A queueing model of an energy harvesting sensor node with data buffering

Cuypere, Eline De; Turck, Koen De; Fiems, Dieter

doi:10.1007/s11235-017-0338-8

Cited by 24 publications

(13 citation statements)

References 57 publications

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“…While the former models account for energy storage, data buffering is not considered. Data buffering however is accounted for in [29][30][31][32][33]. In [32], Gelenbe considers a model with Poisson arrivals of data and energy.…”

Section: Related Literaturementioning

confidence: 99%

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Optimal data collection in wireless sensor networks with correlated energy harvesting

2018

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We study the optimal data collection rate in a hybrid wireless sensor network where sensor data is collected by mobile sinks. In such networks, there is a trade-off between the cost of data collection and the timeliness of the data. We further assume that the sensor node under study harvests its energy from its environment. Such energy harvesting sensors ideally operate energy neutral, meaning that they can harvest the necessary energy to sense and transmit data, and have on-board rechargeable batteries to level out energy harvesting fluctuations. Even with batteries, fluctuations in energy harvesting can considerably affect performance, as it is not at all unlikely that a sensor node runs out of energy, and is neither able to sense nor to transmit data. The energy harvesting process also influences the cost vs. timeliness trade-off as additional data collection requires additional energy as well. To study this trade-off, we propose an analytic model for the value of the information that a sensor node brings to decision making. We account for the timeliness of the data by discounting the value of the information at the sensor over time, we adopt the energy-chunk approach (i.e. discretise the energy level) to track energy harvesting and expenditure over time, and introduce correlation in the energy harvesting process to study its influence on the optimal collection rate.

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Section: Related Literaturementioning

confidence: 99%

“…Therefore, Markov models with two queues (an energy and a data queue) are required. In [29,30] such a Markov model is studied where energy and packet arrivals depend on an exogenous Markovian background process. This allows to include correlation in both harvesting, sensing and transmission processes.…”

Section: Related Literaturementioning

confidence: 99%

Optimal data collection in wireless sensor networks with correlated energy harvesting

2018

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“…As an example, wireless sensor networks (WSNs) are frequently used in many areas for monitoring tasks such as environmental sensing, forest-fire surveillance, danger detection, and military operations [9]. An energy-harvesting WSN constructed by applying the LB scheme has been described in De Cuypere et al [10]. Congestion control in WSNs has attracted interest from many researchers, and various methods have been proposed [11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

Choi

Lim

2020

Sustainability

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This study analyzes the performance of a queue length-dependent overload control policy using a leaky bucket (LB) scheme. This queueing model is applied to the operation of a battery swapping and charging station for electric vehicles (EVs). In addition to the LB scheme, we propose two congestion control policies based on EV queue length thresholds. With these policies, the model determines both EV-arrival and battery-supply intervals, and these depend on the number of EVs waiting in the queue. The queue length distributions, including those at arbitrary epochs, are derived using embedded Markov chain and supplementary variable methods. Performance measures such as blocking probability and mean waiting time are investigated using numerical examples. We study the characteristics of the system using numerical examples and use a cost analysis to investigate situations in which the application of each congestion control policy is advantageous.

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“…A numerical method is developed to compute the average time until the node experiences an outage. De Cuypere et al 3 develop a Markovian queueing model to instigate the impact of randomness in EH, energy expenditure, data acquisition, and data transmission. Numerical examples show that EH correlation considerably affects performance measures of the communication system.…”

Section: Introductionmentioning

confidence: 99%

Modeling and performance analysis of energy harvesting wireless communication systems with reliable energy backup

Tang

Tan

Liu

2020

Int J Communication

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Summary Energy harvesting wireless communication system (EH‐WCS) has the capability of harvesting energy for system operations from the surrounding renewable energy sources. However, the randomness and instability of the harvested energy will result in the depletion of the energy consumption. To provide reliable communication services with the quality of service (QoS) guarantee, it is necessary for the EH‐WCS to use a reliable energy backup (REB) for supplying energy to the system during the failure of its primary energy source. In this paper, a novel stochastic model, i.e., the extended Markov fluid flow model, is proposed to describe the EH‐WCS with REB. The Kolmogorov forward equations of the system model are derived. By solving the corresponding equations, we obtain the stationary distributions of the key performance metrics for the EH‐WCS with REB, including the average energy consumption rate of the EH‐WCS, the residual energy distribution, the average energy supply rate by REB, the packet queue length in data buffer, the data queue delay, and the packet blocking probability. A numerical example is provided to investigate the theoretical results, and the effects of the system parameters on the performance are further studied numerically. Both the theoretical insights and the numerical analyses are believed to be important for the design of EH‐WCSs.

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A queueing model of an energy harvesting sensor node with data buffering

Cited by 24 publications

References 57 publications

Optimal data collection in wireless sensor networks with correlated energy harvesting

Optimal data collection in wireless sensor networks with correlated energy harvesting

Analysis of the State-Dependent Queueing Model and Its Application to Battery Swapping and Charging Stations

Modeling and performance analysis of energy harvesting wireless communication systems with reliable energy backup

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