Increasing the Lifetime of Underwater Acoustic Sensor Networks: Difference Convex Approach

Mohammadi, Zahra; Soleimanpour-Moghadam, Mohadeseh; Askarizadeh, Mohammad; Talebi, Siamak

doi:10.1109/jsyst.2019.2959046

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…Data Acquisition Focused [51], [52] Develop Efficient sensor node placement strategies [53] Develop Optimization strategy of computing processes [54], [55], [56], [57], [58], [59], [60], [61] Develop Output from specific sensors and interpreting process [62] Develop Routing strategies for power consumption efficiency [63], [64] Develop Data transmission speed enhancement for efficient data acquisition [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75] Develop Optimization strategies for ML, DL, RL, and specific algorithms [76], [77], [78], [79], [80], [81] Develop Optimal network resource management [82], [83], [84] Develop Integration of diverse sensors (Fusion), interpretation, and optimization strategies [85], [86], [87], [88], [89], [90], [91], [92] Develop RL model for optimizing interpretation Passive/Active Sound-navigation-and-ranging (SONAR) [93] Develop Efficient routing design for data acquisition optimization [94] Developing strategies for detecting...…”

Section: Researchmentioning

confidence: 99%

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An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

Wibisono,

Piran,

Song

et al. 2024

IEEE Access

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This article introduces an innovative approach to the navigation of autonomous underwater vehicles (AUVs) in inspection and data acquisition missions within underwater wireless sensor networks (UWSNs). We combine at least five underwater navigation techniques to accomplish this mission, adapted from related works. These five techniques are employed to address at least four main challenges identified in inspection and data acquisition missions in UWSNs involving the use of AUVs, namely: communication constraints, energy usage optimization, precise navigation, and effective data acquisition. Limited simulations conducted demonstrate the reliability of the proposed model. The model successfully navigates to the target point in 3D coordinates X Y Z, assuming the launch point as d0 (10 40 40), and reaches the q-goal target point (45 45 0) within 21 seconds, with the addition of uattractive and urepulsive (magnetic beacon attraction force and repulsion force as simulation of underwater current disturbance factor). Furthermore, in the inspection and data acquisition mission in UWSN simulated as node points (o) in pink, AUV (*) in blue effectively follows the predetermined points while acquiring data, as indicated by green lines (-) within just 5 seconds, achievable by increasing the value of α (angle of attack) of the target node to reduce delay time. The evaluation of the experimental simulations has raised issues and future research challenges, including the development of environmental simulation challenges that can closely resemble real conditions, the measurement of energy usage effectiveness to reach each target point, and the potential development of underwater recharging techniques. Furthermore, there is a need for advanced precise navigation and the advancement of effective data acquisition techniques.

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Section: Researchmentioning

confidence: 99%

“…In UWSN, data collection techniques play a crucial role due to the unique challenges posed by the underwater en-vironment [51]- [67]. One such technique involves the use of autonomous underwater vehicles (AUVs) equipped with sensors to gather data from distant underwater nodes [57].…”

Section: B Data Collectionmentioning

confidence: 99%

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

Wibisono,

Piran,

Song

et al. 2024

IEEE Access

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show abstract

“…Different from terrestrial radio-frequency wireless communication, the use of acoustic signal seems to be a better way in OSNs [ 8 ]. Unfortunately, it is difficult to estimate a target with the acoustic signal due to various adverse factors, including the dynamics of the environment, multipath and absorption loss of the signal, and noise interference [ 7 , 9 , 10 ]. In this context, how to locate a target in such a complex ocean environment is a challenge.…”

Section: Introductionmentioning

confidence: 99%

Two-Phase Robust Target Localization in Ocean Sensor Networks Using Received Signal Strength Measurements

Zhang

Mei

et al. 2021

Sensors

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Target localization plays a vital role in ocean sensor networks (OSNs), in which accurate position information is not only a critical need of ocean observation but a necessary condition for the implementation of ocean engineering. Compared with other range-based localization technologies in OSNs, the received signal strength (RSS)-based localization technique has attracted widespread attention due to its low cost and synchronization-free nature. However, maintaining relatively good accuracy in an environment as dynamic and complex as the ocean remains challenging. One of the most damaging factors that degrade the localization accuracy is the uncertainty in transmission power. Besides the equipment loss, the uncertain factors in the fickle ocean environment may result in a significant deviation between the standard rated transmission power and the usable transmission power. The difference between the rated and actual transmission power would lead to an extra error when it comes to the localization in OSNs. In this case, a method that can locate the target without needing prior knowledge of the transmission power is proposed. The method relies on a two-phase procedure in which the location information and the transmission power are jointly estimated. First, the original nonconvex localization problem is transformed into an alternating non-negativity-constrained least square framework with the unknown transmission power (UT-ANLS). Under this framework, a two-stage optimization method based on interior point method (IPM) and majorization-minimization tactic (MMT) is proposed to search for the optimal solution. In the first stage, the barrier function method is used to limit the optimization scope to find an approximate solution to the problem. However, it is infeasible to approach the constraint boundary due to its intrinsic error. Then, in the second stage, the original objective is converted into a surrogate function consisting of a convex quadratic and concave term. The solution obtained by IPM is considered the initial guess of MMT to jointly estimate both the location and transmission power in the iteration. In addition, in order to evaluate the performance of IPM-MM, the Cramer Rao lower bound (CRLB) is derived. Numerical simulation results demonstrate that IPM-MM achieves better performance than the others in different scenarios.

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“…For UASNs, it is important to transmit effectively the crucial information which is captured by sensors in the complex underwater environment. Given that underwater acoustic communication has the characteristics of limited energy, severe fading, time-varying channel and high transmission delay, it is a challenge to guarantee the large information transmission range and high quality of service (QoS) in UASNs [ 3 , 4 ]. The relay-assisted UASNs can achieve effective information transmission in a long range by deploying relay sensor nodes.…”

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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Increasing the Lifetime of Underwater Acoustic Sensor Networks: Difference Convex Approach

Cited by 12 publications

References 30 publications

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

An Autonomous Underwater Vehicle Navigation Technique for Inspection and Data Acquisition in UWSNs

Two-Phase Robust Target Localization in Ocean Sensor Networks Using Received Signal Strength Measurements

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

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