Energy-constrained bi-objective data muling in underwater wireless sensor networks

Li, Ké; Shen, Chien-Chung; Chen, Guaning

doi:10.1109/mass.2010.5664026

Cited by 18 publications

(15 citation statements)

References 29 publications

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“…In [3], authors have proposed a method called UDMP. The UDMP's core concept is shown in Figure 6 below.…”

Section: Underwater Data Muling Problemmentioning

confidence: 99%

“…As a result, sound waves will have different transmission speeds and therefore the transmission times will be different. We establish Docking Stations [1][2][3][4][5][6] to deal with the limited power of the AUV. This way the AUV can use docking stations to charge and continue collecting information within range to send back to the sink.…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, we have to think of a method that allows AUVs to collect all the information according to more reasonable routes. For example, [1][2][3][4][5][6] consider the importance of the docking station. It is a debatable issue where docking stations are best located, and problems concerning exhausting AUV power underwater.…”

Section: Introductionmentioning

confidence: 99%

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Tour Planning for AUV Data Gathering in Underwater Wireless

Chang

Shih

2015

2015 18th International Conference on Network-Based Information Systems

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In this paper, we utilize autonomous underwater vehicle (AUV) to gather data from sensors. Due the limited power of an AUV, it is hard to collect all the information from every sensor in one trip. Therefore, in this paper, we propose using a docking station to deal with this problem. However, acoustic transmission leads to a large propagation delay such that it is quite difficult to plan the tour path of AUVs. In this paper, a tour-planning algorithm is proposed for data gathering in Underwater Acoustic Sensor Networks (UASNs). We will implement this tour-planning algorithm by utilizing simulation software and compared with UDMP [3] method in the near future.

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“…In [3], authors have proposed a method called UDMP. The UDMP's core concept is shown in Figure 6 below.…”

Section: Underwater Data Muling Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tour Planning for AUV Data Gathering in Underwater Wireless

Chang

Shih

2015

2015 18th International Conference on Network-Based Information Systems

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“…A popular feature to optimize is the energy efficiency of the mobile agent deployments [4], [5], [9], [10], [14]. Another feature to optimize for sparse networks is data latency reduction [3], [8], [11], [15].…”

Section: Related Workmentioning

confidence: 99%

Exploitation by Informed Exploration between Isolated Operatives for information-theoretic data harvesting

Axelrod¹,

Chowdhary²,

Karaman³

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-We consider the problem of ferrying data between nodes of a sparsely distributed sensing network of Unattended Ground Sensors (UGS) with endurance-constrained Unmanned Aerial Systems (UAS). The sensing domain wherein the sparsely distributed UGS network is deployed is assumed to be highly nonstationary (time-varying) and noisy. This makes the dataferrying problem very complicated as the expected valueof-information at a sensing location can rapidly change. To address this issue, we present a new data ferrying algorithm termed Exploitation by Informed Exploration between Isolated Operatives (EIEIO), and show that with several reasonable assumptions and a model on the predicted accumulation of value-of-information, the problem can be simplified to a mathematical linear program. To solve the linear program, the UAS learns to anticipate regions in the sensing domain that have the highest degree of change. The degree of change, is learned using a novel implementation of a Cox Process called the CoxGaussian Process (CGP). Our approach does not require a priori knowledge of the sensing domain model to arrive at an optimal UAS allocation strategy.

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“…Previous work has looked at the theoretical performance of data muling [10] and the optimization of the path taken between nodes [7]. In both cases the locations of the nodes are assumed to be known.…”

Section: Introductionmentioning

confidence: 99%

Autonomous, Localization-Free Underwater Data Muling Using Acoustic and Optical Communication

Doniec

Topor

Rus

2013

Springer Tracts in Advanced Robotics

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We present a fully autonomous data muling system consisting of hardware and algorithms. The system allows a robot to autonomously find a sensor node and use high bandwidth, short range optical communication to download 1.2 MB of data from the sensor node and then transport the data back to a base station. The hardware of the system consists of an autonomous underwater vehicle (AUV) paired with an underwater sensor node. The robot and the sensor node use two modes of communication -acoustic for long-range communication and optical for high bandwidth communication. No positioning system is required. Acoustic ranging is used between the sensor node and the AUV. The AUV uses the ranging information to find the sensor node by means of either stochastic gradient descent, or a particle filter. Once it comes close enough to the sensor node where it can use the optical channel it switches to position keeping by means of stochastic gradient descent on the signal quality of the optical link. During this time the optical link is used to download data. Fountain codes are used for data transfer to maximize throughput while minimizing protocol requirements. The system is evaluated in three separate experiments using our Autonomous Modular Optical Underwater Robot (AMOUR), a PANDA sensor node, the UNET acoustic modem, and the AquaOptical modem. In the first experiment AMOUR uses acoustic gradient descent to find the PANDA node starting from a distance of at least 25 m and then switches to optical position keeping during which it downloads a 1.2 MB large file. This experiment is completed 10 times successfully. In the second experiment AMOUR is manually steered above the PANDA node and then autonomously maintains position using the quality of the optical link as a measurement. This experiment is performed two times for 10 minutes. The final experiment does not make use of the optical modems and evaluate the performance of the particle filter in finding the PANDA node. This experiment is performed 5 times successfully.

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Energy-constrained bi-objective data muling in underwater wireless sensor networks

Cited by 18 publications

References 29 publications

Tour Planning for AUV Data Gathering in Underwater Wireless

Tour Planning for AUV Data Gathering in Underwater Wireless

Exploitation by Informed Exploration between Isolated Operatives for information-theoretic data harvesting

Autonomous, Localization-Free Underwater Data Muling Using Acoustic and Optical Communication

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