Tag: Mobile edge computing

Researchers developed a federated reinforcement learning algorithm that jointly optimizes task offloading and trajectory control in multi-UAV mobile edge computing ...

A hierarchical attention-based reinforcement learning framework improved slice admission rates by up to 20 percent and cut resource overhead by ...

Researchers have introduced RAPBS, a reliability-aware framework that places bidirectional service function chains in mobile edge computing using backup estimation, ...

Researchers have built a three-layer AI framework for 5G mobile learning that cuts device energy use by up to 27 ...

Researchers have unveiled SWEET, an inference serving system that jointly optimizes model quantization and device-server workload partitioning to cut edge ...

A deep reinforcement learning method called P2TO extends task offloading from edge and cloud servers down to individual virtual machines, ...

Researchers have developed a reliability-aware adaptive actor-critic reinforcement learning framework that jointly optimizes service function chain deployment in mobile edge ...

Researchers in China have developed VDDM, a framework combining dynamic virtual machine partitioning, matching theory, and deep reinforcement learning to ...

Researchers have developed an adaptive weighted cost-optimization approach that makes optimal task offloading decisions in mobile edge computing within milliseconds, ...

Researchers in China have developed an improved white shark optimization algorithm that substantially lowers user-perceived cost and scales to networks ...