Abstract: A method includes, generating a schedule for an asset tag, the schedule defining: a unicast trigger time for transmission of a unicast trigger to the asset tag; a transmit time succeeding receipt of the multicast trigger; and a wake window intersecting the transmit time and the receipt of the multiact trigger. The method also includes transmitting the schedule from a node network to the asset tag and configuring the asset tag based on the schedule. The method also includes, at the node network, broadcasting the unicast trigger and, at an asset tag: entering a wake mode; receiving the unicast trigger; transmitting a ranging signal; and entering the sleep mode. The method further includes, at the node network: deriving location of the asset tag based on instance of the ranging signal received by nodes in the node network.

Abstract: A method including accessing a network graph including: a set of transceiver nodes representing a set of transceivers operating in a mesh network of transceivers; a set of transmitter nodes representing a set of transmitters communicating with the mesh network of transceivers; and a set of edges, each connecting a pair of nodes in the set of nodes. The method also includes: identifying a subgraph of the network graph associated with a node in the set of nodes, the node representing a transceiver; accessing a network state of the subgraph comprising a set of edge values for each edge in the subgraph; calculating a probability of failure of the transceiver based on the network state of the subgraph; and in response to detecting the probability of failure of the transceiver exceeding a threshold likelihood, triggering a corrective action at the transceiver.

Abstract: A method includes: receiving a ranging signal from the transmitter including a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency in a set of frequencies; calculating a sample-based time-of-arrival estimate based on the series of time-domain samples of the ranging signal; calculating a sample-based uncertainty of the sample-based time-of-arrival; for each sub-signal pair in a subset of multiplexed sub-signals of the set of multiplexed sub-signals, extracting a phase difference of the sub-signal pair; calculating a phase-based time-of-arrival estimate based on the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating a phase-based uncertainty of the phase-based time-of-arrival estimate; and calculating a hybrid time-of-arrival estimate as a weighted combination of the sample-based time-of-arrival estimate, the phase-based time-of-arrival estimate, based on the sample-based uncertainty and the phase-based uncertainty.

Abstract: A method includes accessing a network graph including: a set of nodes, each representing a transceiver; and a set of edges, each edge connecting two nodes and representing a communication channel between a pair of transceivers. The method also includes: accessing a network state comprising a set of edge values for the set of edges; and identifying a set of triangle graphs in the network graph. The method further includes, for each triangle graph in the network graph: calculating a component diagnostic score based on a subset of edge values; and for each node in the triangle graph, updating a cumulative diagnostic score for the node based on the component diagnostic score. The method additionally includes, in response to detecting a cumulative diagnostic score for a node exceeding a threshold cumulative diagnostic score, triggering a corrective action at a transceiver represented by the node.

Abstract: A method includes: receiving a ranging signal from the transmitter comprising a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency in a set of frequencies; calculating a time-based time-of-arrival estimate based on the series of time-domain samples of the ranging signal; calculating a time-based uncertainty of the time-based time-of-arrival; for each sub-signal pair in a subset of multiplexed sub-signals of the set of multiplexed sub-signals, extracting a phase difference of the sub-signal pair; calculating a phase-based time-of-arrival estimate based on the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating a phase-based uncertainty of the phase-based time-of-arrival estimate; and calculating a hybrid time-of-arrival estimate as a weighted combination of the time-based time-of-arrival estimate, the phase-based time-of-arrival estimate, based on the time-based uncertainty and the phase-based uncertainty.

Abstract: A method including accessing a network graph including: a set of transceiver nodes representing a set of transceivers operating in a mesh network of transceivers; a set of transmitter nodes representing a set of transmitters communicating with the mesh network of transceivers; and a set of edges, each connecting a pair of nodes in the set of nodes. The method also includes: identifying a subgraph of the network graph associated with a node in the set of nodes, the node representing a transceiver; accessing a network state of the subgraph comprising a set of edge values for each edge in the subgraph; calculating a probability of failure of the transceiver based on the network state of the subgraph; and in response to detecting the probability of failure of the transceiver exceeding a threshold likelihood, triggering a corrective action at the transceiver.

Abstract: A method includes, generating a schedule for an asset tag group, the schedule defining: a multicast trigger time for transmission of a multicast trigger to the asset tag group; and for each asset tag in the asset tag group, a transmit time succeeding receipt of the multicast trigger and unique to the asset tag, and a wake window intersecting the transmit time and the receipt of the multiact trigger. The method also includes, at the node network: broadcasting the multicast trigger; and, at an asset tag in the asset tag group, entering a wake mode; receiving the multicast trigger; transmitting a ranging signal; and entering the sleep mode. The method further includes, at the node network: receiving ranging signals from the asset tag group; and deriving locations of the asset tags in the asset tag group based on the ranging signals received by the node network.

Abstract: A method includes, generating a schedule for an asset tag, the schedule defining: a unicast trigger time for transmission of a unicast trigger to the asset tag; a transmit time succeeding receipt of the multicast trigger; and a wake window intersecting the transmit time and the receipt of the multiact trigger. The method also includes transmitting the schedule from a node network to the asset tag and configuring the asset tag based on the schedule. The method also includes, at the node network, broadcasting the unicast trigger and, at an asset tag: entering a wake mode; receiving the unicast trigger; transmitting a ranging signal; and entering the sleep mode. The method further includes, at the node network: deriving location of the asset tag based on instance of the ranging signal received by nodes in the node network.

Abstract: A method includes: receiving a ranging signal from the transmitter comprising a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency in a set of frequencies; calculating a time-based time-of-arrival estimate based on the series of time-domain samples of the ranging signal; calculating a time-based uncertainty of the time-based time-of-arrival; for each sub-signal pair in a subset of multiplexed sub-signals of the set of multiplexed sub-signals, extracting a phase difference of the sub-signal pair; calculating a phase-based time-of-arrival estimate based on the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating a phase-based uncertainty of the phase-based time-of-arrival estimate; and calculating a hybrid time-of-arrival estimate as a weighted combination of the time-based time-of-arrival estimate, the phase-based time-of-arrival estimate, based on the time-based uncertainty and the phase-based uncertainty.

Abstract: A method includes accessing a network graph including: a set of nodes, each representing a transceiver; and a set of edges, each edge connecting two nodes and representing a communication channel between a pair of transceivers. The method also includes: accessing a network state comprising a set of edge values for the set of edges; and identifying a set of triangle graphs in the network graph. The method further includes, for each triangle graph in the network graph: calculating a component diagnostic score based on a subset of edge values; and for each node in the triangle graph, updating a cumulative diagnostic score for the node based on the component diagnostic score. The method additionally includes, in response to detecting a cumulative diagnostic score for a node exceeding a threshold cumulative diagnostic score, triggering a corrective action at a transceiver represented by the node.

Abstract: A method includes: receiving a ranging signal from the transmitter comprising a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency in a set of frequencies; calculating a time-based time-of-arrival estimate based on the series of time-domain samples of the ranging signal; calculating a time-based uncertainty of the time-based time-of-arrival; for each sub-signal pair in a subset of multiplexed sub-signals of the set of multiplexed sub-signals, extracting a phase difference of the sub-signal pair; calculating a phase-based time-of-arrival estimate based on the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating a phase-based uncertainty of the phase-based time-of-arrival estimate; and calculating a hybrid time-of-arrival estimate as a weighted combination of the time-based time-of-arrival estimate, the phase-based time-of-arrival estimate, based on the time-based uncertainty and the phase-based uncertainty.

Abstract: A method includes: receiving a ranging signal from the transmitter comprising a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency in a set of frequencies; calculating a time-based time-of-arrival estimate based on the series of time-domain samples of the ranging signal; calculating a time-based uncertainty of the time-based time-of-arrival; for each sub-signal pair in a subset of multiplexed sub-signals of the set of multiplexed sub-signals, extracting a phase difference of the sub-signal pair; calculating a phase-based time-of-arrival estimate based on the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating a phase-based uncertainty of the phase-based time-of-arrival estimate; and calculating a hybrid time-of-arrival estimate as a weighted combination of the time-based time-of-arrival estimate, the phase-based time-of-arrival estimate, based on the time-based uncertainty and the phase-based uncertainty.

Abstract: A method includes: receiving a ranging signal from the transmitter comprising a set of multiplexed sub-signals, each multiplexed sub-signal characterized by a frequency in a set of frequencies; calculating a time-based time-of-arrival estimate based on the series of time-domain samples of the ranging signal; calculating a time-based uncertainty of the time-based time-of-arrival; for each sub-signal pair in a subset of multiplexed sub-signals of the set of multiplexed sub-signals, extracting a phase difference of the sub-signal pair; calculating a phase-based time-of-arrival estimate based on the phase difference of each sub-signal pair in the subset of multiplexed sub-signals; calculating a phase-based uncertainty of the phase-based time-of-arrival estimate; and calculating a hybrid time-of-arrival estimate as a weighted combination of the time-based time-of-arrival estimate, the phase-based time-of-arrival estimate, based on the time-based uncertainty and the phase-based uncertainty.

Abstract: This disclosure provides methods, systems, and devices for user authentication in radio frequency environments. In one aspect, an access point (AP) may collect channel information from a station (STA) in a mesh network, and receive, from a cloud platform, a user profile including a fingerprint of the STA and fingerprint of a user associated with the STA based on the collected channel information. The AP may determine a profile of wireless traffic from the STA in the mesh network and identify the STA and the user associated with the STA based on comparing the user profile to the profile of the wireless traffic. As a result, the AP may authenticate the STA and the user associated with the STA based on the comparison.

Abstract: A method for providing safe-driving support of a vehicle includes obtaining occupant data and vehicle data received at a vehicle hub. The occupant data is related to an identity and health status of an occupant and the vehicle data is related to a status of the vehicle. The method also includes obtaining action data based on an application of the occupant data and vehicle data to a machine learning safe-driving model. The machine learning safe-driving model is associated with a user profile of the occupant that is identified from among a plurality of user profiles based on the occupant data. A server maintains a plurality of user profiles, each having a respective machine learning safe-driving model. The action data relates to an action to be performed by the vehicle while the occupant is located in the vehicle.

Abstract: A method for providing safe-driving support of a vehicle includes obtaining occupant data and vehicle data received at a vehicle hub. The occupant data is related to an identity and health status of an occupant and the vehicle data is related to a status of the vehicle. The method also includes obtaining action data based on an application of the occupant data and vehicle data to a machine learning safe-driving model. The machine learning safe-driving model is associated with a user profile of the occupant that is identified from among a plurality of user profiles based on the occupant data. A server maintains a plurality of user profiles, each having a respective machine learning safe-driving model. The action data relates to an action to be performed by the vehicle while the occupant is located in the vehicle.

Abstract: A method is described. The method includes receiving an event monitoring model generated by a machine learning engine. The event monitoring model is configured to classify network device behavior based on observed events. The method also includes monitoring events in a network based on the event monitoring model. Machine learning features are extracted from network traffic generated by one or more network devices. The method further includes determining a network device classification of the monitored events based on the event monitoring model. The method additionally includes sending the observed events and the network device classification to the machine learning engine to update the event monitoring model.

Abstract: A method for providing an update package to a node in a mesh network comprising a set of nodes and a gateway node arranged to provide access to an update server via a second network. The gateway node collects package information from each set node, including a first node. Each package identifies a respective node and its package version. The gateway node may query the update server based on the package information. The update server may respond to the gateway node with an updated package for the first node. The gateway node broadcasts the updated package into the mesh network as a sequence of mesh messages. Each of a first plurality of nodes of the set may forward the mesh messages to other nodes. The first node stores the mesh messages so as the sequence of mesh messages is received, assemble the updated package.

Abstract: A method for providing an update package to a node in a mesh network comprising a set of nodes and a gateway node arranged to provide access to an update server via a second network. The gateway node collects package information from each set node, including a first node. Each package identifies a respective node and its package version. The gateway node may query the update server based on the package information. The update server may respond to the gateway node with an updated package for the first node. The gateway node broadcasts the updated package into the mesh network as a sequence of mesh messages. Each of a first plurality of nodes of the set may forward the mesh messages to other nodes. The first node stores the mesh messages so as the sequence of mesh messages is received, assemble the updated package.

Abstract: The present invention is related to location positioning systems, and more particularly, to a method and apparatus for making accuracy improvements to a GPS receiver's navigation solutions. According to a first aspect, cartography information from a map database embedded within the GPS receiver is integrated into the position calculations performed by the GPS receiver. According to another aspect, the map database embedded within the GPS receiver is optimized for the purpose of improving the accuracy of the GPS receiver's position calculations.