Communication-Aware Cloud Robotic Task Offloading With On-Demand Mobility for Smart Factory Maintenance

Rahman, Akhlaqur; Jin, Jiong; Cricenti, A.; Rahman, Ashfaqur; Kulkarni, Ambarish

doi:10.1109/tii.2018.2874693

Cited by 53 publications

(20 citation statements)

References 27 publications

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“…3) Placement of Multiple Multi-hop Services: Few studies focus on placing several services composed of multiple microservices simultaneously. For example, Rahman et al [16] proposed a modified genetic algorithm that placed three oil factory maintenance services composed of multiple microservices to the edge-cloud infrastructure to minimize the consumption of resources and the overall service latency.…”

Section: A Latency-aware Service Placement Methodsmentioning

confidence: 99%

“…1). Considerable efforts [4], [12]- [16] have been conducted on exploring aspects like latency models, placement algorithms, and the trade-off among multiple QoS requirements, providing with fruitful results.…”

mentioning

confidence: 99%

“…Since it is quite difficult to profile each service's E2E latency 1 under numerous viable placements in real-world collaborative systems, existing approaches [4], [12]- [14], [18]- [23] usually use mechanism-driven solutions that construct different mathematical models to estimate the E2E service latency. However, as the development of smart manufacturing services (e.g., multi-hop services composed of several microservices with inherently complex dependencies [16]), such idealized estimations cannot accurately depict the non-linear E2E latency of services based on edge-cloud collaborations. For example, the widely adopted transmission time model (i.e., d/b for transmitting load d over a link with bandwidth b) cannot reflect the impact of either the reliability, or the total traffic of a specific link.…”

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confidence: 99%

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MPCSM: Microservice Placement for Edge-Cloud Collaborative Smart Manufacturing

Wang

Zhao

Yang

et al. 2021

IEEE Trans. Ind. Inf.

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Latency-aware service placement is promising in reducing the overall service response latency of proliferating edge-cloud collaborative smart manufacturing systems. However, intuitive latency estimators used by existing service placement approaches cannot accurately depict the non-linear end-to-end (E2E) latency of multi-hop microservices with complex dependencies, which is severely hindering the effectiveness of latencyaware service placement. To address this issue, we present a Microservice Placement mechanism for edge-cloud Collaborative Smart Manufacturing (MPCSM), where a microservice placement algorithm LaECP supported by an accurate data-driven E2E latency estimation method is proposed. We build a realworld collaborative prototype, and conduct a case study on semiconductor manufacturing to elaborate the construction of our latency estimator. Results of extensive experiments demonstrate that the error of our E2E latency estimator is up to 10× less than that of existing ones, and the overall service latency with MPCSM is up to 10× less than that with existing service placement approaches.

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Section: A Latency-aware Service Placement Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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MPCSM: Microservice Placement for Edge-Cloud Collaborative Smart Manufacturing

Wang

Zhao

Yang

et al. 2021

IEEE Trans. Ind. Inf.

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“…Edge computing became edge intelligence, where research was conducted on how AI (Artificial Intelligence) delivers data analysis [4]. Accordingly, data gathering uses wired and wireless methods for data analysis, and it is a recent trend that it is designed wirelessly to allow flexible movement of workers, mobile shelves, and production facilities in the smart factory [5,6]. Thus, in the manufacturing industry, wireless network connectivity continues to be a challenge.…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Analysis for Edge Intelligence‐Based F‐PMIPv6 Mobility Support for Smart Manufacturing

Kim

Park

Moon

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, we propose a new mobility management network, i-FP, to be used in the smart factory that continues to develop in the Fourth Industrial Revolution. i-FP was created to solve the current local mobility management problem of legacy frameworks. MN (mobile node) refers to a mobile device in a manufacturing environment that includes workers, production facilities, and AGV. To allow mobile nodes (MNs) to move from one domain to another, i-FP uses three network entities: LFA (Local Factory Anchor), FAG (Factory Access Gateway), and MN, as an extended concept of PMIPv6. Among the three network entities in i-FP, LFA and FAG can act as edge intelligence devices to reduce the handover latency of the MNs. i-FP also uses IP header-swapping mechanisms to prevent traffic overhead and enhance network throughput. We evaluate new framework i-FP, PMIPv6, and HMIPv6, which are legacy protocols of local mobility management, in various ways and evaluate three schemes. We confirm that i-FP works better than do the other network methods used in the smart factory.

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“…Cloud robotic systems (CRS) appeared at the beginning of this decade, and their presence is increasing in both industrial utilization (Rahimi et al, 2017) and daily life use (Sharath et al, 2018). Hence, applications of CRS range from smart factory (Rahman et al, 2019) including teleoperation (Salmeron-Garcia et al, 2015) to smart city (Ermacora et al, 2013), encompassing surveillance (Bruckner et al, 2012), urban planning (Bruckner et al, 2012), disaster management (Jangid & Sharma, 2016), ambient-assisted living (Quintas et al, 2011), life support (Kamei et al, 2012), and elderly care (Kamei et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Ontologies for cloud robotics

Freitas¹,

Bermejo–Alonso

Khamis

et al. 2020

The Knowledge Engineering Review

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Cloud robotics (CR) is currently a growing area in the robotic community. Indeed, the use of cloud computing to share data and resources of distributed robotic systems leads to the design and development of cloud robotic systems (CRS) which constitute useful technologies for a wide range of applications such as smart manufacturing, aid and rescue missions. However, in order to get coherent agent-to-cloud communications and efficient agent-to-agent collaboration within these CRS, there is a need to formalize the knowledge representation in CR. Hence, the use of ontologies provides a mean to define formal concepts and their relations in an interoperable way. This paper presents standard robotic ontologies and their extension in the CR domain as well as their possible implementations in the case of a real-world CR scenario.

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Communication-Aware Cloud Robotic Task Offloading With On-Demand Mobility for Smart Factory Maintenance

Cited by 53 publications

References 27 publications

MPCSM: Microservice Placement for Edge-Cloud Collaborative Smart Manufacturing

MPCSM: Microservice Placement for Edge-Cloud Collaborative Smart Manufacturing

Design and Performance Analysis for Edge Intelligence‐Based F‐PMIPv6 Mobility Support for Smart Manufacturing

Ontologies for cloud robotics

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