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.