The fourth industrial revolution introduces an ideal opportunity for inclusion of cloud-enabled robots in a factory environment to improve productivity and reduce human intervention.
For this novel paradigm, task offloading plays a critical role in leveraging computation support from resourceful cloud infrastructure. In particular, network connectivity and on-demand mobility of robot significantly influence the task offloading decision-making and vice-versa.
While current studies in the literature separately consider mobility or communication aspects to accommodate offloading, ours is the first approach to integrate these three interdependent factors together in order to formulate a joint optimization problem for the proposed oil factory maintenance application.
A modified genetic algorithm scheme is then developed to solve the problem with a novel 3-layer decision: task offloading, path planning, and access point selection.
Simulation results and comparison with existing techniques suggest that communication-aware and mobility-driven offloading in industrial scenario leads to superior system performance and minimum consumption of resources.