We propose a broadcast message forwarding algorithm for V2V communication in a platooning scenario for heavy duty trucks. The algorithm utilizes link information, which is piggybacked on the original data packet, to estimate which nodes are best suited to forward the packet. The aim is to reach all nodes in the platoon with as few forward messages as possible in order to avoid channel congestion. The algorithm is evaluated by simulation using real world V2V measurement data as input. We show that the algorithm performs almost as good as two ETSI standardized forwarding algorithms with respect to keeping the data age for the entire platoon at a low level. But when it comes to keeping the message intensity low, our algorithm outperforms the better of the ETSI algorithms by 35%.

This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like inmodern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user's physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets. © 2016 Susanna Spinsante et al.