Cloud-Enhanced Robotic System for Smart City Crowd Control

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.

Robot–City Interaction: Mapping the Research Landscape—A Survey of the Interactions Between Robots and Modern Cities

“…[22,40,65,90,110]). This reveals that providing a more semantic representation of the data exchanged is still of little emphasis in RCI, as works tend to focus on building ad-hoc cyber-physical systems [5,10,26,83], where robots are often seen as mobile sensors rather than knowledge producers [60,62,79,81]. This also conforms with the fact that robotics lacks of established standards and best practices for data representation and exchange, while these have been proven to be fundamental in promoting interoperability in applications involving heterogeneous systems (e.g.…”

“…Publicly available passage and exhibit spaces are overall more common, specifically in outdoor scenarios. Figure 13 reports that few works have some form of robotgovernment interaction, typically in cases in which robots are employed by the public administration as a support for security or maintenance [16,[26][27][28]79,81,107]. Figure 14 shows that robots mainly acquire and process data from the city, while dissemination (being more timeconsuming) is less common [16,17,79,81,107], and mostly employed when teams of robots are involved.…”

“…Agent context organized by RCI area intelligent designs of networked infrastructures that integrate robots[27,28,78,81], and planning/ decision-making solutions for monitoring situations such as parkings or road networks[16,79].As reported inFigs. 16, 17, 18 and 19, works in urban security present the following characteristics: • highly mobile platforms such as UAVs and wheeled robots, which are able to precisely perform outdoor navigation, environmental perception and manage a situation; • robots operating mostly autonomously, sometimes partially supervised by humans; • central role of the government as an actor w.r.t.…”

AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR

“…The sparse matrix-vector product (SpMV) is considered to be the most important, time-consuming, and widely used scientific kernel [1] in various scientific disciplines such as computer graphics, computer vision [2][3][4][5], robotics [3], 3D or 2D problems [4], acoustic problems [5,6], thermal problems, healthcare, networking, operational research, and computational fluid dynamics (CFD).…”

ZAKI+: A Machine Learning Based Process Mapping Tool for SpMV Computations on Distributed Memory Architectures

“…Many of these scientific and analytics applications require the solution of sparse linear equation systems. Some examples of these applications where these have been specifically applied to smart city settings include computational fluid dynamics (CFD) [9], [10], computer vision or computer graphics [11]- [13], robotics problems [14], 2D/3D problems [15], thermal problems [16], [17], acoustics problems [18], [19], operational research [20]- [22], healthcare [23], and networking [24], [25]. Some other examples of smart city applications include life sciences [26], smart farming [27], transportation [28]- [33], autonomous vehicles [34], graph computations [35], and social media analytics [36], [37].…”