IoT and Edge Computing for Digital Agriculture
We are developing algorithms for handling large volumes of IoT data and to derive actionable insights from them. This is relevant in digital agriculture with data being generated by ground sensors and aerial drones. Our novel approach allows the data to be stored and processed close to the source of the data rather than being ferried all the way to a data center. This is important to ensure the privacy of the IoT data (e.g., it can be made to stay completely within the premises) and to ensure that the scarce wireless bandwidth and constrained energy resources are not spent carrying raw and irrelevant data to the backend.
Genome reads are still rather error prone, especially as we look toward newer technologies, like nanopore sequencing. ICAN considers these reads as words of a language and just like Natural Language Processing (NLP) can correct errors in language, we are optimizing technologies from there to correct genomic reads. Our approach enables the huge flourishing of the work in deep neural networks (DNNs) to be applied to understanding the “language” of the genome, so that we can derive actionable insights from them, such as, developing precise genome editing technologies. We have published this work in 2019 and have our follow-on work using NanoPore and PacBio reads under submission to BMC Bioinformatics. We have also developed a DSL for genomics to efficiently reuse recurrent building blocks that are used for common computational genomics algorithms and have tested the DSL for genome assembly and microRNA-gene regulation.