QoS-Aware Cooperative Computation Offloading for Robot Swarms in Cloud Robotics

Computation offloading is a promising solution to extend the capacity of robot swarms for computation-intensive applications because it allows robot swarms to benefit from the powerful computing resources of modern data centers. However, the existing computation-offloading approaches still face chal...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2019-04, Vol.68 (4), p.4027-4041
Main Authors: Hong, Zicong, Huang, Huawei, Guo, Song, Chen, Wuhui, Zheng, Zibin
Format: Article
Language:English
Subjects:
Algorithms
cloud robotics
Computation offloading
Computational modeling
Computer simulation
Data centers
Decision making
Energy conservation
Game theory
Optimization
QoS
Quality of service
robot swarms
Robotics
Robots
Routing
Slicing
Spread spectrum communication
Task analysis
Upper bounds
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Summary:Computation offloading is a promising solution to extend the capacity of robot swarms for computation-intensive applications because it allows robot swarms to benefit from the powerful computing resources of modern data centers. However, the existing computation-offloading approaches still face challenges: 1) multi-hop cooperative computation offloading, 2) joint computation offloading and routing, and 3) task slicing. In this paper, we propose a quality of service (QoS)-aware cooperative computation-offloading scheme for robot swarms using game theory. We analyze the multi-hop cooperative communication model in robot swarms and investigate the computation offloading and routing decision-making problems with the goals of both latency minimization and energy efficiency. We formulate the joint optimization problem as a multi-hop cooperative computation-offloading game and show the existence of a Nash equilibrium (NE) of the game for both unsliceable and sliceable tasks. We further propose a QoS-aware distributed algorithm to attain an NE and provide an upper bound on the price of anarchy in the game. Finally, our simulated results show that our algorithm scales well as the swarm size increases and it has a stable performance gain in various parameter settings.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2019.2901761