Abstract
Following the successful results obtained in the previous editions of the TEEM Conference, a new edition of this event was held at ISEP/IPP - School of Engineering, Polytechnic Institute of Porto -, on October 7-9, 2015. The TEEM Conference was organized in thematic tracks, being one of them devoted to the topic of "Robotics in Education". This topic tries to strengthen different skills for future engineers/scientists by the means of the design, construction and programming of robots. These skills are related to various fields like science, technology, engineering and mathematics (STEM). Also, and equally important, the introduction of the robot in the classroom, both at school and college and even as extracurricular activity, promotes a host of outcomes such as technological fluency, creativity, participation, support, collaboration and cooperation. The purpose of the track "A Robot in the Classroom" was to bring together researchers, teachers and practitioners interested in the Robotics in Education. This track emphasized the exchange of experiences, methodologies and materials to motivate students about science and technology.

Abstract

Abstract
Data aggregation is a fundamental building block of modern distributed systems. Averaging based approaches, commonly designated gossip-based, are an important class of aggregation algorithms as they allow all nodes to produce a result, converge to any required accuracy, and work independently from the network topology. However, existing approaches exhibit many dependability issues when used in faulty and dynamic environments. This paper describes and evaluates a fault tolerant distributed aggregation technique, Flow Updating, which overcomes the problems in previous averaging approaches and is able to operate on faulty dynamic networks. Experimental results show that this novel approach outperforms previous averaging algorithms; it self-adapts to churn and input value changes without requiring any periodic restart, supporting node crashes and high levels of message loss, and works in asynchronous networks. Realistic concerns have been taken into account in evaluating Flow Updating, like the use of unreliable failure detectors and asynchrony, targeting its application to realistic environments.

Abstract

Abstract
Uncoordinated Frequency Hopping (UFH) has been proposed as a mechanism to address denial-of-service attacks, and consists of legitimate devices hopping uniformly at random between frequencies to cope with an attacker that aims to disrupt communication. We consider the use of UFH against an eavesdropper adversary that aims to overhear as much information as possible. We characterize the secrecy level of wireless networks under UFH, showing the harmful security effect of broadband eavesdropper adversaries capable of overhearing in multiple frequencies. To counter such eavesdroppers, we consider the use of broadband friendly jammers that are available to cause interference on eavesdroppers. Our results show that adding a limited number of broadband friendly jammers effectively improves the security level of such systems. © 2015 IEEE.

Abstract
Real-time monitoring applications may generate delay sensitive traffic that is expected to be delivered within a firm delay boundary in order to be useful. In this context, a previous work proposed an End-to-End Delay (EED) estimation mechanism for Wireless Sensor Networks (WSNs) to preview potential useless packets, and to early discard them in order to save processing and energy resources. Such estimation mechanism accounts delays using timers that make use of an Exponentially Weighted Moving Average (EWMA) function where the smoothing factor is a constant defined prior to the WSN deployment. Later experiments showed that, in order to enhance the estimation results, such smoothing factor should be defined as a function of the network load. The current work proposes an optimization of the previous estimation mechanism that works by evaluating the network load and by adapting the smoothing factor of the EWMA function accordingly. Results show that this optimization leads to a more accurate EED estimation for different network loads.