LAUSR

Edge computing is to be widely adopted for autonomous systems (ASs) applications as compute-intensive processing tasks can be offloaded to compute-capable servers located at the edge of the network infrastructure. Given the critical nature of numerous AS applications, their tasks are mostly governed by strict execution deadlines to alleviate any safety concerns from delayed responses. Although wireless link uncertainty has prompted recent works to designate redundant local execution as an offloading fail-safe to ensure these deadlines are met, frequent invocation of such fail-safe mechanisms can potentially undermine the extent of performance gains from offloading. In this article, we thoroughly analyze how redundant execution overheads can influence the overall performance. Then, we present TESTUDO, a methodology to optimize the energy consumption for latency-sensitive AS applications employing collaborative edge computing. Primarily, our methodology encompasses two main stages: 1) designing processing pipelines supporting optimal offloading points and fail-safe integration using modular design techniques and 2) developing a context-aware adaptive runtime solution based on deep reinforcement learning to adapt the mode of operation according to the wireless network status. Our experiments for end-to-end control and object detection use-cases have shown that TESTUDO achieved energy gains reaching up to 31% and 13.4% (15.9% and 5.3% on average) for the former and latter, respectively, while incurring little-to-no degradation in prediction scores (< 1% change) from state-of-the-art strategies.