Chew’s Second Delaunay Triangulation Refinement Scheme for Optimal RSUs Deployment to Ensure Maximum Connectivity in Vehicle to Infrastructure Communication

SummaryVehicular ad hoc network (VANET) is most significant for supporting intelligent transportation system (ITS)‐based technologies, but it gets hurdled by sparse distribution of vehicles on highways, and dynamically challenging topology that arises due to increase in traffic. Hence, energy stable and optimized cluster construction maximizes the network lifetime. In this paper, Hybrid Prairie Dogs and Beluga Whale Optimization‐based Node Clustering (HPDBWOA‐NC) mechanism is proposed with the parameters of highway route, node velocity, number of vehicular nodes, and communication for achieving stable cluster construction in VANETs. It is proposed with the balanced exploration and exploitation potential of Prairie Dog Optimization Algorithm (PDOA) and Beluga Whale Optimization Algorithm (BWOA) for establishing optimal clusters that increase the network stability during the routing process. It integrated the exploration and exploitation capabilities of PDOA and BWOA and confirmed better optimized clusters which confirmed reliable data delivery by preventing the issue of premature convergence. It constructed clusters and selected cluster heads (CHs) depending on the fitness factors of energy, interdistance between vehicles, communication range, and vehicular density. The results of the proposed HPDBWOA‐NC generated optimal number of CHs in the network which is comparatively 34.21% better than the benchmarked mechanisms. The mean throughput and packet delivery ratio (PDR) achieved by the proposed HPDBWOA‐NC are identified to be significantly improved by 25.48% and 28.91% better than the investigated metaheuristic clustering protocols. The statistical study also guaranteed an increased factor of 81, during the processing of optimizing the clusters during the employment of ITS applications in VANETs.

Section: Resultsmentioning

confidence: 99%

“…Simulation experiments of proposed HPDBWOA‐NC and standard approaches are performed with parameters depicted in Table 1 37–42 . The experiments are conducted with 100 vehicular nodes for a transmission range between 100 and 500 m. The experiments are run for 30 times, and the mean results are used for plotting.…”

Section: Resultsmentioning

confidence: 99%

An intelligent hybrid prairie dog optimization algorithm‐based stable cluster reliable routing scheme for VANETs

Nithyanandam,

Ambiyaram,

Prabathkumar

2023

“…The simulation experiments of the proposed BCOACS mechanism and baseline AWCP-EWOA, BCOA-NCT, and HGFARP schemes are conducted using MATLAB. [33][34][35][36][37] In this experiment, the velocity of vehicles is dynamically varied from 80 km/h. and 120 km/h.…”

Section: Simulation and Results Discussionmentioning

confidence: 99%

Beetle colony optimization algorithm‐based node clustering scheme for efficient data dissemination in vehicular ad hoc networks

Nithyanandam,

Ambayiram,

Natarajan

2023

SummaryVehicular ad hoc networks (VANETs) are the ultimate solution for preventing road accidents, which result in the loss of precious human life worldwide. In this context, effective communication between the vehicular nodes is essential due to the varying network topology and high vehicular mobility inherent with VANETs. Cluster‐based routing is identified to be a significant approach for achieving efficient routing and improving communication proficiency in VANETs. In this paper, a beetle colony optimization algorithm–based clustering scheme (BCOACS) is proposed for generating optimized clusters for facilitating reliable data dissemination. This BCOACS algorithm includes two vital strategies such as beetle antenna search (BAS) and swarm intelligence for attaining inter‐cluster and intra‐cluster communications. In specific, BAS strategy that includes random search attributed toward gradient direction is used for intra‐cluster communication without using the complete amount of gradient information. On the other hand, a swarm intelligence strategy that encompasses a collective approach of self‐organized and decentralized agents is used for inter‐cluster communication with the view to minimize the load on each cluster head (CH) and to extend the clusters' lifetime. The simulation outcomes of the proposed BCOACS scheme confirmed improved performance in optimizing the number of constructed clusters independent of the increase in the network grid size, transmission range, and number of vehicular nodes in the network compared to the benchmarked approaches. The results also confirmed that the proposed BCOACS scheme achieved a maximized throughput of 13.42%, with reduced delay and protocol overhead of 18.96% and 19.45%, better than the benchmarked schemes used for investigation.

“…Step 11: Identify the priority of RSUs that possesses a greater weight (

R W_{(RSU - i)}

) among m RSUs deployed in the network in order to select them as the fittest RSUs 23–28 that has the possibility of selection during V2I communication. Moreover, a degree of satisfaction with respect to each RSUs complies with the indices of selection that are either maximized or minimized during optimization process.…”

Section: Proposed Cfdtrs For Optimal Rsu Placementmentioning

confidence: 99%

Chew's first Delaunay triangulation refinement scheme‐based positioning of road side units for optimal network coverage in vehicular ad‐hoc networks

Selvakumari¹,

Lakshmi²,

Sheela

et al. 2022

Summary The temporal network fragmentation and uncertain vehicle mobility are considered to impact the communication connectivity among the vehicular nodes of the network. In this context, road side units (RSUs) play an anchor role in enhancing the vehicle‐to‐vehicle (V2V) communication connectivity and support vehicle‐to‐infrastructure (V2I) communication. In the current scenario, deploying a huge number of RSUs in the vehicular networks at an initial stage is completely impossible as it incurs high installation cost and authority restriction. Moreover, the optimal placement of RSUs in the vehicular network needs to be enforced for attaining maximized network coverage. In this paper, Chew's first Delaunay triangulation refinement scheme (CFDTRS) is proposed for optimal positioning of RSUs in order to attain optimal network coverage in the vehicular network with minimized cost. This CFDTRS considered the factors of vehicular density, number of obstacles in the map, intersection popularity, and global coverage into account during the placement of RSUs. It is proposed with the objective of deploying required number of RSUs within the range of data transmission in order to achieve maximal coverage in the convex map, such that each area of the convex map is completely covered with at least a single RSU in the existence of multiple number of obstacles. The simulation results of the proposed CFDTRS attained from the real time situations of simplex, moderate, and complex maps confirmed improved packet delivery rate of 9.32%, throughput by 10.74% with minimized packet loss of 9.14%, and reduced end‐to‐end delay of 19.31$, compared to the benchmarked schemes considered for investigation.