Abstract
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 analyse the optimised link state routing (OLSR) and wired equivalent privacy (WEP) protocol in an indoor scenario, and mean shift clustering algorithm considering sensing data. For evaluation we considered throughput, delay and jitter metrics, and respiratory rate and heart rate metrics. The experimental and simulation results show that the nodes in the testbed were communicating smoothly and the mean shift clustering algorithm have a good performance.
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