On coverage issues in directional sensor networks: A survey

Güvensan, M. Amaç; Yavuz, A. Gökhan

doi:10.1016/j.adhoc.2011.02.003

Cited by 242 publications

(90 citation statements)

References 75 publications

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“…In this study, we develop tractable solutions to the problem of assigning directions to multiple 2-D directional sensors to maximize the information gain corresponding to multiple target locations. Directional sensor control has been studied before in various contexts [2]- [5]; a general survey of this topic can be found in [6], where the focus is on coverage issues in directional sensor networks.…”

Section: Introductionmentioning

confidence: 99%

Directional Sensor Control: Heuristic Approaches

2015

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Abstract-We study the problem of controlling multiple 2-D directional sensors while maximizing an objective function based on the information gain corresponding to multiple target locations. We assume a joint prior Gaussian distribution for the target locations. A sensor generates a (noisy) measurement of a target only if the target lies within the field-of-view of the sensor, where the statistical properties of the measurement error depend on the location of the target with respect to the sensor and the direction of the sensor. The measurements from the sensors are fused to form global estimates of target locations. This problem is combinatorial in nature-the computation time increases exponentially with the number of sensors. We develop heuristic methods to solve the problem approximately, and provide analytical results on performance guarantees. We then improve the performance of our heuristic approaches by applying an approximate dynamic programming approach called rollout. In addition, we address a variant of the above problem, where the goal is to map the sensors to the targets while maximizing the above-mentioned objective function. This mapping problem also turns out to be combinatorial in nature, so we extend one of the above heuristics to solve this mapping problem approximately. We compare the performance of these heuristic approaches analytically and empirically.

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Section: Introductionmentioning

confidence: 99%

Directional Sensor Control: Heuristic Approaches

2015

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“…On the other hand, in the area-based coverage problem, which is the concern of this paper, the objective is to obtain the maximum region covered by geosensors, and is usually evaluated as the ratio of the covered area to the whole area (Huang and Tseng, 2005). The area-based coverage calculation methods are classified into: (i) the methods that consider a raster environment (Akbarzadeh et al, 2013;Argany et al, 2012;Cortés et al, 2004), which are limited by the spatial resolution; and (ii) the methods that model the environment as a vector dataset (Ghosh and Das, 2006;Guvensan and Yavuz, 2011;Ma et al, 2009;Cao, 2006, 2011), which have been mostly proposed for 2D spaces and do not consider the earth topography and human-made obstacles. Furthermore, the sensing models (i.e., binary or probabilistic, omnidirectional or directional, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…The key point of all deployment optimization algorithms, is determining the coverage of an individual geosensor. The coverage problem is classified into target-and area-based coverage: In some of WSN applications, detecting the target points such as building, doors, flags and boxes are desired, while in others the aim is detection of the mobile target points like intruders (Guvensan and Yavuz, 2011). Covering the target points, instead of the whole area, is concerned in the targetbased coverage problem, whose purpose is to achieve the maximum number of target points that have been covered.…”

Section: Introductionmentioning

confidence: 99%

Coevrage Estimation of Geosensor in 3d Vector Environments

Afghantoloee

Doodman

Karimipour

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Sensor deployment optimization to achieve the maximum spatial coverage is one of the main issues in Wireless geoSensor Networks (WSN). The model of the environment is an imperative parameter that influences the accuracy of geosensor coverage. In most of recent studies, the environment has been modeled by Digital Surface Model (DSM). However, the advances in technology to collect 3D vector data at different levels, especially in urban models can enhance the quality of geosensor deployment in order to achieve more accurate coverage estimations. This paper proposes an approach to calculate the geosensor coverage in 3D vector environments. The approach is applied on some case studies and compared with DSM based methods.

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“…The coverage problem is the mainly about monitoring the physical world in hotspots with constrained capacity of network node energy, communication bandwidth and processing [2,7,9]. The deployment of nodes and effective scheduling is the key technology of coverage problem.…”

Section: Introductionmentioning

confidence: 99%

K-coverage prediction optimization for non-uniform motion objects in wireless video sensor networks

Jiang¹,

Sheng²,

Wang³

et al. 2016

Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)

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Abstract. The existing wireless video sensor networks tracking algorithms have much higher performance requirements of the sensor node in networks. For solving this problem, we propose a new target trajectory prediction model for non-uniform motion, which takes all the possible locations of each target at next moment into consideration. Furthermore, we design a K-coverage dynamic optimization algorithm to gain benefit of distributed method based on the aforementioned mathematical model. The experimental results indicate that the proposed algorithm outperforms the existing algorithms.

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On coverage issues in directional sensor networks: A survey

Cited by 242 publications

References 75 publications

Directional Sensor Control: Heuristic Approaches

Directional Sensor Control: Heuristic Approaches

Coevrage Estimation of Geosensor in 3d Vector Environments

K-coverage prediction optimization for non-uniform motion objects in wireless video sensor networks

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