An Energy-Efficient Ensemble Approach for Mitigating Data Incompleteness
  in IoT Applications
Yousef AlShehri Lakshmish Ramaswamy
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.