A microcontroller configured to monitor the input voltage and load conditions, and continuously adjust the switching frequencies in order to optimize the efficiency and longevity of the power supply incorporated in a device. The microcontroller utilizes a combination of GaN switching elements with their efficient high frequency switching capabilities, together with the continuous monitoring of the load conditions, allowing the intelligent microcontroller to vary the switching frequency of the power conversion blocks as needed in order to maintain the highest efficiency of conversion. The microcontroller can be utilized to control a luminaire or other device into which the controller is preferably integrated. The microcontroller can utilize one or more environmental sensors configured for sensing internal environmental conditions and/or external environmental conditions. Preferably the microcontroller utilizes an energy storage device configured to power the microcontroller and associated sensors to allow the mesh network controls to continue functioning in the event of a power outage.

A wireless sensing system includes a first tape node and a second tape node. The first tape node has a low-power wireless-communications interface and an environmental sensor operable to capture and transmit a first set of environmental data of at least one environmental characteristic to the second tape node. The second node includes an environmental sensor, a low-power wireless-communication interface, a first processor, and a first memory communicatively coupled with the first processor, the first memory storing machine-readable instructions that, when executed by the first processor, cause the first processor to: capture a second set of environmental data; compute an environmental differential between the first set of environmental data and the second set of environmental data; compare the environmental differential to a predetermined environmental threshold; and transmit a notification to a client application of the wireless sensing system running on a client device of the wireless sensing system when the environmental differential exceeds the predetermined environmental threshold.

A Portable Network-Connected Signaling and Reporting Device that attaches to a portable power source. includes a potentiometer that controls the pulse width modulation/strobing of the at least one LED. The device includes but not limited to aluminum and plastic housing with a substantially cylindrical exterior profile for attachment to a power source. At least one electrical connection from the circuit and the power source is embedded in the housing and exposed for connection to an external charging source. The Portable Network-Connected Signaling and Reporting Device permits at least 1 electrical slot for external sensors. The external sensors can be attached to the body of the Portable Network-Connected Signaling and Reporting Device.

A Portable Network-Connected Signaling and Reporting Device that attaches to a portable power source. includes a potentiometer that controls the pulse width modulation/strobing of the at least one LED. The device includes but not limited to aluminum and plastic housing with a substantially cylindrical exterior profile for attachment to a power source. At least one electrical connection from the circuit and the power source is embedded in the housing and exposed for connection to an external charging source. The Portable Network-Connected Signaling and Reporting Device permits at least 1 electrical slot for external sensors. The external sensors can be attached to the body of the Portable Network-Connected Signaling and Reporting Device.

A plurality of risk relationship sensors, including at least one image capturing sensor (e.g., a camera), may each include an environment characteristic detection element, a power source, and a communication device to transmit data associated with risk relationship sensor data at a site. A risk relationship data store may contain electronic records associated with prior risk relationship events at other sites along with risk relationship sensor location data for those sites. An enterprise analytics platform may automatically analyze the electronic records in the risk relationship data store to create a predictive analytics algorithm. The data associated with potential risk relationship sensor data at the site may then be automatically analyzed, in substantially real-time, using the predictive analytics algorithm, and a result of the analysis may then be transmitted (e.g., to a party associated with the site).

A plurality of risk relationship sensors, including at least one image capturing sensor (e.g., a camera), may each include an environment characteristic detection element, a power source, and a communication device to transmit data associated with risk relationship sensor data at a site. A risk relationship data store may contain electronic records associated with prior risk relationship events at other sites along with risk relationship sensor location data for those sites. An enterprise analytics platform may automatically analyze the electronic records in the risk relationship data store to create a predictive analytics algorithm. The data associated with potential risk relationship sensor data at the site may then be automatically analyzed, in substantially real-time, using the predictive analytics algorithm, and a result of the analysis may then be transmitted (e.g., to a party associated with the site).

The invention relates cloud based IoT network monitoring and validation to enable optimal network selection and connectivity for IoT sensors. The present invention relates to a system to measure the signal quality directly from the network module of IoT sensors. It comprises of an application programming interface (API) 105, a Network detection dongle 103, communication network 110, server 115, network modules of network operators and IoT sensors 120, to be deployed or installed. The invention also relates to a method for determination of signal strength from network module of IoT sensors, wherein the API 105 is configured to run network detection software to determine and validate an optimal location for IoT sensor/device installation or deployment based on the highest signal strength.

A method and system to interpret sensor transmission patterns to analyze anomalies in a smart environment include obtaining a map of the smart environment, the map including an indication of obstructions and openings. The method includes determining an initial location of each sensor of a plurality of sensors in the smart environment. Each sensor emits a transmission after each detection. The method also includes identifying an initial transmission pattern associated with each sensor, and identifying a change in the initial transmission pattern of a sensor among the plurality of sensors. The change is interpreted to determine whether the change in the initial transmission pattern of the sensor among the plurality of sensors is due to movement or obstruction of the sensor. Action is taken based on a determination that the sensor among the plurality of sensors is obstructed or removed.

A method and system to interpret sensor transmission patterns to analyze anomalies in a smart environment include obtaining a map of the smart environment, the map including an indication of obstructions and openings. The method includes determining an initial location of each sensor of a plurality of sensors in the smart environment. Each sensor emits a transmission after each detection. The method also includes identifying an initial transmission pattern associated with each sensor, and identifying a change in the initial transmission pattern of a sensor among the plurality of sensors. The change is interpreted to determine whether the change in the initial transmission pattern of the sensor among the plurality of sensors is due to movement or obstruction of the sensor. Action is taken based on a determination that the sensor among the plurality of sensors is obstructed or removed.

A system described herein may provide a technique for the monitoring of one or more devices, such as Internet of Things (“IoT”) devices or other suitable types of devices, via one or more networks. The system may maintain readings associated with the IoT devices, and may identify a demand for a set of readings from a particular IoT device. The system may output a request to the particular IoT device. The request may indicate a maximum data size for a response to the request, as well as an indication of the latest time associated with readings received from the particular IoT device. The IoT device may respond with readings that were collected after the indicated latest time. The response may be restricted to the maximum data size. As such, the IoT device may refrain from providing a full set of readings that were collected after the indicated latest time.