Efficient deployment of roadside units in vehicular networks using optimization methods

Abstract

Summary Nowadays, vehicles have become more and more intelligent and equipped with highly sophisticated systems. This allows them to communicate with each other and with the roadside units (RSUs). Furthermore, to ensure efficient data dissemination in vehicular ad hoc networks (VANETs), it is recommended that a Vehicle‐to‐Infrastructure (V2I) architecture be chosen where RSUs will be installed at intersections. Nevertheless, it is not advisable to place an RSU at each intersection because of their high cost. It is therefore appropriate to reduce the number of RSUs by choosing locations at intersections that maximize the surface covered of the urban area and minimize the area of overlapping zones. Moreover, deploying an optimal number of RSUs in an urban area meeting the above requirements is an NP‐hard problem since the number of combinations is very high when the number of intersections is very large. For this purpose, we used metaheuristic approaches. The first approach is represented by the standard version of genetic algorithms (GA‐Basic) and its improved version (GA‐Improved) while the second approach is based on the standard version of simulated annealing (SA‐Basic) and its improved version (SA‐Improved). The proposed approaches are evaluated over OMNET++ simulator. The results obtained showed that the GA‐Improved approach deploys a reduced number of RSUs (37.5%) while guaranteeing acceptable routing performance compared to the GA‐Basic, SA‐Basic, and SA‐Improved approaches which deploy 46.25%, 65%, and 52.50%, respectively, for the same routing performance.

Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

“…Deploying RSUs in urban areas can be a complex task due to the high cost of installing them at intersections and the large number of possible combinations when there are many intersections. To address this issue, Lehsaini et al [ 79 ] used various metaheuristics, including genetic algorithms (GA), simulated annealing (SA), and improved versions of these algorithms, to determine the best approach for achieving high coverage rates on roads in the target area while deploying a minimum number of RSUs at intersections. The GA-Basic approach includes a probability of performing a mutation operation, where two bits are chosen randomly, while the GA-Improved approach focuses on individuals that increase the overlap of coverage areas.…”

Roadside Units Optimization Considering Path Flow Uncertainty

“…(13) where Equation ( 7) is the objective function, which minimizes the total cost of RSU minus the value of reduced travel time; Equation (8) ensures that for each path, at least one link deploys a RSU; Equation (9) ensures that for arbitrary two paths and ', at least one RSU is installed in the non-shared link l ( , ' = 1 ) or on a non-shared link before link l ( , ' = 1 ); Equations ( 8) and ( 9) express the preconditions of fully path flow reconstruction (Salari et al [26]); To address the difficulty in understanding the parameters involved in equation ( 9), this paper introduces two methods for distinguishing different paths in Section II Description, specifically in A. Path Flow Reconstruction, through illustrative examples.…”

“…investigated a new strategy to maximize the spatiotemporal coverage of RSU under a limited budget. Lehsaini et al [8] adopted genetic algorithm, standard version of simulated annealing and their improved versions in order to reduce the number of RSU by choosing locations at intersections that maximize the surface covered of the urban area and minimize the area of overlapping zones. Anbalagan et al [9] proposed an efficient memetic-based RSU (M-RSU) placement algorithm for Software-defined-IoV to reduce communication delay and increase the coverage area among IoV devices.…”

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

“…Existing studies on RSU deployment methods generally focus on determining the optimal locations, interval, minimum number of deployments, maximum coverage range, or maximum connectivity within cost constraints [12][13][14][15]. Lehsaini M. et al [16] proposed the use of metaheuristic methods, Guerna A. et al [17] proposed the use of a bio-inspired RSU placement system using ant colony optimization, Zhang L. et al [18] proposed an improved multi-objective quantum particle swarm optimization (MOQPSO) algorithm for RSU deployment, and Silva C. M. et al [19] presented an integer linear programming formulation and heuristic methods based on taboo search, all of which maximize network coverage with lower cost. Magsino E. R. et al [20] proposed the Enhanced Information SHAring scheme using RSU allocation (EISHA-RSU) to address the coverage and connectivity between vehicles and infrastructure.…”