A Survey of Underwater Magnetic Induction Communications: Fundamental Issues, Recent Advances, and Challenges

Li, Yuzhou; Wang, Shengnan; Jin, Cheng; Zhang, Yu; Jiang, Tao

doi:10.1109/comst.2019.2897610

Cited by 150 publications

(65 citation statements)

References 94 publications

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“…The energy harvesting model of E CH2 i is analyzed as follows. Because CU i and CAU k are equipped with magnetic induction coil, there exist mutual inductance between the receiving coil in CU i and the transmitting coil in CAU k , which is calculated as [28]…”

Section: B Hybrid Energy Harvesting Model Of Mobile Usermentioning

confidence: 99%

Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

Chen

Zhao

et al. 2020

IEEE Access

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Due to the limited battery capacity and computing capability of mobile users, the resource allocation strategy in device-to-device (D2D)-assisted edge computing system with hybrid energy harvesting is investigated in this paper. By employing magnetic induction-based wireless reverse charging technology, mobile user can supplement extra energy from nearby users when the energy harvested from ambient radio frequency sources is about to be exhausted. Moreover, mobile user can not only perform local computation, but also offload computing tasks to nearby users for auxiliary computation through D2D communication links or mobile edge computing (MEC) server under base station (BS) for edge computation. Due to the limited computing resources of MEC server, when the computing capability of the MEC server reaches the maximum value, an adjacent user under another nearby BS can be considered as a relay node. The computing tasks of the remaining users under the previous BS can be transferred to the MEC server with sufficient resources under another nearby BS by establishing D2D relay links. The objective of the resource allocation strategy is to maximize the energy efficiency under the constraints of computation delay and energy harvesting. The resource allocation problem is formulated as a mixed-integer nonlinear programming problem, which is not easy to obtain the optimal solution at low computational complexity. A suboptimal solution is obtained by adopting the quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the performance of the proposed strategy is superior to other benchmark strategies, and QPSO algorithm can achieve higher energy efficiency than the standard particle swarm optimization algorithm.

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Section: B Hybrid Energy Harvesting Model Of Mobile Usermentioning

confidence: 99%

Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

Chen

Zhao

et al. 2020

IEEE Access

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“…A promising alternative for near/mid-range communication is to exploit magnetic fields for digital communication [1]- [8]. Unlike radio waves, which are radiative and hence subject to multipath propagation, operation is in the non-radiative near field.…”

Section: Introductionmentioning

confidence: 99%

Underwater Communication Employing High-Sensitive Magnetic Field Detectors

Hott

Hoeher

2020

IEEE Access

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Magnetic communication is receiving significant interest in RF-challenging environments. Particularly in underwater environments and underground wireless sensor networks, magnetic communication is an emerging research area. In this paper, a new approach for magnetic underwater communication is presented and evaluated. In this approach, the receiver coil of a conventional magnetic induction communication system is replaced by a high-sensitive low-noise wideband magnetic field sensor. This concept enables a good detection sensitivity and, under certain conditions, an extended communication range. Most magnetic field sensors are small compared to equivalent search coils and offer a high bandwidth. Hence, they can be assembled in order to provide multiple-input multiple-output processing. Based on suitable channel modeling, trade-offs between system parameters are analyzed and the channel capacity is derived. Analytical results are supported by a prototype implementation. Potential application scenarios are studied, where emphasis is on mobile applications.

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“…Magneto-induction (MI) communication has recently emerged as an alternative choice for underground and underwater wireless sensor network applications [1][2][3], because of easy penetration, instant speed, high data rates, stable and predictable channel response. Furthermore, MI communication offers a unique advantage of inter-medium communication such as air-to-water/soil and water/soil-to-air due to the same magnetic permeability of air, and water [4,5].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Optimal Relay Position for Magneto-Inductive Wireless Sensor Networks

Qiao

Muzzammil

Ahmed

et al. 2020

Sensors

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Magneto-inductive (MI) waveguide technology is often proposed to increase the MI communication distance without adding significant cost and power consumption to the wireless sensor network. The idea is to add intermediate relaying nodes between transmitter (Tx) and receiver (Rx) to relay the information from Tx to Rx. Our study of MI wave-guides has realized that adding a relay node improves the communication distance, however, the performance is greatly dependent on the position of the relaying node in the network. We therefore, in this work have investigated the effect of placement of a relay node and have determined the optimal relay position. We have performed various sets of experiments to thoroughly understand the behavior and identified three main regions: (a) for region 1, when the distance between Tx and Rx is equal or less than the diameter of the coils ( d ≤ 2 r ), the optimal relay position is close to Tx, (b) for region 2, when the distance between Tx and Rx is greater than diameter of the coils but less than twice the diameter ( 2 r < d < 4 r ), the optimal relay position lies in the center of Tx and Rx, and (c) for region 3, when the distance between the Tx and Rx is equal or greater than twice the diameter of the coils ( d ≥ 4 r ), the optimal relay position is close to Rx.

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A Survey of Underwater Magnetic Induction Communications: Fundamental Issues, Recent Advances, and Challenges

Cited by 150 publications

References 94 publications

Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

Underwater Communication Employing High-Sensitive Magnetic Field Detectors

Experimental Investigation of Optimal Relay Position for Magneto-Inductive Wireless Sensor Networks

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