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Industrial Internet of Things
Published in Bhawana Rudra, Anshul Verma, Shekhar Verma, Bhanu Shrestha, Futuristic Research Trends and Applications of Internet of Things, 2022
An M2M messaging system is proposed [26] for Industrial IoT architecture. A ZeroMQ based messaging system is found to be an efficient tool for data transmission and messaging purposes. It provides good connectivity, a rich sensing environment, and ubiquitous data access for IIoT applications. This ZMQ based data-oriented messaging system provides a detailed view of M2M-based IIoT applications. The proposed messaging technique is flexible with a different software platform and uses low-level UPD and TCP sockets. It can implement lightweight devices and powerful machines because of its unique features like compatibility with cross platforms, flexibility, and efficiency. Future research could optimize the ZMQ data-oriented messaging system to make it more efficient and suitable for various devices.
Workspace Sharing Assembly Robots: Applying IEC 61499 to System Integration and Application Development
Published in Alois Zoitl, Thomas Strasser, Distributed Control Applications, 2017
Matthias Plasch, Ebenhofer Gerhard, Michael Hofmann, Martijn Rooker, Sharath Chandra Akkaladevi, Andreas Pichler
ZeroMQ (ZMQ) is a high performance, asynchronous messaging library used in the fields of distributed computing and concurrency frameworks. Communication endpoints are based on common sockets, supporting protocols like inter-process, in-process, TCP and multicasts. The message distribution is based on a queue, which does not require a broker system to execute properly. Based on basic communication patterns like publish-subscribe, request-response, and push-pull, a huge variety of approaches to implement a concurrency framework is described in the user guide [11]. ZMQ is an open source project and its integration is based on an application programming interface (API), which is available in a wide range of programming languages [11].
Block size, parallelism and predictive performance: finding the sweet spot in distributed learning
Published in International Journal of Parallel, Emergent and Distributed Systems, 2023
Filipe Oliveira, Davide Carneiro, Miguel Guimarães, Óscar Oliveira, Paulo Novais
Once the Coordination module receives the information relative to the best candidate where to allocate each task of a specific ML project, the training process begins. The process starts with the coordinator node broadcasting the task information to all of the worker nodes in the cluster. This communication process is implemented with ZeroMQ: a brokerless asynchronous messaging library that implements multiple socket communication patterns that are useful for implementing distributed systems.