IoT and Emerging Tech: Presentations
This paper presents a framework to design a platform for smart cities which collects, stores, enriches, distributes and enables the re-use of urban data. By using state of the art business models, supply chain management techniques, technologies and communication protocols, the proposed platform shifts cities to a new state in which intelligence and integration is pervasive throughout its physical infrastructure. Results confirm the viability and efficiency of the proposed platform.
We investigated the contribution of wearable sensor data in addition to medical record information to predict discharge clinical outcome scores for 20 patients at an inpatient rehabilitation institute. Wearable inertial sensors can offer more objective measures of patient movement and progress. While models trained only with clinical features predicted discharge scores well, we were able to achieve a higher level of prediction accuracy when also including wearable sensor derived features.
We provide a session manager suitable for IoT traffic over LTE. Our analysis starts with a Markov Chain analysis of the impact of DRX parameters. This is followed by an optimal scheduler design and an IoT-aware adaptive DRX algorithm at the client, both of which modulate the tradeoff among signal load, delay and power consumption. Scalability is considered in this work by providing a clustering-based adaptive DRX algorithm at eNB.