Earticle

Estimating the locations of workers in a shipyard is beneficial for a variety of applications such as selecting potential forwarders for transferring data in IoT services and quickly rescuing workers in the event of industrial disasters or accidents. In this work, we propose a human movement stream processing system for estimating worker locations in shipyards based on Apache Spark and TensorFlow serving. First, we use Apache Spark to process location data streams. Then, we design a worker location prediction model to estimate the locations of workers. TensorFlow serving manages and executes the worker location prediction model. When there are requirements from clients, Apache Spark extracts input data from the processed data for the prediction model and then sends it to TensorFlow serving for estimating workers’ locations. The worker movement data is needed to evaluate the proposed system but there are no available worker movement traces in shipyards. Therefore, we also develop a mobility model for generating the workers' movements in shipyards. Based on synthetic data, the proposed system is evaluated. It obtains a high performance and could be used for a variety of tasks in shipyards.

The recent advance of sensor networks and ubiquitous techniques allow collecting and analyzing of the data which overcome the limitation imposed by time and space in real-time for making decisions. Also, analysis and prediction of collected data can support useful and necessary information to users. The collected data in sensor networks environment is the stream data which has continuous, unlimited and sequential properties. Because of the continuous, unlimited and large volume properties of stream data, managing stream data is difficult. And the stream data needs dynamic processing method because of the memory constraint and access limitation. Accordingly, we analyze correlation stream data using principal component analysis. And using result of analysis, it helps users for making decisions.

유비쿼터스 홈 네트워크 환경에서 사용자는 한 장소에서 다른 장소로 이동하면서도 연속적인 서비스를 제공받길 원한다. 기존의 멀티미디어 스트리밍 서비스는 사용자를 인식하지 못하고 항상 새 세션 생성됨에 따라 사용자가 제공받던 멀티미디어를 이어서 보고자 할 때 프레임위치를 기억해 놓고 재설정 해주어야한다. 이에 본 연구에서는 위치인식시스템을 사용하여 사용자의 위치를 추적하고 사용자의 이동성을 지원하며 개인의 상황(기존에 제공받던 미디어를 이어 전송받기를 원하는지, 사용자의 위치, 사용자와 가까운 단말기 등)을 고려한 위치기반의 개인화된 멀티미디어 스트리밍 시스템을 제안하고 구현하였다.