According to a source node and a destination node of each data packet p in a given data packet set required to be transmitted by the wireless vehicular network, the joint scheduling based wireless vehicular network routing algorithm determines overall transmission performance of the wireless vehicular network and a transmission latency and transmission cost of the data packets, and determines an optimal transmission strategy. A multi-order Markov chain is used to model position change processes of vehicle nodes, and a next moment and several moments in the future are predicted and estimated under the condition that positions at previous several moments are given. On the basis of position actual values and predicted estimated values of the vehicle nodes, the optimal transmission strategy is combined to achieve data routing in the wireless vehicular network. The data-aware routing method is mainly used for data communication of a vehicular ad hoc network.

Systems, apparatuses, methods, and computer-readable media, are provided for providing connectivity-based and/or connectivity-considered routing with supplemental wireless connections in driving assistance-related activities. Embodiments may be relevant to multi-access edge computing (MEC) and Automotive Edge Computing Consortium (AECC) technologies. Other embodiments may be described and/or claimed.

A central trajectory controller including a cell interface configured to establish signaling connections with one or more backhaul moving cells and to establish signaling connections with one or more outer moving cells, an input data repository configured to obtain input data related to a radio environment of the one or more outer moving cells and the one or more backhaul moving cells, and a trajectory processor configured to determine, based on the input data, first coarse trajectories for the one or more backhaul moving cells and second coarse trajectories for the one or more outer moving cells, the cell interface further configured to send the first coarse trajectories to the one or more backhaul moving cells and to send the second coarse trajectories to the one or more outer moving cells.

The technologies described herein are generally directed to selecting network routes based on aggregating models that can predict routing performance in a fifth generation (5G) network or other next generation networks. For example, a method described herein can include communicating, to second routing equipment, a first model describing a delay predicted to be caused to a future communication by the future communication being transited via the first routing equipment. The method can further include receiving, from the second routing equipment, a current communication for transit via the first routing equipment to destination equipment, wherein the first routing equipment was selected by the second routing equipment based on the first model, and second models, other than the first model, describing respective predicted delays from other routing equipment other than the first routing and second routing equipment.

An architecture to perform resource management among multiple network nodes and associated resources is disclosed. Example resource management techniques include those relating to: proactive reservation of edge computing resources; deadline-driven resource allocation; speculative edge QoS pre-allocation; and automatic QoS migration across edge computing nodes. In a specific example, a technique for service migration includes: identifying a service operated with computing resources in an edge computing system, involving computing capabilities for a connected edge device with an identified service level; identifying a mobility condition for the service, based on a change in network connectivity with the connected edge device; and performing a migration of the service to another edge computing system based on the identified mobility condition, to enable the service to be continued at the second edge computing apparatus to provide computing capabilities for the connected edge device with the identified service level.

There is disclosed a method of staging real-time data in proximity to a mobile device. The method includes determining a geographic location associated with the mobile, device and identifying a storage device located in proximity to the determined geographic location. The method also includes enabling real-time data published by the mobile device or provided to the mobile device to be stored on the identified storage device.

There is disclosed a method of staging real-time data in proximity to a mobile device. The method includes determining a geographic location associated with the mobile, device and identifying a storage device located in proximity to the determined geographic location. The method also includes enabling real-time data published by the mobile device or provided to the mobile device to be stored on the identified storage device.

Embodiments of this application relates to a communication method and a related device. In an example, a device receives a first data packet in a first network segment, the first data packet comprises a first backhaul adaptation protocol (BAP) routing identity, and the first BAP routing identity is used for transmission of the first data packet in the first network segment. The device further obtains a first correspondence. In response to determining that the first correspondence comprises the first BAP routing identity in the first data packet, the device determines that the first data packet meets a preset condition. In response to that the first data packet meets the preset condition, the device replaces the first BAP routing identity in the first data packet with a second BAP routing identity according to the first correspondence, to obtain a second data packet.

A wireless communication network serves User Equipment (UE) responsive to an Artificial Intelligence (AI) network. The UE determines Quality-of-Service (QoS) levels for a wireless data service at geographic locations. The UE transfers the QoS levels for the wireless data service at the geographic locations to a network core. The network core transfers the QoS levels for the wireless data service at the geographic locations for the UE to the AI network. The network core receives a future QoS level, future geographic location, and future time for the wireless data service for the UE from the AI network. The network core signals a network control-plane to deliver the wireless data service to the UE at the future geographic location and the future time using the future QoS level. The UE receives the wireless data service at the future geographic location and the future time using the future QoS level.

A wireless communication network serves User Equipment (UE) responsive to an Artificial Intelligence (AI) network. The UE determines Quality-of-Service (QoS) levels for a wireless data service at geographic locations. The UE transfers the QoS levels for the wireless data service at the geographic locations to a network core. The network core transfers the QoS levels for the wireless data service at the geographic locations for the UE to the AI network. The network core receives a future QoS level, future geographic location, and future time for the wireless data service for the UE from the AI network. The network core signals a network control-plane to deliver the wireless data service to the UE at the future geographic location and the future time using the future QoS level. The UE receives the wireless data service at the future geographic location and the future time using the future QoS level.