| CN120676439A (en) | Remote wake-up method, system, storage medium and device for Bluetooth low-power-consumption device | |
| CN113920678B (en) | Remote smoke alarm system on M5311 chip based on middle moving object | |
| CN119136288B (en) | A dual-band low-power Wi-Fi keep-alive wake-up method and system for the Internet of Things | |
| CN120302392A (en) | A sleep method for Wi-Fi module based on ZIGBEE | |
| CN113889109A (en) | Method for adjusting voice wake-up mode, storage medium and electronic device | |
| WO2023246106A1 (en) | Methods and devices for information transmission | |
| CN107094286B (en) | Ultra-low power consumption implementation method for sparse flow wireless self-organizing network | |
| CN113691930A (en) | Mobile terminal, control method thereof, internet of things system and storage medium | |
| WO2010099664A1 (en) | Method and device for accessing network | |
| CN110365895A (en) | Photographic device adjusting method, system, readable storage medium storing program for executing and equipment | |
| CN116627046A (en) | Household power supply control method and system based on intelligent gateway |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |