Integration of Data Distribution Service and distributed partitioned systems

García-Valls, Marisol; Domínguez-Poblete, Jorge; Touahria, Imad Eddine; Lu, Chenyang

doi:10.1016/j.sysarc.2017.11.001

Cited by 21 publications

(7 citation statements)

References 18 publications

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“…In [36], the design of a scheme for attending simultaneous requests is provided. In [32], a model for integrating the Data Distribution Software with single board computers and Raspberry Pi is provided; this is further reworked in [40] for a different domain such as avionics. Also, there have been a number of dedicated research contributions to building real-time facilities in middleware such as [38,39,49], among others; or building abstractions for utilization of multiple interaction paradigms such as [66] or [43].…”

Section: Background On Distribution Sofware Designmentioning

confidence: 99%

Medical systems, the role of middleware and survey on middleware design

Touahria

2018

Preprint

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The integration of medical devices in the patient treatment process becomes increasingly important due to the efficiency of the technology. On the one hand, medical devices hardware is more powerful and its integration with the software platforms is improved. These devices are able to transfer accurate data to the clinician and offer the possibility of sharing data with other devices or computational servers for advanced analysis. On the other hand, medical software platforms are appearing to provide advanced functionality on top of these medical hardware devices. In this context, the role of the software is essential, not only at the highest abstraction level that provides the application business logic; the role of the underlying connecting software (the communication middleware or distribution software) is essential. These are capable of providing advanced connectivity functions, very efficiently, and within appropriate time bounds. This paper reviews the state of the art on middleware as facilitator for interconnection among devices and also describes a number of initiatives (such as the Integrated Clinical Environment -ICE) and projects that further extend the underlying distribution software towards the clinical domain as device interconnection facilitators.

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Section: Background On Distribution Sofware Designmentioning

confidence: 99%

Medical systems, the role of middleware and survey on middleware design

Touahria

2018

Preprint

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“…In [56], the design of a scheme for attending simultaneous requests is provided. In [46], a model for integrating the Data Distribution Software with single board computers and Raspberry Pi is provided; this is further reworked in [61] for a different domain such as avionics. Also, there have been a number of dedicated research contributions to building real-time facilities in middleware such as [58,59], among others; or building abstractions for utilization of multiple interaction paradigms such as [95] or [67].…”

Section: Baselinementioning

confidence: 99%

An incomplete and biased survey on selected resource management for distributed applications as basis for interoperability of medical devices

Touahria

2018

Preprint

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The popularity and wide spread of IoT technology has brought about a rich hardware infrastructure over which it is possible to run powerful applications that were not previously imagined. Among this infrastructure, there are the medical hardware that is progressively advancing but at a slower pace. Nevertheless, medical devices are more powerful now to run more sophisticated functions and applications and exchange big data with external systems in a secure and safe fashion. Towards the design of an architecture for interoperability of medical devices, this paper initially focuses on the background work that is taken by the author for this objective. The paper briefly describes the role of the software in the advances of medical systems and their possibilities for interoperability. It focuses attention on the distribution software layer that is responsible for connectivity, efficiency, and time-sensitivity in the basic operation of medical systems such as exchange of information and commands across devices and systems. The paper analyses a number of previous work on middleware (mostly performed at his research group and also in a broader research community), and pay especial attention to the middleware for web-based systems and how they relate to the development of distributed medical systems.

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“…The deployed nodes are proportional to the data error and transmissions. Much to our knowledge, the research limitations in [5][6][7][8][9][10][11] necessitated this work. These were significantly resolved with our proposed memory-efficient automatic discovery (MAD)-DDS model.…”

Section: Introductionmentioning

confidence: 99%

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

et al. 2023

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The rampant deployment of data distribution service (DDS) as middle‐ware service providers for industrial network platforms has been widely investigated. All DDS‐based node discovery protocols establish communication with its intended target systems by accessing matched endpoints/nodes information. These matched endpoints/nodes information is usually embedded with the system’s programmable control plane network which acts as the conveyor vehicle. The introduction of software defined networking (SDN) is to characterize the control plane from embedded data plane. The DDS implements the simple discovery protocol (SDP) as its inherent node discovery protocol. Deploying DDS for data packet exchange in server‐based collaborative distributed networked control (DNC) systems has gained traction. The current automatic discovery protocol (ADP) based on SDP is fraught with real‐time limitations such as high memory consumption and poor packet transmission. This work presents novel memory‐efficient automatic discovery data distribution service (MAD‐DDS) with enhanced threshold bloom filters (ETBF) where ETBF stores transmission packets at simulation end‐nodes. The packet is further adjusted using optimized binarization and decision thresholds inside ADP, hence guaranteeing memory reduction. The testbed computation recorded significantly improved quality of service (QoS) whereas numerical results depict significant decline in memory consumption with consistent packet transmission rates that produces increased computational capacity.

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Integration of Data Distribution Service and distributed partitioned systems

Cited by 21 publications

References 18 publications

Medical systems, the role of middleware and survey on middleware design

Medical systems, the role of middleware and survey on middleware design

An incomplete and biased survey on selected resource management for distributed applications as basis for interoperability of medical devices

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

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