Performance Evaluations of Multi-hop WirelessHART Network and 6LoWPAN using Different Topologies

Benoit, Joshua B.; Yao, Aiping; Saladis, Luke; Zheng, Yi

doi:10.1109/glosic.2018.8570126

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Meanwhile, the use of wireless communication technology will make the deployment of equipment more flexible, which is an important component of future factory upgrades and smart factory construction [1][2][3][4]. Industrial wireless standards mainly include WirelessHART standard [5], ISA100.11a standard [6], and China's independently developed WIA-PA standard [7]. Ensuring that data is transmitted within the deadline is particularly important in industrial wireless networks, and deterministic scheduling technology can effectively solve the problem of periodic data flows under deadline constraints.…”

Section: Research Content and Main Workmentioning

confidence: 99%

“…Fig.6and Fig.7illustrates the performance comparison of the three scheduling methods under different channel numbers. The simulation parameters are set as follows: n = 60, α = 0.8, ρ = 0.6, m =[1,2,3,4,5,6], β = 0.8. It can be observed that, within a certain range, the scheduling success rate of the three scheduling methods significantly increases with the increase of the number of channels.…”

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confidence: 99%

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Deterministic Scheduling Algorithm Based on Proportional Conflict and Deadline in Industrial Wireless Networks

Xuan Zhao,

Bo Shen

et al. 2024

Journal of Computers

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<p>Deterministic scheduling technology is of great significance for the real-time and deterministic transmission of industrial wireless network data. In view of the fact that the industrial wireless network data stream itself has priority classification attribute, this paper, based on multi-channel time-division multiple access (TDMA) technology, analyzes link conflict delay and channel contention delay caused by high priority data stream to low priority data stream, and performs scheduling preprocessing on the network, so as to eliminate the network with unreasonable parameters, and feedback to the network manager. For preprocessed networks, the scheduling algorithm prioritizes the allocation of time slots and channel resources for links of high priority data streams, while for data streams belonging to the same priority class, a scheduling scheme based on Maximum Proportional Conflict and Deadline First (MPC-D) is proposed. Under the premise of meeting schedulability conditions, time slots and channel resources are allocated sequentially according to the proportional conflict deadline values of each link, from largest to smallest. The experimental results show that the proposed scheduling algorithm can achieve a high network scheduling success rate.</p> <p> </p>

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Section: Research Content and Main Workmentioning

confidence: 99%

mentioning

confidence: 99%

Deterministic Scheduling Algorithm Based on Proportional Conflict and Deadline in Industrial Wireless Networks

Xuan Zhao,

Bo Shen

et al. 2024

Journal of Computers

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“…The diverse connectivity between industrial field devices and edge infrastructure, depending on the communication medium and protocols from the media access control (MAC) layer all the way toward the application layer as defined in the open systems interconnection (OSI) model, leads to widely divergent performance. Many researchers have provided thorough study on the performance of existing industrial connectivity protocols from different perspectives, such as IEEE 802.11 [10], [11], IEEE 802.15.4 [12], [13], ISA100.11a [14], [15], WIA-PA [16], [17], wirelessHART [18], [19], Thread [20], [21], and LoRaWAN [22]- [24], whereas the performance of the backbone, i.e., the edge-cloud computing stack, is still unknown. Therefore, in this study, we limit our scope to the performance of containerization-based edge-cloud computing stacks in IIoT applications.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Liu

Lan

Pang

et al. 2021

IEEE Open J. Ind. Electron. Soc.

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Today, the edge-cloud computing paradigm starts to gain increasing popularity, aiming to enable short latency, fast decision-making and intelligence at the network edge, especially for industrial applications. The container-based virtualization technology has been put on the roadmap by the industry to implement edge-cloud computing infrastructures. Has the performance of the container-based edge-cloud computing stacks reached industry requirement? In this paper, from the industrial client perspective, we provide a performance evaluation methodology and apply it to the state-of-the-art containerization-based edge-cloud computing infrastructures. The influences of the message sending interval, payload, network bandwidth and concurrent devices on full stack latency are measured, and the processing capability of executing machine learning tasks are benchmarked. The results show that containerization on the edge does not introduce noticeable performance degradation in terms of communication, computing and intelligence capabilities, making it a promising technology for the edge-cloud computing paradigm. However, there is a large room for performance improvement between current implementation of the edge-cloud infrastructure and the demanding requirements anticipated by time-critical industrial applications. We also emphasize and showcase that partitioning of an industrial application into microservices throughout the whole stack can be considered during solution design. The proposed evaluation methodology can be a reference to users of edge-cloud computing as well as developers to get a client perspective overview of system performance.

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“…Significant gains in network performance are achieved through the use of wider channels, improved modulation efficiency, and multi-user connections (MIMO) [3][4]. Along with the introduction of new standards in WLAN technology, performance improvements can be achieved by using multi-hop wireless mesh networks [5][6][7]. At the same time, performance largely depends on the used frequency channel allocation mechanism.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Approach for Optimal Routing in IoT Wireless Mesh Networks

Nurlan

Zhukabayeva²,

Othman

et al. 2021

IEEE Access

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Performance Evaluations of Multi-hop WirelessHART Network and 6LoWPAN using Different Topologies

Cited by 7 publications

References 4 publications

Deterministic Scheduling Algorithm Based on Proportional Conflict and Deadline in Industrial Wireless Networks

Deterministic Scheduling Algorithm Based on Proportional Conflict and Deadline in Industrial Wireless Networks

Performance Evaluation of Containerization in Edge-Cloud Computing Stacks for Industrial Applications: A Client Perspective

Resource Allocation Approach for Optimal Routing in IoT Wireless Mesh Networks

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