The Internet of Things (IoT) systems, as any electronic or mechanical system, are prone to failures. Hard failures in hardware due to aging and degradation are particularly important since they are irrecoverable, requiring maintenance for the replacement of defective parts, at high costs. In this paper, we propose a novel dynamic reliability management (DRM) technique for IoT edge computing systems to satisfy the Quality of Service (QoS) and reliability requirements while maximizing the remaining energy of the edge device batteries. We formulate a state-space optimal control problem with a battery energy objective, QoS, and terminal reliability constraints. We decompose the problem into low-overhead subproblems and solve it employing a hierarchical and multi-timescale control approach, distributed over the edge devices and the gateway. Our results, based on real measurements and trace-driven simulation demonstrate that the proposed scheme can achieve a similar battery lifetime compared to the state-of-the-art approaches while satisfying reliability requirements, where other approaches fail to do so.
Automatic modulation classification for radio signals is an important task in many applications, including cognitive radio, radio spectrum monitoring and signal decoding in non-cooperative communications. Recent studies in this area apply various deep learning methods to achieve accurate classification. However, due to the nature of radio signals, distortions during transmission are often unforeseen and unpredictable, which poses a need for robust learning models. At the same time, there is the need for fast real-time modulation classification to meet strict timing requirements. In this work, we propose a lightweight deep learning model that accurately and quickly classifies the modulation of signals having different types of distortions, without the need to be trained using distorted signals. Our model trains 25% faster and classifies 36% faster compared to the state-of-the-art [1], with smaller accuracy degradation on datasets generated using distortion parameters that do not appear in the training set.