Abstract: In one aspect, a computerized method for electing an asset tracker to communicate the location of each asset tracker in a cluster of asset trackers comprising: detecting an n-number of asset trackers within a low power radio range and form a cluster of these asset trackers; polling the n-number of asset trackers for one or more attributes of each asset tracker; based on an optimization of the one or more attributes of each asset tracker, electing an asset tracker to collect all the asset tracking and location data from the other asset trackers in the n-number of asset trackers; and communicating the collecting of all the asset tracking and location data to a remote server system.

Abstract: In one aspect, a method for machine-learning (ML) based adaptive Internet of Things (IoT) asset tracking comprising: providing a plurality of asset trackers following a route, wherein each asset tracker tracks one or more IoT assets; configuring each asset tracker to autonomously change and update a tracking technology and a tracking rate or an upload time utilized by the asset tracker; and training and validating a plurality of autonomous asset machine-learned (ML) models based on learned behavior of similar trackers that followed a same route or a similar route to the route followed by the plurality of asset trackers.

Abstract: In one aspect, a system for auto-tracking with machine-learning trained local-area identifier tracking for radio state control of asset trackers 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: training an asset tracker location model using at least one machine learning (ML) method to identify a region by building a multi-modality fingerprint of a region, and with the asset tracker location model, automatically identifying a relationship of the region with respect to a location of each asset tracker of the plurality of asset trackers; with each asset tracker, use the relationship of the region with respect to the location of each asset tracker to determine a behavior of each asset tracker without a need turn on a high-power radio in each asset track

Abstract: An intelligent tracking system generally includes one or more tracking devices, some of which may be passive tracking devices. Each passive tracking device includes one or more transceivers and is energized by an energizing signal. Some of these passive tracking devices may operate in a first communication mode or a second communication mode based on the energizing signal. Some tracking devices may include encryption modules or authentication modules. Some of these devices may incorporate a bulk acoustic wave oscillator.

Abstract: 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.

Abstract: In one aspect, a computerized method for augmented reality tracking of asset trackers comprising: integrating an asset tracker with a specific asset; configuring the asset tracker to communicate a location of the asset tracker to an Augmented Reality (AR) tracking system; and overlaying an AR visualization system overlay digital location data with a real-time camera view of a search area, wherein the search area comprises the specific asset; wherein the AR tracking system comprises an RTL (Real-Time Location) system, with the AR tracking system software: detecting an RTL link of the RTL system is within a range of the specific asset, and automatically launching the AR viewer with an AR view of the RTL link over laying a view of the specific asset.

Abstract: In one aspect, a system of an adaptive asset tracking insurance rates for IoT asset tracking comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; 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: obtain data from the plurality of asset trackers, and dynamically issue an insurance coverage cost of each IoT asset tracked by the plurality of asset t

Abstract: In one aspect, a system for auto-tracking with just-in-time training and goal seeking AI agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; 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: wherein one or more asset trackers transmit raw data directly to the server computing device, instead of device ML model-derived inference or device

Abstract: In one aspect, a system for auto-tracking with just-in-time training and goal seeking AI agents comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; wherein in the one or more communications hubs are configured to be in communication with one or more of the mobile units, and the one or more network; wherein the base station is configured to be in communication with the plurality of asset trackers; 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: wherein one or more asset trackers transmit raw data directly to the server computing device, instead of device ML model-derived inference or device

Abstract: In one aspect, a system of a live-stock carbon footprint assessment in an internet of things network tracking comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; 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: periodically received sensor data from an attached live-stock carbon footprint tracker that is attached to a live-stock entity; and use the sensor data to calculate a livestock carbon footprint.

Abstract: In one aspect, a computer system for implementing an asset tracker-based virtual tether 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: attach an asset tracker of the plurality of asset tracker to an animal to be tracked; and use precision location technology in a training regime of animals and the recording of animal interactions; wherein an asset tracker tether device in the asset tracker is equipped with a precision location and a feedback mechanism that reinforces for the animal a need to remain with a pre-specified boundary.

Abstract: In one aspect, a system for application-free tracker interfaces and auto-tracking for asset trackers comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; 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: with a underlying tracking system operative in the server computing device, wherein the underlying tracking system manages a goal-seeking large language model (LLM) that is trained to translate a natural language into specific tracker device configurations in order to fulfill set goals, and wherein the LLM receives a user's spoken or written request and: constructs a data display based on a user's spoken or written request, answers a user inquiry in the user's spoken or written request with the underlying tracking system facilitates answering to enhance the user experie

Abstract: In one aspect, a system of a live-stock carbon footprint assessment in an internet of things network tracking comprising: a plurality of asset trackers, wherein each asset tracker tracks one or more IoT assets and obtains a set of IoT data; one or more communications hubs; a base station; one or more communication networks; wherein each asset tracker is configured to be in communication with a base station and one or more of the communications hubs; 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: periodically received sensor data from an attached live-stock carbon footprint tracker that is attached to a live-stock entity; and use the sensor data to calculate a livestock carbon footprint.

Abstract: A computerized method of an integrated microphone to monitor environmental conditions of battery-operated asset tracker comprising: integrating one or more microphones into each asset tracker of a cluster of asset trackers; using the one or more microphones to monitor an environmental condition of a battery-operated asset tracker; correlate a set of sound data received form the one or more microphones with another set of data from other sensors of each asset tracker of the cluster of asset trackers; using an IMU (Inertial Management Unit) to determine an orientation in space of each asset tracker; with the data of the one or more microphones and the orientation in space of each asset tracker, determining a direction of a sound in a proximity of the cluster of asset trackers; and providing a pre-learned machine learning sound identification model to identify a sound source of the sound.

Abstract: In one aspect, a method for detecting lithium polymer battery swell due to exposure to heat or battery aging comprising: integrating an integrated force sensor with a lithium polymer battery; monitoring a lithium polymer battery swell of the lithium polymer battery with the integrated force sensor; with the integrated force sensor, detecting the lithium polymer battery swell beyond a specific swelling threshold; and determining that the lithium polymer battery swell due to exposure to heat or battery aging.

Abstract: According to some embodiments of the present disclosure, an intelligent tracking system is disclosed. The intelligent tracking system includes one or more passive tracking devices, an exciter, and a tracker. Each passive tracking device includes one or more transceivers and is energized by an electromagnetic frequency. In response to being energized each passive tracking device transmits a short message. The exciter emits the electromagnetic frequency. The tracker receives short messages from the one or more passive tracking devices and confirms the presence of the one or more passive tracking devices in a vicinity of the tracker based on the received messages.

Abstract: Systems and methods are described for managing communications amongst mobile computing units. The invention includes location detection, three-dimensional proximity detection and alert systems for and amongst mobile units, through the utilization of low-power local communications protocols. The systems and methods minimize power consumption by the mobile units, and enable for robust, reconfigurable real-time location and asset tracking.

Abstract: An intelligent tracking system generally includes one or more tracking devices, some of which may be passive tracking devices. Each passive tracking device includes one or more transceivers and is energized by an energizing signal. Some of these passive tracking devices may operate in a first communication mode or a second communication mode based on the energizing signal. Some tracking devices may include encryption modules or authentication modules. Some of these devices may incorporate a bulk acoustic wave oscillator.

Abstract: According to some embodiments of the present disclosure, an intelligent tracking system is disclosed. The intelligent tracking system includes one or more passive tracking devices, an exciter, and a tracker. Each passive tracking device includes one or more transceivers and is energized by an electromagnetic frequency. In response to being energized each passive tracking device transmits a short message. The exciter emits the electromagnetic frequency. The tracker receives short messages from the one or more passive tracking devices and confirms the presence of the one or more passive tracking devices in a vicinity of the tracker based on the received messages.

Abstract: An intelligent tracking system generally includes one or more tracking devices, some of which may be passive tracking devices. Each passive tracking device includes one or more transceivers and is energized by an energizing signal. Some of these passive tracking devices may operate in a first communication mode or a second communication mode based on the energizing signal. Some tracking devices may include encryption modules or authentication modules. Some of these devices may incorporate a bulk acoustic wave oscillator.