Refereed International Conference Publications

Integrated IoT Programming with Selective Abstraction [abstract] (ACM DL, PDF)
Gyeongmin Lee, Seonyeong Heo, Bongjun Kim, Jong Kim, and Hanjun Kim
Proceedings of the 18th ACM SIGPLAN/SIGBAD Conference on Languages, Compilers, Tools, and Theory for Embedded Systems (LCTES), June 2017.
Accept Rate: 23% (12/52).

The explosion of networked devices has driven a new computing environment called the Internet of Things (IoT), enabling various services such as home automation and health monitoring. Despite the promising applicability of the IoT, developing an IoT service is challenging for programmers, because the programmers should integrate multiple programmable devices and heterogeneous third-party devices. Recent works have proposed integrated programming platforms, but they either require device-specific implementation for third-party devices without any device abstraction, or abstract all the devices to the standard interfaces requiring unnecessary abstraction of programmable devices. To integrate IoT devices with selective abstraction, this work revisits the object oriented programming (OOP) model, and proposes a new language extension and its compiler-runtime framework, called Esperanto. With three annotations that map each object to its corresponding IoT device, the Esperanto language allows programmers to integrate multiple programmable devices into one OOP program and to abstract similar third-party devices into their common ancestor classes. Given the annotations, the Esperanto compiler automatically partitions the integrated program into multiple sub-programs for each programmable IoT device, and inserts communication and synchronization code. Moreover, for the ancestor classes, the Esperanto runtime dynamically identifies connected third-party devices, and links their corresponding descendent objects. Compared to an existing approach on the integrated IoT programming, Esperanto requires 33.3% fewer lines of code to implement 5 IoT services, and reduces their response time by 44.8% on average.