Browsing by Subject "Risk theory"

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Open Access
Energy management in energy harvesting wireless sensor nodes with lifetime constraints
(2016-06) Tunç, Çağlar
Advancements in the \Internet of Things (IoT)" concept enables large numbers of low-power wireless sensors and electronic devices to be connected to the Internet and outside world over a wide area wireless network without a need for human interaction. Using rechargeable batteries with energy harvesting to power these wireless sensors has been shown to preserve the self-sustainability and selfsu fficiency of a sensor node and prolong its lifetime, hence the whole network it belongs to. However, it brings the question of how to intelligently manage the energy in the battery so that the node maintains its functionalities by keeping the battery level over zero for an extended duration of time, known as the lifehorizon. We propose a risk-theoretic Markov uid queue model to compute the battery outage probability of a wireless sensor node for a given finite life-horizon. The proposed method enables the performance evaluation of a wide spectrum of energy management policies including those with adaptive sensing rate (or duty cycling). In this model, the node gathers data from the environment according to a Poisson process whose rate is to depend on the instantaneous battery level and/or the state of the energy harvesting process (EHP) which is characterized by a Continuous time Markov Chain (CTMC). Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing rate policies are obtained that maximize the information sensing rate of the sensor node while meeting lifetime constraints given in terms of battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using two-state CTMC-based EHPs.
Open Access
Markov fluid queue model of an energy harvesting IoT device with adaptive sensing
(Elsevier B.V., 2017) Tunc C.; Akar, N.
Energy management is key in prolonging the lifetime of an energy harvesting Internet of Things (IoT) device with rechargeable batteries. Such an IoT device is required to fulfill its main functionalities, i.e., information sensing and dissemination at an acceptable rate, while keeping the probability that the node first becomes non-operational, i.e., the battery level hits zero the first time within a given finite time horizon, below a desired level. Assuming a finite-state Continuous-Time Markov Chain (CTMC) model for the Energy Harvesting Process (EHP), we propose a risk-theoretic Markov fluid queue model for the computation of first battery outage probabilities in a given finite time horizon. The proposed model enables the performance evaluation of a wide spectrum of energy management policies including those with sensing rates depending on the instantaneous battery level and/or the state of the energy harvesting process. Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing policies are obtained that maximize the information sensing rate of the IoT device while meeting a Quality of Service (QoS) constraint given in terms of first battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using a two-state CTMC-based EHP.