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Öğe An energy prediction algorithm for wind-powered wireless sensor networks with energy harvesting(Elsevier Ltd, 2017) Kosunalp S.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. © 2017 Elsevier LtdÖğe Harvesting solar energy for limited-energy problem in wireless sensor networks(Institute of Electrical and Electronics Engineers Inc., 2017) Kosunalp S.; Cihan A.The major problem in wireless sensor networks (WSNs) is the limited-energy source, typically small batteries, employed by sensors. In order to prolong the lifetime of the WSNs, a lot of approaches have taken the limited-energy problem as a primarily design criterion. However, inevitable energy depletion will eventually disturb the operation of the WSNs. Recent studies have shown that renewable energy sources would potentially provide an infinite energy source for powering WSNs. Solar energy is the most effective energy source for WSNs as it has the largest power intensity. Therefore, each sensor which requires a small amount of energy for efficient operation can harvest sufficient energy from solar. In this study, a platform is designed in order to allow sensors to exploit solar energy. The performance of the sensors in terms of the lifetime is practically studied. The results demonstrate that sensors can also survive when there is no energy available through storing energy in a super-capacitor. IRIS nodes are used in experiments as they are very popular in WSNs domain. © 2017 IEEE.Öğe MAC protocols for energy harvesting wireless sensor networks: Survey(ETRI, 2015) Kosunalp S.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. © 2015 ETRI.Öğe A New Energy Prediction Algorithm for Energy-Harvesting Wireless Sensor Networks With Q-Learning(Institute of Electrical and Electronics Engineers Inc., 2016) Kosunalp S.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. © 2016 IEEE.Öğe Practical implementation and stability analysis of ALOHA-Q for wireless sensor networks(ETRI, 2016) Kosunalp S.; Mitchell P.D.; Grace D.; Clarke T.This paper presents the description, practical implementation, and stability analysis of a recently proposed, energy-efficient, medium access control protocol for wireless sensor networks, ALOHA-Q, which employs a reinforcement-learning framework as an intelligent transmission strategy. The channel performance is evaluated through a simulation and experiments conducted using a real-world test-bed. The stability of the system against possible changes in the environment and changing channel conditions is studied with a discussion on the resilience level of the system. A Markov model is derived to represent the system behavior and estimate the time in which the system loses its operation. A novel scheme is also proposed to protect the lifetime of the system when the environment and channel conditions do not sufficiently maintain the system operation. © 2016 ETRI.Öğe Use of Q-learning approaches for practical medium access control in wireless sensor networks(Elsevier Ltd, 2016) Kosunalp S.; Chu Y.; Mitchell P.D.; Grace D.; Clarke T.This paper studies the potential of a novel approach to ensure more efficient and intelligent assignment of capacity through medium access control (MAC) in practical wireless sensor networks. Q-Learning is employed as an intelligent transmission strategy. We review the existing MAC protocols in the context of Q-learning. A recently-proposed, ALOHA and Q-Learning based MAC scheme, ALOHA-Q, is considered which improves the channel performance significantly with a key benefit of simplicity. Practical implementation issues of ALOHA-Q are studied. We demonstrate the performance of the ALOHA-Q through extensive simulations and evaluations in various testbeds. A new exploration/exploitation method is proposed to strengthen the merits of the ALOHA-Q against dynamic the channel and environment conditions. © 2016 Elsevier Ltd
