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- MakaleAçık Erişim2017Scopus SJRQ1An energy prediction algorithm for wind-powered wireless sensor networks with energy harvesting
Kosunalp, Selahattin
Energy harvesting (EH) from environmental energy sources has the potential to ensure unlimited, uncontrollable and unreliable energy for wireless sensor networks (WSNs), bringing a need to predict future energy availability for the effective utilization of the harvested energy. The majority of previous prediction approaches have exploited the diurnal cycle dividing the whole day into equal-length time slots in which predictions were carried out in each slot independently. This is not, however, efficient for wind energy as it exhibits non-controllable behaviour in that the amount of energy to be harvested varies over time. This paper proposes a novel approach to predict the wind energy for EH-WSNs depending on the energy generation profile of latest condition. The distinctive feature of the proposed approach is to consider the recent conditions in current-day, instead of past-day's energy generation profiles. The performance of the proposed algorithm is evaluated using real measurements in comparison with state-of-art approaches. Results show that the proposed strategy significantly outperforms the two popular energy predictors, EWMA and Pro-Energy.
AtıfScopus48WoS36OpenAlex51
İncele - MakaleAçık Erişim2016Scopus SJRQ1A New Energy Prediction Algorithm for Energy-Harvesting Wireless Sensor Networks With Q-Learning
Kosunalp, Selahattin
Traditional wireless sensor networks (WSNs) face the problem of a limited-energy source, typically batteries, resulting in the need for careful and effective utilization of the energy source. However, inevitable energy depletion will eventually disturb the operation of a WSN. Energy harvesting (EH) technology is acquiring particular interest, because it has the potential to provide a continuous energy supply in battery-powered WSNs. Solar energy is the most effective environmental energy for EH-WSNs because of its high energy intensity, which comes from a non-controllable source. Therefore, the prediction of future energy availability is a critical issue, as the amount of the harvestable energy may vary over time. In this paper, a novel solar energy prediction algorithm with Q-learning, called Q-learning-based solar energy prediction (QL-SEP), is proposed. Q-learning is an effective way of predicting future actions based on past observations. The distinctive feature of QL-SEP is that not only past days' observations but also the current weather conditions are considered for prediction. The performance of QL-SEP is simulated in this paper using real-world measurements obtained from a solar panel in comparison with the state-of-art approaches.
AtıfScopus128WoS96OpenAlex135İncele - MakaleAçık Erişim2015Scopus SJRQ2MAC protocols for energy harvesting wireless sensor networks: Survey
Kosunalp, Selahattin
Energy harvesting (EH) technology in the field of wireless sensor networks (WSNs) is gaining increasing popularity through removing the burden of having to replace/recharge depleted energy sources by energy harvester devices. EH provides an alternative source of energy from the surrounding environment; therefore, by exploiting the EH process, WSNs can achieve a perpetual lifetime. In view of this, emphasis is being placed on the design of new medium access control (MAC) protocols that aim to maximize the lifetime of WSNs by using the maximum possible amount of harvested energy instead of saving any residual energy, given that the rate of energy harvested is greater than that which is consumed. Various MAC protocols with the objective of exploiting ambient energy have been proposed for energy-harvesting WSNs (EH-WSNs). In this paper, first, the fundamental properties of EH-WSN architecture are outlined. Then, several MAC protocols proposed for EH-WSNs are presented, describing their operating principles and underlying features. To give an insight into future research directions, open research issues (key ideas) with respect to design trade-offs are discussed at the end of this paper.
AtıfScopus56WoS45OpenAlex57İncele