The present disclosure pertains to utilizing hardware and software to control and record environmental and other data obtained from sensors and other devices, placed throughout a facility, and analyzing and displaying the information in a detailed status report of the environmental conditions inside facility, and once analyzed, the software can provide recommendations to implement measures that increase the efficiency of the facility.

Systems, methods, devices, computer readable media, and other various embodiments are described for location management processes in wearable electronic devices. One embodiment involves pairing a client device with a wearable device, capturing a first client location fix at a first time using the first application and location circuitry of the client device. The client device then receives content from the wearable device, where the content is associated with a content capture time and location state data. The client device then updates a location based on the available data to reconcile the different sets of location data. In some embodiments, additional sensor data, such as data from an accelerometer, is used is used to determine which location data is more accurate for certain content.

The present disclosure relates to a method and device for recording a parking location of a vehicle more accurately at a more accurate time point by performing machine learning through situation recognition by the device. A method for recording parking according to an embodiment of the present disclosure includes, when a communication is established between the terminal and the vehicle, recognizing that a user gets in the vehicle, when a strength of a communication connection signal between the terminal and the vehicle is lowered to be equal to or lower than a predetermined level, recognizing that the user exits the vehicle, when it is determined by the motion sensor of the terminal that the user has moved a predetermined number of walking steps or more, recognizing that parking of the vehicle is completed.

A method performed in a wireless device for positioning the wireless device in a wireless communications network is provided. The wireless device activates a positioning unit comprised in the wireless device to perform a first position measurement. Then, the wireless device de-activates the positioning unit after a first position has been measured. The wireless device may also determine that the wireless device has moved away from the measured first position based on received radio signals from one or more network nodes in the wireless communications network. Further, when determined that the wireless device has moved away from the measured first position, the wireless device re-activate the positioning unit to perform at least one second position measurement.

According to an embodiment, an electronic device may include: a cellular communication module; a Wi-Fi communication module; a sensor module; a first processor; a second processor; and a memory. The memory may store first instructions that, when executed, cause the first processor to recognize a change in a user state to a measurement state to perform positioning, based on data received from the sensor module or the cellular communication module, determine a time at which the positioning is to be performed, based on the change, and transmit a timing notification to the second processor corresponding to the time at which the positioning is performed. The memory may store second instructions that, when executed, cause the second processor to measure a first position of the electronic device using the Wi-Fi communication module, in response to the timing notification. In addition, various other embodiments may be provided.

In one aspect of the present disclosure, an apparatus for locating a mobile railway asset is provided that includes a power source, GNSS circuitry configured to utilize electrical power from the power source to receive GNSS data, and a controller operatively coupled to the power source and the GNSS circuitry. The controller has a power saving mode wherein the controller inhibits the GNSS circuitry from receiving GNSS data and a standard accuracy mode wherein the controller permits the GNSS circuitry to receive GNSS data for a first time period. The controller has a higher accuracy mode wherein the controller permits the GNSS circuitry to receive GNSS data for a second time period longer than the first time period, and subsequently across multiple instances, in order to collect more GNSS data that can be qualified, filtered, sorted, and averaged to produce a more accurate result.

Biometric monitoring devices, including various technologies that may be implemented in such devices, are discussed herein. Additionally, techniques, systems, and apparatuses are discussed herein for providing power-conserving techniques and systems for efficiently utilizing a GPS receiver are described. The positional fix frequency of the GPS receiver may, according to some implementations, be modified or adjusted between various levels according to data from one or more non-GPS sensors. Such non-GPS sensors may include, for example, ambient light intensity or spectrum sensors, accelerometers, gyroscopes, magnetometers, heart rate sensors, galvanic skin response sensors, infrared sensors, etc.

A tracking device is described. The tracking device receives, from a management server, configuration parameters including a first data transmission rate and a second data transmission rate. The tracking device automatically enters into an active mode of the tracking device. When operating in an active mode, the tracking device, is operative to transmit, in response to determining based on first motion sensor measurements that the asset is stationary, first location data at the first data transmission rate. In response to determining, based on second motion sensor measurements and motion definition parameters, that the asset is mobile, the tracking device is operative to transmit, second location data of the asset at the second data transmission rate.

Disclosed are methods and systems for estimating a location of a wireless device. In some embodiments, received signal strength indication (RSSI) values of signals from a first wireless device are determined. A rate of motion of the first wireless device is then determined based on a rate at which the RSSI values vary with time. A machine learning model is conditionally updated based on the determined rate of motion, and path loss parameters are then derived from the machine learning model. The path loss parameters are used to estimate the location of the first wireless device.

A computer is programmed to receive, via a vehicle wireless receiver, a wireless signal from a mobile device, and determine a presence of the mobile device within a distance of the vehicle. The computer is programmed to classify a movement of the mobile device based at least on the determined device presence, and actuate a vehicle component based on the determined device presence and the classified movement of the mobile device.