Data Mining in Internet of Things Systems: A Literature Review

The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur

Understanding internal state to predict habitat selection

An important part of managing wildlife populations is predicting how they will distribute after environmental change. Because distributions are driven by selection of habitats, many studies make predictions based on our understanding of how habitat availability and other aspects of the external environment impact habitat selection. However, the external environment is only one driver of habitat selection. Animals are also motivated to move by aspects of their internal state, including energetic state and life-history stage. My thesis focusses on understanding how internal state influences habitat selection decisions by individual animals. I first test whether modelling changes in habitat selection with habitat availability — i.e., the functional response in habitat selection — can improve predictions of habitat selection. I show that only accounting for the functional response does not improve predictions because individuals differ in their responses to changing habitat availability. I next show how internal state might motivate these individual responses to habitat availability, ultimately producing population distributions that depend on the internal states of individual animals in the population. I tested this connection by modelling habitat selection by female elk in response to glucocorticoid hormones, a physiological indicator of their internal state and energetic needs after experiencing stressors. I found that glucocorticoid hormones drive selection for energy-rich forage by female elk. This demonstrates glucocorticoids are a mechanism for habitat selection, and individual differences in its production and physiological effects can shape how individuals respond to stressors. I next present a novel method for collecting non-invasive samples of glucocorticoids and other physiological biomarkers from wild animals. Finally, I demonstrate glucocorticoids — and thus internal state — reveal how animals manage resource acquisition, competition, and predator avoidance in social contexts. Overall, my thesis provides a framework for integrating internal state with habitat selection. I argue this integration is necessary to make better predictions about wildlife distributions, a critical endeavour as human land use and climate change accelerate environmental change