Abstract: An animal monitoring device includes memory circuitry, interface circuitry configured to communicate via short-range wireless communication to a second animal monitoring device and via cellular communication, and processor circuitry configured to obtain, from a first sensor, sensor data having a first transmission deadline and provide the sensor data into a sensor data set.

Abstract: Drones are controlled by one or more control devices and comprise a respective camera for image capture. The one or more control devices perform a method including obtaining projected flight paths of the drones, obtaining a projected camera setting of the respective camera, computing, as a function of the projected camera setting, a projected viewing frustum of the respective camera, defining, for the drones, projected time-space trajectories of no-fly zones based on the projected viewing frustum of the respective camera, analyzing the projected flight paths of the drones in relation to the projected time-space trajectories for detection of a violation of one or more of the no-fly zones, and setting an operative flight path and/or an operative camera setting for at least one selected drone to prevent the violation.

Abstract: A portable electronic device is controlled, for example to selectively activate a flight mode, based on first event data and/or second event data. The first event data is generated by operating a first model for detection of airplane flight events, AFEs, on first sensing data representing ambient pressure. The second event data is generated by operating a second model for detection of AFEs on second sensing data representing own motion or ambient sound. A control method in the portable electronic device generates training data comprising groups of time-aligned data samples from the first event data and the second sensing data, generates an updated second model by use of the training data, and replaces the second model by the updated second model. The control method facilitates adaptation of the portable electronic device to detect airplane flight events in new environments and enables improved robustness when detecting AFEs.

Abstract: A first electronic device includes an interface; microphone circuitry; memory circuitry; and processor circuitry. The first electronic device is configured to discover, via the interface, a first wireless network. The first electronic device is configured to receive, via the interface, from a second electronic device discovering the first wireless network, a voice biometric indicative of a second user. The first electronic device is configured to activate the microphone circuitry to detect an input audio signal. The first electronic device is configured to determine whether the detected input audio signal satisfies one or more criteria. The first electronic device is configured to, when the detected input audio signal satisfies the one or more criteria, determine a parameter indicative of a level of risk of exposure; and generate, based on the parameter, contact data.

Abstract: An electronic device that includes a power source and a wireless transmitter is controlled by a method. The method predicts a magnitude of surplus energy in the power source under the assumption that the electronic device is operated in accordance with a default schedule over a time period. The default schedule defines sampling of sensor data from at least one sensor and transmission of the sensor data by use of the wireless transmitter. The method further determines, based on the magnitude of surplus energy, a modified schedule that results in sampling of an increased amount of sensor data per unit time compared to the default schedule, and configures the electronic device to operate in accordance with the modified schedule for at least part of the time period. The method increases availability of sensor data without compromising the mission of the electronic device.

Abstract: A method (100) is disclosed, the method (100) being performed by a server device, where the server device (400) is configured to communicate with a first electronic device (300) of a plurality of electronic devices (300, 300A-300I), the first electronic device being configured to operate using a processing scheme based on a machine-learning model. The method (100) comprises receiving (S102), from the first electronic device (300), an update request for updating the machine-learning model, the update request comprising sensor data indicative of a context surrounding the first electronic device (300); generating (S107) an updated machine-learning model based on the sensor data of the update request; and transmitting (S110) an update response comprising the updated machine-learning model to the first electronic device (300).

Abstract: A method for updating a neural network model on a computation device. The method includes estimating a bandwidth for data download from a server device to the computation device; estimating a time point of available computation capacity of the computation device; computing a maximum partition size as a function of the bandwidth and the time point; and causing download of a selected partition of the neural network model from the server device to the computation device, the selected partition being determined based on the maximum partition size. The method enables the selected partition to be executed by the computation device upon downloading and thereby provides for seamless updating of the neural network model while it is being executed on the computation device.

Abstract: A method for controlling upload of data, performed in an electronic device (100), wherein said electronic device is configured to intermittently upload data (520) to a wireless network to operate as a tracker, said method comprising: obtaining (500) a current position of the electronic device, determining, dependent on network coverage associated with said position, to either upload data (530) to the network or postpone (430) upload of data.

Abstract: The present disclosure provides a method, performed at an electronic device, for determining a geofence parameter of a geofence area related to a point of interest, POI. The method comprises obtaining a location of the POI, obtaining first POI data based on the location of the POI. The method may comprise determining, based on the location of the POI, one or more entities in proximity of the POI. The method comprises obtaining second POI data related to at least one entity of the one or more entities. The method comprises generating a set of enclosing features related to the POI based on the second POI data, wherein generating the set of enclosing features comprises applying a processing scheme to the second POI data; and determining a geofence parameter based on the first POI data and the set of enclosing features.

Abstract: A control system performs a method for configuring a delivery system that includes first storages with predefined positions, second storages with variable positions, and unmanned aerial vehicles which are operable to transport goods between destination points and the first and second storages. The method includes obtaining position data for the destination points, operating a clustering algorithm on the position data to determine clusters of destination points, and evaluating the clusters in relation to the predefined positions of the first storages to determine a set of stopping positions for at least one of the second storages. The stopping position(s) may thereby be adapted in a resource efficient way to improve the overall capabilities of the delivery system.

Abstract: A system performs a control method to launch an unmanned aerial vehicle, UAV. The control method includes calculating a selected altitude, relative to a reference level, for launching the UAV, operating a lifting arrangement to vertically elevate the UAV to the selected altitude, and causing the UAV to be launched from the selected altitude. Elevating the UAV may improve the performance of the UAV in terms of range, power consumption, ability to carry payload, transit time to destination, etc. The selected altitude may be calculated as a function of parameter data representing any of the UAV, the lifting arrangement, or surrounding conditions. An apparatus is further provided to determine an optimal location of the system in relation to a plurality of destinations of UAVs to be elevated by the system.

Abstract: A control system in a delivery system performs a method of controlling unmanned aerial vehicles, UAVs, which are organized in a group or swarm to perform one or more missions to deliver and/or pick up goods. The method includes obtaining drag data indicative of air resistance for one or more UAVs in the group, determining, as a function of the drag data, a respective relative position of at least the one or more UAVs within the group, and controlling at least the one or more UAVs to attain the respective relative position. Such adjustment in the relative position of one or more UAVs enables improved energy efficiency and may be made to reduce energy consumption, increase reach or decrease travel time of at least one UAV in the group.

Abstract: A control system in a delivery system performs a method to deliver a payload by an unmanned aerial vehicle, UAV, without requiring presence of the recipient even if the payload comprises sensitive or vulnerable goods. The method comprises obtaining a designated location for delivery of the payload, obtaining at least one payload vulnerability index for the payload, and obtaining a pick-up time indicative of a time point when the payload is expected to be retrieved by the recipient. The method further comprises selecting a final delivery area at or near the designated location as a function of the at least one payload vulnerability index and the pick-up time, and operating the UAV to deliver the payload to the final delivery area.

Abstract: A scheduling device performs a method of scheduling transports by a fleet of unmanned aerial vehicles, UAVs, to enable improved utilization of the fleet. The method includes computing, for the fleet, aggregated cost data, ACM, that associates utilization cost with distributed sub-regions within a geographic region, the utilization cost of a respective sub-region representing an estimated cost for directing at least one of the UAVs to the respective sub-region. The method further includes receiving a query indicative of one or more potential locations for pick-up or delivery of a payload, determining, based on the ACM, a transportation price for at least one potential location, and presenting the transportation price for the at least one potential location. The scheduling device thereby provides an interactive ordering system that enables utilization of the fleet to be automatically optimized by swarm intelligence.

Abstract: A coordinator electronic device includes a memory circuitry, a processor circuitry, and an interface circuitry. The processor circuitry is configured to obtain sensor data from a first sensor device. The processor circuitry is configured to provide to the first sensor device, based on the sensor data obtained, a first configuration parameter indicative of scheduling of reporting from the first sensor device.

Abstract: A method for controlling upload of data, performed in an electronic device (100), wherein said electronic device is configured to intermittently upload data (520) to a wireless network to operate as a tracker, said method comprising: obtaining (500) a current position of the electronic device, determining, dependent on network coverage associated with said position, to either upload data (530) to the network or postpone (430) upload of data.

Abstract: An electronic device includes a memory circuitry, an interface circuitry, a processor circuitry having a controller circuitry, and an inference circuitry configured to operate according to a first inference model of a plurality of inference models. The processor circuitry is configured to obtain first primary detection data from a detection circuitry. The processor circuitry is configured to obtain one or more criteria. The processor circuitry is configured to obtain, from the inference circuitry, a first primary probability associated with the first primary detection data, based on the first inference model. The processor circuitry is configured to generate a first set, based on the one or more criteria, by determining whether the first primary probability associated with the first primary detection data satisfies at least one of the one or more criteria.

Abstract: A tag (10) is configured to be attached to an object (12). The tag includes a sensor (24) configured to generate data indicative of movement of the tag in three dimensions. A control circuit (14) of the tag is configured to determine an amount of change in orientation of the tag over a predetermined period of time based on the data indicative of movement of the tag in three dimensions generated by the sensor; and to determine that the amount of change in orientation indicates that the tag is in one of an attached state relative to the object or is in a detached state relative to the object. A signal may be transmitted to a host computing device (28) if it is determined that the tag has become detached from the object.

Abstract: A portable electronic device comprises at least one sensor configured to sense sensor data, including at least one first sensor data representing a first sensed condition. The portable electronic device comprises a processor configured to create a learned sensor data pattern and create a learning function. The processor is also configured to obtain the first sensor data, estimate using the learning function a first estimated transmission cost associated with an upcoming data transmission when the device experiences the first sensed condition, predict using the learned sensor data pattern an upcoming second sensor data representing a predicted second sensed condition, and estimate using the learning function a second estimated transmission cost associated with the upcoming data transmission when the device experiences the predicted second sensed condition. The processor is configured to compare the first estimated transmission cost with the second estimated transmission cost.

Abstract: A monitoring system detects and mitigates traffic risks among a group of vehicles. The group of vehicles includes a ground-based vehicle (GBV), e.g. an automotive vehicle, and an air-based vehicle (ABV), e.g. a drone, which is operated to track a ground-based object (GBO), e.g. an unprotected road user or an animal. The monitoring system performs a method comprising: obtaining (301) predicted navigation data for the ground-based vehicle and the air-based vehicle, processing (302) the predicted navigation data to obtain one or more future locations of the ground based-object and to detect an upcoming spatial proximity between the ground-based object and the ground-based vehicle, and causing (305), upon detection of the upcoming spatial proximity, an alert signal to be provided to at least one of the ground-based object and the ground-based vehicle.