To manage and maintain large-scale cellular networks, operators need to know which sectors underperform at any given time. For this purpose, they use the so-called hot spot score, which is the result of a combination of multiple network measurements and reflects the instantaneous overall performance of individual sectors. While operators have a good understanding of the current performance of a network and its overall trend, forecasting the performance of each sector over time is a challenging task, as it is affected by both regular and non-regular events, triggered by human behavior and hardware failures. In this paper, we study the spatio-temporal patterns of the hot spot score and uncover its regularities. Based on our observations, we then explore the possibility to use recent measurements' history to predict future hot spots. To this end, we consider tree-based machine learning models, and study their performance as a function of time, amount of past data, and prediction horizon. Our results indicate that, compared to the best baseline, tree-based models can deliver up to 14 better forecasts for regular hot spots and 153 better forecasts for non-regular hot spots. The latter brings strong evidence that, for moderate horizons, forecasts can be made even for sectors exhibiting isolated, non-regular behavior. Overall, our work provides insight into the dynamics of cellular sectors and their predictability. It also paves the way for more proactive network operations with greater forecasting horizons.
After an exhaustive search, we have not been able to find any work that characterizes performance of hot spots in cellular networks using real data. The only exception could be the work by Nika et al. @cite_4 , where a data set with more than 700K users is analyzed to understand temporal characteristics of data hot spots. Predictability is assessed, but considering only hot spot repetitions at the exact time and location in the forthcoming week. However, we find that the focus of the work is not on performance, but on load, with hot spots being defined on the basis of relative daily cell traffic.
@cite_4
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Various graphical passwords have been proposed as an alternative to traditional alphanumeric passwords and Microsoft has applied a graphical scheme in the operating system Windows 8. As a new type of password scheme, potential security problems such as hot-spots may exist. In this paper, we study user choice in Windows 8 graphical password scheme by both lab and field studies and analyze the hot-spots caused by user choice. Our analysis shows that there are many significant hot-spots in the background image when users set their passwords using Microsofts guidance. Then, based on the data of field study, we conducted a simulated human-seeded attack to prove our conclusion. The success rate of 66.69 and 54.46 also provide strong proof of the hot-spots in Windows 8 graphical password scheme. Finally, we designed a simulated automated attack and obtained a success rate of 42.86 .