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

Summary Vehicular Ad hoc NETwork (VANET) facilitates ubiquitous connectivity for establishing Vehicle‐to‐Vehicle (V2V) communication and supporting Intelligent Transportation Systems (ITSs). This vehicle communication requires complete coverage within the target range for ensuring reliable message dissemination. High density of vehicles in the intersections introduces challenges due to obstacles such as buildings, foliage, and other moving vehicles, preventing exchange of information about location and message update between vehicles. Non‐Line‐of‐Sight (NLOS) nodes also introduce broadcasting storm problem leading to congestion that prevents emergency messages from reaching the target vehicular nodes. Integration of meta‐heuristic Crow Search Algorithm (CSA) and Gray Wolf Optimization Algorithm (GWOA) minimizes the objective function of NLOS localization problem without the solution being trapped into local optima. In this paper, a Hybrid Crow Search and GWOA‐based NLOS Positioning Scheme (HCSGWOA‐NLOS‐PS) is proposed to handle the issue of broadcast storm and facilitate reliability in emergency message delivery. The proposed HCSGWOA‐NLOS‐PS utilizes the benefits of Time of Arrival (ToA) and geographical information‐based cooperative localization agent for attaining efficient NLOS node positioning in the network. It uses the benefits of CSA and GWOA for positioning the NLOS nodes based on the intelligence derived from the crows' conduit and the social attacking behavior of gray wolves that are ideal for balancing the tradeoff between exploration and exploitation. The simulation results of the proposed HCSGWOA‐NLOS‐PS confirm a mean emergency message delivery of 11.82%, neighborhood awareness of 12.38% with reduced localization error rate of 2.36% and minimized delay of 8.64% when compared to the baseline approaches.

Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

Malar¹,

Priya²,

Janakiraman³

2020

“…It was identified to be significant in reducing the network delay and response time to a maximum extent on par with CVP‐NLOS‐LT scheme considered for investigation. Then, NLOS detection scheme using Weighted Distance Hyperbolic Prediction (WDHP‐NLOS‐LT) was proposed for effective prevention of delay and broadcast storming in the network 23 . This prediction scheme used the merits of hyperbolic distance factors for the actual estimation of NLOS nodes in a specified point of time with respect to the reference nodes considered in the localization process.…”

Section: Related Workmentioning

confidence: 99%

Lamarckian learning‐based whale optimization algorithm to localize non‐line‐of‐sight nodes for achieving reliable data dissemination in vehicular ad‐hoc networks

Balamurugan

2021

Summary Non‐line‐of‐sight (NLOS) nodes in vehicular ad‐hoc networks (VANETs) are responsible for introducing channel congestion and broadcast storm during data dissemination for sustaining reliable connectivity between vehicular nodes. In this paper, an Integrated Lamarckian Learning and Whale Optimization Algorithm‐based NLOS Localization Technique (ILL‐WOA‐NLOS‐LT) is proposed for NLOS localization in order to improve high reliability and low latency during the emergency message sharing. This proposed ILL‐WOA‐NLOS scheme inherits an optimization process that concentrates on the objective of minimizing latency during warning message delivery. It included the merits of Lamarckian evolution‐based learning strategy for strengthening and speeding up the rate of local search. It possesses maximum probability of acquiring higher adaptability through active learning for improving the global convergence speed to attain better localization of NLOS nodes in emergency situations. It also incorporated a better tradeoff between exploitation and exploration for effective NLOS localization with reduced error. The simulation experiments of the proposed ILL‐WOA‐NLOS scheme conducted through EstiNet 8.1 confirmed its predominance in achieving an excellent mean emergency message dissemination rate of 12.96% mean NLOS node localization rate of 14.21% and the mean neighborhood awareness rate of 13.62% with different number of vehicular nodes, compared to the benchmarked localization approaches considered for investigation. The localization error of the proposed ILL‐WOA‐NLOS scheme was also identified to be significantly reduced by 12.82% compared to the baseline schemes used for investigation.

“…Weighted Distance Hyperbolic Prediction‐based NLOS nodes Localization Scheme (WDHP‐NLOS‐LS) is proposed by Amuthan and Kaviarasan 25 for resolving the problem of broadcasting storm and channel contention in vehicular networks. WDHP‐NLOS‐LS inherits the benefits of range‐free algorithms for dynamically forecasting the location of the NLOS nodes by combining the dynamic characteristics of the hyperbolic forecasting process.…”

Section: Related Workmentioning

confidence: 99%

Harris Hawk Optimization Algorithm‐based Effective Localization of Non‐Line‐of‐Sight Nodes for Reliable Data Dissemination in Vehicular Ad hoc Networks

Malar¹,

Priya²,

Janakiraman³

2020

SummaryVehicular Ad hoc NETwork (VANET) is considered as the appropriate candidate for provisioning risk‐free environment that confirms secure cooperation and very minimal congestion among the vehicular nodes in the network. The establishment and maintenance of connectivity between vehicular nodes are determined to be influenced by the existence of Non‐Line‐of‐Sight (NLOS) nodes that introduce channel congestion and broadcasting storm into the network during emergency message delivery. Thus, NLOS nodes need to be localized with optimality for enhancing the emergency data delivery rate with minimized latency degree and energy consumption in the network. In this paper, Harris Hawk Optimization Algorithm (HHOA)‐based NLOS nodes Localization Scheme (NLOS‐LS) (HHOA‐NLOS‐LS) is proposed for facilitating reliable data dissemination among vehicular nodes under emergency situations. HHOA utilizes chasing styles and cooperative behavior of Harris hawks termed as surprise pounce for efficient localization based on reference nodes. In particular, the intelligent strategy of Harris hawks' behavior in attacking the prey in all directions is included for localizing the NLOS nodes from the reference nodes positioned in all directions of the network. It is capable of localizing the NLOS nodes based on adaptive localizing (chasing) styles attained through reference nodes dependingon the dynamic nature of NLOS nodes. The simulation results prove that the mean localization rate is improved by 23.21%, mean neighborhood awareness rate by 19.82%, mean emergency message delivery rate by 18.32% and mean channel utilization by 17.28% when compared to the baseline Weighted Inertia‐based Dynamic Virtual Bat Algorithm (WIDVBA)‐based NLOS‐LS (WIDVBA‐NLOS‐LS), Cooperative Volunteer Vehicular Nodes (CVVN)‐based NLOS‐LS (CVVN‐NLOS‐LS), Vote Selection Mechanisms and Probabilistic Data Association (VSMPDA)‐based NLOS‐LS (VSMPDA‐NLOS‐LS), and Weighted Distance Hyperbolic Prediction (WDHP)‐based NLOS‐LS (WDHP‐NLOS‐LS) for a varying number of vehicular nodes in the network.