{"title":"Towards location error resilient geographic routing for VANETs","authors":"Reena Kasana, Sushil Kumar, Omprakash Kaiwartya","doi":"10.1109/CCAA.2017.8229890","DOIUrl":null,"url":null,"abstract":"Geographic routing has received a lot of attention from researchers all over the world due to availability of low cost Global Positioning System (GPS) devices. It is considered as efficient routing for large scale networks and offers encouraging solutions for information dissemination in Vehicular ad hoc Networks (VANETs). The efficacy and scalability of all the geographic routing depends on the accuracy of location information obtained from positioning systems. Related literature implicitly assumed perfect location information. However, such belief is unrealistic in the real world. Measured location information inherently has inaccuracy, leading to performance degradation of geographic routing. In this paper, a novel location error tolerant geographical routing (LETGR) in vehicular environment is proposed that can reduce the impact of location inaccuracy in measurement due to instrument imprecision and obstacles in the realistic scenarios in highly mobile environment. LETGR takes the statistical error characteristic into account in its next forwarding vehicle selection logic to maximize the probability of message delivery. To alleviate the effect of mobility, LETGR exploits future locations of vehicles instead of current locations. Extended Kalman filter is used in the proposed algorithm for predicting and correcting future locations of the vehicles. Performance of the LETGR algorithm is evaluated via simulation and results show that LETGR algorithm performance is encouraging when the objective is to maximize the reception of data packets at the destination vehicle.","PeriodicalId":6627,"journal":{"name":"2017 International Conference on Computing, Communication and Automation (ICCCA)","volume":"5 1","pages":"691-697"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computing, Communication and Automation (ICCCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCAA.2017.8229890","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Geographic routing has received a lot of attention from researchers all over the world due to availability of low cost Global Positioning System (GPS) devices. It is considered as efficient routing for large scale networks and offers encouraging solutions for information dissemination in Vehicular ad hoc Networks (VANETs). The efficacy and scalability of all the geographic routing depends on the accuracy of location information obtained from positioning systems. Related literature implicitly assumed perfect location information. However, such belief is unrealistic in the real world. Measured location information inherently has inaccuracy, leading to performance degradation of geographic routing. In this paper, a novel location error tolerant geographical routing (LETGR) in vehicular environment is proposed that can reduce the impact of location inaccuracy in measurement due to instrument imprecision and obstacles in the realistic scenarios in highly mobile environment. LETGR takes the statistical error characteristic into account in its next forwarding vehicle selection logic to maximize the probability of message delivery. To alleviate the effect of mobility, LETGR exploits future locations of vehicles instead of current locations. Extended Kalman filter is used in the proposed algorithm for predicting and correcting future locations of the vehicles. Performance of the LETGR algorithm is evaluated via simulation and results show that LETGR algorithm performance is encouraging when the objective is to maximize the reception of data packets at the destination vehicle.