9154results about "Machine-to-machine/machine-type communication service" patented technology

The invention discloses an urban greening system based on big data and an optimization method thereof, relates to the technical field of artificial intelligence, and solves the problems of difficulty in heterogeneous data integration, insufficient real-time analysis capability of dynamic data and low management efficiency in an existing urban greening system. Multi-source heterogeneous data are collected through an API interface and Internet of Things communication, and are cleaned, aligned, converted and fused through a distributed real-time data stream processing platform; image recognition and a machine learning algorithm are used for monitoring diseases and insect pests in real time, and a growth prediction model is trained in combination with an LSTM network, so that the quick response capability of the system to environmental changes is enhanced; according to the method, target weights are dynamically adjusted and optimized through the deep Q network, a multi-target optimization model is constructed in combination with mixed integer linear programming, context awareness and a genetic algorithm, intelligent optimization of irrigation, fertilization, pest control and greening layout is achieved, and the management efficiency and the resource utilization rate of an urban greening system are remarkably improved.

The Internet can be configured to provide communications to a large number of Internet-of-Things (IoT) devices. Devices can be designed to address the need for network layers, from central servers, through gateways, down to edge devices, to grow unhindered, to discover and make accessible connected resources, and to support the ability to hide and compartmentalize connected resources. Network protocols can be part of the fabric supporting human accessible services that operate regardless of location, time, or space. Innovations can include service delivery and associated infrastructure, such as hardware and software. Services may be provided in accordance with specified Quality of Service (QoS) terms. The use of IoT devices and networks can be included in a heterogeneous network of connectivity including wired and wireless technologies.

The Internet can be configured to provide communications to a large number of Internet-of-Things (IoT) devices. An example non-transitory, machine readable medium comprises instructions that, when executed, direct a processor to: cause a device to connect to a network of a decentralized database, discover a namespace of a node of the decentralized database, create a shared database partition in response to being accepted by the node, advertise a service to the decentralized database; and route data of a service between a private database partition and the shared database partition

In some implementations, an ultra-wideband (UWB) management server may send a first message to a UWB anchor, wherein: the first message is indicative of a first threshold to use in a UWB positioning session, the first threshold specifying at least one trigger condition for making available one or more reserved time slots in a Contention Access Period (CAP) of the UWB positioning session. The UWB management server may determine a second threshold for identifying a UWB responder having a respective reserved time slot in the CAP. The UWB management server may send a second message to the UWB anchor, wherein the second message causes the UWB anchor to either: (i) move the respective reserved time slot in the CAP for the UWB responder to a respective one or more time slots in a Contention Free Period (CFP), or (ii) terminate the UWB positioning session with the UWB responder.

Methods, systems, and computer program products for managing Internet of Things (IoT) network-connected devices.

The invention discloses an intelligent water quality monitoring device based on Internet of Things, relates to the technical field of environmental monitoring, and aims to analyze a complex nonlinear relationship in water quality data, accurately predict a water quality change trend and identify potential water quality problems in advance through a deep learning model and a system. The dynamic weight calculation module can dynamically adjust the importance of water quality parameters according to real-time data and environmental changes, the monitoring precision and adaptability are improved, an intelligent feedback mechanism automatically adjusts the sampling frequency and the sensor position when an early warning signal is triggered, accurate monitoring of the water quality changes is ensured, and the monitoring accuracy is improved. The Internet of Things technology supports real-time transmission of water quality data and analysis results to the cloud, managers can check water quality states, historical trends and early warning records through a remote monitoring interface, the response capability and management efficiency of the system are improved, and the intelligent water quality monitoring device has the advantages of being high in precision, high in intelligence and high in adaptability.

A system for real-time data aggregation and analysis within an Internet of Things (IoT) network, consisting of: a multitude of IoT sensor nodes configured to collect data from multiple sources, each sensor node comprising a data acquisition unit for capturing data in real time and a local processing unit for preprocessing the captured data by performing data filtering, normalization and compression prior to transmission; a centralized aggregation server communicating with the multitude of IoT sensor nodes, the aggregation server comprising: a data aggregation module configured to receive, integrate, and synchronize pre-processed data streams from the multitude of sensor nodes, thereby enabling consolidated data storage; a timestamped metadata indexing unit coupled with the data aggregation module and configured to add metadata to each data entry, including a unique identifier, a geographic location, and a timestamp; an analysis engine integrated into the aggregation server, which includes the following: a data processing unit configured to perform real-time analysis of the aggregated data by applying pattern recognition and anomaly detection techniques and to generate processed results based on predefined parameters; a predictive analytics module that is connected to the data processing unit and configured to perform predictive modeling tasks by extrapolating trends from historical and real-time data; a communication interface connected to the aggregation server, configured to enable bidirectional data exchange with external monitoring devices, user interfaces, or remote management systems via secure communication protocols; and a dynamic load balancing unit embedded in the aggregation server, configured to dynamically distribute computing resources between the data processing unit and the predictive analytics module depending on data volume and complexity.

The invention relates to the field of geological disaster prediction, and discloses a geological disaster real-time monitoring and early warning system based on Internet of Things and artificial intelligence, and the system comprises a sensing module which is used for collecting multi-source data of geological disasters in real time through all Internet of Things sensors, and visually displaying potential geological disaster hidden danger signs on the ground surface; the transmission module is used for transmitting various data and guaranteeing the data transmission security by adopting an encryption technology; the processing module is used for sorting and processing the multi-source data and converting the multi-source data into processing data in a standardized data format; and the model module is used for processing data and applying a model fusing a physical principle and a machine learning algorithm. An advanced early warning model is constructed by applying an artificial intelligence technology and fusing a physical principle and a machine learning algorithm, a deep learning optimization unit customizes a strategy for geological disaster images and time sequence data characteristics, and a knowledge graph unit introduces geological domain knowledge to assist modeling.

Disclosed are methods, systems, and computer-readable medium to perform operations including: generating a registration request comprising a capability of the UE to serve as a gateway for a personal Internet-of-things network (PIN); sending the registration request to a base station serving the UE; receiving, from the base station, a registration accept message comprising a PIN gateway authorization; and initiating establishment of the PIN with at least one other device, wherein the UE serves as the gateway of the PIN to the base station.

The invention provides a real-time map route construction method and system. The method comprises the following steps: installing various sensors on an unmanned aerial vehicle to obtain multi-modal data of an environment; converting the multi-modal features into a high-dimensional embedding space, and constructing a real-time three-dimensional environment model based on a multi-modal point cloud reconstruction algorithm; deploying edge computing nodes, and distributing computing tasks to the edge computing nodes; planning a cooperative path among the multiple unmanned aerial vehicles; constructing a multi-unmanned aerial vehicle cooperative global adaptive adjustment mechanism to dynamically adjust the flight path of the unmanned aerial vehicle; and performing priority ranking on the tasks of the flight path of the unmanned aerial vehicle, constructing a path adjustment optimization mechanism according to the real-time flight condition to minimize the task failure risk, and updating the task priority and the scheduling strategy according to the feedback after each task is executed. According to the invention, a plurality of key problems in the prior art are effectively solved, and a technical guarantee is provided for successful application of the unmanned aerial vehicle in more complex application scenes.

Embodiments of the present disclosure relate to methods, devices and computer readable media for communication. A first network device receives a first paging message from a second network device in a RAN, the first paging message comprising information on a MT-SDT for a terminal device, and determines, based on the information, whether the MT-SDT is performed for the terminal device. If the MT-SDT is performed for the terminal device, the first network device transmits, to the terminal device, a second paging message comprising an indication indicating that the MT-SDT is performed for the terminal device. In this way, the MT-SDT procedure can be triggered.

A method by a first node (101), for handling a receiver at a wireless device (130). The first node (101) and the wireless device (130) operate in a wireless communications network (100). The first node (101) determines (502) whether or not a condition is met. The condition is a measurement of a signal received by the wireless device (130) being above or below a first threshold. The first node (101) initiates (503), based on a result of the determining (502), a procedure to wake-up the wireless device (130). The first node (101) initiates the procedure by applying at least one of: a downlink signal to be used, and a receiver to receive the downlink signal to wake-up the wireless device (130) at the wireless device (130).

In one aspect, a computer system for implementing asset tracker safety enhancements in an internet of things network comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; a server computing device configured to be in communication with the one or more networks, wherein the server computing device is further configured to implement the following logic: enhance the plurality of asset trackers with a plurality of sensors and a plurality of algorithms to detect and transmit an alert when a hazardous condition is detected, wherein the plurality of asset trackers monitor one or more IoT assets sensor data; and initiate a risk prevention or risk detection warning protocol when a specified probability of a hazardous material is detected by at least one asset tracker of the plurality of asset trackers.

Examples described herein include apparatuses and methods for full duplex device-to-device cooperative communication. Example systems described herein may include self-interference noise calculators. The output of a self-interference noise calculator may be used to compensate for the interference experienced due to signals transmitted by another antenna of the same wireless device or system. In implementing such a self-interference noise calculator, a selected wireless relaying device or wireless destination device may operate in a full-duplex mode, such that relayed messages may be transmitted as well as information from other sources or destinations during a common time period (e.g., symbol, slot, subframe, etc.).

A user equipment (UE) is described. The UE includes receiving circuitry configured to receive signaling that includes a configuration for an Enhanced Machine Type Communication (eMTC) physical downlink shared channel (PDSCH) in a non-terrestrial network (NTN). The receiving circuitry is also configured to receive signaling that includes first information to indicate whether Hybrid Automatic Repeat Request (HARQ) feedback is disabled for the eMTC PDSCH. The receiving circuitry is further configured to receive signaling that comprises second information to indicate a timing offset for the eMTC PDSCH. The UE also includes transmitting circuitry configured to transmit the eMTC PDSCH based on the configuration and the second information. The UE further includes a processor configured to flush a data buffer of the eMTC PDSCH based on the first information.

Systems and methods for balancing remote oil and gas equipment are disclosed. An example system may include analog sensors coupled to a piece of equipment and an analog switch with a plurality of analog sensor channels, wherein a first analog sensor channel comprises a trigger channel coupled to a first of the analog sensors, and wherein a second one of the analog sensor channels comprises an input channel coupled to a second sensors. The analog switch may digitally derive a relative phase between the trigger channel and the input channel, utilize a PLL band-pass tracking filter to determine at least one of slow-speed RPMs or phase information for the piece of equipment, and a response circuit that provides a process change command to remotely balance at least one component of the piece of equipment based on the RPMs or the phase information.

An operation mode from a plurality of operation modes can be determined (310) based on at least one traffic type originated from a UE. The plurality of operation modes can include at least one group operation mode and an individual operation mode. The at least one group operation mode can support a plurality of UEs grouped into a group of UEs. The at least one group operation mode can support at least one network entity communicating associated messages with the group of UEs including the UE. The associated messages can be associated with the group of UEs. A network entity can be informed (320) of the determined operation mode.

The invention provides a marine ecological environment influence tracking and monitoring method based on an offshore wind power project, and relates to the technical field of marine ecological environment, and the method comprises the steps: carrying out the ecological risk index calculation based on an obtained multi-modal feature vector, and obtaining ecological risk data, and configuring a space-time sequence based on the preprocessed comprehensive data source and the ecological risk data, and predicting a pollution diffusion path and migration blocking based on the preprocessed comprehensive data source. By introducing a dynamic evaluation model of an ecological risk index and combining time sequence modeling and spatial distribution modeling, dynamic analysis and future trend prediction of the ecological risk are realized, and by generating a risk thermodynamic diagram in real time, identifying a high-risk area and simulating a pollutant diffusion path and a biological migration blocking condition, the risk of the ecological risk can be predicted. According to the invention, the monitoring strategy can be dynamically adjusted according to the real-time monitoring data, a scientific basis is provided for construction optimization and ecological restoration, and the accuracy and timeliness of monitoring and evaluation are improved.

A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.

A system can include a backend system and a sensor kit configured to monitor an industrial setting. The sensor kit can include an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network and transmits sensor kit packets to the backend system via a public network. The backend system can include a processing system and a storage system, where the processing system performs backend operations on the sensor data and the storage systems stores the sensor data.

Provided in an embodiment of the present disclosure is a method for determining a resource. The method is executed by a network device. The method comprises: determining a predetermined resource according to predetermined information, wherein the predetermined information indicates that a multicast and broadcast service (MBS) is to be provided or not to be provided to a first-type terminal, and the predetermined resource comprises at least one of the following: a common frequency resource (CFR), or an initial downlink bandwidth part (DL BWP) for use by the first-type terminal.

A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.

The invention discloses a gas safety monitoring system based on the Internet of Things, and relates to the technical field of monitoring and alarming, the gas safety monitoring system comprises a monitoring and alarming platform, the monitoring and alarming platform is in communication connection with a sensor module, a communication module, an abnormity analysis module, an alarming and response module and a user interaction module, and the modules are in electric signal connection; the sensor module is used for collecting key parameters in a gas environment, including gas concentration, temperature and pressure. By collecting key parameters such as gas concentration, environment temperature, humidity, pressure and equipment state, and utilizing data analysis to quickly identify potential safety hazards, detect abnormities and trigger early warning by the system, an operator can take measures before an accident occurs, so that disastrous consequences are effectively avoided, and the safety of the operator is improved. Moreover, the time delay from detection to response is greatly reduced, and the response speed to gas safety accidents is increased.

An integrated system for detecting dam breaches, as an early warning system and for evacuation assistance, consisting of: A geoinformatics analysis module comprising a modeling processor with a graphics processing unit, configured to: analyze spatial, hydrological, and topographic data of a dam and its downstream area; generate hydrological models using probable maximum rainfall (PMP) data to determine probable maximum flood discharge (PMF); generate hydraulic models to simulate flood propagation and determine flood depth, flood extent, flow velocity, and flood arrival time; create flood vulnerability models, vulnerability models, and flood hotspot models using a multi-criteria decision-making (MCDM) framework; and generate evacuation plans for high-risk zones based on the models. A sensor monitoring subsystem includes: a distributed fiber optic sensor (DFOS) configured to monitor the structural load and deformation of a dam, and a radar level gauge configured to measure the water level in the reservoir; a microcontroller unit configured to receive and process sensor data from the sensor monitoring subsystem, to detect dam breaches based on abnormal structural movements detected by the DFOS or sudden drops in water level below predefined thresholds detected by the radar level gauge; a communication module configured to transmit warning messages when dam breaches are detected; a warning system configured to: send SMS notifications to residents in downstream high-risk zones via a GSM communication module, generate real-time alerts via an IoT platform on user devices, and activate siren systems in villages to issue loud alarms and voice announcements; and an evacuation assistance platform configured to provide real-time evacuation assistance in emergencies due to dam breaks, displaying site maps with nearby emergency shelters, evacuation routes, hospitals, government buildings and aid centers, showing hydrological layers including flood extent, water depth, flow velocity and arrival time of the water, and enabling real-time location tracking in relation to safety zones and flood-prone areas.

Methods, systems, and devices for wireless communications are described. A UE may be configured with a machine learning model by a core network to perform analytics, training, or inferences. The UE may indicate capability information to a core network entity, including a list of machine learning models supported at the UE. A centralized core network entity may manage different machine learning models and may send information for a machine learning model to the UE, such as through another core network entity. The UE or the core network may initiate the configuration. For example, the UE may request to be configured with a machine learning model. The core network may send control signaling that indicates a configuration for the machine learning model to the UE. The UE may perform analytics based on the machine learning model.

Methods, systems, and devices for wireless communication are described. A mobility function network entity may transmit inventory request messages to reader wireless devices requesting that the readers report an inventory of ambientpowered wireless devices associated with each reader. The readers may request and receive information from the ambient-powered wireless devices, and the readers may transmit inventory response messages to the mobility function network entity identifying the ambient-powered wireless devices and their associated information. The mobility function network entity may transmit a message to a first reader for relay to a first ambient-powered wireless device based on a first inventory response message that indicates that the first ambient-powered wireless device is associated with the first reader.

The invention provides a data transmission method and a related device, and relates to the technical field of communication, and the method comprises the steps that an A-IoT device sends first information to a network device, the first information comprises state information and / or indication information of the A-IoT device, and the indication information is used for indicating whether the first information comprises the state information of the A-IoT device or not. According to the invention, the network equipment identifies whether the first information contains the state information of the A-IoT equipment by analyzing the first information, thereby preventing the network equipment from decoding the A-IoT data packet by mistake, and improving the accuracy of A-IoT data transmission. Furthermore, after the network equipment obtains the state information of the A-IoT equipment, the communication process can be decided according to the state information of the A-IoT equipment, so that the communication performance and efficiency between the network equipment and the A-IoT equipment are improved.

The present disclosure relates to the technical field of communications. Provided are a sidelink positioning reference signal (S-PRS) transmitting method and apparatus, and an S-PRS receiving method and apparatus. The S-PRS transmitting method of the present disclosure comprises: a first terminal performing resource-sensing by means of detecting sidelink control information (SCI) transmitted by a second terminal, or determining a first resource by means of random selection in a first resource pool; and the first terminal sending an S-PRS on the first resource, wherein the first resource pool is a dedicated resource pool for randomly selecting a transmission resource for the S-PRS.