Weighted Distance Hyperbolic Prediction-Based Detection Scheme for Non Line Of Sight nodes in VANETs

Vehicular Ad hoc Network (VANET) is considered as the potential network for implementing risk-free environment that facilitates least cluttered and safer interaction between the vehicular nodes. The issue of handling channel contention and broadcasting storm during the exchange of warning messages between the emergency vehicles and the Non Line Of Sight (NLOS) nodes is an area of major concern in congestive scenarios for vehicular network. NLOS nodes under emergency situation need to be effectively predicted such that the emergency vehicle is efficient enough in delivering the required information to the NLOS nodes in the stipulated time period with reduced overhead and time delay under a high degree of channel utilization. Weighted Distance Hyperbolic Prediction-Based Detection Scheme (WDHPBDS) is proposed as an effective mechanism that forecasts the NLOS nodes under dynamic moments of vehicular nodes using the merits of range free algorithm that integrates dynamic properties of hyperbolic properties. The simulation of WDHPBDS is investigated using EstiNet 8.1 network simulator by combining the data and management planes of the vehicular network. The results of WDHPBDS analyzed using warning message delivery rate, neighborhood awareness rate and channel utilization rate is confirmed to be 24%, 21% and 27% better to the compared NLOS detection schemes. (c) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Vehicular Ad hoc Network (VANET) is considered as a potential network for safe interaction, but handling NLOS nodes during emergencies remains a major concern. WDHPBDS effectively predicts NLOS nodes using weighted distance and hyperbolic properties, with experimental results showing better performance compared to other NLOS detection schemes.