Edge Computing in IoT-Based Manufacturing

Chen, Baotong; Wan, Jiafu; Celesti, Antonio; Li, Di; Abbas, Haider; Zhang, Qin

doi:10.1109/mcom.2018.1701231

Cited by 247 publications

(111 citation statements)

References 14 publications

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“…Even though the public cloud service mode provides a relatively secure and convenient environment for enterprises as an alternative, transmission latency and congestion remain to be considered as the rigid characters for cloud computing. On the other hand, due to cost and efficiency factors, minor enterprises still want to have a solution that makes the most use of their local computing devices [24]. A flexible technology is needed to take up such challenges.…”

Section: Edge Computingmentioning

confidence: 99%

“…To deploy edge computing, a software container on the gateway can be installed. To ensure the requirements of low congestion and latency, edge computing can be regarded as a time-aware scheduler by introducing the time-sensitive networks (TSN) [24]. In such a scenario, edge computing allocates the information communication and affords the real-time capability on the time nodes.…”

Section: Network Communication Channelmentioning

confidence: 99%

“…In the case of no network coverage, devices can separate their connections from nearby devices and form local sub-networks intelligently. Then, they can access the core network through the intermediate connections [24].…”

Section: Network Communication Channelmentioning

confidence: 99%

“…In this section, a distributed manufacturing environment [24] is built using a Hadoop-based framework. The Hadoop Distributed File System (HDFS) is set to implement the real-time acquisition and analysis of the local data, which is composed of machine log data and status data.…”

Section: Case Study 2: Experiments Of the Proposed Frameworkmentioning

confidence: 99%

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A Novel Multi-Agent-Based Collaborative Virtual Manufacturing Environment Integrated with Edge Computing Technique

et al. 2019

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This paper proposes a multi-agent-based collaborative virtual manufacturing environment (VME) to save energy consumption and improve efficiency in the manufacturing process. In order to achieve the high autonomy of the manufacturing system, a multi-agent system (MAS) is designed to build a collaborative VME. In this new VME environment, edge computing is embedded to strengthen the cyber resource utilization and system economy. Moreover, an efficient communication channel between networks is proposed. The subsequent cooperation and collaboration protocols among agents are designed to ensure flexible and process-oriented operations. Furthermore, the fuzzy resolution algorithm is employed to resolve the competition conflicts among function-similar MASs in the distributed manufacturing scenario. Lastly, a simulation and case study are performed to evaluate the performance of the proposed VME in Internet of Things (IoT)-based manufacturing. The analysis results have demonstrated the feasibility and effectiveness of the proposed VME system.

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Section: Edge Computingmentioning

confidence: 99%

Section: Network Communication Channelmentioning

confidence: 99%

Section: Network Communication Channelmentioning

confidence: 99%

Section: Case Study 2: Experiments Of the Proposed Frameworkmentioning

confidence: 99%

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A Novel Multi-Agent-Based Collaborative Virtual Manufacturing Environment Integrated with Edge Computing Technique

et al. 2019

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“…Plus, the great paradigm shift from PLCs to ePLCs is resulting in some great advances in multiprocessing [32,33], customized compilation tools [29], support for market-friendly embedded hardware (e.g., Raspberry-Pi), and more. Some of this market-friendly embedded hardware can be used with IoT platforms [34,35].…”

Section: Edgementioning

confidence: 99%

Edge Computing in an IoT Base Station System: Reprogramming and Real‐Time Tasks

Sun

et al. 2019

Complexity

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There are millions of base stations distributed across China, each containing many support devices and monitoring sensors. Conventional base station management systems tend to be hosted in the cloud, but cloud-based systems are difficult to reprogram and performing tasks in real-time is sometimes problematic, for example, sounding a combination of alarms or executing linked tasks. To overcome these drawbacks, we propose a hybrid edge-cloud IoT base station system, called BSIS. This paper includes a theoretical mathematical model that demonstrates the dynamic characteristics of BSIS along with a formulation for implementing BSIS in practice. Embedded programmable logic controllers serve as the edge nodes; a dynamic programming method creates a seamless integration between the edge nodes and the cloud. The paper concludes with a series of comprehensive analyses on scalability, responsiveness, and reliability. These analyses indicate a possible 60% reduction in the number of alarms, an edge response time of less than 0.1s, and an average downtime ratio of 0.66%.

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