Domain Specific Languages for Low Power Sensor Nodes: Bridging Expressiveness and Efficiency in the IoT Edge

Abstract

Abstract:  Energy constrained sensor nodes operate at the periphery of the Internet of Things, yet the majority of contemporary languages remain tailored to desktop class hardware. This paper investigates the design of ultra-lightweight domain specific languages (DSLs) that balance expressiveness with the extreme memory, instruction cycle, and energy budgets of microcontroller based motes. We first analyze instruction set telemetry from representative TI MSP430, ARM Cortex M0, and RISC V RV32E platforms to uncover execution phase bottlenecks. On this empirical foundation, we propose EmberScript, a statically typed DSL compiling to an 8 bit threaded byte code that fits within 6?kB of flash and 512?B of RAM while supporting higher order functions, tuple pattern matching, and energy aware annotations. A dual tier compiler pipeline introduces profile guided inlining and region based memory allocation, trimming dynamic allocations by 71?% compared with a C baseline. Evaluation on a 64 node environmental monitoring testbed shows EmberScript reduces duty cycle energy by 38?% and code development effort by 44?% over state of practice TinyOS nesC. We conclude with a taxonomy of IoT edge language trade offs, underscoring that embedded DSLs can elevate programming productivity without sacrificing the nanowatt hour economy vital to battery operated deployments.

Keywords: Edge Computing, Embedded DSL, Energy Optimization, Sensor Networks, Static Typing