3D Coverage Analysis under Heterogeneous Deployment Strategies in Wireless Sensor Networks

Oktuğ, Sema; Халилов, А. И.; Tezcan, Hakan

doi:10.1109/aict.2008.50

Cited by 28 publications

(15 citation statements)

References 9 publications

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“…The coverage is basically related to the concept of area and node coverage. The parameter of area coverage is calculated by dividing the covered area to the total area of the terrain and the node coverage should be calculated by dividing number of redundant nodes to the total number of sensor nodes [7].…”

Section: Coveragementioning

confidence: 99%

Connectivity and Coverage Preserving Schemes for Surveillance Applications in WSN

Sharma¹,

Singh²,

Agnihotri³

2012

IJCA

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In applications such as surveillance and target monitoring, high degree of coverage and connectivity are required. This paper investigates the problem of energy efficient coverage and connectivity for random placement of nodes such that active sensor nodes are minimized. We introduce an algorithm based on connected dominating set (CDS) and use it as a virtual backbone for network connectivity. Some nodes are refined from isolation to the backbone network, while others are connected under the tributaries of backbone network. If all the nodes are activated simultaneously, it leads to redundancy and wastage of resources in the network. In our work, coverage is achieved such that overlapping area is minimized, while connectivity of network is maintained via backbone network and its tributaries.

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Section: Coveragementioning

confidence: 99%

Connectivity and Coverage Preserving Schemes for Surveillance Applications in WSN

Sharma¹,

Singh²,

Agnihotri³

2012

IJCA

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“…Therefore, many approaches are proposed to deal with the 2D coverage problem [7,10]. Recently, Oktug et al [9] proposed an approach to solve coverage problem by simulating sensor deployment strategies on a 3D terrain model and to find answers to questions that how many sensors are needed to cover a specified 3D terrain at a specified coverage percentage.…”

Section: Wsn Deployment Problemmentioning

confidence: 99%

“…In many WSN applications, the main task is the surveillance of certain geographical areas [9]. Target location can be simplified considerably if the sensors are placed in such a way that every grid point in the sensor field is covered by sensors [3].…”

Section: Maximization Of Coveragementioning

confidence: 99%

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A force-driven evolutionary approach for multi-objective 3D differentiated sensor network deployment

Wei

Kang

Chen

2009

2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems

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This paper describes a novel force-driven evolutionary approach for solving multi-objective 3D deployment problems in differentiated wireless sensor networks (WSNs). WSN is a wireless network consisting ofspatially distributed autonomous sensors to monitor physical or environmental conditions. Deciding the location of sensor to be deployed on a terrain with the consideration ofdifferent criteria is an important issue for the design of wireless sensor network. A multi-objective genetic algorithm with a force-driven method is proposed to solve 3D differentiated WSN deployment problems with the objectives of the coverage of sensors, satisfaction of detection levels, and energy conservation. The preliminary experimental results demonstrated that the proposed approach is capable of obtaining a set of non-dominated solutions for multi-objective 3D differentiated WSN deployment problems.

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“…Finally, in order to check the accuracy of the proposed formulation, numerical results are presented. [17] Addressed the problem of determining an optimal deployment pattern that achieves both coverage and k-connectivity in a wireless sensor network. In [18], authors investigate the connectivity problem when directional antennas are used.…”

Section: Related Workmentioning

confidence: 99%

Impact of Nakagamim Fading Model on Multihop Mobile Ad Hoc Network

Tarique¹,

Tanvir²

2011

IJCA

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Several theoretical and experimental based models have been proposed to predict the fading envelope of the received signal in multipath condition. In this paper, we considered a model named Nakagami-m model. Nakagami-m can model a variety of fading environments, where it closely approximates the Nakagami-q (Hoyt) and the Nakagami-n (Rice) models, and has the Rayleigh and one sided Gaussian models as special cases. This model provides the best fit to land-mobile system like wireless mobile ad hoc Network (MANET). Under Nakagami-m fading model, received packet may not be clearly understood by the receiving node, which affects the routing protocol as well as the medium access control protocol of a network. The severity of Nakagami-m fading model on the network performance has been presented in this paper which is demonstrated via simulation results. Simulation results illustrate that the performance of a network may become unable to meet the expectation if Nakagami-m fading model is used in contrast to the simple two-ray model. A physical layer solution and a Medium Access Control (MAC) layer solution been proposed in this paper to overcome the effects of Nakagami-m fading model. Simulation results prove that these two solutions condense the Nakagami-m fading effect and improve network performance.

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3D Coverage Analysis under Heterogeneous Deployment Strategies in Wireless Sensor Networks

Cited by 28 publications

References 9 publications

Connectivity and Coverage Preserving Schemes for Surveillance Applications in WSN

Connectivity and Coverage Preserving Schemes for Surveillance Applications in WSN

A force-driven evolutionary approach for multi-objective 3D differentiated sensor network deployment

Impact of Nakagamim Fading Model on Multihop Mobile Ad Hoc Network

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