A range-free tracking algorithm in vehicular ad-hoc networks

Kumar, Sudhir; Kislay, Kumar; Singh, Mohit Kumar; Hegde, Rajesh M.

doi:10.1109/ncc.2014.6811310

Cited by 7 publications

(10 citation statements)

References 11 publications

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“…Recently, the authors in [21] have introduced adapted the work in [17] for vehicular ad-hoc networks in cities (it will be called here, Vehicular Monte Carlo Boxed, VMCB). Similar to Monte Carlo localization principles, generated samples for vehicular nodes are filtered by 1-hop and 2-hop anchors' constraints.…”

Section: Research Articlementioning

confidence: 99%

“…In addition, VMCB cannot achieve higher localization accuracy in noisy The proposed scheme (COPO) is based on the network connectivity in which radio ranging techniques are completely avoidable. This work exploits the principles of Monte Carlo localization similar to MCL [16], VMCB [21] which new heuristics are proposed to avoid the problem of untruthful 2-hop reference nodes improving the localization accuracy in underground vehicular ad-hoc networks. The vehicular node movement direction in road tunnels is also exploited.…”

Section: Research Articlementioning

confidence: 99%

“…The transmission time period (t) is constrained by the maximum allowed speed in the road tunnel (vmax) and RN transmission range (R). After a VN discovers the nearest and farthest RNs, Monte Carlo principles can be used to estimate VN position such as [16] and [21]. However, underground environmental effects, such as path loss and multipath and shadow fading, have greatly impact on the signal propagation.…”

Section: Vehicular Node Position Estimation Stagementioning

confidence: 99%

“…Two values are stored for the next time slot, the value of estimated location and the current speed reading as shown in Lines (15,16). In what follows, simulation results are introduced which the performance of COPO system is compared to existing Monte Carlo schemes such as MCL [16] and VMCB [21]. In addition, MCL and VMCB are adapted to generate samples only in the movement direction and compared with COPO scheme.…”

Section: Explanation Of Remark 44mentioning

confidence: 99%

“…In this section, a performance comparison is presented to compare the performance of proposed system with existing range-free schemes such as MCL [16] and VMCB [21]. We adapt MCL and VMCB to exploit VN movement direction (they are called MCLwMD and VMCBwMD).…”

Section: (T+t) = D(t) + S(t+t)t the Term S(t+t)mentioning

confidence: 99%

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Connectivity Based Positioning System for Underground Vehicular Ad Hoc Networks

Mageid¹

2017

ijcna

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-Underground vehicular ad-hoc networks are indorsing wireless networks because they can realize many goals such as improving the driving safety and monitoring the emergency alerts in underground environments (i.e., road tunnels). It is necessary for vehicular nodes to recognize their positions to achieve these goals. However, Global Positioning System (GPS) devices cannot operate in underground environments; furthermore, the signal propagation faces many effects such as attenuation, multipath and shadow fading. Traditional distance measurement techniques are inadequate to estimate vehicular node locations in underground environments because the expected measurement errors lead to poor positioning. In this paper, the network connectivity is exploited to estimate vehicular node positions instead of radio ranging methods. This work investigates one of the most important techniques that are based on the network connectivity (i.e., Monte Carlo) and proposes new heuristics that achieve an appropriate position estimation accuracy for vehicular nodes. As the underlying method is predictable, it enables these nodes to know their positions all the time inside underground environments. In addition, an efficient deployment strategy is proposed in this work to well organize reference nodes (i.e., fixed nodes that their positions are preconfigured) inside a road tunnel. The proposed scheme performance is verified by NS2 simulator and compared with the current Monte Carlo localization schemes where the simulation results indicate the superiority of the proposed scheme.

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Section: Research Articlementioning

confidence: 99%

Section: Research Articlementioning

confidence: 99%