Robotic Edge Resource Allocation for Agricultural Cyber-Physical System

被引:18
作者
Afrin, Mahbuba [1 ,2 ]
Jin, Jiong [1 ]
Rahman, Ashfaqur [2 ]
Gasparri, Andrea [3 ]
Tian, Yu-Chu [4 ]
Kulkarni, Ambarish [5 ]
机构
[1] Swinburne Univ Technol, Sch Sci Comp & Engn Technol, Melbourne, Vic 3122, Australia
[2] CSIRO, Data61, Sandy Bay, Tas 7005, Australia
[3] Roma Tre Univ, Dept Engn, I-00146 Rome, Italy
[4] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4001, Australia
[5] Swinburne Univ Technol, Sch Engn, Melbourne, Vic 3122, Australia
来源
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING | 2022年 / 9卷 / 06期
基金
澳大利亚研究理事会;
关键词
Edge computing; Robot sensing systems; Resource management; Cloud computing; Service robots; Energy consumption; Smart manufacturing; Quality of service; Internet of Things; Cyber-physical systems; Social factors; cloud computing; resource allocation; Quality-of-Service; SOCIAL SYSTEMS; CLOUD; MODEL;
D O I
10.1109/TNSE.2021.3103602
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Cloud-aided robots are increasingly adopted in realizing Cyber-Physical-Social System (CPSS) to reduce human efforts and enhance the system performance with diverse robotic applications. However, the multi-hop distance from robot to cloud data centre elevates the data transfer delay that often inhibits the deadline-satisfied task execution of robotic applications. Thanks to edge computing, this issue can be well addressed by harnessing computing resources in proximity of data sources. As edge resources are constrained in energy and processing capacity, sharing resources among the tasks of multiple robotic applications is critical to ensure. Therefore, in this paper, we develop a congestion game-theoretic robotic edge resource allocation mechanism for CPSS, which not only maintains the Quality-of-Service (QoS) by meeting task completion deadlines but also satisfies the energy constraints of resources. Here, Agriculture 4.0 is considered as a use case for the proposed mechanism which can be extended for other domains. Nevertheless, the performance evaluation of proposed mechanism is conducted in an iFogSim simulated edge computing environment. In comparison with existing greedy, heuristic, and evolutionary benchmarks, our mechanism is proven to offer overall 20% improvement in deadline satisfaction, energy consumption, and resource utilization.
引用
收藏
页码:3979 / 3990
页数:12
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