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Long-Term Energy Consumption and Transmission Delay Tradeoff in Wireless-Powered Body Area Networks

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In this article, we investigate the long-term energy consumption and transmission delay (EC-TD) tradeoff in a wireless-powered body area network that consists of a multiantenna hybrid access point and a… Click to show full abstract

In this article, we investigate the long-term energy consumption and transmission delay (EC-TD) tradeoff in a wireless-powered body area network that consists of a multiantenna hybrid access point and a number of single-antenna sensor nodes (SNs). The beamforming technique and the simultaneous wireless information and power transfer (SWIPT) technique are adopted. Each SN is equipped with a battery and data buffer for storing harvested energy and sensory data. The long-term energy consumption minimization problem is addressed subject to the constraint of transmission delay. Meanwhile, the residual energy constraints of SNs are considered, which enable the setting up of the available energy of the SNs according to requirements. By employing the Lyapunov optimization theory, the original stochastic optimization problem is transformed into an equivalent instantaneous nonconvex problem in which the long-term EC-TD tradeoff can be adjusted using a system control parameter $V$ . A joint power and time allocation scheme is then proposed to solve this instantaneous problem. Moreover, based on the derived upper bounds of the long-term energy consumption and data buffer length, we reveal that the proposed resource allocation scheme achieves an EC-TD tradeoff as $[\mathcal {O}(1/V),\mathcal {O}(V)]$ . Since the value of $V$ can be adjusted to achieve different energy consumption and transmission delay, the flexibility and applicability of the proposed scheme are enhanced. The simulation results validate the theoretical analysis and verify the effectiveness of the proposed scheme.

Keywords: energy; long term; energy consumption; term energy

Journal Title: IEEE Internet of Things Journal
Year Published: 2022

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