A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks

Khan, Jawaad Ullah; Cho, Ho-Shin

doi:10.3390/s150819331

Cited by 90 publications

(94 citation statements)

References 25 publications

(40 reference statements)

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“…Three kinds of data transmission methods are designed according to the distance, which can reduce the same frequency interference with each other. Khan [8] proposed a hierarchical clustering structure and the bottom nodes are divided into three categories of underwater gateway node, path node and ordinary node. The underwater gateway node is the cluster head, and the path nodes on the AUV polling path that will be interactive, and the ordinary node is used as an alternative to replace the energy node with too large energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…A network data collection framework is effectively established for different situations, and can be used for more flexible implementation of different service strategies. In [17], four AUVs interactive polling optimization algorithms are designed on the basis of hierarchical clustering structure in Khan [8]. The method is defined to share the state information of four AUVs.…”

Section: Related Workmentioning

confidence: 99%

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Reliable Data Collection Algorithm Based on AUVs Online Prediction

Chen

Dai

et al. 2017

IRATJ

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The communication process in Underwater Acoustic Sensor Networks (UASNs) is susceptible to interference, which leads to higher latency and Bit Error Ratio (BER). The Autonomous Underwater Vehicle (AUV) polling to collect data has been used to provide reliable transmission of data and extend the network lifecycle. The target events underwater are random appeared. In order to effectively capture the target event, an algorithm for the AUV polling to collect data with online prediction is proposed, namely RCAP (Reliable Collection based on AUV with Prediction). Firstly, the polling objects to AUV should be determined. The clustered network structure is established. The cluster head will be polled by the AUV to transfer the data from the cluster. Secondly, the forecast model is build. The first N rounds of data collected are the historical data. Using the historical data, the data of the N+1th round is predicted based on the regression method. Then, the N+1 to 2N rounds of data are used to calibrate the predicted values, and the prediction model is optimized. Finally, the AUV polling trajectory would be plan. All the cluster head have storage threshold. According to the data volume prediction of each cluster head, the AUV polling objects of each round can be determined to maximize the amount of data collection and to improve network efficiency. The simulation results show that the algorithm can adapt to the prediction of target events in a variety of distributions. Especially, when the target events obey the linear distribution, compared with AAEERP, RCAP has a great optimization in network energy consumption, throughput, data transmission efficiency. It increases nearly 10% throughput especially. Keywords: IntroductionThe underwater acoustic sensor networks (UASNs) is a kind of new marine measurement and control technology that combines autonomous data acquisition, data fusion and transmission applications in oceanographic data collection, water pollution monitoring, earthquake and tsunami prediction, marine navigation and underwater military surveillance, enemy target tracking and other aspects of the potential application value. So that the military and scientific research departments attach great importance to it. In recent years, China has proposed the strategic requirements of building a strong marine power, and the key technologies of the underwater acoustic sensor network are listed as the key research directions. The related researches mainly focus on the fields of MAC protocol, routing protocol, clock synchronization location, target recognition tracking and so on. However, the main problem affecting the performance of the underwater acoustic network is that the energy of the underwater nodes is limited and easy to disappear. Although the current research scholars have adopted a variety of routing protocols to minimize node energy consumption, such as the choice of the next hop node, the node between the rotation of the cluster head to balance the energy, the communication between the network nodes...

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Reliable Data Collection Algorithm Based on AUVs Online Prediction

Chen

Dai

et al. 2017

IRATJ

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“…The plane, A, is divided into several regions by means of the Voronoi region and generator-point concept, as described in [34]. One AUV is assigned to each region, where the AUV follows a specific trajectory called the tour-path .…”

Section: System Descriptionmentioning

confidence: 99%

“…In the network-setup phase, first, an AUV moves to the Voronoi generator-point that is assigned by the sink and forms a cluster around the generator-point [34,35]. …”

Section: Operation Of the Proposed Schemementioning

confidence: 99%

Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach

Khan¹,

Cho²

2016

Sensors

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In this paper, we propose a data-gathering scheme for hierarchical underwater sensor networks, where multiple Autonomous Underwater Vehicles (AUVs) are deployed over large-scale coverage areas. The deployed AUVs constitute an intermittently connected multihop network through inter-AUV synchronization (in this paper, synchronization means an interconnection between nodes for communication) for forwarding data to the designated sink. In such a scenario, the performance of the multihop communication depends upon the synchronization among the vehicles. The mobility parameters of the vehicles vary continuously because of the constantly changing underwater currents. The variations in the AUV mobility parameters reduce the inter-AUV synchronization frequency contributing to delays in the multihop communication. The proposed scheme improves the AUV synchronization frequency by permitting neighboring AUVs to share their status information via a pre-selected node called an agent-node at the static layer of the network. We evaluate the proposed scheme in terms of the AUV synchronization frequency, vertical delay (node→AUV), horizontal delay (AUV→AUV), end-to-end delay, and the packet loss ratio. Simulation results show that the proposed scheme significantly reduces the aforementioned delays without the synchronization time-out process employed in conventional works.

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“…Time synchronization solutions for underwater sensor networks aim to solve the challenges, including the long and dynamic propagation delay, and especially the dynamic propagation delay caused by the mobility among nodes [11,12]. This is an important issue on which recent synchronization schemes focus.…”

Section: Introductionmentioning

confidence: 99%

DE-Sync: A Doppler-Enhanced Time Synchronization for Mobile Underwater Sensor Networks

Zhou

Wang

Nie

et al. 2018

Sensors

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Time synchronization is the foundation of cooperative work among nodes of underwater sensor networks; it takes a critical role in the research and application of underwater sensor networks. Although numerous time synchronization protocols have been proposed for terrestrial wireless sensor networks, they cannot be directly applied to underwater sensor networks. This is because most of them typically assume that the propagation delay among sensor nodes is negligible, which is not the case in underwater sensor networks. Time synchronization is mainly affected by a long propagation delay among sensor nodes due to the low propagation speed of acoustic signals. Furthermore, sensor nodes in underwater tend to experience some degree of mobility due to wind or ocean current, or some other nodes are on self-propelled vehicles, such as autonomous underwater vehicles (AUVs). In this paper, we propose a Doppler-enhanced time synchronization scheme for mobile underwater sensor networks, called DE-Sync. Our new scheme considers the effect of the clock skew during the process of estimating the Doppler scale factor and directly substitutes the Doppler scale factor into linear regression to achieve the estimation of the clock skew and offset. Simulation results show that DE-Sync outperforms existing time synchronization protocols in both accuracy and energy efficiency.

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A Distributed Data-Gathering Protocol Using AUV in Underwater Sensor Networks

Cited by 90 publications

References 25 publications

Reliable Data Collection Algorithm Based on AUVs Online Prediction

Reliable Data Collection Algorithm Based on AUVs Online Prediction

Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach

DE-Sync: A Doppler-Enhanced Time Synchronization for Mobile Underwater Sensor Networks

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