FCPP+Miosix: Scaling Aggregate Programming to Embedded Systems

Abstract

As the density of nodes capable of sensing, computing and actuation increases, it becomes increasingly useful to model an entire network of physical devices as a single, continuous space-time computing machine. The emergent behaviour of the whole software system is then induced by local computations deployed within each node and by the dynamics of the information diffusion. A relevant example of this distribution model is given by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aggregate programming</i> and its minimal set of functional constructs used to manipulate distributed data structures evolving over space and time, and resulting in robustness to changes. In this paper, we propose the first implementation of the aggregate computing paradigm targeting microcontrollers, by integrating FCPP, a C++ implementation of the paradigm, with Miosix, a modern operating system for microcontrollers with full C++ support. To the best of the author's knowledge, we are the first to present results on the effectiveness of FCPP in an embedded operating system setting as opposed to a simulation environment, thus considering tight memory and computational constraints and accounting for packet losses due to nonidealities of the radio channel. We implemented and tested on a network of WandStem nodes two benchmark applications: a network connectivity checker for network planning and preventive maintenance, and a decentralised contact tracing application. Additionally, we show that common problems in sensor networks such as neighbour discovery, construction of a graph of the network topology, coarse grain clock synchronisation as well as network monitoring and the collection of statistics (such as memory occupation data) can be easily performed thanks to the expressive semantics of aggregate programming.