A pattern-oriented PIM for resource management of educational services in mobile networks

Zadahmad, Manouchehr; YousefzadehFard, Parisa; Arasteh, Bahman

doi:10.1016/j.sbspro.2011.11.019

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Other MDA-based approaches [ 30 , 31 ] take advantage of design patterns to facilitate reusability, accurate automation, and granularity between transformations. These approaches can be useful for smart spaces development since there are many entities, interactions and behaviors that take place in many different scenarios following similar patterns.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Model-Driven Methodology for Rapid Deployment of Smart Spaces Based on Resource-Oriented Architectures

Corredor

Bernardos

Iglesias

et al. 2012

Sensors

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Advances in electronics nowadays facilitate the design of smart spaces based on physical mash-ups of sensor and actuator devices. At the same time, software paradigms such as Internet of Things (IoT) and Web of Things (WoT) are motivating the creation of technology to support the development and deployment of web-enabled embedded sensor and actuator devices with two major objectives: (i) to integrate sensing and actuating functionalities into everyday objects, and (ii) to easily allow a diversity of devices to plug into the Internet. Currently, developers who are applying this Internet-oriented approach need to have solid understanding about specific platforms and web technologies. In order to alleviate this development process, this research proposes a Resource-Oriented and Ontology-Driven Development (ROOD) methodology based on the Model Driven Architecture (MDA). This methodology aims at enabling the development of smart spaces through a set of modeling tools and semantic technologies that support the definition of the smart space and the automatic generation of code at hardware level. ROOD feasibility is demonstrated by building an adaptive health monitoring service for a Smart Gym.

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Section: Related Work and Backgroundmentioning

confidence: 99%