Based on better game theory, a computation offloading strategy for cloud robots

The Internet of Things (IoT) and cloud computing have ushered in a paradigm change that has resulted in the creation of integrated robotic applications and services. The computation-intensive jobs are moved to the cloud to fulfil the rising need for robots' energy-intensive applications. As a result, job offloading is crucial in cloud networked robotics (CNR) for exploiting cloud infrastructure compute support. However, given the time limitation, the additional expenses of data transmission, and remote processing, making optimal offloading decisions is not simple. Despite the fact that many attempts have been made to investigate various elements of offloading, the most of them are focused on mobile cloud computing. In fact, the CNR's offloading process is more complicated due to the robot's on-demand mobility, which has a substantial impact on the link between offloading, movement, and communication. To overcome these restrictions, more extensive offloading strategies that can handle higher levels of complication must be established during system modelling. Unlike earlier research that focused on each of the aforementioned problems independently, our method intends to consider path planning, link selection, and offloading as part of the decisionmaking process for various types of CNR systems.

Author(s): Owino Roto

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