DYAMAND: DYnamic, Adaptive MAnagement of Networks and Devices

Nelis, Jelle; Verschueren, Tom; Verslype, Dieter; Develder, Chris

doi:10.1109/lcn.2012.6423604

Cited by 19 publications

(19 citation statements)

References 13 publications

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“…In the presented use cases, the sensor integration is supported by the DYAMAND platform [39], which was developed at the IBCN 16 research group. The DYAMAND platform allows the detection and integration of sensors and devices.…”

Section: Discussionmentioning

confidence: 99%

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

et al. 2016

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In the Internet of Things (IoT), data-producing entities sense their environment and transmit these observations to a data-processing platform for further analysis. Applications can have a notion of context-awareness by combining this sensed data, or by processing the combined data. The processes of combining data can consist both of merging the dynamic sensed data, as well as fusing the sensed data with background and historical data. Semantics can aid in this task, as they have proven their use in data integration, knowledge exchange and reasoning. Semantic services performing reasoning on the integrated sensed data, combined with background knowledge, such as profile data, allow extracting useful information and support intelligent decision making. However, advanced reasoning on the combination of this sensed data and background knowledge is still hard to achieve. Furthermore, the collaboration between semantic services allows to reach complex decisions. The dynamic composition of such collaborative workflows that can adapt to the current context, has not received much attention yet.In this paper, we present MASSIF, a data-driven platform for the semantic annotation of and reasoning on IoT data. It allows the integration of multiple modular reasoning services, that can collaborate in a flexible manner to facilitate complex decision making processes. Data-driven workflows are enabled by letting services specify the data they would like to consume. After thorough processing, these services can decide to share their decisions with other consumers. By defining the data these services would like to consume, they can operate on a subset of data, improving reasoning efficiency. Furthermore, each of these services can integrate the consumed data with background knowledge in its own context model, for rapid intelligent decision making. To show the strengths of the platform, two use cases are detailed and thoroughly evaluated.

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Section: Discussionmentioning

confidence: 99%

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

et al. 2016

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“…Next we have OpenRemote [16], a software integration platform for residential and commercial building automation from OpenRemote Inc. Finally, DYAMAND [14] is a lightweight plugin based interoperability framework developed at Ghent University -iMinds. We evaluated the procedure to integrate a new technology they all claim to support (i.e.…”

Section: Existing Integration Platformsmentioning

confidence: 99%

“…A technology-agnostic model is used to be able to represent all information about the discovered devices and services. This model is based on work done at Ghent University on the DYAMAND platform [14]. This model includes among others the concept of state variables that model the state of services in a generic way.…”

Section: Decomposition Of the Remote Management Systemmentioning

confidence: 99%

Remote Management of a Large Set of Heterogeneous Devices Using Existing IoT Interoperability Platforms

Vandaele

Nelis

Verbelen

et al. 2016

Internet of Things. IoT Infrastructures

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Abstract. With the evolution of the Internet of Things, devices of many different technologies and manufacturers are being developed (e.g. for use cases ranging from home automation to smart cities). This creates challenges regarding interoperability between these heterogeneous devices, as well as integrating them to enable innovative applications. Currently, several integration platforms already exist to integrate technologies in a local gateway (e.g. OpenHAB, Zodianet, etc.). Yet, the local set-up and configuration still is overly complex, especially for non-technical users. In this paper, we present a remote management platform that focuses on ease of configuration and installation. It allows monitoring, configuration, diagnostics and service provisioning without manual intervention of a technical person. The platform reacts on local changes such as the installation of a new device or state changes of discovered services. This information can then be used to install required plugins, generate alarms or take problem-solving actions.

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“…we use the DYAMAND [20] framework. DYAMAND is an extendable interoperability framework for service discovery and device access protocols.…”

Section: A General Sensors and Actorsmentioning

confidence: 99%

Middleware Platform for Distributed Applications Incorporating Robots, Sensors and the Cloud

Coninck

Bohez

Leroux

et al. 2016

2016 5th IEEE International Conference on Cloud Networking (Cloudnet)

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Abstract-Cyber-physical systems in the factory of the future will consist of cloud-hosted software governing an agile production process executed by autonomous mobile robots and controlled by analyzing the data from a vast number of sensors. CPSs thus operate on a distributed production floor infrastructure and the set-up continuously changes with each new manufacturing task. In this paper, we present our OSGibased middleware that abstracts the deployment of servicebased CPS software components on the underlying distributed platform comprising robots, actuators, sensors and the cloud. Moreover, our middleware provides specific support to develop components based on artificial neural networks, a technique that recently became very popular for sensor data analytics and robot actuation. We demonstrate a system where a robot takes actions based on the input from sensors in its vicinity.

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DYAMAND: DYnamic, Adaptive MAnagement of Networks and Devices

Cited by 19 publications

References 13 publications

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

The MASSIF platform: a modular and semantic platform for the development of flexible IoT services

Remote Management of a Large Set of Heterogeneous Devices Using Existing IoT Interoperability Platforms

Middleware Platform for Distributed Applications Incorporating Robots, Sensors and the Cloud

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