A method for automatically training a machine learning system to detect and identify a sensor triggering event associated with an internet of things (IoT) device is provided. The method may include capturing sensor data and capturing sound clips associated with the IoT device. The method may further include identifying the sensor triggering event associated with the IoT device. The method may further include sending an alert of the identified sensor triggering event. The method may also include correlating the captured sensor data, the captured sound clips, and the identified sensor triggering event. The method may further include identifying a second sensor triggering event by determining similarities between the correlated data associated with the identified sensor triggering event and additional sensor and sound data that is captured based on the second sensor triggering event.

A method for automatically training a machine learning system to detect and identify a sensor triggering event associated with an internet of things (IoT) device is provided. The method may include capturing sensor data and capturing sound clips associated with the IoT device. The method may further include identifying the sensor triggering event associated with the IoT device. The method may further include sending an alert of the identified sensor triggering event. The method may also include correlating the captured sensor data, the captured sound clips, and the identified sensor triggering event. The method may further include identifying a second sensor triggering event by determining similarities between the correlated data associated with the identified sensor triggering event and additional sensor and sound data that is captured based on the second sensor triggering event.

A system and method for automatically managing a living space relates to Internet of Things (IoT) implementations within a commercial or residential location that allow for fully private management of collected data. Such a system enables users to automate many different functions within their homes and properties, including temperature management, room illumination, humidity, and more, in order to optimize for a chosen metric, such as comfort, energy efficiency, or cost efficiency. Because the user takes ownership over sensors, servers, and output devices, collected data may be parsed, organized, stored, applied, and otherwise utilized without risk of exposing collected data to external parties. This control enables users to develop various interfaces, algorithms, command signals, visuals, and more at will, thereby ensuring the user may customize a living space according to whatever desirable outcomes the user chooses. A user may add further subsystems and features at will, enabling complex and varied implementations.

A system and method for automatically managing a living space relates to Internet of Things (IoT) implementations within a commercial or residential location that allow for fully private management of collected data. Such a system enables users to automate many different functions within their homes and properties, including temperature management, room illumination, humidity, and more, in order to optimize for a chosen metric, such as comfort, energy efficiency, or cost efficiency. Because the user takes ownership over sensors, servers, and output devices, collected data may be parsed, organized, stored, applied, and otherwise utilized without risk of exposing collected data to external parties. This control enables users to develop various interfaces, algorithms, command signals, visuals, and more at will, thereby ensuring the user may customize a living space according to whatever desirable outcomes the user chooses. A user may add further subsystems and features at will, enabling complex and varied implementations.

A computer system is provided which includes a sensor system and a processor. The sampling rate of the sensor system is not predetermined, and instead depends on the acquisition rate of the algorithm acquiring the data stream provided by the sensor system. A corresponding method is also provided.

A computer system is provided which includes a sensor system and a processor. The sampling rate of the sensor system is not predetermined, and instead depends on the acquisition rate of the algorithm acquiring the data stream provided by the sensor system. A corresponding method is also provided.

The embodiments of the present disclosure provide a method for predicting gas transmission loss of smart gas, implemented by a smart gas equipment management platform of an Internet of Things (IoT) system for predicting gas transmission loss of smart gas, comprising: obtaining gas flow data, gas pressure data, and ambient temperature data of a plurality of time points respectively based on gas metering devices, pressure detection devices, and temperature monitoring devices at a plurality of positions of a gas pipeline network; predicting a gas metering error based on the ambient temperature data; determining whether gas loss is abnormal loss based on the gas flow data, the gas pressure data, and the gas metering error; and in response to a determination that the gas loss is the abnormal loss, sending a warning notice.

The embodiments of the present disclosure provide a method for predicting gas transmission loss of smart gas, implemented by a smart gas equipment management platform of an Internet of Things (IoT) system for predicting gas transmission loss of smart gas, comprising: obtaining gas flow data, gas pressure data, and ambient temperature data of a plurality of time points respectively based on gas metering devices, pressure detection devices, and temperature monitoring devices at a plurality of positions of a gas pipeline network; predicting a gas metering error based on the ambient temperature data; determining whether gas loss is abnormal loss based on the gas flow data, the gas pressure data, and the gas metering error; and in response to a determination that the gas loss is the abnormal loss, sending a warning notice.

A system and method are described for ensuring that a product has been transported in acceptable environmental conditions. For example, one embodiment of the invention comprises: one or more sensors to measure environmental conditions during transport of the IoT device from an origin location to a destination location; a sensor interface coupled to the one or more sensors to communicate environmental values associated with the environmental conditions; a storage device to store monitor application program code; a processor coupled to the sensor interface and storage device, the processor to process the monitor application program code to perform operations based on the environmental values including storing the environmental values on the storage device; and a wireless communication interface to connect to an Internet of Things (IoT) service at one or more intermediate locations between the origin location and the destination location and/or at the destination location, the wireless communication interface to transmit the environmental values and/or event data associated with the environmental values to the IoT service; wherein the IoT service is to provide the environmental values and/or event data associated with the environmental values to one or more apps running on one or more client devices, the one or more apps associated with one or more accounts on the IoT service.

A method for pollutant sensor placement is described. Data about environmental characteristics across a geographic region is received from a plurality of environmental sensors. The geographic region includes one or more pollutant sources that emit a pollutant. The received data is transformed from one or more of the plurality of environmental sensors into common data having a common grid across the geographic region. The geographic region is divided into a plurality of sub-regions based on the common data. Locations within the geographic region are determined for placement of pollutant sensors based on estimated dispersion of the pollutant through the plurality of sub-regions.