Aeerp

Ahmad, Awais; Wahid, Abdul; Kim, Dongkyun

doi:10.1145/2512840.2512848

Cited by 41 publications

(6 citation statements)

References 12 publications

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“…The first is when the trajectory of the AUV is fixed. In [13][14][15], the trajectory of the AUV is assumed to be elliptical. In [13], fixed gate nodes (GNs) gather data from member nodes (MNs) and transmit the data to the mobile AUV to enhance the performance of the network in terms of data delivery rate and energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, the rapid energy consumption of GNs results in a shortened network lifetime. In [14], on the basis of the received signal strength index (RSSI) value, a new clustering protocol is designed, and MNs are assigned to GNs by establishing the shortest path tree (SPT). In addition, the function of the gateway varies according to the residual energy to balance the energy consumption.…”

Section: Related Workmentioning

confidence: 99%

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An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT

Shi,

Tang,

Jin

et al. 2023

JMSE

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The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV’s navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT

Shi,

Tang,

Jin

et al. 2023

JMSE

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“…In [9], it was determined that mobility increases the sensing coverage area. In [15], the authors proposed the AUV-aided energy-efficient routing protocol (AEERP).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In [9], it was determined that mobility increases the sensing coverage area. In [15], the authors proposed the AUV-aided energy-efficient routing protocol (AEERP). This scheme employs GNs that rotate on the basis of their residual energy to enhance the network lifetime by reducing energy consumption, but it does not restrict the association of MNs with GNs and this becomes the reason for a high amount of energy consumption at the GNs; moreover, there are excessive possibilities of data losses at the GNs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In UWSNs, the process of data gathering holds the most important position [14]. Recent work shows that data collection with the help of an AUV minimizes the multihop data transmission, improving the energy efficiency in the UWSN [15]. For data collection, instead of visiting each node or cluster, the AUV visits only the selected nodes, called gateway nodes (GNs) or path nodes, to reduce the transmission power of the sensor nodes.…”

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confidence: 99%

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Atomic-shaped efficient delay and data gathering routing protocol for underwater wireless sensor networks

Farooq¹,

Ali²,

Shaf³

et al. 2019

Turk J Elec Eng & Comp Sci

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High end-to-end delay is a major challenge in autonomous underwater vehicle (AUV)-aided routing protocols for underwater monitoring applications. In this paper, a new routing protocol called atomic-shaped efficient delay and data gathering (ASEDG) has been introduced for underwater wireless sensor networks. The ASEDG is divided into two phases; in the first phase, the atomic-shaped trajectory model with horizontal and vertical ellipticals was designed for the movement of the AUV. In the second phase, two types of delay models were considered to make our protocol more delay efficient: member nodes (MNs) to MNs and MNs to gateway nodes (GNs). The MNs-to-MNs delay in the network specifies how long is required for the selection of the next possible forwarders by eliminating the chances of backtracking and a higher number of association links. The MNs-to-GNs delay is considered to choose the path from a multipath environment that takes a minimum amount of time for sending the packet from its generation to destination node. For efficient data gathering, this new trajectory model creates the maximum possible GNs for the association of the MNs. Furthermore, our protocol, ASEDG, has been evaluated by using the aquasim network simulator (NS-2), and its results were compared with the already existing protocol, an efficient data gathering (AEDG) routing protocol. The simulation results show that the ASEDG performed better than the AEDG in terms of end-to-end delay and throughput.

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A systematic literature review on mobility in terrestrial and underwater wireless sensor networks

Ghanem

Mansoor

Ahmad

2021

Int J Communication

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Wireless sensor network (WSN) is one of the most significant technologies that gets much attention from researchers due to its promising applications in various environments. Mobility in wireless sensor networks contributes significantly to improving the performance of terrestrial wireless sensor networks (TWSNs) and underwater wireless sensor networks (UWSNs). This research's primary goal is to highlight the roles of mobility and mobile element tasks in enhancing terrestrial and underwater wireless sensor networks. Our systematic literature review (SLR) analyzed studies published between the periods of 2003 and January 2020. Out of 1762 studies retrieved by searching six online digital libraries and other resources, only 107 of them were selected for primary studies based on selection criteria and relevant to three identified research questions. This was followed by data extraction from the primary studies for answering the three SLR questions. The study reveals that mobility plays an essential role in improving network performance in TWSNs and UWSNs, especially in the aspects of coverage, energy efficiency, throughput, and connectivity. The study also identifies the tasks of the mobile elements in TWSNs and UWSNs, including data collection, localization, and charging. Moreover, it demonstrates the parameters of the mobile elements that should be considered when building networks. Finally, this study concludes by highlighting some open research issues that can be considered for future research.

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Aeerp

Cited by 41 publications

References 12 publications

An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT

An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT

Atomic-shaped efficient delay and data gathering routing protocol for underwater wireless sensor networks

A systematic literature review on mobility in terrestrial and underwater wireless sensor networks

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