An infrastructure for robotic applications as cloud computing services

Mouradian, Carla; Errounda, Fatima Zahra; Belqasmi, Fatna; Glitho, Roch

doi:10.1109/wf-iot.2014.6803195

Cited by 13 publications

(8 citation statements)

References 45 publications

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“…Moreover, there are some application specific researches for disaster management [74], vision acquisition [131], where the integration of customized IaaS with robotic systems are considered explicitly. Additionally, in [115], the confederation of cloud infrastructure is considered for supporting robotic systems. While providing IaaS for the robotic systems, the infrastructures are required to support diversity so that these resources can be beneficial to heterogeneous robots.…”

Section: Service Modelmentioning

confidence: 99%

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Afrin

Jin

Rahman

et al. 2021

IEEE Commun. Surv. Tutorials

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Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robotto-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.

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Section: Service Modelmentioning

confidence: 99%

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Afrin

Jin

Rahman

et al. 2021

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…These models can be applied to robot clouds and multiple studies regarding them have been introduced. Mourandian et al [13] highlighted aspects of IaaS in robotic applications. They proposed an architecture that enables cost-effectiveness by delegating virtualization and dynamic tasks to robots, including robots that could belong to other clouds.…”

Section: Related Workmentioning

confidence: 99%

A Function as a Service Based Fog Robotic System for Cognitive Robots

Ahn

2019

Applied Sciences

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Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a function as a service based fog robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency, and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.

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“…On the one hand, the robot nodelevel virtualization enables multiple applications to reside in a single robot and run concurrently. On the other hand, the robot network-level virtualization enables the dynamic formation of subsets of robots services, with each subset dedicated to a certain application at a given time [8].…”

Section: B Scope and Challengesmentioning

confidence: 99%

“…Although in its early days, enabling robots in the cloud has started to be the topic of a few research works. For instance, the authors in [8] propose an architecture for robotic applications as cloud computing services. The proposed architecture enables the network-level virtualization of robots and considers delegating tasks to robots belonging to other IaaSs.…”

Section: State Of the Artmentioning

confidence: 99%

Robots as-a-service in cloud computing: Search and rescue in large-scale disasters case study

Mouradian

Yangui

Glitho

2018

2018 15th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Internet of Things (IoT) is expected to enable a myriad of applications by interconnecting objects -such as sensors and robots -over the Internet. IoT applications range from healthcare to autonomous vehicles and include disaster management. Enabling these applications in cloud environments requires the design of appropriate IoT Infrastructure-as-a-Service (IoT IaaS) to ease the provisioning of the IoT objects as cloud services. This paper discusses a case study on search and rescue IoT applications in large-scale disaster scenarios. It proposes an IoT IaaS architecture that virtualizes robots (IaaS for robots) and provides them to the upstream applications as-a-Service. Node-and Network-level robots virtualization are supported. The proposed architecture meets a set of identified requirements, such as the need for a unified description model for heterogeneous robots, publication/discovery mechanism, and federation with other IaaS for robots when needed. A validating proof of concept is built and experiments are made to evaluate its performance. Lessons learned and prospective research directions are discussed.

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An infrastructure for robotic applications as cloud computing services

Abstract: Robotic applications are becoming ubiquitous. They are widely used in several areas (e.g., healthcare, disaster management, and manufacturing). However, their provisioning still faces several challenges such as cost efficiency. Cloud computing is an emerging paradigm that may

Cited by 13 publications

References 45 publications

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

A Function as a Service Based Fog Robotic System for Cognitive Robots

Robots as-a-service in cloud computing: Search and rescue in large-scale disasters case study

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