A monitoring-based approach for WSN security using IEEE-802.15.4/6LowPAN and DTLS communication

International audienceIn this paper, we present a monitoring based approach for securing upper layer communications of WSN (Wireless Sensor Networks), the latter using IEEE802.15.4/6LoWPAN stacks and tinyDTLS. The monitoring techniques have been integrated as an extension to the industrial tool MMT (Montimage Monitoring Tool). The MMT-extension verifies that the network is working following a set of security rules that have been defined by ETSI. The security rules check if the protocol stack is working properly. If MMT detects a security rule that was not respected, then it sends an alarm to the system manager so that he can take properly reactive adjustments. We tested each of the security rules in MMT's extension using point-to-point configuration. After all these tests were verified, we tested our MMT-extension using real data gathered from the FIT IoT-LAB platform. The results of these tests shown that our MMT's extension for WSN using IEEE-802.15.4/6LowPAN and DTLS communication is feasible

Mathematical modelling based learning strategy

We present the Mathematical Modelling Learning strategy in which students create a model that will predict behaviour of existing phenomena using real data. In our implementation students create models from atmospheric data and solve them to determine which weather conditions favour high levels of pollutants in the atmosphere of Monterrey metropolitan area in Mexico. To carry out the strategy we structure course topics around this single comprehensive and integrative project. Students follow a procedure consisting of 4 stages. In the first stage they do statistical analysis of the data. In the second stage, students interpolate missing data and project component data to a 2D map of the metro area. In the third stage students create the mathematical models by carrying out curve fitting through least squares technique. In the third stage, students solve the models by finding roots, solving systems of equations, solving differential equations or integrating. The final deliverable is to determine under which weather conditions there can be an environmental situation that put people's health in danger. Analysis of the strategy is presented as well as statistical results. © The Authors. Published by Elsevier B.V

A parallel implementation of singular value decomposition for video-on-demand services design using principal component analysis

We have developed a mathematical model for video on demand server design based on principal component analysis. Singular value decomposition on the video correlation matrix is used to perform the PCA. The challenge is to counter the computational complexity, which grows proportionally to n3, where n is the number of video streams. We present a solution from high performance computing, which splits the problem up and computes it in parallel on a distributed memory system. © The Authors. Published by Elsevier B.V

Overcoming uncertainty on video-on-demand server design by using self-similarity and principal component analysis

In this paper we use a small amount of video files to design a video-on-demand server. We use the available video information to overcome uncertainties such as future user preference, type of video file (movie, cartoon, documentary), video compression technique, etc. Using principal component analysis we overcome such uncertainties by reducing the dimensionality of the video data, creating a new video trace that captures statistical characteristics of most video files; we call this the characteristic video trace. Using the Pareto probability distribution for the size of the video frames (of the characteristic video trace) and self-similarity we develop a non-asymptotic model which predicts memory buffer size for a required quality of service. By obtaining the necessary parameters for the mathematical model from the characteristic video trace we could design the server without more information. © 2013 The Authors. Published by Elsevier B.V