An enormous amount of research has been conducted in the area of positioning systems and thus it calls for a detailed literature review of recent localization systems. This article focuses on recent developments of non-Global Positioning System (GPS) localization/positioning systems. We have presented a new hierarchical method to classify various positioning systems. A comprehensive performance comparison of the techniques and technologies against multiple performance metrics along with the limitations is presented. A few indoor positioning systems have emerged as more successful in particular application environments than others, which are presented at the end.

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Machine learning, like its various applications, has received a great interest in outlier detection in Wireless Sensor Networks. Support Vector Machines (SVM) are a special type of Machine learning techniques which are computationally inexpensive and provide a sparse solution. This work presents a detailed analysis of various formulations of one-class SVMs, like, hyper-plane, hyper-sphere, quarter-sphere and hyper-ellipsoidal. These formulations are used to separate the normal data from anomalous data. Various techniques based on these formulations have been analyzed in terms of a number of characteristics for harsh environments. These characteristics include input data type, spatio-temporal and attribute correlations, user specified thresholds, outlier types, outlier identification(event/error), outlier degree, susceptibility to dynamic topology, non-stationarity and inhomogeneity. A tabular description of improvement and feasibility of various techniques for deployment in the harsh environments has also been presented.

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