Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter

Jaiswal, Raj Kumar; Jaidhar, C. D.

doi:10.1007/s11276-016-1265-4

Cited by 37 publications

(9 citation statements)

References 39 publications

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“…According to Jaiswal et al [23], the location of the mobile nodes in the VANETs predicted based on the method known as the KALAR. The idea is to improve the performance of LAR protocol by using the Kalman Filter, which helps to reduce the position error.…”

Section: Related Workmentioning

confidence: 99%

Genetic Optimized Location Aided Routing Protocol for VANET Based on Rectangular Estimation of Position

2020

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Vehicle Ad-Hoc Network (VANET) is a dynamic decentralized network that consists of various wireless mobile vehicles with no individual user management. Several routing protocols can be used for VANETs, for example, the Location-Aided Routing (LAR) protocol that utilizes location information provided by the Global Positioning System (GPS) sensors. It can help to reduce the search space for the desired route—limiting the search space results in fewer route discovery messages. However, two essential aspects are ignored while applying the LAR protocol in the VANET-based environment. Firstly, the LAR does not exploit the fact that nodes in VANET do not have pure random movement. In other words, nodes in LAR predict the position of destination node by ignoring the fact that the pre-defined constraint on the destination node navigation is met. Secondly, the nodes in the conventional LAR (or simply stated as LAR) protocol use the location information of the destination node before selecting the route location, which is most likely to expire because of the fast movement of the nodes in the VANET environment. This study presents an estimation based on a heuristic approach that was developed to reject weak GPS location data and accept accurate ones. The proposed routing protocol stated as Rectangle-Aided LAR (RALAR) is based on a moving rectangular zone according to the node′s mobility model. Additionally, the proposed RALAR protocol was optimized by using the Genetic Algorithm (GA) by selecting the most suitable time-out variable. The results were compared with LAR and Kalman-Filter Aided-LAR (KALAR), the most commonly utilized protocols in VANET for performance metrics using Packet Delivery Ratio (PDR), average End-to-End Delay (E2E Delay), routing overhead and average energy consumption. The results showed that the proposed RALAR protocol achieved an improvement over the KALAR in terms of PDR of 4.7%, average E2E delay of 60%, routing overhead of 15.5%, and 10.7% of energy consumption. The results proved that the performance of the RALAR protocol had outperformed the KALAR and LAR protocol in terms of regular network performance measures in the VANET environment.

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Section: Related Workmentioning

confidence: 99%

Genetic Optimized Location Aided Routing Protocol for VANET Based on Rectangular Estimation of Position

2020

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“…Reference [10] was trying to predict location of the mobile node in VANETs, relying on a new method called Kalman filter that enables to use fundamental analysis of location information with technical analysis, and evaluated the performance of location prediction using both real vehicle mobility traces and model-driven traces. And [11] proposes a location prediction method for a nonlinear vehicular movement using extended Kalman filter (EKF). EKF is more appropriate contrasted with the Kalman filter (KF), as it is designed to work with the nonlinear system.…”

Section: B Prediction Based On Statistical Modelsmentioning

confidence: 99%

Location Prediction Model Based on the Internet of Vehicles for Assistance to Medical Vehicles

et al. 2020

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Along with the Internet of Vehicles, some intelligent systems can help the medical vehicles transport medical supplies and patients. In terms of emergency issues like catastrophic natural disasters or serious accidents, safe and timely transportation for medical vehicles is particularly important. For assistance to medical vehicles on the road, models with position prediction can provide accurate position information of ambient vehicles in the next seconds. However, with the increasing number of vehicles on the road and the changing road environment, it is an important challenge to predict the location of vehicles in the road correctly. Current location prediction models for vehicles usually use the previous trajectory of vehicles, lacking the consideration of vehicle's state and real-time traffic information, which leads to relatively low accuracy and safety. Based on the deep belief nets (DBN) and long short-term memory (LSTM), this paper presents a location prediction model for assistance to medical vehicles (LPMVs), which fully considers vehicle's attributes, road information and driving environment as well as the relationship between the factors that influence vehicle driving behaviors and vehicle positions. By experiment, we prove that LPMVs can predict more accurately than current location prediction models and be a good choice for assistance to medical vehicles. INDEX TERMS Internet of Vehicles, medical vehicles, neural network, prediction model.

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“…In recent years, the hotspots of social media research are as follows: Statistical analysis of complex networks. Using user data and user relationship data on social media, a complex network is abstracted as the research object to study how to use a known probability distribution to better fit the degree distribution of nodes; to study the evolution of nodes and edges in complex networks; to study the measurement indicator of the importance of edges and nodes; to study how to extract a sub-network as the basic framework of the network and keep some statistical characteristics unchanged; and to study the robustness of network [1][2].…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction Model based on Internet News Buzzword Data

Lei¹

2019

Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

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The partial classification algorithm is mainly used to predict the popularity of network news and to explore the best model to predict the popularity of network news, so as to help network news service providers predict the popularity of news before publication. The popularity of network news is predicted according to the data analysis process: first, UCI data sets are pre-processed; secondly, feature selection is conducted for the data sets by using recursive feature elimination algorithm; then modelling and analysis is carried out, and finally through the confusion matrix, risk map and ROC (Receiver Operating Characteristic) chart performance evaluation, the performance of the model is compared and analyzed. Through comparison, it is found that random forest is the best prediction model.

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Location prediction algorithm for a nonlinear vehicular movement in VANET using extended Kalman filter

Cited by 37 publications

References 39 publications

Genetic Optimized Location Aided Routing Protocol for VANET Based on Rectangular Estimation of Position

Genetic Optimized Location Aided Routing Protocol for VANET Based on Rectangular Estimation of Position

Location Prediction Model Based on the Internet of Vehicles for Assistance to Medical Vehicles

Prediction Model based on Internet News Buzzword Data

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