Data Distribution Service for industrial automation

Yang, Jinsong; Sandström, Kristian; Nolte, Thomas; Behnam, Moris

doi:10.1109/etfa.2012.6489544

Cited by 33 publications

(11 citation statements)

References 7 publications

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“…The infrastructure is built using the MQTT protocol [40]. MQTT is not the only communication protocol available for IoT scenarios; for instance, possible alternatives are AMQP (Advanced Message Queuing Protocol) [41], DDS (Data Distribution Service) [42], CoAP (Constrained Application Protocol) [43], OPC UA (OPC Foundation Unified Architecture) [44], etc. ; for a more comprehensive survey we refer to the work of Dizdarevic et al [45].…”

Section: Framework Descriptionmentioning

confidence: 99%

ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

Borghesi¹,

Burrello²,

Bartolini³

2023

IEEE Internet Things J.

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In recent years, the Industrial Internet of Things (IIoT) has led to significant steps forward in many industries, thanks to the exploitation of several technologies, ranging from Big Data processing to Artificial Intelligence (AI). Among the various IIoT scenarios, large-scale data centers can reap significant benefits from adopting Big Data analytics and AIboosted approaches since these technologies can allow effective predictive maintenance. However, most of the off-the-shelf currently available solutions are not ideally suited to the HPC context, e.g., they do not sufficiently take into account the very heterogeneous data sources and the privacy issues which hinder the adoption of the cloud solution, or they do not fully exploit the computing capabilities available in loco in a supercomputing facility. In this paper, we tackle this issue, and we propose an IIoT holistic and vertical framework for predictive maintenance in supercomputers. The framework is based on a big lightweight data monitoring infrastructure, specialized databases suited for heterogeneous data, and a set of high-level AI-based functionalities tailored to HPC actors' specific needs. We present the deployment and assess the usage of this framework in several in-production HPC systems.

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Section: Framework Descriptionmentioning

confidence: 99%

ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

Borghesi¹,

Burrello²,

Bartolini³

2023

IEEE Internet Things J.

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“…Publishers and subscribers are the entities for data distribution and consumption, which publish and receive data through the GDS, but they can not do it on their own. Instead, publishers use Data Writers to send data and subscribers use Data Readers to receive data [81] with the matching between the two through topics, that is in order to communicate with each other, publishers and subscribers must use the same topic (same name, type and a compatible QoS).…”

Section: Data Distribution Service (Dds)mentioning

confidence: 99%

A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration

et al. 2019

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The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This paper surveys on the application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog-and cloud-based IoT systems. To this end, the paper first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the paper surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture. Continuous innovations in hardware, software and connection solutions in the last decade have lead to the expansion of the Internet of Things (IoT) with the number of connected devices growing by the day [1] [2]. The huge amount of data generated by these devices require to find a proper system architecture able to both process and store all the data. While cloud-based architectures are being currently used for that purpose, the new fog computing paradigm is envisioned to scale and optimize the IoT infrastructures [3]. Examples of the cloud-based IoT solutions have been proposed in [4], [5], [6] and a detailed analysis of properties for IoT cloud providers has been conducted in [7]. These studies have shown that cloud computing has the potential to satisfy many IoT requirements, such as monitoring of services, powerful processing of sensor data streams and visualization tasks. On the other hand, fog-based solutions are suited to address real-time processing, fast data response, and latency issues, thus extending the cloud capabilities closer to the edge of the network [8]. Among many factors that will determine the performance in a combined IoT, fog and cloud computing paradigm, the application layer communication, which in turn depends on the selected communication protocols, is one of the main ones.Despite the popularity and wide spread usage of HTTP, the currently used protocols in various domains of IoT, fog and cloud domains are de-facto fragmented with many different solutions. This is due to the different requirements and areas that IoT needs to cover, combining the functionalities of sensors, actuators and computing power with security, connectivity and a myriad of other features. As a result, there is no common agreement on the reference architecture or adopted standards of co...

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“…Other researchers have measured the performance qualities of DDS, such as latency [14][15][16][17]. While measuring latency is an essential benchmark for real-time communication middleware, there are methods to collect the information and many other factors that influence end-to-end latency.…”

Section: Dds Performance Evaluationsmentioning

confidence: 99%

Quantifying DDS-cerberus network control overhead

et al. 2022

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Securing distributed device communication is critical because the private industry and the military depend on these resources. One area that adversaries target is the middleware, which is the medium that connects different systems. This paper evaluates a novel security layer, DDS-Cerberus (DDS-C), that protects in-transit data and improves communication efficiency on data-first distribution systems. This research contributes a distributed robotics operating system testbed and designs a multifactorial performance-based experiment to evaluate DDS-C efficiency and security by assessing total packet traffic generated in a robotics network. The performance experiment follows a 2:1 publisher to subscriber node ratio, varying the number of subscribers and publisher nodes from three to eighteen. By categorizing the network traffic from these nodes into either data message, security, or discovery+ with Quality of Service (QoS) best effort and reliable, the mean security traffic from DDS-C has minimal impact to Data Distribution Service (DDS) operations compared to other network traffic. The results reveal that applying DDS-C to a representative distributed network robotics operating system network does not impact performance.

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Data Distribution Service for industrial automation

Cited by 33 publications

References 7 publications

ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

ExaMon-X: A Predictive Maintenance Framework for Automatic Monitoring in Industrial IoT Systems

A Survey of Communication Protocols for Internet of Things and Related Challenges of Fog and Cloud Computing Integration

Quantifying DDS-cerberus network control overhead

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