Advances in Internet, Data and Web Technologies The 7th International Conference on Emerging Internet, Data and Web Technologies (EIDWT-2019)

This book presents original contributions on the theories and practices of emerging Internet, Data and Web technologies and their applications in businesses, engineering and academia. As a key feature, it addresses advances in the life-cycle exploitation of data generated by digital ecosystem technologies.

The Internet has become the most proliferative platform for emerging large-scale computing paradigms. Among these, Data and Web technologies are two of the most prominent paradigms, manifesting in a variety of forms such as Data Centers, Cloud Computing, Mobile Cloud, Mobile Web Services, and so on. These technologies altogether create a digital ecosystem whose cornerstone is the data cycle, from capturing to processing, analysis and visualization. The need to investigate various research and development issues in this digital ecosystem has been made even more pressing by the ever-increasing demands of real-life applications, which are based on storing and processing large amounts of data.

Given its scope, the book offers a valuable asset for all researchers, software developers, practitioners and students interested in the field of Data and Web technologies.

A Fuzzy-based System for Cloud-Fog-Edge Selection in VANETs.- A Fuzzy-based System for Selection of Actor Nodes in WSANs Considering Actor Reliability and Load Distribution.- IoT Device Selection in Opportunistic Networks: A Fuzzy Approach Considering IoT Device Failure Rate.- A Comparison Study for Chi-square and Uniform Client Distributions by WMN-PSOSA Simulation System for WMNs.- Linguistic-based Security in Fog and Cloud Computing.- Terminal Access Data Anomaly Detection Based on GBDT for Power User Electric Energy Data Acquisition System.- Evaluating Indoor Location Triangulation Using Wi-Fi Signals.- Load and Price Forecasting in Smart Grids Using Enhanced Support Vector Machine.- Understanding Students Personality to Detect Their Learning Differences.- Hybrid Approach for Heart Disease Prediction Using Data Mining Techniques.- TCP with Network Coding Performance Under Packet Reordering.