DARTS: Distributed IoT Architecture for Real-Time, Resilient and AI-Compressed Workflows
Ragini Gupta, Bo Chen, Shengzhong Liu, Tianshi Wang, Sandeep Singh Sandha, Abel Souza, Klara Nahrstedt, Tarek Abdelzaher, Mani B. Srivastava, Prashant J. Shenoy, Jeffrey A. Smith, Maggie Wigness, Niranjan Suri · 2022
IoT (Internet of Things) sensor devices are becoming ubiquitous in diverse smart environments, including smart homes, smart cities, smart laboratories, and others. To handle their IoT sensor data, distributed edge-cloud infrastructures are emerging to capture, distribute, and analyze them and deliver important services and utilities to different communities. However, there are several challenges for these IoT-edge-cloud infrastructures to provide efficient and effective services to users: (1) how to deliver real-time distributed services under diverse IoT devices, including cameras, meteorological and other sensors; (2) how to provide robustness and resilience of distributed services within the IoT-edge-cloud infrastructures to withstand failures or attacks; (3) how to handle AI workloads are in an efficient manner under constrained network conditions. To address these challenges, we present DARTS, which is composed of different IoT, edge, cloud services addressing application portability, real-time robust data transfer and AI-driven capabilities. We benchmark and evaluate these services to showcase the practical deployment of DARTS catering to application-specific constraints.