Grid Coverage Algorithm &amp; Analyzing for wireless sensor networks

Hawbani, Ammar; Wang, XingFu; Husaini, Naji; Karmoshi, Saleem

doi:10.5296/npa.v6i4.6449

Cited by 15 publications

(10 citation statements)

References 9 publications

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“…Another difference with the terrestrial networks is that sensors can be easily deployed in the underwater environment using 3-D space [7] and other sensor placement algorithms [8]. It has brought new researches focused on the optimization of mobility, coverage and connectivity [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

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Underwater Acoustic Modems

et al. 2016

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Abstract-Due to the growing interest using underwater acoustic networks, there are more and more research papers about underwater communications. These papers are mainly focused on deployments and studies about the constraints of the underwater medium. The underwater acoustic channel is highly variable and the signal transmission can change according to environmental factors such as the temperature, pressure or salinity of the water. For this reason, it is important to know how these devices are developed and the maximum distance and data transfer rates they can achieve. To this end, this paper presents an exhaustive study of existing underwater acoustic modems where their main features are highlighted. We also review the main features of their hardware. All presented proposals in the research literature are compared with commercial underwater acoustic modems. Finally, we analyze different programs and improvements of existing network simulators that are often used to simulate and estimate the behavior of underwater networks.

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Section: Introductionmentioning

confidence: 99%

“…The related literature includes several surveys about underwater acoustic networks, about WSN architectures [10] [11], about location techniques [13] and other general issues [18]. However there is no specific survey about acoustic modems, where authors focus the content on showing their main features.…”

mentioning

confidence: 99%

Underwater Acoustic Modems

et al. 2016

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“…If the distance is less than a threshold 0 = √ fs / mp (meters), the free space (fs) model is used; otherwise, the multipath (mp) model is used, as shown in (13). A sensor node will consume ( , ) of energy to transmit (a) Grid coverage scheme 1 [27] (b) Grid coverage scheme 2 [27] (c) Zigzag coverage scheme [28] Figure 7: The network topologies used to simulate the performance of GHS. The arrow indicated the position of base station (sink node).…”

Section: Resultsmentioning

confidence: 99%

“…For grid based coverage schemes, we have selected two algorithms provided in [27], Grid Square Coverage version (1) and Grid Square Coverage version (2). Grid Square Coverage (1) algorithms have 78% coverage efficiency while Grid Square Coverage (2) Notes: (i) PTP = peer-to-peer, DC = dynamic clustering, and SC = static clustering.…”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

Sensors Grouping Hierarchy Structure for Wireless Sensor Network

Hawbani

Wang

Karmoshi

et al. 2015

International Journal of Distributed Sensor Networks

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There are many challenges in implementation of wireless sensor network systems: clustering and grouping are two of them. The grouping of sensors is computational process intended to partition the sensors of network into groups. Each group contains a number of sensors and a sensor can be an element of multiple groups. In this paper, we provided a Sensors Grouping Hierarchy Structure (GHS) to split the nodes in wireless sensor network into groups to assist the collaborative, dynamic, distributed computing and communication of the system. Our idea is to partition the nodes according to their geographical maximum covered regions such that each group contains a number of nodes and a number of leaders. To evaluate the performance of our proposed grouping structure, we have implemented a Grouped based routing and Grouped based object tracking. The proposed grouping structure shows a good performance in energy consumption and energy dissipation during data routing and it generates a little redundant data during object tracking.

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“…The applications of underwater wireless sensor networks (UWSNs) mostly include oceanography data collection, intrusion detection applications, disaster prediction, and mineral exploitation [1,2,3,4], which involve nodes that are capable of underwater data collection and communication processing through underwater acoustic signals [5,6,7]. The underwater acoustic signal communication involves a low propagation speed of sound at approximately 1500 m/s, with slow attenuation in underwater environments.…”

Section: Introductionmentioning

confidence: 99%

A High-Efficiency Uneven Cluster Deployment Algorithm Based on Network Layered for Event Coverage in UWSNs

Shu

Liu

Jiang

2016

Sensors

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Most existing deployment algorithms for event coverage in underwater wireless sensor networks (UWSNs) usually do not consider that network communication has non-uniform characteristics on three-dimensional underwater environments. Such deployment algorithms ignore that the nodes are distributed at different depths and have different probabilities for data acquisition, thereby leading to imbalances in the overall network energy consumption, decreasing the network performance, and resulting in poor and unreliable late network operation. Therefore, in this study, we proposed an uneven cluster deployment algorithm based network layered for event coverage. First, according to the energy consumption requirement of the communication load at different depths of the underwater network, we obtained the expected value of deployment nodes and the distribution density of each layer network after theoretical analysis and deduction. Afterward, the network is divided into multilayers based on uneven clusters, and the heterogeneous communication radius of nodes can improve the network connectivity rate. The recovery strategy is used to balance the energy consumption of nodes in the cluster and can efficiently reconstruct the network topology, which ensures that the network has a high network coverage and connectivity rate in a long period of data acquisition. Simulation results show that the proposed algorithm improves network reliability and prolongs network lifetime by significantly reducing the blind movement of overall network nodes while maintaining a high network coverage and connectivity rate.

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Grid Coverage Algorithm & Analyzing for wireless sensor networks

Cited by 15 publications

References 9 publications

Underwater Acoustic Modems

Underwater Acoustic Modems

Sensors Grouping Hierarchy Structure for Wireless Sensor Network

A High-Efficiency Uneven Cluster Deployment Algorithm Based on Network Layered for Event Coverage in UWSNs

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