Hybrid seagull and thermal exchange optimization algorithm‐based NLOS nodes detection technique for enhancing reliability under data dissemination in VANETs

Abstract

SummaryThe reliability of data dissemination in vehicular ad hoc network (VANET) necessitates maximized cooperation between the vehicular nodes and the least degree of congestion. However, non‐line of sight (NLOS) nodes prevent the establishment and sustenance of connectivity between the vehicular nodes. In this paper, a hybrid seagull and thermal exchange optimization (TEO) algorithm‐based NLOS node detection technique is proposed for enhancing cooperative data dissemination in VANETs. It inherits three different versions of the proposed hybridized algorithm; three different approaches for localization of NLOS nodes depending upon its distance from the reference nodes are incorporated. It is considered as a reliable attempt in effective NLOS node localization as it is predominant in maintaining the balancing the degree of exploration and exploitation in the search process. In the first variant, the method of the roulette wheel is utilized for selecting one among the two optimization algorithm. In the second adoption, this hybridization algorithm combines TEO algorithm only after the iteration of SEOA algorithm. In the final adoption, the predominance of the seagull attack mode is enhanced by including the heat exchange formula of TEO algorithms for improving exploitation capability. The simulation experiments of the proposed HS‐TEO‐NLOS‐ND scheme conducted using EstiNet 8.1 exhibited its reliability in improving the emergency message delivery rate by 14.86%, a neighborhood awareness rate by 13%, and the channel utilization rate by 11.24%, compared to the benchmarked techniques under the evaluation done with different number of vehicular nodes and NLOS nodes in the network.

Hybrid invasive weed optimization and squirrel search algorithm‐based NLOS nodes localization mechanism for improving reliable data dissemination in VANETs

“…In addition, the predominance of the proposed HIWO‐SSA‐LM scheme is investigated with the existing meta‐heuristic NLOS localization schemes such as Weighted Inertia‐Based Dynamic Virtual Bat Algorithm (WIBDVBA) to detect NLOS nodes, Hybrid Seagull and Thermal Exchange Optimization Algorithm (HSTEOA), 22 and Trust‐Inspired meta‐Heuristic Optimization Algorithm (TIHOA) 24 . Table 2 presents the percentage increase in localization accuracy, the percentage decrease in localization error in localization, and percentage decrease in packet latency with different vehicle densities.…”

“…The degree of localization was determined to be predominant; however, a room of possible improvement exists in these factors considered for exploration. Then, Hybrid Seagull and Thermal Exchange Optimization Algorithm (HSTEOA) 22 was proposed for improving reliable warning message delivery. This HSTEOA scheme incorporated three different inherent algorithms depending on the distance between the unknown NLOS nodes and the reference nodes considered for localization.…”

Spotted Hyena Optimization and Simulated Annealing-Based NLOS Nodes Localization Scheme for Improving Warning Message Dessimination in VANETs

“…The simulation experiments of the proposed SHSAOA and the competitive NLOS localization schemes such as ROALS [22], GWCSOALS [24] and ISTEOA [25] are conducted using Veins version 4.5 framework which is integrated with SUMO and OMNET++ simulator. In specific, this SUMO simulator version of mobility 0.29 and network simulator version 5.0 of OMNET++ are integrated with the Veins framework.…”

“…The simulation results of GWCSOALS also proved reduced localization error rate of 2.36% with mean neighborhood awareness rate of 12.38% and average warning message delivery rate of 11.82%, superior to the compared approaches. An Integrated Seagull and Thermal Exchange Optimization Algorithm (ISTEOA)-based NLOS node positioning scheme was proposed for ensuring relaible warning message delivery [25]. This ISTEOA adopted three variants of optimization strategy based on the distance estimated between the unknown NLOS node and the reference nodes that could be possibly utilized for better localization.…”

NLOS node localization for improving warning message distribution in VANETS

“…In 2022, Janakiraman, et al 29 proposed a cooperative vehicle localization augmentation technique using Improved Weighted Distance Hop Hyperbolic Prediction‐based Reliable Data Dissemination (IWDH‐HP‐RDD). When comparison with the existing techniques, the the recommended IWDH‐HP‐RDD technique increases the performance by 5.12%, 6.62%, and 7.82% than existing techniques.…”