Performance Evaluation of an AmI Testbed for Improving QoL: Evaluation Using Clustering Approach Considering Distributed Concurrent Processing
Ryoichiro Obukata, Miralda Cuka, Donald Elmazi, Shinji Sakamoto, Tetsuya Oda, Leonard Barolli · 2017
Ambient intelligence (AmI) deals with a new world of ubiquitous computing devices, where physical environments interact intelligently and unobtrusively with people. AmI environments can be diverse, such as homes, offices, meeting rooms, schools, hospitals, control centers, vehicles, tourist attractions, stores, sports facilities, and music devices. In this paper, we present the design and implementation of a testbed for AmI using Raspberry Pi mounted on Raspbian OS. We analyze the performance of k-means clustering algorithm considering sensing data. For evaluation we considered respiratory rate and heart rate metrics. We speeded up the k-means clustering algorithm by using distributed concurrent processing.