WIA-FA and Its Applications to Digital Factory: A Wireless Network Solution for Factory Automation

Liang, Wei; Zheng, Meng; Zhang, Jialin; Shi, Huaguang; Huang, Yu; Yang, Yi; Liu, Shuai; Yang, Wenhua; Zhao, Xuefeng

doi:10.1109/jproc.2019.2897627

Cited by 81 publications

(33 citation statements)

References 51 publications

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“…Generally, in the past decade, developments in AMR and AGV technologies became more interrelated, with AMRs generally having more onboard intelligence than AGVs [55]. In the past decade, majority of research works regarding FoF have consistently reiterated the need for equipment in factory floors including AGV systems to be more agile, reactive and operable in a dynamic factory environment [80], [81], [82]. In industry 4.0 or smart manufacturing environments, different complex systems that constitute a smart factory will employ industrial Internet of Things (IIoT), fault-tolerant systems, distributed edge computing, multi-access or mobile edge computing systems and low-latency wireless networks to institute a reliable and resilient FoF systems [83], [84], [85], [86].…”

Section: Review Of Agv Research Work In the Past Decadementioning

confidence: 99%

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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In industrial environments, over several decades, Automated Guided Vehicles (AGVs) and Autonomous Mobile Robots (AMRs) have served to improve efficiencies of intralogistics and material handling tasks. However, for system integrators, the choice and effective deployment of improved, suitable and reliable communication and control technologies for these unmanned vehicles remains a very challenging task. Specifics of communication for AGVs and AMRs imposes stringent performance requirements on latency and reliability of communication links which many existing wireless technologies struggle to satisfy. In this paper, a review of latest AGVs and AMRs research results in the past decade is presented. The review encompasses results from different past and present research domains of AGVs. In addition, performance requirements of communication networks in terms of their latencies and reliabilities when they are deployed for AGVs and AMRs coordination, control and fleet management in smart manufacturing environments are discussed. Integration challenges and limitations of present state-of-the-art AGV and AMR technologies when those technologies are used for facilitating AGV-based smart manufacturing and factory of the future applications are also thoroughly discussed. The paper also present a thorough discussion of areas in need of further research regarding the application of 5G networks for AGVs and AMRs fleet management in smart manufacturing environments. In addition, novel integration ideas by which tactile Internet, 5G network slicing and virtual reality applications can be used to facilitate AGV and AMR based factory of the future (FoF) and smart manufacturing applications were motivated.INDEX TERMS Intelligent factory, factory of the future, 5G, smart manufacturing, industry 4.0, autonomous industrial equipment, AGV, AMR, tactile Internet, virtual reality, lean manufacturing.

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Section: Review Of Agv Research Work In the Past Decadementioning

confidence: 99%

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

et al. 2020

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“…In [ 18 ], a modification of the IEEE 802.11 physical layer is introduced in order to achieve very low packet delivery times, which were comparable with those of the wired counterparts. In [ 19 ], the authors describe WIA–FA, a communication system for factory automation based on Wi-Fi, standardized by the International Electrotechnical Commission (IEC) [ 20 ], along with some interesting case studies and examples of application to real manufacturing plants. Real–time industrial multimedia applications, particularly video streaming, are addressed in [ 21 ], where the authors show the capability of Wi-Fi to handle such a kind of traffic.…”

Section: Related Work and Contributionmentioning

confidence: 99%

The Impact of Rate Adaptation Algorithms on Wi-Fi-Based Factory Automation Systems

Fedullo

Tramarin

Vitturi

2020

Sensors

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Factory automation systems based on the IEEE 802.11 Wi-Fi standard may benefit from its Multi-Rate Support (MRS) feature, which allows for dynamically selecting the most suitable transmission rate for the targeted application context. The MRS is implemented by means of rate adaptation algorithms (RAAs), which has already demonstrated to be effective to improve both timeliness and reliability, which are typically strict requirements of industrial real-time communication systems. Indeed, some of such algorithms have been specifically conceived for reliable real-time communications. However, the computational complexity of such algorithms has not been effectively investigated yet. In this paper, we address such an issue, particularly focusing on the execution times of some specific rate adaptation algorithms, as well as on their impact on the automation tasks. In this respect, after a formal description of the algorithms, we present the outcomes of an extensive experimental session, which includes practical measurements and realistic simulations. The obtained results are encouraging, since the measured execution times indicate that rate adaptation algorithms can be profitably adopted by industrial automation systems, allowing for improving their reliability and timeliness without impacting on the overall performance.

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“…This paper focuses on a particular category of WSNs, namely the industrial ones. Industrial WSNs (IWSNs) are addressed by several standards, including ISA100.11a [ 2 ] (IEC-62734 [ 3 ]), WirelessHART (IEC-62591) [ 4 ], ZigBee [ 5 ], Bluetooth Mesh Networking [ 6 ], Wireless Networks for Industrial Automation-Factory Automation (WIA-FA) (IEC-62948) [ 7 , 8 ], and Process Automation (WIA-PA) (IEC-62601) [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of an ISA100.11a Simulation Model for Accurate Industrial WSN Planning and Deployment

Padrah

Pastrav

Palade

et al. 2021

Sensors

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During the planning, design, and optimization of an industrial wireless sensor network (IWSN), the proposed solutions need to be validated and evaluated. To reduce the time and expenses, highly accurate simulators can be used for these tasks. This paper presents the development and experimental validation of an ISA100.11a simulation model for industrial wireless sensor networks (IWSN). To achieve high simulation accuracy, the ISA100.11a software stack running on two types of certified devices (i.e., an all-in-one gateway and a field device) is integrated with the ns-3 simulator. The behavior of IWSNs is analyzed in four different types of test scenarios: (1) through simulation using the proposed ISA100.11a simulation model, (2) on an experimental testbed using ISA100.11a certified devices, (3) in a Gateway-in-the-loop Hardware-in-the-loop (HIL) scenario, and (4) in a Node-in-the-loop HIL scenario. Moreover, the scalability of the proposed simulation model is evaluated. Several metrics related to the timing of events and communication statistics are used to evaluate the behavior and performance of the tested IWSNs. The results analysis demonstrates the potential of the proposed model to accurately predict IWSN behavior.

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WIA-FA and Its Applications to Digital Factory: A Wireless Network Solution for Factory Automation

Cited by 81 publications

References 51 publications

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

A Review of Recent Advances in Automated Guided Vehicle Technologies: Integration Challenges and Research Areas for 5G-Based Smart Manufacturing Applications

The Impact of Rate Adaptation Algorithms on Wi-Fi-Based Factory Automation Systems

Development and Validation of an ISA100.11a Simulation Model for Accurate Industrial WSN Planning and Deployment

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