A Hexagon Tessellation Approach for the Transmission Energy Efficiency in Underwater Wireless Sensor Networks

Kim, Sungun; Cheon, Hyunsoo; Seo, Sangbo; Song, Seung-Mi; Park, Seonyeong

doi:10.3745/jips.2010.6.1.053

Cited by 11 publications

(14 citation statements)

References 12 publications

(16 reference statements)

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“…Next, the papers from each class are separated based on the specific application. The search and selection results are given in Table II for circular and Table III [3], [5], [6], [8][9][10][11][12][13][14][15], [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31], [33-42] 37 Total #papers 42 We further categorized the papers from each application in sub-classes depending on whether authors provide formulas on how to calculate the total number of embedded hexagonal cells, their vertices or edges.…”

Section: Paper Search and Selection Resultsmentioning

confidence: 99%

A Review of Embedding Hexagonal Cells in the Circular and Hexagonal Region of Interest

Prvan¹,

Ožegović²,

Mišura³

2019

IJACSA

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Hexagonal cells are applied in various fields of research. They exhibit many advantages, and one of the most important is their possibility to be closely packed and to form a hexagonal grid that fully covers the Region of Interest (ROI) without overlaps or gaps. ROI can be of various geometrical shapes, but this paper deals with the circular or hexagonal ROI approximations. The main purpose of our research is to provide a short review on the literature concerning the hexagonal grid, summarizing the existing state-of-the-art approaches on embedding hexagonal cells in the targeted ROI shapes and offering application-specific advantages. We report on formulas and algebraic expressions given in the existing researches that are used for calculating the number of embedded inner hexagonal cells or their vertices and/or edges. We contribute by integrating all researches in one place, finding a connection between previously unrelated applications concerning the use of embedded hexagonal grid and extracting commonality between previous researches on whether it provides the formulas on calculating the inner hexagon cells. In case only the number of edges or vertices is provided for the targeted application, we derive formulas for calculating the number of inner hexagons. Therefore, our survey results with the overview on solving the problem of embedding hexagonal cells in the desired circular or hexagonal ROI. The contribution of the review is the following: first it provides the existing and the derived formulas for calculating the embedded hexagons and second, it provides a theoretical background that is necessary to encourage further research. Namely, our main motivation, that is the geometrical design of the one of the world's largest CERN particle detectors, Compact Muon Solenoid (CMS) is analyzed as a source for the future research directions.

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Section: Paper Search and Selection Resultsmentioning

confidence: 99%

A Review of Embedding Hexagonal Cells in the Circular and Hexagonal Region of Interest

Prvan¹,

Ožegović²,

Mišura³

2019

IJACSA

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“…We built MATLAB-based LLS and SLS platforms and employed the DL power allocation strategy for the next-generation UAC networks by referring the terrestrial cellular network communication approaches [6][7][8]. This work is a continuation of our previous work in [15] where we analyzed effective SNR mapping and link adaptation strategies for UAC networks.…”

Section: System Model For Uac Networkmentioning

confidence: 99%

“…It should be noted that the P B 0 power offset for pilot spacing [0, 0] is equivalent to a non-PA strategy. Therefore, only the three PA cases based on pilot spacing [6,3], [4,2], and [2,1] are considered. Figure 5.…”

Section: Power Offset Using Three Types Of Pilot Spacingmentioning

confidence: 99%

“…In addition, bandwidth availability is the most prominent challenge in UAC networks. Therefore, cellular and frequency-reuse concepts are more tempting when trying to improve the coverage and capacity of UAC networks [6][7][8]. In this paper, we consider a cellular type of UAC network architecture and employ a downlink (DL) power allocation (PA) strategy to analyze system throughput and outage performance in a system-level simulation (SLS).…”

Section: Introductionmentioning

confidence: 99%

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Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks

Ahmad

Chang

2019

Electronics

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The increasing interest in next-generation underwater acoustic communications networks is due to vast investigation of oceans for oceanography, commercial operations in maritime areas, military surveillance, and more. A surface buoy or underwater base station controller (UBSC) communicates with either transceivers or underwater base stations (UBSs) via acoustic links. Transceivers further communicate with underwater sensor nodes using acoustic links. In this paper, we employ a downlink (DL) power allocation (PA) strategy using an orthogonal frequency-division multiple access (OFDMA) technique for underwater acoustic communications (UAC) networks. First, we present an approach to power offsets using three kinds of pilot spacing and apply the power boosting (PB) concept on orthogonal frequency-division multiplexing (OFDM) symbols for the UAC network. Secondly, we draw the block error rate (BLER) curves from link-level simulation (LLS) and analyze the signal-to-noise ratio (SNR) for both PA and non-PA strategies. Lastly, we adopt the best PB for system-level simulation (SLS) and compare the throughput and outage performance for PA and non-PA strategies. Hence, the simulation results confirm the effectiveness of the DL PA strategy for UAC networks.

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“…Therefore, the intermediate nodes use the unintended energy for the packet forwarding of the source node, which induces high energy consumption of the nodes and thus accelerates network partitioning. Therefore, the energy efficiency of the nodes is the key factor that affects the network performance in distributed networks for IoT [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Probabilistic Routing Algorithm for Internet of Things

Park¹,

Cho²,

Lee

2014

Journal of Applied Mathematics

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In the future network with Internet of Things (IoT), each of the things communicates with the others and acquires information by itself. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose energy-efficient probabilistic routing (EEPR) algorithm, which controls the transmission of the routing request packets stochastically in order to increase the network lifetime and decrease the packet loss under the flooding algorithm. The proposed EEPR algorithm adopts energy-efficient probabilistic control by simultaneously using the residual energy of each node and ETX metric in the context of the typical AODV protocol. In the simulations, we verify that the proposed algorithm has longer network lifetime and consumes the residual energy of each node more evenly when compared with the typical AODV protocol.

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A Hexagon Tessellation Approach for the Transmission Energy Efficiency in Underwater Wireless Sensor Networks

Cited by 11 publications

References 12 publications

A Review of Embedding Hexagonal Cells in the Circular and Hexagonal Region of Interest

A Review of Embedding Hexagonal Cells in the Circular and Hexagonal Region of Interest

Downlink Power Allocation Strategy for Next-Generation Underwater Acoustic Communications Networks

Energy-Efficient Probabilistic Routing Algorithm for Internet of Things

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