Abstract: Embodiments described herein are directed to creating a mesh network and using dynamic radio parameter selection for communications within the mesh network. A first participant computing device in the mesh network receives a communication for a destination computing device. The first participant computing device determines a route from the first participant computing device to the destination computing device and selects a second participant computing device in the mesh network along the selected route. The first participant computing device selects one or more radio parameters based on the selection of the second participant computing device along the selected route and transmits the communication to the second participant computing device based on the one or more selected radio parameters.

Abstract: Embodiments described herein are directed to creating a cognitive heterogeneous ad hoc mesh network via generation and utilization of a participant table. Participant object transmit notification signals to inform other participants objects in line-of-sight of their position and movement. Mobile participants that receive these notification signals update a local version of the participant table. If the receiving mobile participant is responsible for forwarding participant table updates to the sending mobile participant, then the receiving mobile participant includes the received notification signal with the transmission of its next notification signal to propagate the participant table update to a stationary participant. The stationary participant can then transmit participant table updates to other mobile participants. If a stationary participant is not accessible or not functioning, the participants continue to propagate participant table changes when a responsible forwarding participant changes.

Abstract: Embodiments described herein are directed to establishment of a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. These notification signals are used by each participant to establish an ad hoc mesh network between the recipient and the sender. The participants can also utilize echoes of the notification signals to detect and estimate the position and movement of participant or non-participant objects. Participant objects can share this positional information with one another to refine the estimated position and movement of non-participant objects or to correct errors in participant-reported positioning information. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to utilizing a tokenized system to manage network bandwidth. A total network bandwidth availability is determined for a network, and a total number of tokens is determined for that total network bandwidth. The system also determines a total number of users for the network. When a user sends a network usage request to use or access the network, the system selects and allocates a number of tokens for the user based on the network usage request, the total number of network users, and the total number of tokens. The user's device can then access and use the network if the user has a sufficient number of available tokens for that usage. The number of tokens for the user is reduced based on the amount of data used by the user.

Abstract: Embodiments described herein are directed to employing an aggregation of participant sensor coverage areas to determine if there are missing coverage areas or unwanted overlapping coverage areas. If there are missing coverage areas, then at least one participant is instructed to modify its sensor coverage area to at least partially cover the missing coverage area. Conversely, if at least one participant has a sensor that is providing unwanted overlap of other sensor coverage areas, then that participant may be instructed to stop using that sensor, utilize that sensor for other data transmission purposes, or modify the coverage area to be non- or less-overlapping.

Abstract: Embodiments described herein are directed to establishment of a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. These notification signals are used by each participant to establish an ad hoc mesh network between the recipient and the sender. The participants can also utilize echoes of the notification signals to detect and estimate the position and movement of participant or non-participant objects. Participant objects can share this positional information with one another to refine the estimated position and movement of non-participant objects or to correct errors in participant-reported positioning information. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to creating a cognitive heterogeneous ad hoc mesh network via generation and utilization of a participant table. Participant object transmit notification signals to inform other participants objects in line-of-sight of their position and movement. Mobile participants that receive these notification signals update a local version of the participant table. If the receiving mobile participant is responsible for forwarding participant table updates to the sending mobile participant, then the receiving mobile participant includes the received notification signal with the transmission of its next notification signal to propagate the participant table update to a stationary participant. The stationary participant can then transmit participant table updates to other mobile participants. If a stationary participant is not accessible or not functioning, the participants continue to propagate participant table changes when a responsible forwarding participant changes.

Abstract: Embodiments described herein are directed to creating a cognitive heterogeneous ad hoc mesh network via generation and utilization of a participant table. Participant object transmit notification signals to inform other participants objects in line-of-sight of their position and movement. Mobile participants that receive these notification signals update a local version of the participant table. If the receiving mobile participant is responsible for forwarding participant table updates to the sending mobile participant, then the receiving mobile participant includes the received notification signal with the transmission of its next notification signal to propagate the participant table update to a stationary participant. The stationary participant can then transmit participant table updates to other mobile participants. If a stationary participant is not accessible or not functioning, the participants continue to propagate participant table changes when a responsible forwarding participant changes.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to employing an aggregation of participant sensor coverage areas to determine if there are missing coverage areas or unwanted overlapping coverage areas. If there are missing coverage areas, then at least one participant is instructed to modify its sensor coverage area to at least partially cover the missing coverage area. Conversely, if at least one participant has a sensor that is providing unwanted overlap of other sensor coverage areas, then that participant may be instructed to stop using that sensor, utilize that sensor for other data transmission purposes, or modify the coverage area to be non- or less-overlapping.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to employing an aggregation of participant sensor coverage areas to determine if there are missing coverage areas or unwanted overlapping coverage areas. If there are missing coverage areas, then at least one participant is instructed to modify its sensor coverage area to at least partially cover the missing coverage area. Conversely, if at least one participant has a sensor that is providing unwanted overlap of other sensor coverage areas, then that participant may be instructed to stop using that sensor, utilize that sensor for other data transmission purposes, or modify the coverage area to be non- or less-overlapping.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.

Abstract: Embodiments described herein are directed to a tracking objects using a cognitive heterogeneous ad hoc mesh network. Participant objects transmit notification signals to inform other participant objects in line-of-sight of their position and movement. The participants also utilize echoes of the notification signals to detect and estimate the position and movement of non-participant objects. Participant objects can then share this positional information with one another to refine the estimated position and movement of non-participant objects. The position of each other participant and non-participant object is updated based on an individualized update rate that dynamically changes based on the distance and velocity of closure between the participant and the other participant or non-participant object.