Energy Management and Power Allocation for Underwater Acoustic Sensor Network

Jing, Lianyou; He, Chengbing; Huang, Jianguo; Ding, Zhi

doi:10.1109/jsen.2017.2737229

Cited by 38 publications

(13 citation statements)

References 36 publications

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“…The major difference in this study is that we employ the power allocation scheme for non-orthogonal multiple access while utilizing the orthogonal frequency-division multiplexing (OFDM) technique for UAC networks. In [33], the authors investigated the power allocation strategy for energy harvesting in UAC networks. They considered two scenarios for knowing the channel state information and applied stochastic dynamic programming to find the optimal power allocation for UAC networks.…”

Section: Differences In System Methodologies In the Literaturementioning

confidence: 99%

“…The major difference in this study is that we select the proper center frequency, bandwidth, and transmission using the routing protocols. Hence, the different approaches and system design in the existing works [32][33][34] resulted in different system parameters. Therefore, it is very difficult to compare the proposed DL PA strategy with other research.…”

Section: Differences In System Methodologies In the Literaturementioning

confidence: 99%

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Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks

Ahmad

Chang

2019

Electronics

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The increasing interest in next-generation underwater acoustic communications networks is due to vast investigation of oceans for oceanography, commercial operations in maritime areas, military surveillance, and more. A surface buoy or underwater base station controller (UBSC) communicates with either transceivers or underwater base stations (UBSs) via acoustic links. Transceivers further communicate with underwater sensor nodes using acoustic links. In this paper, we employ a downlink (DL) power allocation (PA) strategy using an orthogonal frequency-division multiple access (OFDMA) technique for underwater acoustic communications (UAC) networks. First, we present an approach to power offsets using three kinds of pilot spacing and apply the power boosting (PB) concept on orthogonal frequency-division multiplexing (OFDM) symbols for the UAC network. Secondly, we draw the block error rate (BLER) curves from link-level simulation (LLS) and analyze the signal-to-noise ratio (SNR) for both PA and non-PA strategies. Lastly, we adopt the best PB for system-level simulation (SLS) and compare the throughput and outage performance for PA and non-PA strategies. Hence, the simulation results confirm the effectiveness of the DL PA strategy for UAC networks.

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Section: Differences In System Methodologies In the Literaturementioning

confidence: 99%

Section: Differences In System Methodologies In the Literaturementioning

confidence: 99%

Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks

Ahmad

Chang

2019

Electronics

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“…Wang et al [13] applied a cluster-based method to exploit spatial and temporal correlations for data aggregation, in which the data collected by the sensors were transmitted to the data fusion center through a hierarchy of cluster heads that used wavelet decomposition to build the wavelet coefficients of the data. Jing et al [14] used a dynamic programming approach to decide the energy allocation ratio. Gonglaing et al [15] proposed interleave division multiple-access based compressed sensing for information acquisition with sensor networks to ensure the data transmission reliability of information map reconstruction.…”

Section: Related Workmentioning

confidence: 99%

Data Uploading Strategy for Underwater Wireless Sensor Networks

Huang

Sun

Yang

2019

Sensors

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Underwater wireless sensor networks (UWSNs) have become a popular research topic due to the challenges of underwater communication. The existing mechanisms for collecting data from UWSNs focus on reducing the data redundancy and communication energy consumption, while ignoring the problem of energy-saving transmission after compression. In order to improve the efficiency of data collection, we propose a data uploading decision-making strategy based on the high similarity of the collected data and the energy consumption of the high similarity data compression. This decision-making strategy efficiently optimizes the energy consumption of the networks. By analyzing the data similarity, the quality of network communication, and uploading energy consumption, the decision-making strategy provides an energy-efficient data upload strategy for underwater nodes, which reduces the energy consumption in various network settings. The simulation results show that compared with several existing data compression and uploading methods, the proposed data upload methods has better energy saving effect in different network scenarios.

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“…Under the dynamic energy harvesting [ 16 ], Jiao et al developed a novel evolutionary algorithm to jointly optimize the data rates and power allocation to decrease the total network delay for wireless sensor networks. In Reference [ 17 ], Jing et al employed a channel-aware dynamic programming approach to allocate power to maximize capacity of EH-UASNs. In this approach, the authors concentrated on the distribution of battery energy for sensing and transmission.…”

Section: Introductionmentioning

confidence: 99%

Deep Q-Network-Based Cooperative Transmission Joint Strategy Optimization Algorithm for Energy Harvesting-Powered Underwater Acoustic Sensor Networks

Han

2020

Sensors

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Cooperative transmission is a promising technology for underwater acoustic sensor networks (UASNs) to ensure the effective collection of underwater information. In this paper, we study the joint relay selection and power allocation problem to maximize the cumulative quality of information transmission in energy harvesting-powered UASNs (EH-UASNs). First, we formulate the process of cooperative transmission with joint strategy optimization as a Markov decision process model. In the proposed model, an effective state expression is presented to better reveal interactive relationship between learning and environment, thereby improving the learning ability. Then, we further propose a novel reward function which can guide nodes to adjust power strategy adaptively to balance instantaneous capacity and long-term quality of service (QoS) under the dynamic unpredictable energy harvesting. More specifically, we propose a deep Q-network-based resource allocation algorithm for EH-UASNs to solve the complex coupled strategy optimization problem without any prior underwater environment information. Finally, simulation results verify the superior performance of the proposed algorithm in improving the cumulative network capacity and reducing outages.

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Energy Management and Power Allocation for Underwater Acoustic Sensor Network

Cited by 38 publications

References 36 publications

Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks

Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks

Data Uploading Strategy for Underwater Wireless Sensor Networks

Deep Q-Network-Based Cooperative Transmission Joint Strategy Optimization Algorithm for Energy Harvesting-Powered Underwater Acoustic Sensor Networks

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