The state-of-the-art Internet-of-Things (IoT) ecosystem is facing critical challenges, which call for efficient data compression techniques capable of a) compressing small blocks of time-series data at IoT devices, b) supporting parallel updates, and c) allowing analytics directly on compressed data. Light-IoT aims to develop an end-to-end IoT framework addressing those challenges and that can perform a wide range of data analytics on compressed data by relying on a new compression technique pioneered by our team at AU. We will jointly design data compression, store and analytics to optimize the overall cost of IoT ecosystem. The research results will serve as a foundation for developing sustainable IoT solutions and have a profound impact on digitalization