Self-Adaptation with Imperfect Monitoring in Solar Energy Harvesting Systems

Ubiquitous and perpetual nature of cyber-physical systems (CPSs) have made them mostly battery-operated in many applications. The batteries need recharge via environmental energy sources. Solar energy harvesting is a conventional source for CPSs, whereas it is not perfectly predictable due to environmental changes. Thus, the system needs to adaptively control its consumption with respect to the energy harvesting. In this paper, we propose a model-driven approach for analyzing self-adaptive solar energy harvesting systems; it uses a feedback control loop to monitor and analyze the behavior of the system and the environment, and decides which adaptation action must be triggered against the changes. We elaborate a data-driven method to come up with the prediction of the incoming changes, especially those from the environment. The method takes the energy harvesting data for prediction purposes, and models the environment as a Markov chain. We empower the proposed system against the runtime monitoring faults as well. In this regard, the system is able to verify an incomplete model, i.e. when some data is missed. To this aim, we propose a pattern-matching system that simulates the current behavior of the system using random walk, and matches it with the history to estimate the omitted data. The results show an accuracy of at least 96% when decisions are made by imperfect monitoring.