Binary programming model to optimize RSU placement for information dissemination

Abstract

Vehicular communication systems are developed not only to increase safety but also for mobility of road transportation. Roadside units (RSU) are the prominent elements of this technology. This equipment is installed in roadside and intersections to gather traffic information from vehicles and to send messages and alarms to vehicles. Due to costly implementation and maintenance of this equipment, determining the number of RSUs and placement of them is an important problem. In this paper, we proposed a novel binary programming (BP) model to a placement of RSUs beside a road for maximizing information dissemination to vehicles. This approach makes decisions based on the number of curves, the number of on-ramps, accident rate, weather condition, and cost limitations. The proposed model is applied on Tehran to Pardis freeway. According to computational experiments, four operational phases to equip the whole road for information dissemination is obtained.

Multiobjective Differential Evolution with Discrete Elite Guide in Internet of Vehicles Roadside Unit Deployment

“…Ou et al [19] proposed a connection-oriented maximum coverage RSU deployment approach, which improves the communication performance of urban areas by deploying fewer RSU. Eftekhari et al [20] proposed a new binary planning approach, in which RSU are deployed alongside roads to maximize the transmission of information to vehicles. Anbalagan et al [21] proposed the memetic-based RSU placement algorithm to reduce communication delay and increase the coverage area between devices in IoV.…”

“…Existing work regarding RSU deployment schemes can be roughly divided into three categories: mathematical models, heuristic algorithms with game theory, and graph theory. Although greedy algorithm, genetic algorithm, and other ones are used in aforementioned works [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] to solve the RSU deployment problem, multiobjective optimization algorithm is not leveraged to solve multiple conflicting ones. The deployment of RSU often involves multiple conflicting objectives, thus facilitating the utilization of multiobjective evolutionary 2…”

Combining Capital and Operating Expenditure Costs in Vehicular Roadside Unit Placement

“…For more of these approaches, see References (Campolo et al, 2011;Barrachina et al, 2013;Cavalcante et al, 2012;Xie et al, 2013;Jiang et al, 2014;Brahim et al, 2014;Eftekhari et al, 2015). …”