An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness in IoT Applications
This work addresses energy constraints and sensor failures in IoT systems, offering an incremental improvement over existing techniques like SECOE.
The paper tackles data incompleteness in IoT applications by proposing ENAMLE, an energy-efficient ensemble method that adapts to missing sensor data, achieving up to 30% energy savings while maintaining accuracy comparable to baseline methods.
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 incompleteness while simultaneously being energy efficient. This paper presents an empirical study of SECOE - a recent technique for alleviating data incompleteness in IoT - with respect to its energy bottlenecks. Towards addressing the energy bottlenecks of SECOE, we propose ENAMLE - a proactive, energy-aware technique for mitigating the impact of concurrent missing data. ENAMLE is unique in the sense that it builds an energy-aware ensemble of sub-models, each trained with a subset of sensors chosen carefully based on their correlations. Furthermore, at inference time, ENAMLE adaptively alters the number of the ensemble of models based on the amount of missing data rate and the energy-accuracy trade-off. ENAMLE's design includes several novel mechanisms for minimizing energy consumption while maintaining accuracy. We present extensive experimental studies on two distinct datasets that demonstrate the energy efficiency of ENAMLE and its ability to alleviate sensor failures.