in Proceeding
| Authors | Gabriel Orsini, Wolf Posdorfer, Winfried Lamersdorf |
| Title | Efficient Mobile Clouds: Forecasting the Future Connectivity of Mobile and IoT Devices to Save Energy and Bandwidth |
| Published in | The 14th International Conference on Future Networks and Communications (FNC 2019) / The 16th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2019) / Affiliated Workshops, August 19-21, 2019, Halifax, Nova Scotia, Canada |
| Published in | Procedia Computer Science |
| Publisher | Elsevier Science |
| Medium | Procedia Computer Science Volume 155 |
| Date | August 2019 |
| Pages | 121--128 |
| URL | https://www.sciencedirect.com/science/article/pii/S1877050919309342 |
| Abstract | Use cases in the Internet of Things (IoT) and in mobile clouds often require the interaction of one or more mobile devices with their infrastructure to provide services to users. In an ideal scenario this interaction is based on a reliable connection between the communicating devices, which is often not the case. Since most use cases do not adequately address this issue, the quality of service is often compromised. Aimed to address this issue, this paper proposes a novel approach to predict the future connectivity and bandwidth of mobile devices by applying machine learning to the context data recorded by the various sensors of the mobile device. This concept, designed as a microservice, has been implemented in the mobile middleware CloudAware, a system software infrastructure for mobile cloud computing that integrates easily with mobile device operating systems, such as Android. Furthermore, we evaluate our approach with real sensor data and show how to enable mobile devices in the IoT to make assumptions about their future connectivity, allowing for intelligent and distributed decision making at the mobile edge of the network. |
| Document |  |
| Other formats | Din 1501 |