An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks

Wu, Guangyuan; Chen, Zhigang; Guo, Lin; Wu, Jia

doi:10.3390/a11020014

Cited by 2 publications

(4 citation statements)

References 45 publications

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“…For the sensing task in wireless sensor networks with heterogeneous energy supplies, the Lyapunov drift‐plus‐penalty with perturbation mechanism is applied to solve a discrete‐time stochastic optimization problem of cross‐layer in a fully distributed way 30 . Wu et al 31 utilize Lyapunov optimization technology to transform the user utility optimization problem in body area networks into three sub‐problems: collecting rate control, transmission power allocation and battery management. Amirnavaei et al 32 have designed an online energy control policy by Lyapunov framework to maximize the long‐term average wireless transmission rate under a finite battery storage limitation with energy harvesting.…”

Section: Related Workmentioning

confidence: 99%

“…According to Lyapunov optimization, 28 we denote a virtual queue Q ( t ) for the E q ( t ) as

Q (t) = E_{q} (t) - A

where A is constant of time‐independent. It can be seen 31 that stabilizing the queue Q ( t ) is equivalent to satisfying the constraint (11). According to (3), the dynamics of queue Q ( t ) can also be represented by

Q (t + 1) = Q (t) + E_{a} (t) - J (t)

…”

Section: Node Model and Energy Allocationmentioning

confidence: 99%

“…For any value of Q ( t ), the drift‐plus‐penalty can be upper bounded as 31

Δ Q (t) \leq B + Q (t) [E_{a} (t) - J (t) | Q (t)] - V l o g (1 + J (t)) | Q (t)

where

B ≜ m a x {false{E_{h, m a x}, J_{m a x} false}}^{2} / 2

. Since B is a constant, instead of directly using the objective in (11), we derive the equivalent optimization problem as

\underset{0 \leq J (t) \leq J_{m a x}}{m i n} Q (t) (E_{a} (t) - J (t)) - V l o g (1 + J (t))

…”

Section: Node Model and Energy Allocationmentioning

confidence: 99%

“…To ensure sure that the online power allocation is feasible for the original problem, we need to check whether E q ( t ) satisfies the battery constraint by determining the values of A and V . Then, we provide upper and lower bounds of the virtual queue Q ( t ) 31 . With the given energy allocation solution J ∗ ( t ), the virtual queue Q ( t ) is limited on all t as

Q_{m i n} ⩽ Q (t) ⩽ Q_{m a x}

, where Q min = − J ( t ) − V and Q min = E a , max .…”

Section: Node Model and Energy Allocationmentioning

confidence: 99%

See 3 more Smart Citations

Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

Ling

Meng²,

Tian

et al. 2021

Softw Pract Exp

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Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low‐power. Energy allocation is essential for motion‐based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra‐low‐power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real‐world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices.

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Section: Related Workmentioning

confidence: 99%

“…According to Lyapunov optimization, 28 we denote a virtual queue Q ( t ) for the E q ( t ) as