Methods and systems for managing temperature of 5G UE by TX/RX path switching. If temperature of the UE increases beyond a preconfigured threshold due to heat contributed by a first TX/RX path of the UE, embodiments perform switching from the first TX/RX path to a second TX/RX path to control the temperature of the UE. The first TX/RX path and the second TX/RX path correspond to the same or different RF bands. If the first TX/RX path and the second TX/RX path correspond to different RF bands, the UE sends, to a wireless network, either: a measurement report, indicating that RSRP of a cell corresponding to the second TX/RX path is greater than RSRP of the serving cell corresponding to the first TX/RX path; or a UE Assistance Information indicating that the UE intends to camp on the cell corresponding to the second TX/RX path of the UE.

Methods and systems for managing temperature of 5G UE by TX/RX path switching. If temperature of the UE increases beyond a preconfigured threshold due to heat contributed by a first TX/RX path of the UE, embodiments perform switching from the first TX/RX path to a second TX/RX path to control the temperature of the UE. The first TX/RX path and the second TX/RX path correspond to the same or different RF bands. If the first TX/RX path and the second TX/RX path correspond to different RF bands, the UE sends, to a wireless network, either: a measurement report, indicating that RSRP of a cell corresponding to the second TX/RX path is greater than RSRP of the serving cell corresponding to the first TX/RX path; or a UE Assistance Information indicating that the UE intends to camp on the cell corresponding to the second TX/RX path of the UE.

A method can include obtaining device data for a set of edge devices. The method can further include obtaining a predicted travel path of a focal entity. The method can further include determining, for a first edge device of the set of edge devices and based on the device data, a first proximity of the first edge device to the predicted travel path. The method can further include selecting the first edge device based, at least in part, on the first proximity. The method can further include transmitting, in response to the selecting the first edge device, a workload to the first edge device. The method can further include receiving, in response to the transmitting the workload, first captured data obtained by the first edge device. The method can further include transmitting the first captured data to an electronic user device.

A method can include obtaining device data for a set of edge devices. The method can further include obtaining a predicted travel path of a focal entity. The method can further include determining, for a first edge device of the set of edge devices and based on the device data, a first proximity of the first edge device to the predicted travel path. The method can further include selecting the first edge device based, at least in part, on the first proximity. The method can further include transmitting, in response to the selecting the first edge device, a workload to the first edge device. The method can further include receiving, in response to the transmitting the workload, first captured data obtained by the first edge device. The method can further include transmitting the first captured data to an electronic user device.

Communication network architectures, systems and methods for supporting a network of mobile nodes. As a non-limiting example, various aspects of this disclosure provide communication network architectures, systems, and methods for supporting a dynamically configurable communication network comprising a complex array of both static and moving communication nodes (e.g., the Internet of moving things).

Communication network architectures, systems and methods for supporting a network of mobile nodes. As a non-limiting example, various aspects of this disclosure provide communication network architectures, systems, and methods for supporting a dynamically configurable communication network comprising a complex array of both static and moving communication nodes (e.g., the Internet of moving things).

There is disclosed in one example a computing apparatus, including: a hardware platform; and a service chain pre-placement analyzer to operate on the hardware platform and configured to: receive a total utility input for a service chain placement; predict a mobility pattern for the service chain placement; and compute an average utility for the service chain placement, wherein the average utility is a product of the total utility and the mobility pattern.

There is disclosed in one example a computing apparatus, including: a hardware platform; and a service chain pre-placement analyzer to operate on the hardware platform and configured to: receive a total utility input for a service chain placement; predict a mobility pattern for the service chain placement; and compute an average utility for the service chain placement, wherein the average utility is a product of the total utility and the mobility pattern.

A system and method of propagating signal links by using artificial neural networks and a relay link selection protocol to predict an optimal signal path. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated.

A system and method of propagating signal links by using artificial neural networks and a relay link selection protocol to predict an optimal signal path. The artificial neural networks used in the method classify training and testing datasets into sufficient signal strengths and insufficient signal strengths, such that paths are evaluated for predicted propagation links, such that the strongest propagation link can be selected. Specifically, a multilayer perceptron method is used to identify and characterize new link candidates using the path loss parameter or the received signal strength, such that optimal links can be selected and updated.