Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Unsupervised Understanding of Location and Illumination Changes in Egocentric Videos

Wearable cameras stand out as one of the most promising devices for the upcoming years, and as a consequence, the demand of computer algorithms to automatically understand the videos recorded with them is increasing quickly. An automatic understanding of these videos is not an easy task, and its mobile nature implies important challenges to be faced, such as the changing light conditions and the unrestricted locations recorded. This paper proposes an unsupervised strategy based on global features and manifold learning to endow wearable cameras with contextual information regarding the light conditions and the location captured. Results show that non-linear manifold methods can capture contextual patterns from global features without compromising large computational resources. The proposed strategy is used, as an application case, as a switching mechanism to improve the hand-detection problem in egocentric videos.Comment: Submitted for publicatio

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Trace element contamination in the arms of the Danube Delta (Romania/Ukraine): Current state of knowledge and future needs

This paper provides the first critical synopsis of contamination by selected trace elements in the whole Danube Delta (Romania/Ukraine) to: identify general patterns of contamination by trace elements across the Delta, provide recommendations to refine existing monitoring networks and discuss the potential toxicity of trace elements in the whole Delta. Sediment samples were collected between 2004 and 2007 in the three main branches of the Delta (Chilia, Sulina and Sfantu Gheorghe) and in the secondary delta of the Chilia branch. Samples were analyzed for trace elements (Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) and TiO2, Fe2O3, MnO, CaCO3 and total organic carbon. Cluster analysis (CA) and Principal Component Analysis (PCA) showed that levels of Cd, Cu, Pb, and Zn were influenced by anthropogenic activities. At the opposite, concentrations of Cr and Ni largely originated from the weathering of rocks located in the Romanian part of the Danube catchment and naturally rich in these elements. Data analysis using Self- Organizing Maps confirmed the conclusions of CA/PCA and further detected that the contamination tended to be higher in the Chilia and Sulina arms than in the Sfantu Gheorghe arm. The potential ecological risks due to trace element contamination in the Danube Delta could be identified as moderate and localized, provided that the presence of the natural sources of Cr and Ni was properly considered. The available results suggest that monitoring sediment quality at the mouths of Sulina and Sfantu Gheorghe arms is probably enough to get a picture of the sediment quality along their entire lengths. However, a larger network of monitoring points is necessary in the Chilia and secondary Chilia delta to account for the presence of local point sources and for the more complex hydrodynamic of this part of the Danube Delta