Trade-off between accuracy, cost, and QoS using a beacon-on-demand strategy and Kalman filtering over a VANET

Houssaini, Zineb Squalli; Zaimi, Imane; Drissi, Maroua; Oumsis, Mohammed; Ouatik, Saïd El Alaoui

doi:10.1016/j.dcan.2017.09.001

Cited by 19 publications

(15 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This adds great difficulty to the implementation of a RSSI-based low-latency, high-precision positioning system [ 28 ]. The researchers tried to use a Kalman filter or improved Kalman filter to reduce the noise [ 29 , 30 , 31 ]. As presented in Figure 2 , the measurement error in RSSI does not usually have a Gaussian distribution, so even with the adoption of a Kalman filter, it is difficult to achieve good results.…”

Section: Related Workmentioning

confidence: 99%

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Geng

et al. 2018

Sensors

182

111

View full text Add to dashboard Cite

The Global Navigation Satellite System (GNSS) cannot achieve accurate positioning and navigation in the indoor environment. Therefore, efficient indoor positioning technology has become a very active research topic. Bluetooth beacon positioning is one of the most widely used technologies. Because of the time-varying characteristics of the Bluetooth received signal strength indication (RSSI), traditional positioning algorithms have large ranging errors because they use fixed path loss models. In this paper, we propose an RSSI real-time correction method based on Bluetooth gateway which is used to detect the RSSI fluctuations of surrounding Bluetooth nodes and upload them to the cloud server. The terminal to be located collects the RSSIs of surrounding Bluetooth nodes, and then adjusts them by the RSSI fluctuation information stored on the server in real-time. The adjusted RSSIs can be used for calculation and achieve smaller positioning error. Moreover, it is difficult to accurately fit the RSSI distance model with the logarithmic distance loss model due to the complex electromagnetic environment in the room. Therefore, the back propagation neural network optimized by particle swarm optimization (PSO-BPNN) is used to train the RSSI distance model to reduce the positioning error. The experiment shows that the proposed method has better positioning accuracy than the traditional method.

show abstract

Section: Related Workmentioning

confidence: 99%

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Geng

et al. 2018

Sensors

182

111

View full text Add to dashboard Cite

show abstract

“…It can be seen that two sets of RSSI values are generated with the change of sampling time due to the instability of Bluetooth system and the maximum fluctuation is about 12dBm. Therefore, in order to reduce the influence of random fluctuation of RSSI values on location accuracy, we use Kalman filter [38] to filter and denoise the collected RSSI values to obtain more accurate and smooth RSSI value distribution. We compare the original RSSI values collected at two Bluetooth beacon point A and point B in figure 7 with the RSSI values processed by Kalman filtering, the experimental results are shown in figure 3.…”

Section: B Predefined Landmark Settingsmentioning

confidence: 99%

The Technology of Crowd-Sourcing Landmarks-Assisted Smartphone in Indoor Localization

et al. 2020

View full text Add to dashboard Cite

In recent years, with the popularity of smartphones, mobile sensor technologies have been widely used in indoor localization. Its advantage is that data can be collected directly from smartphones without installing any other equipment. Meanwhile, it has the disadvantages of high requirements on positioning conditions and large deviations in positioning results. A technology of crowd-sourcing landmarks-assisted smartphone in indoor localization (CLS-IL) is proposed in this paper so as to solve these disadvantages. Firstly, the accelerometer, magnetometer and gyroscope sensors are used to acquire initial positioning of users. The characteristics of magnetometer and gyroscope sensors are analyzed through a series of experiments in allusion to the key problem of heading estimation errors, then corresponding data is matched for different experimental conditions. After that, the corresponding landmark database is established for experimental environment and an adaptive optimization algorithm of landmarks is proposed to correct drift errors caused by the readings of sensors influenced by surrounding environment and long positioning time excessively during traditional pedestrian dead reckoning. Experimental results show that the location accuracy of CLS-IL system is significantly improved compared with traditional technologies, which provides strong support for subsequent studies.

show abstract

“…Houssain et al, [21] an improvement of GPSR, named KF-GPSR, where each vehicle estimate in real time the position of its neighbours by using kalman filter algorithm.…”

Section: Scenarios Of Travelingmentioning

confidence: 99%

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

Dhasarathan¹,

Dumka

Loganathan

2020

Technol Econ Smart Grids Sustain Energy

View full text Add to dashboard Cite

The aim of this paper is to focus on energy dissemination for green vehicle transportation. In current scenario population of urban areas keeps increasing as the people move towards the cities in search of jobs and betterlife style. This leads to an increase in the number of vehicles on the road. But the transport network which is accessible to the citizens is less when compared to their demand. This situation results in more vehicles on the road. The result of which is one of the factors that aggravate the traffic congestion. It is identified the vehicle volume on road is 91.7% which increases the CO2 emission to 93.25% every month. 2M2C-R2ED shows its efficiency in identification and segregation of alternative route by IWS with an average frequency of 3.82%. Traffic congestion occurs when the available transport resources are less when compared to the number of vehicles that share the resource. As the number of vehicles increases the resources become scarce and congestion is more. The demand for the resources is higher than the actual capacity of the roads and the streets. There are some circumstances that which will aggravate the traffic congestion. The circumstances can be the road condition (potholes, road repair), accidents and some natural calamities. There is a lot of research being done to predict the traffic and model it in order to find a solution that will make the condition better. But still it is an open issue. The accuracy of the predictions done is less.

show abstract

Trade-off between accuracy, cost, and QoS using a beacon-on-demand strategy and Kalman filtering over a VANET

Cited by 19 publications

References 12 publications

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

Indoor Positioning Algorithm Based on the Improved RSSI Distance Model

The Technology of Crowd-Sourcing Landmarks-Assisted Smartphone in Indoor Localization

2M2C-R2ED: Multi-Metric Cooperative Clustering Based Routing for Energy Efficient Data Dissemination in Green-VANETs

Contact Info

Product

Resources

About