An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness in IoT Applications

arXiv:2403.10371v1 Announce Type: new
Abstract: Machine Learning (ML) is becoming increasingly important for IoT-based applications. However, the dynamic and ad-hoc nature of many IoT ecosystems poses unique challenges to the efficacy of ML algorithms. One such challenge is data incompleteness, which is manifested as missing sensor readings. Many factors, including sensor failures and/or network disruption, can cause data incompleteness. Furthermore, most IoT systems are severely power-constrained. It is important that we build IoT-based ML systems that are robust against data …

abstract algorithms applications arxiv challenge challenges cs.ai cs.lg cs.ni data dynamic ecosystems energy ensemble however iot machine machine learning ml algorithms nature sensor type