• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.94-96
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• 2022

Plug & Play techniques have been proposed in various ways to overcome the complexity of sensors/drivers in IoT application development. IEEE1451 standard abstracts sensors/drivers into a data structure called TEDS. As a result, characteristics of the sensor/driver are unnecessary when connecting the sensor/driver to the host device. The method proposed by ETRI is a format in which device drivers of sensors/drivers are dynamically loaded and connected to hosts, and there is no particular abstraction data structure. Both schemes are located at both ends in terms of the abstraction unit of the sensor/driver. We present the problem based on this fact, and what optimized methods can exist in the middle of it. In this paper, we analyze existing Plug&Play techniques. Also, we specify abstraction units of sensors/drivers, and analyze them.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.370-381
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• 2012

Multi-criteria result extraction is crucial in many scientific applications that support real-time stream processing, such as habitat research and disaster monitoring. Skyline evaluation is computational intensive especially over continuous time-interval data streams where each object has its own customized expiration time. In this work, we propose TI-Sky - a continuous skyline evaluation framework. To ensure correctness, the result space needs to be continuously maintained as new objects arrive and older objects expire. TI-Sky strikes a perfect balance between the costs of continuously maintaining the result space and the costs of computing the final skyline result from this space whenever a pull-based user query is received. Our key principle is to incrementally maintain a partially precomputed skyline result space - however doing so efficiently by working at a higher level of abstraction. TI-Sky's algorithms for insertion, deletion, purging and result retrieval exploit both layers of granularity. Our experimental study demonstrates the superiority of TI-Sky over existing techniques to handle a wide variety of data sets.