In an Internet of Things (IoT) environment, sensors collect and send data to application servers through IoT gateways. However, these data may be missing values due to networking problems or sensor malfunction, which reduces applications’ reliability. This work proposes a mechanism to predict and impute missing data in IoT gateways to achieve greater autonomy at the network edge. These gateways typically have limited computing resources. Therefore, the missing data imputation methods must be simple and provide good results. Thus, this work presents two regression models based on neural networks to impute missing data in IoT gateways. In addition to the prediction quality, we analyzed both the execution time and the amount of memory used. We validated our models using six years of weather data from Rio de Janeiro, varying the missing data percentages. The results show that the neural network regression models perform better than the other imputation methods analyzed, based on the averages and repetition of previous values, for all missing data percentages. In addition, the neural network models present a short execution time and need less than 140 KiB of memory, which allows them to run on IoT gateways.
The OSN's (On-line Social Networks) have reached an incredible popularity in modern Internet. Those systems have been present in the daily lives of countless people helping them to share personal experiences, expectations and opinions. So high popularity has made of such networks complex systems. To understand the operation and phenomena that occur in such networks, there are metrics and models that capture aspects of their structures.The purpose of this work is to understand the complex reality of eBay ecommerce network, their connections and the dynamics of its users. Data were collected using a script developed in this work, and it resulted in a database of approximately 87 million transactions and 15 million different dealer users. From these data, the characterization was made estimating network metrics, like dealer users' degree distribution, that gave us key insights about the eBay negotiation network. We found that there are users who bought/sold for more than 100.000 different persons. We also found that a user A interacted over 4.000 times with another user B in just 3 months. Those and other interesting results, such as average distance and feedbacks ratings, were obtained, analyzed and discussed in this work. 1
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