Weighted localization in Vehicular Ad Hoc Networks using vehicle-to-vehicle communication

Altoaimy, Lina; Mahgoub, Imad; Rathod, Monika

doi:10.1109/giis.2014.6934270

Cited by 28 publications

(13 citation statements)

References 12 publications

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“…(21) and (22). (22) Also, the lists of time-varying GPS STD for all 7 vehicles and 9 ve-546 hicles are depicted in Tables 7 and 8. 547…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The key features of the VANETs over the conventional Q3 Q4 19 ITS are their network-based structure and the capability of commu- 20 nication between vehicles, i.e. vehicle-to-vehicle (V2V), and vehicles 21 to roadside units (V2R) or other available infrastructures (V2I), by 22 utilizing the underlying communication platforms and standardized 23 protocols [2,3]. In V2V, the vehicles communicate with each other to 24 share the information needed by an application or so called "service".…”

Section: Introductionmentioning

confidence: 99%

“…applications. 176 In [22], a localization method for VANETs is proposed based on 177 the weighted localization using the SINR (signal to interference-noise 178 ratio) obtained from the becons and the distance between the neigh-179 boring vehicles where it is assumed that closer vehicles have higher 180 SINR values and thus will have larger weights, and vice-versa. 181 In [23], the idea of using distance measurements to improve the …”

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confidence: 99%

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Localization in vehicular ad hoc networks using data fusion and V2V communication

Golestan

Sattar

Karray

et al. 2015

Computer Communications

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“…(21) and (22). (22) Also, the lists of time-varying GPS STD for all 7 vehicles and 9 ve-546 hicles are depicted in Tables 7 and 8. 547…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Localization in vehicular ad hoc networks using data fusion and V2V communication

Golestan

Sattar

Karray

et al. 2015

Computer Communications

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“…Then, extended Kaman filter uses the measured distance in addition to the kinematic motion model and road map to update the current vehicle's position. Similarly, the authors in [13] have considered the noise and interference associated with the received signal strength for better distance estimation from the RSS. However, such RSS based ranging will suffer from limited performance due to its sensitivity to the used path loss model that relates the distance to the RSS [14].…”

Section: A Related Workmentioning

confidence: 98%

Integrated Cooperative Localization for Connected Vehicles in Urban Canyons

Elazab¹,

Noureldin²,

Hassanein³

2014

2015 IEEE Global Communications Conference (GLOBECOM)

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The goal to achieve accurate and ubiquitous localization is the driving force for location based services in vehicular ad hoc networks (VANETs). In urban areas, global positioning system (GPS) and in-vehicle navigation sensors (e.g. odometers) suffer from prolonged outages and unsustainable error accumulation, respectively. The need for precise vehicle localization remains paramount, and cooperative vehicle localization based on ranging techniques are being exploited to this end. This paper presents a novel cooperative localization scheme that utilizes round trip time (RTT) for inter-vehicle distance calculation, integrated with inertial sensor measurements to update the position of not only the vehicle to be localized, but its neighbors as well. We adopted the extended Kalman filter (EKF), to limit the effect of errors in both the sensors and the neighbors' positions, in computing the new location. In comparison to the existing cooperative localization techniques, our proposed cooperative scheme does not depend on GPS updates for the neighbors' positions thus making it far more suitable in urban canyons and tunnels. In addition, our scheme considers updating the neighbors' positions using their current inertial sensor measurements resulting in; better position estimation. The scheme is implemented and tested using the network simulator 3 (ns-3), vehicle traces are generated using SUMO and error models are introduced to the sensors and initial positions for different velocities and densities. Results show that our scheme outperforms the inertial navigation systems (INS) technology typically used in environments where GPS fails.

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“…Therefore, the values of an, bn, cn, and dn may be slightly higher than the respective actual values, and consequently, the four circles may not intersect at one common point (Figure 10a). The received signal strength indication (RSSI) algorithm [50][51][52][53][54][55][56], such as quadrilateral localization (Figure 10a), provides an alternative to solve this problem and to improve the accuracy and precision. The plane OXY is perpendicular to the gravity direction, the origin O is the location of anchor A, and the x-axis is the projection direction from anchor A to anchor B.…”

Section: Estimation Algorithm Of a Tree Positionmentioning

confidence: 99%

An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions

Sun

Fang

Weng

et al. 2019

Sensors

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Accurately measuring tree diameter at breast height (DBH) and estimating tree positions in a sample plot are important in tree mensuration. The main aims of this paper include (1) developing a new, integrated device that can identify trees using the quick response (QR) code technique to record tree identifications, measure DBH, and estimate tree positions concurrently; (2) designing an innovative algorithm to measure DBH using only two angle sensors, which is simple and can reduce the impact of eccentric stems on DBH measures; and (3) designing an algorithm to estimate the position of the tree by combining ultra-wide band (UWB) technology and altitude sensors, which is based on the received signal strength indication (RSSI) algorithm and quadrilateral localization algorithm. This novel device was applied to measure ten 10 × 10 m square plots of diversified environments and various tree species to test its accuracy. Before measuring a plot, a coded sticker was fixed at a height of 1.3 m on each individual tree stem, and four UWB module anchors were set up at the four corners of the plot. All individual trees’ DBHs and positions within the plot were then measured. Tree DBH, measured using a tree caliper, and the values of tree positions, measured using tape, angle ruler, and inclinometer, were used as the respective reference values for comparison. Across the plots, the decode rate of QR codes was 100%, with an average response time less than two seconds. The DBH values had a bias of 1.89 mm (1.88% in relative terms) and a root mean square error (RMSE) of 5.38 mm (4.53% in relative terms). The tree positions were accurately estimated; the biases on the x-axis and the y-axis of the tree position were −8.55–14.88 cm and −12.07–24.49 cm, respectively, and the corresponding RMSEs were 12.94–33.96 cm and 17.78–28.43 cm. The average error between the estimated and reference distances was 30.06 cm, with a standard deviation of 13.53 cm. The device is cheap and friendly to use in addition to its high accuracy. Although further studies are needed, our method provides a great alternative to conventional tools for improving the efficiency and accuracy of tree mensuration.

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Weighted localization in Vehicular Ad Hoc Networks using vehicle-to-vehicle communication

Cited by 28 publications

References 12 publications

Localization in vehicular ad hoc networks using data fusion and V2V communication

Localization in vehicular ad hoc networks using data fusion and V2V communication

Integrated Cooperative Localization for Connected Vehicles in Urban Canyons

An Integrated Method for Coding Trees, Measuring Tree Diameter, and Estimating Tree Positions

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