Task offloading optimization for cloud enhanced robotic system using hybrid intelligent generic algorithm

Abstract

The cloud computing and the Internet of Things (IoT) are the key materializations that leads to the emergence of cloud robotics application. Considering the energy demanding applications of robots, the complex computation tasks are offloaded to the cloud. This task offloading process is a significant job in Cloud Enhanced Robotics (CER) for utilizing computation help from the cloud framework. Conversely, thinking about the delay requirement, the additional expenses of information communication and cloud computing are considered to be the essential needs to optimize superior offloading decisions. Despite the fact that numerous endeavours have been made to consider various features of offloading, the vast majority of them are devoted towards mobility supported cloud computing. The robot offloading in CER is crucial because of robot s dynamic mobility that drastically disturbs association among the skill learning, offloading decision-making capabilities, robot mobility and data communication. To address these impediments, it is subsequently fundamental to set up a progressively complete offloading procedure during framework demonstration that are fit to take care of more significant level of difficulties. Unlike earlier Exhaustive Search, All on Robot and Greedy method, our methodology aims to mutually consider path planning, Access Point (AP) determination and offloading as the major aspects of decision -making for various sorts of CER frameworks. At first, we present a Hybrid Intelligent Generic Algorithm (HIGA) based task offloading framework for cloud-enhanced robotic system that holds mobility and communication as part of its offloading. In order to emphasize the impact of the aforesaid parameters on task offloading, we at newline