In recent years, the use of wireless sensor networks has been increasing. Localization is a fundamental problem in wireless sensor networks (WSNs), since location information is essential for diverse applications such as tracking, quality network coverage, health, and energy efficiency. In this paper performance of localization algorithms such as range-free, range-based, and fuzzybased decision is evaluated. We introduce a modification of an algorithm by providing weights to the correlation matrix to improve correctness. In all the cases the accuracy, precision, and computational complexity are evaluated as performance metrics. Location algorithms are evaluated using two scenarios, a first stage where all nodes are randomly distributed in a given area and a second scenario where four APs (access points) are placed on fixed positions and unknown nodes are randomly distributed within the sensing area. The received signal strength (RSS) is used to estimate the position of a node of interest. In the simulation results we show how our modified algorithm improves localization. On the other hand, we also have acceptable accuracy using distance-based algorithms, but they are more complex computationally.

This paper presents a high performance vision-based system with a single static camera for traffic surveillance, for moving vehicle detection with occlusion handling, tracking, counting, and One Class Support Vector Machine (OC-SVM) classification. In this approach, moving objects are first segmented from the background using the adaptive Gaussian Mixture Model (GMM). After that, several geometric features are extracted, such as vehicle area, height, width, centroid, and bounding box. As occlusion is present, an algorithm was implemented to reduce it. The tracking is performed with adaptive Kalman filter. Finally, the selected geometric features: estimated area, height, and width are used by different classifiers in order to sort vehicles into three classes: small, midsize, and large. Extensive experimental results in eight real traffic videos with more than 4000 ground truth vehicles have shown that the improved system can run in real time under an occlusion index of 0.312 and classify vehicles with a global detection rate or recall, precision, and F-measure of up to 98.190%, and an F-measure of up to 99.051% for midsize vehicles.

TCP is the most used transport protocol in the Internet and it relies on RTT (Round Trip Time) predictions for the retransmission control algorithm. Most of the algorithms reported in the literature consider memoryless traffic characteristics and do not study the performance under heavy tailed scenarios present in the Internet. In this paper, an algorithm for RTT prediction in a heavy-tailed environment is introduced, and it is shown to follow closely and accurately the actual RTT. The proposed algorithm is simple and permits online implementations. Results are compared with those obtained with other methodologies for real trace sets. It is shown that the proposed algorithm leads to a lower prediction error.

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Network anomaly detection and classification is an important open issue in network security. Several approaches and systems based on different mathematical tools have been studied and developed, among them, the Anomaly-Network Intrusion Detection System (A-NIDS), which monitors network traffic and compares it against an established baseline of a "normal" traffic profile. Then, it is necessary to characterize the "normal" Internet traffic. This paper presents an approach for anomaly detection and classification based on Shannon, Rényi and Tsallis entropies of selected features, and the construction of regions from entropy data employing the Mahalanobis distance (MD), and One Class Support Vector Machine (OC-SVM) with different kernels (Radial Basis Function (RBF) and Mahalanobis Kernel (MK)) for "normal" and abnormal traffic. Regular and non-regular regions built from "normal" traffic profiles allow anomaly detection, while the classification is performed under the assumption that regions corresponding to the attack classes have been previously characterized. Although this approach allows the use of as many features as required, only four well-known significant features were selected in our case. In order to evaluate our approach, two different data sets were used: one set of real traffic obtained from an Academic Local Area Network (LAN), and the other a subset of the 1998 MIT-DARPA Entropy 2015, 17 6240 set. For these data sets, a True positive rate up to 99.35%, a True negative rate up to 99.83% and a False negative rate at about 0.16% were yielded. Experimental results show that certain q-values of the generalized entropies and the use of OC-SVM with RBF kernel improve the detection rate in the detection stage, while the novel inclusion of MK kernel in OC-SVM and k-temporal nearest neighbors improve accuracy in classification. In addition, the results show that using the Box-Cox transformation, the Mahalanobis distance yielded high detection rates with an efficient computation time, while OC-SVM achieved detection rates slightly higher, but is more computationally expensive.

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