Distributed computing and big data techniques for efficient fault detection and data management in wireless networks

Due to social media, internet websites, and cellular networks, the world is undergoing a digital avalanche. Extensive information will mask this pattern, emerging quickly and in many ways. Big data analytics will filter large amounts of unprocessed data to provide more manageable data to help parties make intelligent decisions. This research demonstrates how large geographical datasets are essential to numerous cutting-edge wireless communication technologies. We also argue that geospatial and spatio-temporal concerns matter differently in massive datasets than interpersonal issues. We present three significant geospatial information use cases with distinct architectural and analytical challenges. Next, using map-based Reduce computing, we offer our research on developing highly available multi-processing systems for geographical information on Hadoop. Our results show that Hadoop allows for highly extendable spatial data analysis methodologies. However, designing such applications requires specialized skills, stressing the need for simpler alternatives.

Automated summary

Due to the prevalence of social media, internet websites, and cellular networks, the world is experiencing a digital revolution. Big data analytics can filter large amounts of unprocessed data to provide more manageable information for intelligent decision-making. This research showcases the significance of large geographical datasets in cutting-edge wireless communication technologies and highlights the differences between geospatial and interpersonal concerns in massive datasets. The study presents three significant geospatial information use cases and explores the development of highly available multi-processing systems for geographical information using Hadoop. The findings demonstrate the scalability of spatial data analysis methodologies in Hadoop but emphasize the need for simpler alternatives due to the specialized skills required.