Scalable location estimation using smart antennas in wireless sensor networks

Küçük, Kerem; Kavak, Adnan

doi:10.1016/j.adhoc.2010.04.005

Cited by 12 publications

(8 citation statements)

References 22 publications

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“…26 It requires only one CN to determine SNs' locations. In this technique, the CN is to be integrated with a SAS, which has array signal processing capabilities.…”

Section: Sectoral Sweeper-based Localization Estimation Techniquementioning

confidence: 99%

“…The SSLE technique was proposed as a localization technique using the sectoral sweeping approach through the sensing area. 26 It requires only one CN to determine SNs’ locations. In this technique, the CN is to be integrated with a SAS, which has array signal processing capabilities.…”

Section: Sectoral Sweeper-based Localization Estimation Techniquementioning

confidence: 99%

“…The M-SSLE method is based on the previously proposed SSLE method. 26 Compared to a previously reported technique (SSLE), this proposed method requires simplified message format and elimination of the transmission of additional antenna beams, and shows performance improvement on the localization accuracy. This technique can be classified as a centralized technique, because it involves only a CN to determine the location of the SNs.…”

Section: Introductionmentioning

confidence: 97%

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A modified sectoral sweeper-based localization estimation and its implementation in a multi-hop wireless sensor networking environment by using OPNET

et al. 2013

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In wireless sensor networks, location awareness along with the sensed data is important for most of the applications. Although there are many localization techniques in the literature, some of these techniques involve tiny sensor nodes, which need to be computationally powerful and complex in hardware to estimate their locations. This paper proposes a modified sectoral sweeper-based localization estimation (M-SSLE) method, which is the improved version of our previously studied SSLE method, to achieve position information of sensor nodes. This technique requires only a central node having a smart antenna capability to estimate locations of sensor nodes and simplified message formats. Using the SSLE technique, the computational burden for location estimation is carried out by a central node and no hardware or software modification to tiny sensor devices is required. The M-SSLE is implemented using an OPNET modeler for realistic fading channel conditions. The detailed implementation methodology in OPNET is presented in terms of node and process models. The performance of the M-SSLE method is evaluated through different network scenarios and channel parameters in terms of localization error, average throughput, and node energy consumption. Furthermore, its localization performance is compared with the SSLE and Cramér Rao Bound methods in the two different scenarios for the deployment of the sensor nodes. The M-SSLE average localization errors of 4.3 and 8.9 m are demonstrated under the log-distance and the log-normal shadowing channel conditions in a 250 m × 250 m area, respectively.

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“…26 It requires only one CN to determine SNs' locations. In this technique, the CN is to be integrated with a SAS, which has array signal processing capabilities.…”

Section: Sectoral Sweeper-based Localization Estimation Techniquementioning

confidence: 99%

Section: Sectoral Sweeper-based Localization Estimation Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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A modified sectoral sweeper-based localization estimation and its implementation in a multi-hop wireless sensor networking environment by using OPNET

et al. 2013

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“…Sectoral sweeper based location estimation (SSLE) algorithm is already in use for location estimation in a twodimensional wireless networks in terms of range and azimuth sweeping [8]. However, the proposed algorithm is designed to be compatible with the three dimensional location identification.…”

Section: Algorithmmentioning

confidence: 99%

Smart antenna system design for localization of wireless capsule

Reza

Ahmad

2014

2014 IEEE MTT-S International Microwave Workshop Series on RF and Wireless Technologies for Biomedical and Healthcare Applicati

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Smart antenna is recognized as an intelligent antenna system in modern wireless communication for its adaptive beam forming technique and direction of arrival (DoA) estimation features. However, this system is yet to be introduced extensively for biomedical applications. A sectoral sweeper based algorithm is proposed in this article for RF localization of wireless capsule inside the human gastro intestinal (GI) tract. This algorithm basically works using the alteration of task beam power level and direction of smart antenna array. It is proposed that, one transmitting antenna element of antenna array transmits signals towards the GI tract at a time and others will be in receive mode. The received signals are then processed based on space-frequency signal processing concept which is derived in this paper for cylindrical shaped antenna array. Particle swarm optimization model is developed to predict the 2D location of robotic capsule from the received information of smart antenna system with about 99% successful.Index Terms -adaptive beamforming, capsule localization, particle swarm optimization, space-frequency signal processing, smart antenna array.

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“…Usually in real engineering applications, sensor nodes are placed around the monitoring area in a random fashion, which are distributed in three-dimensional space, thus twodimensional solution method cannot meet the requirements for actual. So it's need to study the three-dimensional localization to meet the WSN application scenarios [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

A New Three-Dimension Spatial Location Algorithm Of Wireless Sensor Network

Xiang

Zhang

Lei

2016

International Journal on Smart Sensing and Intelligent Systems

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Aiming at three-dimensional space localization problem in wireless sensor network, a method of three-dimensional centroid algorithm with spherical perception radius (3DCSPR) is proposed. The algorithm uses Gaussian probability density function to estimate the sensor node radius, and uses tetrahedron method and three-dimensional center to locate unknown node with estimated. Firstly, RSSI value between beacon nodes and destination node is selected by RSSI distribution density function, that confidence interval of nodes perception radius is estimated. Secondly, satisfied tetrahedron is got by the intersections sphere coverage of beacon nodes in three-dimensional. Finally, destination node localization is calculated with weighted centroid in three-dimensional. Simulation results show that the proposed method can significantly reduce positioning errors in three-dimensional network environment in reality and can be widely applied to many fields.

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Scalable location estimation using smart antennas in wireless sensor networks

Cited by 12 publications

References 22 publications

A modified sectoral sweeper-based localization estimation and its implementation in a multi-hop wireless sensor networking environment by using OPNET

A modified sectoral sweeper-based localization estimation and its implementation in a multi-hop wireless sensor networking environment by using OPNET

Smart antenna system design for localization of wireless capsule

A New Three-Dimension Spatial Location Algorithm Of Wireless Sensor Network

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