Paper Info
Title
An Energy Sensitive Computation Offloading Strategy In Cloud Robotic Network Based On Ga
Abstract
Cloud robotic network (CRN) normally contains multiple mobile robots and a cloud computing center providing feasible solutions for many multiagent applications. One of the most critical issues in CRN and its application is how to effectively assign/offload computational tasks. This paper presents a novel energy sensitive task offloading strategy to answer the question particularly for CRN. First, we propose a novel strategy to offload tasks to cloud center, as well as other robots to greatly improve the computing ability and execution efficiency. Second, an energy sensitive model is developed to balance the energy level of the robots and eventually prolong the lifetime of the robot network. A modified genetic algorithm (GA), named energy sensitive GA, is finally developed and integrated into the strategy to get the optimized task offloading result as soon as possible, which is critical to most CRN applications. The correctness, efficiency, and scalability of the proposed strategy are proved with both theoretical analysis and experimental simulations. The evaluation results show that the proposed method can effectively assign tasks and prolong the lifetime of the network to a certain extent.
Year
DOI
Venue
2019
10.1109/JSYST.2018.2830395
IEEE SYSTEMS JOURNAL
Keywords
Field
DocType
Cloud robotic network (CRN) systems, computation offloading, energy sensitive, genetic algorithm (GA)
Computer science,Correctness,Server,Computer network,Computation offloading,Robot,Genetic algorithm,Mobile robot,Scalability,Cloud computing,Distributed computing
Journal
Volume
Issue
ISSN
13
3
1932-8184
Citations 
PageRank 
References 
2
0.38
0
Authors
4
Name
Order
Citations
PageRank
Yu Guo165.89
Zhenqiang Mi256.54
yang yang3476.29
Mohammad S. Obaidat42190315.70