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• W2013591489 abstract "Smart scheduling can be used to reduce infrastructure-to-vehicle energy costs in delay-tolerant vehicular networks. In this paper, we show that, by combining this with vehicle-to-vehicle (V2V) forwarding, downlink (DL) traffic schedules can be generated, whose energy costs are lower than that in the single-hop case. This is accomplished by having the roadside units (RSUs) dynamically forward packets through vehicles, which are in energy-favorable locations. This paper considers both constant bit rate (CBR) and variable bit rate (VBR) air-interface options. We first derive offline schedulers for the DL RSU energy usage when V2V forwarding is added to RSU-to-vehicle communication. Both in-channel and off-channel forwarding cases are considered. The CBR and VBR cases are obtained using integer linear programming (ILP) and time-expanded graph (TEG) formulations, respectively. These schedulers provide lower bounds on energy performance and are used for comparisons with a variety of proposed online scheduling algorithms. The first algorithm is based on a greedy local optimization (GLOA). A version of this algorithm, which uses a minimum-cost flow graph (MCFG) scheduler, is also introduced. A more sophisticated algorithm is then proposed, which is based on a finite-window group optimization (FWGO). Results from various experiments show that the proposed algorithms can generate traffic schedules with much improved DL energy requirements compared with the case where V2V packet forwarding is not used. The performance improvements are particularly strong when under heavy loading conditions and when the variation in vehicle communication requirements or vehicle speed is high. Results that compare the proposed algorithms with conventional nonenergy-aware schedulers are also presented." @default.
• W2013591489 created "2016-06-24" @default.
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• W2013591489 date "2016-02-01" @default.
• W2013591489 modified "2023-10-03" @default.
• W2013591489 title "Vehicle-to-Vehicle Forwarding in Green Roadside Infrastructure" @default.
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• W2013591489 doi "https://doi.org/10.1109/tvt.2015.2402177" @default.
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