A data gathering protocol using AUV in underwater sensor networks

Khan, Jawaad Ullah; Cho, Ho-Shin

doi:10.1109/oceans-taipei.2014.6964549

Cited by 16 publications

(12 citation statements)

References 11 publications

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“…The transmission loss in link‐( i , j ) is expressed as

\overset{A_{i j} false(f false)}{true} = \overset{A_{0}}{true} + 10 κ \log_{10} false(d_{i j} false) + d_{i j} \times 1 0^{- 3} \times \overset{α false(f false)}{true},

where

\overset{A_{0}}{true} =

30 dB is the transmission anomaly, κ = 1.5 is the spreading factor, d ij is the distance of the link ( i , j ) in meters, and

\overset{α false(f false)}{true}

is the absorption coefficient (in dB/km), which is calculated by using Thorp's equation as

\overset{α false(f false)}{true} = \frac{0.11 f^{2}}{1 + f^{2}} + \frac{44 f^{2}}{4100 + f^{2}} + 2.75 \cdot 1 0^{- 4} f^{2} + 0.003 .

…”

Section: System Modelmentioning

confidence: 99%

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Yildiz

2019

Int J Communication

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Summary Underwater acoustic sensor networks (UASNs) are subjected to harsh characteristics of underwater acoustic channel such as severe path losses, noise, and high propagation delays. Among these constraints, propagation delay (more generally, end‐to‐end delay) is the most dominating limitation especially for time‐critical UASN applications. Although the minimization of end‐to‐end delay can be achieved by using the minimum hop routing, this solution cannot lead prolonged lifetimes since nodes consume excessive energy for transmission over long links. On the other hand, the maximization of network lifetime is possible by using energy efficient paths, which consist of relatively short links but high number of hops. However, this solution results in long end‐to‐end delays. Hence, there is a trade‐off between maximizing the network lifetime and minimizing the end‐to‐end delay in UASNs. In this work, we develop a novel multi‐objective–optimization (MOO) model that jointly maximizes the network lifetime while minimizing the end‐to‐end delay. We systematically analyze the effects of limiting the end‐to‐end delay on UASN lifetime. Our results reveal that the minimum end‐to‐end delay routing solution results in at most 72.93% reduction in maximum network lifetimes obtained without any restrictions on the end‐to‐end delay. Nevertheless, relaxing the minimum end‐to‐end delay constraint at least by 30.91% yields negligible reductions in maximum network lifetimes.

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“…The transmission loss in link‐( i , j ) is expressed as

\overset{A_{i j} false(f false)}{true} = \overset{A_{0}}{true} + 10 κ \log_{10} false(d_{i j} false) + d_{i j} \times 1 0^{- 3} \times \overset{α false(f false)}{true},

where

\overset{A_{0}}{true} =

30 dB is the transmission anomaly, κ = 1.5 is the spreading factor, d ij is the distance of the link ( i , j ) in meters, and

\overset{α false(f false)}{true}

is the absorption coefficient (in dB/km), which is calculated by using Thorp's equation as

\overset{α false(f false)}{true} = \frac{0.11 f^{2}}{1 + f^{2}} + \frac{44 f^{2}}{4100 + f^{2}} + 2.75 \cdot 1 0^{- 4} f^{2} + 0.003 .

…”

Section: System Modelmentioning

confidence: 99%

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Yildiz

2019

Int J Communication

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“…The hierarchical underwater network described in [8,33] is considered; it consists of three layers, sensor-layer, AUV-layer, and surface-layer, as shown in Figure 1. The sensor-layer, where N sensor nodes are distributed uniformly, is at a depth, D, in a plane, A, of a given region, R 3 .…”

Section: System Descriptionmentioning

confidence: 99%

“…In [6,7,8,9,10], several schemes have been proposed, wherein a mobile node such as an AUV acts as a relay node to collect data from the normal sensors. In these schemes, the mobile node uses a specific trajectory called the “ tour-path ” to traverse the network.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a data-gathering scheme using multiple AUVs is proposed to reduce the data-gathering latency over the large-scale hierarchical UWSN described in [8]. In the proposed scheme, all the AUVs share their status information via a selected node named the “ agent-node ”, to create a schedule for inter-AUV communication, minimizing both the vertical and horizontal delays.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The preliminary version of this work was presented in a prior conference paper [ 12 ]. The present paper extends the conference version with additional results related to energy consumption during the network partitioning phase, protocol overhead, and distribution of energy consumption in the network.…”

Section: Introductionmentioning

confidence: 99%

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

Khan

Cho

2015

Sensors

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In this paper, we propose a distributed data-gathering scheme using an autonomous underwater vehicle (AUV) working as a mobile sink to gather data from a randomly distributed underwater sensor network where sensor nodes are clustered around several cluster headers. Unlike conventional data-gathering schemes where the AUV visits either every node or every cluster header, the proposed scheme allows the AUV to visit some selected nodes named path-nodes in a way that reduces the overall transmission power of the sensor nodes. Monte Carlo simulations are performed to investigate the performance of the proposed scheme compared with several preexisting techniques employing the AUV in terms of total amount of energy consumption, standard deviation of each node’s energy consumption, latency to gather data at a sink, and controlling overhead. Simulation results show that the proposed scheme not only reduces the total energy consumption but also distributes the energy consumption more uniformly over the network, thereby increasing the lifetime of the network.

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A data gathering protocol using AUV in underwater sensor networks

Cited by 16 publications

References 11 publications

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

Investigation of maximum lifetime and minimum delay trade‐off in underwater sensor networks

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

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

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