Data Redundancy Reduction in IoT Weather Station

Abstract

Portable weather station allows us to track fluctuations in temperature, humidity, wind speed, wind direction, rainfall and barometric pressure to create a prediction of forthcoming weather conditions. Weather forecast helps farmers/gardeners plan for crop irrigation & protection, helps power generation industry to plan & optimize power production & utilization, and people to plan their outdoor activities. The scope of this work is to retrieve the above said environmental parameters at a place of interest and   then remove the redundancy in the retrieved parameters before uploading them in to the IoT Cloud. Technical experts have predicted that 44 zettabytesof data generation expected in 2020, data storage optimization is one of the biggest challenges that are ahead of us, this work aims at reducing the redundant data and to use the storage space efficiently, by doing so the traffic and congestion at the cloud layer can be averted and also the memory requirement can be reduced where unprecedented data are stored.