Memory Optimized Lifetime Vehicle Data Acquisition Framework

Data acquisition is done from road vehicles by many service providers for delivering various services to the end user. Apart from this, car manufacturers also collect a fair share of data which allows them to design better components and robust systems in future. But, the lifetime data acquisition, storage and processing leads to huge costs and lot of resources. Existing data collection procedure in road vehicles has limitations like huge memory requirements to store such volume of data, delay in accessing the data from vehicle for analysis and etc. Although remote connectivity and cloud storage facilities look promising, the transfer of large volume of data from vehicle to cloud storage is still a challenge. This paper describes a life-time data acquisition and storage framework for automobiles with reduced memory consumption and with possibility of quick access to data for analysis. The framework is tested using a sample application which collects lifetime data from OBD Socket of a car and stores it at a remote storage space. From this sample application, the memory requirements & efficiency of system are analyzed, compared with existing system and proven to be effective.