Joint Power Allocation in Classified WBANs With Wireless Information and Power Transfer

Li, Shuang; Hu, Fengye; Wang, Chao; Mao, Zhi; Ling, Zhuang; Li, He

doi:10.1109/jiot.2020.3010950

Cited by 16 publications

(10 citation statements)

References 36 publications

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“…In this section, we present simulated results to validate the analytical expressions and obtain some insights. To verify the superiority of the BEP and OOP, a buffer-less policy (BLP) 7 is adopted as a benchmark scheme [32], where R is not equipped with energy buffer. Figs.…”

Section: Resultsmentioning

confidence: 99%

“…To integrate the advantages of relaying and WPT systems, relaying WPT system has been applied into the WBAN [28]- [32]. More specifically, in [28], spectral efficiency of the relaying WPT-based WBAN system has been studied over a block fading channel.…”

Section: Introductionmentioning

confidence: 99%

“…In [31], a cooperative communication framework for the WPT-based WBAN system with full duplex has been constructed and its throughput maximization problem has been investigated under the limited delay. Recently, a dynamic weights algorithm for the relaying WPT-based system has been designed in [32]. In such system, the relay and sensors collect energy from the AP and use the harvested energy to transmit information to the AP by using time division multiple access.…”

Section: Introductionmentioning

confidence: 99%

“…In such system, the relay and sensors collect energy from the AP and use the harvested energy to transmit information to the AP by using time division multiple access. However, in these works [28]- [32], all of them utilize relaying and WPT in WBAN, but energy harvesting nodes of these systems use a harvest-use (HU) architecture, which does not buffer the energy for use in future time slots and limits system performance. Moreover, they do not manage the energy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Energy Buffer-Aided Wireless-Powered Relaying System for Self-Sustainable Implant WBAN

Ning,

Lin,

Cai

et al. 2024

IEEE Open J. Commun. Soc.

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Wireless body area networks (WBANs) can provide continuous monitoring of human biological signals. Due to the limited energy of the sensors, wireless-powered system has been adopted to prolong network lifetime for implant WBANs. In this paper, we propose an energy buffer-aided wireless-powered relaying system for self-sustainable implant WBAN. The proposed system is composed of an off-body access point (AP) and many users, where an implant device is placed in body while a dual-mode wearable device with an energy buffer is attached on body for each user. In the downlink, the dual-mode wearable device simultaneously harvests energy that is stored in the energy buffer from a radio-frequency signal broadcasted by the AP and provides energy supply via wireless energy transfer to the implant device. In the uplink, the implant device uses harvested energy to transmit its collected data to the wearable device. Then the wearable device adopts the stored energy to decode and forward the data to the AP in an orthogonal multiple access way. We investigate two transmission policies for the wearable device, namely best-effort policy (BEP) and on-off policy (OOP). We derive the limiting distribution of the energy for both the BEP and OOP. Furthermore, the outage probability and average throughput of the proposed system with both the BEP and OOP are analyzed. Simulation results are presented to validate the analytical expressions and provide some useful insights.INDEX TERMS Implant wireless body area network, best-effort policy, on-off policy, energy buffer, relaying system.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Energy Buffer-Aided Wireless-Powered Relaying System for Self-Sustainable Implant WBAN

Ning,

Lin,

Cai

et al. 2024

IEEE Open J. Commun. Soc.

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show abstract

“…At present, for some different exercise methods, the human knee joint cartilage is subjected to different forms of loading conditions, and the mechanism of its mechanical properties has not been clearly expressed [5]. This requires us to strengthen the study of the changes in the mechanical behavior of the knee joint in different ways of movement and explore the inducing mechanism of the occurrence and exacerbation of knee cartilage damage under different loading conditions, as well as the changes in contact stress and mechanical behavior [6].…”

Section: Introductionmentioning

confidence: 99%

Wireless Pressure Sensor Assisted Orthopedic Nursing Effectiveness Evaluation

Wang¹,

Shen²

2021

Journal of Sensors

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This paper combines flexible pressure sensing technology, wireless sensor network, and cloud platform technology to design and manufacture a medical miniature pressure sensor and its supporting system. The problem of noninvasive monitoring of the syndrome encountered in the clinic is used for real-time monitoring and auxiliary diagnosis of the disease. Different from the current clinical use of “puncture” to measure intrafascial pressure, this system focuses on the noninvasive monitoring of compartment syndrome, using medical tape to paste a flexible microsensing unit on the injured area. The flexible sensor unit can measure the pressure here in real time and then can know the pressure in the fascia chamber. The flexible pressure sensor unit combines with the subsequent flexible circuit to send the measured data to the data in real time through wireless communication. The data aggregation node transmits the collected data to the upper computer through serial communication, and the upper computer software processes and stores the data and uploads it to the cloud server. In this experiment, it was observed that the concentrations of Ca and P showed the same fluctuating trend. With the gradual progress of the stretch, the concentrations of Ca and P increased with the increase in time, reaching approximately at the end of the extension. The peak value indicates that the osteoclast activity is enhanced at this time, the bone matrix is largely destroyed, and the Ca and P in the matrix are released into the serum in a large amount, thereby increasing the serum concentration. After the distraction ceases, it enters the healing period of the callus. At this time, the concentrations of Ca and P decrease with the increase in time and gradually reach a stable level, indicating that the osteoblast activity is enhanced at this time, the bone matrix begins to rebuild, and the Ca and P gradually increase. The deposited bone matrix gradually forms new bone and finally reaches a balance. Since the speed of extension in each experimental group is inconsistent, the time required to reach the same extension length is also inconsistent, so that the peak time is also inconsistent. After plotting the stress difference ( △ F ) before and after stretching against time and speed, it is found that the relationship is linear. However, these two variables affect △ F at the same time, so they cannot be isolated. Based on this, this subject uses multiple regression equations to fit the three relationships of stress difference ( △ F ), time, and speed. In the process of distraction osteogenesis, with each distraction, the bone stress presents a trend from high to low. And as the stretch progresses, the measured stress value increases linearly at the same time point every day.

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Distributed Power Controller of Massive Wireless Body Area Networks based on Deep Reinforcement Learning

Liu

Lan

et al. 2021

Mobile Netw Appl

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Joint Power Allocation in Classified WBANs With Wireless Information and Power Transfer

Cited by 16 publications

References 36 publications

Energy Buffer-Aided Wireless-Powered Relaying System for Self-Sustainable Implant WBAN

Energy Buffer-Aided Wireless-Powered Relaying System for Self-Sustainable Implant WBAN

Wireless Pressure Sensor Assisted Orthopedic Nursing Effectiveness Evaluation

Distributed Power Controller of Massive Wireless Body Area Networks based on Deep Reinforcement Learning

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