A New Comprehensive RSU Installation Strategy for Cost-Efficient VANET Deployment
Department
Computer Science
Document Type
Article
Publication Date
8-4-2016
Abstract
Recently, the studies on vehicular adhoc network (VANET) are booming due to the huge potential. Road side unit (RSU) is a key component of the VANET infrastructure connecting mobile vehicles and the rest of the infrastructure. To maximize the availability of RSUs, RSUs should be densely deployed. Otherwise, blind spots may exist in which vehicles lose the connection to the infrastructure. Unfortunately, the massive deployment of RSUs to seamlessly cover the whole area of interest, which could be a vast metropolitan, can be very expensive. As the effectiveness and the benefits of the VANET are not fully proven yet, such large scale deployment can hardly be a viable option as of today. Motivated by this observation, this paper investigates a new strategy to best deploy RSUs so that their spatio-temporal coverage is maximized under a limited budget. In detail, for the first time in the literature, we consider an innovative RSU deployment framework, which is a well-balanced combination of three different approaches, deploying RSUs on static locations, public mobile transportation, and fully controllable vehicles owned by the local government. We first introduce a new strategy to abstract a map of city area into a grid graph. Then, we formulate the problem as a new optimization problem and show its NP-hardness. To solve this problem, we transform this problem into another optimization problem. Then, we propose a new polynomial running time approximation algorithm for the problem and show that the performance ratio (the ratio between the quality of an output of the proposed algorithm and the quality of the best possible solution) is at least half of the best possible ratio. We also conduct simulations under various setting to study the effectiveness of the proposed
Journal Title
IEEE Vehicular Technology Society
Journal ISSN
1939-9359
Volume
PP
Issue
99
First Page
1
Last Page
13
Digital Object Identifier (DOI)
10.1109/TVT.2016.2598253