Beyond the Fog: Bringing Cross-Platform Code Execution to Constrained IoT Devices - Pisani, Flávia; Brunetta, Jeferson Rech; Rosario, Vanderson Martins Do; Borin, Edson (Anais do International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD); 2017: Anais do XXIX International Symposium on Computer Architecture and High Performance Computing, 2017)
Considering the prediction that there will be over 50 billion devices connected to the Internet of Things (IoT) in the near future, the demand for efficient ways to process data streams generated by sensors grows ever larger, highlighting the necessity to re-evaluate current approaches, such as sending all data to the cloud for processing and analysis. In this paper, we explore one of the methods for improving this scenario: bringing the computation closer to data sources. By executing the code on the IoT devices themselves instead of on the network edge or the cloud, solutions can better meet the latency requirements of several applications, avoid problems with slow and intermittent network connections, prevent network congestion, and potentially save energy by reducing communication. To this end, we propose the LMC framework and compare it with Edgent, an open-source project that is under development by the Apache Incubator. By using a DragonBoard 410c to execute a simple filter, an outlier detector, and a program that calculates the FFT, we obtained results that indicate that LMC outperforms Edgent when dynamic translation is disabled for both of them and is more suitable for lightweight quick queries otherwise. More importantly, the LMC also enables us to perform cross-platform code execution on small, cheap devices that do not have enough resources to run Edgent, like the NodeMCU 1.0.