With the ongoing advances in sensor technology and miniaturization of electronic chips, more applications are researched and developed for wearable devices. Hydration monitoring is among the problems that have been recently researched. Athletes, battlefield soldiers, workers in extreme weather conditions, people with adipsia who have no sensation of thirst, and elderly people who lost their ability to talk are among the main target users for this application. In this paper, we address the use of machine learning for hydration monitoring using data from wearable sensors: accelerometer, magnetometer, gyroscope, galvanic skin response sensor, photoplethysmography sensor, temperature, and barometric pressure sensor. These data, together with new features constructed to reflect the activity level, were integrated with personal features to predict the last drinking time of a person and alert the user when it exceeds a certain threshold. The results of applying different models are compared for model selection for on-device deployment optimization. The extra trees model achieved the least error for predicting unseen data; random forest came next with less training time, then the deep neural network with a small model size, which is preferred for wearable devices with limited memory. Embedded on-device testing is still needed to emphasize the results and test for power consumption.

The actual performance of video delivery over wireless networks is best captured through experimental studies over real testbed, which is crucial to uncover the technical problems and practical challenges of realizing the proposed video delivery techniques. Network coding (NC) provides a paradigm shift to optimize the network resources for enhancing the video quality as a result of delivery over multi-hop wireless networks. In this paper, we propose a novel framework for evaluating the performance of NC-based video delivery over multi-hop wireless networks. Based on an experimental testbed implementation of inter-layer video coding and delivery, we propose a new generalized technique for multi-hop interlayer NC in enhancing video delivery quality. We evaluate the trade-off between complexity of multi-hop NC, and video quality gain. We provide clear recommendations for best design choices of NC-based inter-layer video coding and delivery over multi-hop wireless mesh network considering link conditions and Packet Delivery Ratio (PDR). The results show that using NC improves the layered video performance in single and multi-hop network. The average number of decoded layers when using NC in single hop outperforms that when using NC in multi-hop.