Reward Rate Maximization and Optimal Transmission Policy of EH Device With Temporal Death in EH-WSNs

Tang, Shengda; Tan, Liansheng

doi:10.1109/twc.2016.2640285

Cited by 31 publications

(17 citation statements)

References 31 publications

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“…The temporal energy outage duration is estimated by analytical model of absorbing DTMC as in (22). But, leakage energy ζ u is almost same with the energy ζ u .…”

Section: Numerical Results and Cost-effective Hybrid Esu Designmentioning

confidence: 99%

“…The time slot duration changes for different x = 8, x = 9, x = 10, x = 11, x = 12, and x = 13, and leakage energy also changes. The temporal energy outage duration is estimated by analytical model of absorbing DTMC as in (22). Then, we compare analytical results with the simulation results for different parameters.…”

Section: Numerical Results and Cost-effective Hybrid Esu Designmentioning

confidence: 99%

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Performance evaluation of low‐power wireless sensor node with hybrid energy storage

Haldar

Hossain

Panda

2018

Int J Communication

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In this paper, we evaluate the performance of low-power energy-harvesting wireless sensor node (SN) which has hybrid energy storage unit (ESU) for monitoring applications. This hybrid ESU has super capacitor (SC) as well as thin film battery (TFB) for energy storage. After the deployment of SN, temporal energy outage (TEO) occurs when the stored energy in ESU falls to a minimum level due to uncertain energy-harvesting process and leakage of energy, rendering it unable to transmit information to base station. In this paper, we estimate the duration to reach TEO state which is an important parameter for evaluating the performance of the hybrid ESU. The proposed model considers variation of leakage energy in SC. We also determine design perspectives of hybrid ESU for different range of performance parameters to maximize the performance criteria. Further, given total cost constraints, we determine the SC and TFB capacity that optimizes the performance criteria. The analytical results are validated by Monte Carlo simulation using MATLAB. The estimated TEO duration can ensure precautions in monitoring applications. KEYWORDS energy harvesting, energy outage, hybrid energy storage, super capacitor, thin film battery, wireless sensor node the available energy varies due to fluctuating environment conditions. Hence, SN is unable to harvest energy continuously from ambient sources due to uncertain EH process leading to energy scarcity in ESU. For these reasons, temporal energy outage (TEO) occurs when the EH SN runs out of energy for a while until it harvests energy again. In TEO condition, the SN becomes inactive and thereby fails to achieve its objective of sending information to BS. In control and monitor applications, BS receives data from SNs and applies the control. Thereafter, BS fails to apply appropriate control when TEO occurs in the system. Consequently, the loss of information significantly affects the quality of service (QoS) of the EH wireless sensor network. A prior assessment of TEO duration would help avert information loss. TEO duration refers to the interval of time the EH SN remains active using harvested energy as well as the stored ESU energy before going to TEO condition.The difficulties arise when we estimate TEO duration of EH SN with hybrid energy storage unit (ESU). Mostly, the hybrid ESU unit comprises of either a thin film battery (TFB) or a super capacitor (SC). 12-14 SC has more rechargeable cycle compared to TFB enabling SNs to sustain for a longer lifetime. However, SC leads to a faster battery drain out since it suffers from more energy leakage compared to TFB. Because of these challenges, hybrid ESU is a prime concern and is also used in the literature 15,16 for low-power energy-harvested wireless sensor network applications and in Thangaraj et al 17 for aircraft health monitoring applications. In order to estimate the TEO duration of a hybrid ESU of SN, a consistent performance criteria is required that can handle the uncertain harvesting process and volatility of TFB and SC energy storag...

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“…The temporal energy outage duration is estimated by analytical model of absorbing DTMC as in (22). But, leakage energy ζ u is almost same with the energy ζ u .…”

Section: Numerical Results and Cost-effective Hybrid Esu Designmentioning

confidence: 99%

Section: Numerical Results and Cost-effective Hybrid Esu Designmentioning

confidence: 99%

Performance evaluation of low‐power wireless sensor node with hybrid energy storage

Haldar

Hossain

Panda

2018

Int J Communication

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“…The purpose of this type of algorithm, after the initial organization of the network, is primarily to minimize the risk of data loss during transmission, which is associated with the organization of network node structures, and secondly to maximize the battery life of individual sensors (see e.g., [1,2,3]). Another challenge for ensuring the appropriate level of the quality of service (QoS) is the occurrence of temporal death in some sensor nodes (see e.g., [4,5]). There are two different main approaches to solving the problem of energy saving in WSNs.…”

Section: Introductionmentioning

confidence: 99%

Analytical Model of a Wireless Sensor Network (WSN) Node Operation with a Modified Threshold-Type Energy Saving Mechanism

Kempa

2019

Sensors

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In this article, a model of the operation of a wireless sensor network (WSN) node with an energy saving mechanism based on a threshold-controlled multiple vacation policy is considered. When the queue of packets directed to the node becomes empty, a multiple vacation period is started during which the receiving/transmitting of packets is blocked. In such a period, successive vacations of a fixed constant duration are taken until a predetermined number of N packets accumulated in the queue is detected. Then, at the completion epoch of this vacation, the processing restarts normally. The analytic approach is based on the conception of an embedded Markov chain; integral equations and renewal theory are applied to study the queue-size transient behaviour. The representations for the Laplace transforms of the queue-size distribution at an arbitrary fixed time t and on the idle and processing periods are obtained. The compact-form formulae for the distributions of the idle and processing period duration are derived. Numerical examples are attached as well.

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“…The lifetime of current wireless devices is significantly limited by the energy capacity of their batteries, especially in some energy-constrained scenarios, such as wireless sensor networks (WSNs) [1], wireless body area networks (WBANs) [2], and low-power wide-area networks (LPWANs) [3]. To cope with this limitation, the energy harvesting (EH) has emerged as a promising technology to enable the sustainable energy for the devices without replacing their battery [4,5] been extensively researched and applied in industry.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Power Beacon Assisted Multi-Source Transmission Using Markov Chain

Tang¹,

Deng

Cai³

et al. 2019

IEEE Access

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Wireless power transmission (WPT) is envisioned to be a promising technology for prolonging the lifetime of wireless devices in energy-constrained networks. This paper presents a general power beacon (PB) assisted multi-source transmission, where a practical source selection scheme with information transmission (IT) mode or non-IT mode is developed to maximize the transmission reliability. In the IT mode, a zeroforcing (ZF) beamformed signal with no interference to the destination is transmitted at the multi-antenna PB to supply wireless energy for the sources, and bring non-negative effect to the destination. Among multiple sources, the energy-sufficient source with the best channel quality is selected for wireless information transmission (WIT), while the other sources remain for energy harvesting. In the non-IT mode, the equal power transmission is adopted at PB to focus on energy delivery. Using Markov chain theory, the energy arrival and departure of each finite-capacity storage at the source is characterized mathematically, and the comprehensive analytical expressions of the energy outage probability (EOP), the connection outage probability (COP), and the average transmission delay (ATD) are formulated and derived. Our results reveal that the EOP, COP, and ATD can be significantly improved via increasing the number of sources deployed in the proposed network with finite transmit power of PB. We also prove that the multi-source network will never experience energy outage with infinite transmit power of PB.Index Terms-Wireless power transfer, Markov chain, energy storage, energy outage probability, average transmission delay.

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Reward Rate Maximization and Optimal Transmission Policy of EH Device With Temporal Death in EH-WSNs

Cited by 31 publications

References 31 publications

Performance evaluation of low‐power wireless sensor node with hybrid energy storage

Performance evaluation of low‐power wireless sensor node with hybrid energy storage

Analytical Model of a Wireless Sensor Network (WSN) Node Operation with a Modified Threshold-Type Energy Saving Mechanism

Analyzing Power Beacon Assisted Multi-Source Transmission Using Markov Chain

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