DSM: Dynamic Sink Mobility Equipped DBR for Underwater WSNs

Khan, Ayesha; Jafri, Mohsin Raza; Javaid, Nadeem; Khan, Zahoor Ali; Qasim, Umar; Imran, Muhammad

doi:10.1016/j.procs.2015.05.036

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…We took into consideration the underwater environment and all the necessary equations and coefficients for underwater channel. 10,20 Figure 2 shows a sample screen F I G U R E 2 A sample screen from our simulator with a network of 500m × 500m × 500m divided into four layers with 10 × 10 grid size of our simulator representing a network with a size of 500m × 500m × 500m divided into four layers, a 10 × 10 grid size and with settings that can be adjusted. Data packet size is set to 64 bytes while the control packet size is set to 4 bytes.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have been focused on achieving this by considering one or more optimization metrics. A dynamic sink mobility (DSM) technique presented by Khan et al 20 was shown to enhance the network lifetime. Authors assumed nodes are deployed in a 2D region where the network topology is partitioned into four regions (quadrants).…”

Section: Auv Path Planning For Data Collectionmentioning

confidence: 99%

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Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

Al-Bzoor

Al‐assem

Alawneh

et al. 2021

Trans Emerging Tel Tech

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The Internet of underwater things (IoUT) has become a promising and vibrant paradigm which mainly relies on the underwater acoustic wireless sensor networks (UASN). IoUT was first proposed to facilitate offshore real-time monitoring and exploration of underwater environments. It is now becoming a key component for building and connecting the futuristic underwater smart cities. However, Acoustic waves used in underwater communication suffer from long propagation delays and high transmission energy requirements. Therefore, approaches for autonomous underwater vehicle (AUV) assisted data collection and cluster-based routing were proposed to overcome these drawbacks. In this article, we present a smart genetic-based AUV path planning algorithm for data collection to enhance the performance of UASN. In addition, we combine our smart data collection technique with a dynamic location-unaware clustering algorithm to further reduce energy consumption with mobility consideration. Our simulation results showed that our genetic-based technique achieves more than 145% increase in network lifetime and almost a 75% reduction in energy expenditure compared with traditional diagonal path selection technique. More simulations comparing our combined technique with a novel clustering technique showed that the network lifetime is twice as better, total energy consumption is nearly 30% less, and the delivery ratio is 15% more at low densities. K E Y W O R D S acoustic wireless sensor networks, autonomous underwater vehicles, clustering, Internet of underwater things, underwater communications Recently, the Internet of things (IoT) has gained the attention and interest of the scientific, engineering, and industrial communities. In IoT, smart devices from different heterogeneous systems are connected and controlled over the Internet to exchange important data for several purposes. 1 IoT can be further extended and established in oceans, seas, and big rivers to form the Internet of underwater things (IoUT). 2,3 IoUT is a promising field for a vast number of applications such as tsunami early warning systems, connecting offshore oil and gas platforms, real-time tracking of the submarine ecosystem, and analyzing waste and pollution of specific underwater environments. 4

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Section: Discussionmentioning

confidence: 99%

Section: Auv Path Planning For Data Collectionmentioning

confidence: 99%

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

Al-Bzoor

Al‐assem

Alawneh

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Based on the routing strategy and the major parameters it utilizes for routing purposes, UASN routing can be classified into reliable data forwarding protocols (Nicolaou et al, 2007;Yan et al, 2008;Ayaz and Abdullah, 2009;Wahid et al, 2014;Noh et al, 2016) and predication-based data forwarding protocols (Ayaz and Abdullah, 2009;Wahid and Kim, 2012;Chen and Lin, 2013;Jafri et al, 2013;Wei et al, 2013;Jafri et al, 2014;Khan et al, 2015;Umar et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Mobility Prediction Routing Protocol for Freely Floating Underwater Acoustic Sensor Networks

Alqahtani

Bouabdallah

2021

Front. Comms. Net.

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Recently, there has been an increasing interest in monitoring and exploring underwater environments for scientific applications such as oceanographic data collection, marine surveillance, and pollution detection. Underwater acoustic sensor networks (UASNs) have been proposed as the enabling technology to observe, map, and explore the ocean. The unique characteristics of underwater aquatic environments such as low bandwidth, long propagation delays, and high energy consumption make the data forwarding process very difficult. Moreover, the mobility of the underwater sensors is considered an additional constraint for the success of the data forwarding process. That being said, most of the data forwarding protocols do not realistically consider the dynamic topology of underwater environment as sensor nodes move with the water currents, which is a natural phenomenon. In this research, we propose a mobility prediction optimal data forwarding (MPODF) protocol for UASNs based on mobility prediction. Indeed, by considering a realistic, physically inspired mobility model, our protocol succeeds to forward every generated data packet through one single best path without the need to exchange notification messages, thanks to the mobility prediction module. Simulation results show that our protocol achieves a high packet delivery ratio, high energy efficiency, and reduced end-to-end delay.

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“…The vehicle moves through the stations following the path generated by the base layer, where on-time visit to the target station is the main concern of this layer. The UUV broadcast its position in the map to the closest sink, as they get visited [15]. By doing so, the other sensors associate their regions with the UUV.…”

Section: A Underwater Wireless Sensor Network (Uwsn) Integration and ...mentioning

confidence: 99%

“…The local path generated by the inner layer gets evaluated by the path time (proportional to energy consumption and travelled distance) and its collision avoidance capability. It should be feasible and meet the environmental and vehicle kinematic constraints (described by (15)). The environmental constraints are associated with coastal area of map and fixedmobile obstacles; and the kinematic constraints associated with UUV's yaw surge and sway rates.…”

Section: B Multilayered Motion Planning Optimization Criterionmentioning

confidence: 99%

UUV’s Hierarchical DE-Based Motion Planning in a Semi Dynamic Underwater Wireless Sensor Network

MahmoudZadeh

Powers

Atyabi

2019

IEEE Trans. Cybern.

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This paper describes a reflexive multilayered mission planner with a mounted energy efficient local path planner for unmanned underwater vehicle's (UUV) navigation throughout complex subsea volume in a time variant semi-dynamic operation network. The UUV routing protocol in underwater wireless sensor network is generalized with a homogeneous dynamic knapsack-traveler salesman problem emerging with an adaptive path planning mechanism to address UUV's long-duration missions on dynamically changing subsea volume. The framework includes a base layer of global path planning, an inner layer of local path planning and an environmental sublayer. Such a multilayer integrated structure facilitates the framework to adopt any algorithm with real-time performance. The evolutionary technique known as differential evolution (DE) algorithm is employed by both base and inner layers to examine the performance of the framework in efficient mission timing and its resilience against the environmental disturbances. Relying on reactive nature of the framework and fast computational performance of the DE algorithm, the simulations show promising results and this new framework guarantees a safe and efficient deployment in a turbulent uncertain marine environment passing through a proper sequence of stations considering various constraint in a complex environment.

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DSM: Dynamic Sink Mobility Equipped DBR for Underwater WSNs

Cited by 14 publications

References 10 publications

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

Autonomous underwater vehicles support for enhanced performance in the Internet of underwater things

Energy-Efficient Mobility Prediction Routing Protocol for Freely Floating Underwater Acoustic Sensor Networks

UUV’s Hierarchical DE-Based Motion Planning in a Semi Dynamic Underwater Wireless Sensor Network

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