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J. Sens. Actuator Netw. 2016, 5(4), 20; doi:10.3390/jsan5040020

Cloud-Enhanced Robotic System for Smart City Crowd Control

1
School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn VIC 3122, Australia
2
Data61, CSIRO, Sandy Bay TAS 7005, Australia
3
Department of Electrical and Electronic Engineering, The University of Melbourne, Parkville VIC 3010, Australia
4
Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
*
Author to whom correspondence should be addressed.
Academic Editor: Jamil Y. Khan
Received: 25 May 2016 / Revised: 28 October 2016 / Accepted: 25 November 2016 / Published: 21 December 2016
(This article belongs to the Special Issue Wireless Sensor Networks for Smart Grid and Smart City Applications)
View Full-Text   |   Download PDF [3765 KB, uploaded 21 December 2016]   |  

Abstract

Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure. It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS). However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities. In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities. Specifically, we present an integrated framework for a crowd control system where cloud-enhanced robots are deployed to perform necessary tasks. The task offloading is formulated as a constrained optimization problem capable of handling any task flow that can be characterized by a Direct Acyclic Graph (DAG). We consider two scenarios of minimizing energy and time, respectively, and develop a genetic algorithm (GA)-based approach to identify the optimal task offloading decisions. The performance comparison with two benchmarks shows that our GA scheme achieves desired energy and time performance. We also show the adaptability of our algorithm by varying the values for bandwidth and movement. The results suggest their impact on offloading. Finally, we present a multi-task flow optimal path sequence problem that highlights how the robot can plan its task completion via movements that expend the minimum energy. This integrates path planning with offloading for robotics. To the best of our knowledge, this is the first attempt to evaluate cloud-based task offloading for a smart city crowd control system. View Full-Text
Keywords: cloud robotics; smart city; task offloading; cloud computing; crowd control; genetic algorithm cloud robotics; smart city; task offloading; cloud computing; crowd control; genetic algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rahman, A.; Jin, J.; Cricenti, A.; Rahman, A.; Palaniswami, M.; Luo, T. Cloud-Enhanced Robotic System for Smart City Crowd Control. J. Sens. Actuator Netw. 2016, 5, 20.

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