Packet Size Optimization for Lifetime Maximization in Underwater Acoustic Sensor Networks

Yildiz, Hüseyin Uğur; Güngör, Vehbi Çağrı; Tavlı, Bülent

doi:10.1109/tii.2018.2841830

Cited by 88 publications

(45 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…signal amplitude and delay, using these simulated channel realizations. For example, Yildiz et al [36] propose a framework for jointly optimizing the packet size and transmit power in UANs and use BELLHOP to simulate a PHY UAN simulation/emulation suite based on ns2-MIRACLE…”

Section: State-of-the-art In Underwater Acoustic Channel Simulationmentioning

confidence: 99%

Channel Modeling for Underwater Acoustic Network Simulation

Morozs¹,

Gorma²,

Henson³

et al. 2020

Preprint

View full text Add to dashboard Cite

This manuscript was submitted to IEEE Access on 12 Jun 2020.<div><br></div><div>Abstract:</div><div><br></div><div>Simulation forms an important part of the development and empirical evaluation of underwater acoustic network (UAN) protocols. The key feature of a credible network simulation model is a realistic channel model. A common approach to simulating realistic underwater acoustic (UWA) channels is by using specialised beam tracing software such as BELLHOP. However, BELLHOP and similar modeling software typically require knowledge of ocean acoustics and a substantial programming effort from UAN protocol designers to integrate it into their research. In this paper, we bridge the gap between low level channel modeling via beam tracing and automated channel modeling, e.g. via the World Ocean Simulation System (WOSS), by providing a distilled UWA channel modeling tutorial from the network protocol design point of view. The tutorial is accompanied by our MATLAB simulation code that interfaces with BELLHOP to produce channel data for UAN simulations. As part of the tutorial, we describe two methods of incorporating such channel data into network simulations, including a case study for each of them: 1) directly importing the data as a look-up table, 2) using the data to create a statistical channel model. The primary aim of this paper is to provide a useful learning resource and modeling tool for UAN protocol researchers. Initial insights into the UAN protocol design and performance provided by the statistical channel modeling approach presented in this paper demonstrate its potential as a powerful modeling tool for future UAN research.<br></div>

show abstract

Section: State-of-the-art In Underwater Acoustic Channel Simulationmentioning

confidence: 99%

Channel Modeling for Underwater Acoustic Network Simulation

Morozs¹,

Gorma²,

Henson³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Such as network construction framework has been provided, which is by modeling the sensors' roles according to the special abilities, special tasks and time periods [10]. Ugur et al [11] indicate that two factors of the forwarding packet size and the transmission energy consumption have important effect on prolonging the network lifetime of sensor networks. By considering the forwarding packet size and the forward energy consumption, one integer linear programming based lifetime maximization framework has been proposed.…”

Section: Network Lifetimementioning

confidence: 99%

A Comprehensive Data Gathering Network Architecture in Large-Scale Visual Sensor Networks

et al. 2020

View full text Add to dashboard Cite

The fundamental utility of the Large-Scale Visual Sensor Networks (LVSNs) is to monitor specified events and to transmit the detected information back to the sink for achieving the data aggregation purpose. However, the events of interest are usually not uniformly distributed but frequently detected in certain regions in real-world applications. It implies that when the events frequently picked up by the sensors in the same region, the transmission load of LVSNs is unbalanced and potentially cause the energy hole problem. To overcome this kind of problem for network lifetime, a Comprehensive Visual Data Gathering Network Architecture (CDNA), which is the first comparatively integrated architecture for LVSNs is designed in this paper. In CDNA, a novel α-hull based event location algorithm, which is oriented from the geometric model of α-hull, is designed for accurately and efficiently detect the location of the event. In addition, the Chi-Square distribution event-driven gradient deployment method is proposed to reduce the unbalanced energy consumption for alleviating energy hole problem. Moreover, an energy hole repairing method containing an efficient data gathering tree and a movement algorithm is proposed to ensure the efficiency of transmitting and solving the energy hole problem. Simulations are made for examining the performance of the proposed architecture. The simulation results indicate that the performance of CDNA is better than the previous algorithms in the realistic LVSN environment, such as the significant improvement of the network lifetime.

show abstract

“…Routing takes place in a greedy multihop fashion to the sink by using the pressure level of sensor nodes. In [27], the authors addressed the problem of network lifetime by considering two basic elements: packet size and transmission power. They proposed a framework that jointly optimizes these two factors and increases the network lifetime by using integer linear programming.…”

Section: Literature Reviewmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

Packet Size Optimization for Lifetime Maximization in Underwater Acoustic Sensor Networks

Cited by 88 publications

References 43 publications

Channel Modeling for Underwater Acoustic Network Simulation

Channel Modeling for Underwater Acoustic Network Simulation

A Comprehensive Data Gathering Network Architecture in Large-Scale Visual Sensor Networks

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

Contact Info

Product

Resources

About