Simulation is the most frequently adopted approach for evaluating protocols and algorithms for Vehicular Ad hoc Networks (VANETs) and Delay-Tolerant Networks (DTNs). Usually, simulation tools use mobility traces to build the network topology based on the existing contacts between mobile nodes. However, quality of the traces, in terms of spatial and temporal granularity of each entry in the logfile, is a key factor that impacts the network topology directly. Therefore, the reliability of the results depends strongly on the accurate representation of the real network topology by the vehicular mobility model. We show that five widely adopted existing real vehicular mobility traces present gaps, leading to fallible outcomes. In this work, we propose a solution to fill those gaps, leading to more fine-grained traces, which lead to more trustworthy simulation results. We propose and evaluate a data-based solution using clustering algorithms to fill the gaps of real-world traces. In addition, we also present the evaluation results that compare the communication graph of the original and the calibrated traces using network metrics. The results reveal that the gaps do indeed induce network topologies differing from reality, decreasing the quality of the evaluation results. To contribute to the research community, we have made the calibrated traces publicly available, so that other researchers may adopt them to improve their evaluation results.

Internet of Things arises as a computational paradigm that promotes the interconnection of objects to the Internet and enables interaction, operational efficiency, and communication. With the increasing inclusion in the network of intelligent objects that have characteristics such as diversity, heterogeneity, mobility and low computational power, it is fundamental to develop mechanisms that allow management and control. In addition, it is important to identify whether the assets are working properly or have anomalies. Traffic classification techniques are important to aid in network analysis and to handle many other key aspects such as security, management, access control, provisioning, and resource allocation. In order to promote the identification of network devices, especially IoT, this article presents a technique that uses Random Forest, a supervised automatic learning algorithm, together with the inspection of the contents of the packages for this purpose. Also, we use the same algorithm to perform the classification of network traffic. In the end, the identification of the devices showed an accuracy of approximately 99%.