LAUSR

Energy harvesting is increasingly used as a long-term energy supply for the Internet of Things, wireless sensor networks, and cyber-physical systems. However, the challenge of mitigating the variability of energy harvesting sources needs to be addressed before ubiquitous adoption can happen. Otherwise, an unreliable operation of devices with frequent shutdowns during times of energy scarcity would be encountered. One finds probabilistic performance metrics helpful in designing power management solutions for the long-term operation of energy harvesting nodes. These metrics include the probability of battery depletion, expected energy consumption, expected battery level, and similar. This article proposes a stochastic modeling technique and corresponding analysis which can provide such metrics. Our advanced analysis is based on Markov chains. By modeling harvested energy with random variables, new and existing energy management policies are analyzed and compared. We propose an adaptive energy management strategy inspired by mixed-criticality systems. In the proposed strategy, the system can degrade or drop less essential tasks in real time to ensure a graceful degradation of service in adverse harvesting conditions. We compare the proposed energy management approach to several existing alternatives. To this end, we conduct extensive simulations for indoor and outdoor environments, where our strategy matches or outperforms the state of the art. Initial results also validate the high precision of our stochastic model and analysis in comparison to simulations using real-world data.