The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Cristiano M Silva - One of the best experts on this subject based on the ideXlab platform.
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solutions for the deployment of communication Roadside Infrastructure for streaming delivery in vehicular networks
Journal of Network and Systems Management, 2021Co-Authors: Cristiano M Silva, Fernanda S H Souza, Andreas Pitsillides, Daniel L GuidoniAbstract:The future of mobility involves the interconnection of the entities of the transportation system (vehicles, roads, traffic lights, pedestrians) in high speed networks providing real-time information to drivers, entertainment for passengers, and a wide variety of applications and systems dedicated to smart transportation. Furthermore, in a few years, autonomous vehicles are going to massively reach the streets, and their interconnection may drastically improve the urban mobility by reducing the travel time and the number of accidents. In this work, we consider the design and management of the network Infrastructure for vehicular communication focusing on streaming delivery. We intend to allow a given share of vehicles driving along the road network permanently playing streams received from the network Infrastructure, and our main question is where we must provide coverage for achieving a given share of vehicles receiving the media. As parameters, we consider the download data rate that vehicles receive content from the Infrastructure, and data consumption rate inside vehicles. An Integer Linear Program formulation along with a tabu search-based heuristic are presented. We consider as baseline the intuitive deployment strategy of covering the most popular locations of the road network. All strategies are evaluated considering a realistic vehicular mobility trace composed of 75, 515 vehicles. Results indicate that the tabu search heuristic is able to solve a large instance composed of 75, 515 vehicles requiring less covered area than greedy heuristics. Considering the optimal solution, we investigate the solutions on a reduced subset composed of 100 vehicle trips and, considering this reduced scenario, the tabu search heuristic is able to find the optimal solution.
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a grasp based heuristic for allocating the Roadside Infrastructure maximizing the number of distinct vehicles experiencing contact opportunities
Network Operations and Management Symposium, 2016Co-Authors: João F. M. Sarubbi, Daniel Craviee De A Vieira, Elizabeth F Wanner, Cristiano M SilvaAbstract:In this work the allocation of Roadside Units (RSUs) in a V2I network is modeled as a Maximum Coverage Problem. The main objective is to maximize the number of distinct vehicles contacting the Infrastructure. Two different approaches are presented to solve the problem. The first one is an ILP model that can found optimal solutions or give sharp upper and lower bounds for the problem. The second one is a GRASP-based heuristic that can found close-to-optimal solutions. The GRASP-based heuristic is compared with a previous work achieving better results. Furthermore, a new metric to measure the efficiency of a Deployment strategy is presented.
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delta r a novel and more economic strategy for allocating the Roadside Infrastructure in vehicular networks with guaranteed levels of performance
Network Operations and Management Symposium, 2016Co-Authors: João F. M. Sarubbi, Cristiano M SilvaAbstract:In this work we propose Delta-r, a new greedy heuristic for solving the allocation of Roadside units in order to meet a Δρ1ρ2-Deployment. The Δρ1ρ2-Deployment is a metric for specifying minimal levels of performance from the Infrastructure supporting vehicular networks. As far as we are concerned, this is the first QoS-bounded deployment strategy considering both the contact probability, and the contact duration. We compare Delta-r to two baselines: DL allocates the Roadside units at the densest locations of the road network, while Delta-g uses the absolute V2I contact time. Differently from Delta-r, our proposal evaluates the deployment performance when using the relative V2I contact time considering vehicles and locations of the road network. Our results demonstrate Delta-r requiring less Roadside units to achieve the same performance of the Infrastructure supporting the V2I communication.
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Non-Intrusive Planning the Roadside Infrastructure for Vehicular Networks
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Cristiano M Silva, Wagner Meira, João F. M. SarubbiAbstract:In this article, we describe a strategy for planning the Roadside Infrastructure for vehicular networks based on the global behavior of drivers. Instead of relying on the trajectories of all vehicles, our proposal relies on the migration ratios of vehicles between urban regions in order to infer the better locations for deploying the Roadside units. By relying on the global behavior of drivers, our strategy does not incur in privacy concerns. Given a set of α available Roadside units, our goal is to select those α-better locations for placing the Roadside units in order to maximize the number of distinct vehicles experiencing at least one V2I contact opportunity. Our results demonstrate that full knowledge of the vehicle trajectories are not mandatory for achieving a close-to-optimal deployment performance when we intend to maximize the number of distinct vehicles experiencing (at least one) V2I contact opportunities.
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design of Roadside communication Infrastructure with qos guarantees
International Symposium on Computers and Communications, 2015Co-Authors: Cristiano M Silva, Wagner MeiraAbstract:There are several kinds of envisioned vehicular applications: video delivery, accidents detection, dissemination of traffic announcements, and so forth. Such applications demand minimal (and possibly distinct) QoS guarantees that must couple the vehicular network. Given that vehicular networks will soon become reality, we demand strategies for planning and managing such networks. In this work we propose Delta (A), a QoS-based strategy for planning the Roadside Infrastructure supporting a vehicular network. Thus, the network provider may employ our strategy to design a new network, compare the performance of distinct vehicular networks, and even evaluate the adherence between vehicular applications and the network. Delta is based on two metrics: i) connectivity duration, and ii) percentage of vehicles presenting such connectivity duration. For instance, if a given vehicular application requires that 20% of the vehicles are connected during 30% of the trip, we say that such application requires a deployment Delta (0.3, 0.2). Complementary, we also present Delta-g, a greedy heuristic for solving Delta. A deployment Delta (0.1, 0.1) requires the coverage of 0.09% of the road network, while a deployment Delta (0.9, 0.9) requires 21.67% of coverage.
George Karakostas - One of the best experts on this subject based on the ideXlab platform.
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capacity augmentation in energy efficient vehicular Roadside Infrastructure
Ubiquitous Computing, 2017Co-Authors: Naby Nikookaran, Terence D. Todd, George KarakostasAbstract:This paper considers the problem of capacity augmentation in energy efficient road-side unit (RSU) deployments. RSU placements for a road network, and a set of vehicular traffic flow design traces are used as inputs. The objective is to find an RSU radio capacity assignment that minimizes the long-term operating expenditure costs subject to meeting packet deadline constraints with a given packet loss rate target. A procedure, referred to as the capacity augmentation (CA) algorithm, is proposed that iterates over the RSUs, selecting candidates for capacity augmentation based on their packet loss rate sensitivities. A variety of results are presented that characterize and compare the performance of the CA Algorithm using a greedy online packet scheduler. In particular, we show how to counterbalance the lack of causality in designing the RSU network when it is used for the online scheduling of incoming transmission requests. The comparisons include those using energy-optimal offline scheduling obtained by solving an integer linear program (ILP) formulation. It is shown that the CA Algorithm is an efficient way to assign RSU radio capacity that achieves the desired performance objectives.
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Vehicle-to-Vehicle Forwarding in Green Roadside Infrastructure
IEEE Transactions on Vehicular Technology, 2016Co-Authors: Morteza Azimifar, Amir Khezrian, Terence D. Todd, George KarakostasAbstract: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.
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energy efficient scheduling in green vehicular Infrastructure with multiple Roadside units
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Amir Khezrian, Terence D. Todd, George Karakostas, Morteza AzimifarAbstract:In this paper, we propose low-complexity algorithms for downlink traffic scheduling in green vehicular Roadside Infrastructure. In multiple Roadside unit (RSU) deployments, the energy provisioning of the RSUs may differ, and it is therefore desirable to balance RSU usage from a normalized min-max energy viewpoint. This paper considers both splittable RSU assignment (SRA) and unsplittable RSU asssignment (URA) scheduling. An offline integer linear programming bound is first derived for normalized min-max RSU energy usage. We then show that in the SRA case, there is a polynomial complexity 2-approximation bound for the normalized min-max energy schedule. This paper then proposes several online scheduling algorithms. The first is a greedy online algorithm that makes simple RSU selections, followed by minimum-energy time slot assignments. A normalized min-max algorithm is then proposed [2-approximation online algorithm (TOAA)], which is an online version of the 2-approximation bound. Two algorithms are then introduced based on a potential function scheduling approach. The 1-objective algorithm uses an objective based on normalized min-max energy, and we show that it has an upper bounded worst-case competitive ratio performance. The 2-objective algorithm uses the same approach but incorporates a total-energy secondary objective as well. Results from a variety of experiments show that the proposed scheduling algorithms perform well. In particular, we find that in the SRA case, the TOAA algorithm performs very close to the lower bound but at the expense of having to reassign time slots whenever a new vehicle arrives. In the URA case, our low-complexity 1-objective algorithm performs better than the others over a wide range of traffic conditions.
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on off sleep scheduling in energy efficient vehicular Roadside Infrastructure
International Conference on Communications, 2013Co-Authors: Shokouh Mostofi, Terence D. Todd, Abdulla Hammad, George KarakostasAbstract:Smart downlink scheduling can be used to reduce Infrastructure-to-vehicle energy costs in delay tolerant Roadside networks. In this paper we incorporate this type of scheduling into ON/OFF Roadside unit sleep activity, to further reduce Infrastructure power consumption. To achieve significant power savings however, the OFF-to-ON sleep transitions may be very lengthy, and this overhead must be taken into account when performing the ON state scheduling. We first incorporate the OFF/ON sleep transitions into a lower bound on energy usage that can be computed for given input sample functions. An online scheduling algorithm referred to as the Flow Graph Sleep Scheduler (FGS) is then introduced, which makes locally optimum decisions about when to initiate new ON/OFF cycles. This is done by computing an estimate of the energy needed to fulfill known vehicle communication requirements with and without the OFF period. This calculation is efficiently done using a novel minimum flow graph formulation. Results from a variety of experiments show that the proposed scheduling algorithm performs well when compared to the energy lower bound. It is especially attractive in situations where vehicle demands and arrival rates are such that the energy costs permit frequent ON/OFF cycling.
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downlink traffic scheduling in green vehicular Roadside Infrastructure
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Abdulla Hammad, Terence D. Todd, George Karakostas, Dongmei ZhaoAbstract:In this paper we consider the problem of scheduling for energy-efficient Roadside Infrastructure. In certain scenarios, vehicle locations can be predicted with a high degree of accuracy, and this information can be used to reduce downlink Infrastructure-to-vehicle energy communication costs. Offline scheduling results are first presented that provide lower bounds on the energy needed to satisfy arriving vehicular communication requirements. We show that the packet-based scheduling case can be formulated as a generalization of the classical single-machine job scheduling problem with a tardiness penalty, which is referred to as α-Earliness-Tardiness. A proof is given that shows that even under a simple distance-dependent exponential radio path loss assumption, the problem is NP-complete. The remainder of the paper then focuses on timeslot-based scheduling. We formulate this problem as a Mixed-Integer Linear Program (MILP) that is shown to be solvable in polynomial time using a proposed minimum cost flow graph construction. Three energy-efficient online traffic scheduling algorithms are then introduced for common vehicular scenarios where vehicle position is strongly deterministic. The first, i.e., Greedy Minimum Cost Flow (GMCF), is motivated by our minimum cost flow graph formulation. The other two algorithms have reduced complexity compared with GMCF. The Nearest Fastest Set (NFS) scheduler uses vehicle location and velocity inputs to dynamically schedule communication activity. The Static Scheduler (SS) performs the same task using a simple position-based weighting function. Results from a variety of experiments show that the proposed scheduling algorithms perform well when compared with the energy lower bounds in vehicular situations where path loss has a dominant deterministic component so that energy costs can be estimated. Our results also show that near-optimal results are possible but come with increased computation times compared with our heuristic algorithms.
João F. M. Sarubbi - One of the best experts on this subject based on the ideXlab platform.
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delta r a novel and more economic strategy for allocating the Roadside Infrastructure in vehicular networks with guaranteed levels of performance
Network Operations and Management Symposium, 2016Co-Authors: João F. M. Sarubbi, Cristiano M SilvaAbstract:In this work we propose Delta-r, a new greedy heuristic for solving the allocation of Roadside units in order to meet a Δρ1ρ2-Deployment. The Δρ1ρ2-Deployment is a metric for specifying minimal levels of performance from the Infrastructure supporting vehicular networks. As far as we are concerned, this is the first QoS-bounded deployment strategy considering both the contact probability, and the contact duration. We compare Delta-r to two baselines: DL allocates the Roadside units at the densest locations of the road network, while Delta-g uses the absolute V2I contact time. Differently from Delta-r, our proposal evaluates the deployment performance when using the relative V2I contact time considering vehicles and locations of the road network. Our results demonstrate Delta-r requiring less Roadside units to achieve the same performance of the Infrastructure supporting the V2I communication.
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a grasp based heuristic for allocating the Roadside Infrastructure maximizing the number of distinct vehicles experiencing contact opportunities
Network Operations and Management Symposium, 2016Co-Authors: João F. M. Sarubbi, Daniel Craviee De A Vieira, Elizabeth F Wanner, Cristiano M SilvaAbstract:In this work the allocation of Roadside Units (RSUs) in a V2I network is modeled as a Maximum Coverage Problem. The main objective is to maximize the number of distinct vehicles contacting the Infrastructure. Two different approaches are presented to solve the problem. The first one is an ILP model that can found optimal solutions or give sharp upper and lower bounds for the problem. The second one is a GRASP-based heuristic that can found close-to-optimal solutions. The GRASP-based heuristic is compared with a previous work achieving better results. Furthermore, a new metric to measure the efficiency of a Deployment strategy is presented.
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Non-Intrusive Planning the Roadside Infrastructure for Vehicular Networks
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Cristiano M Silva, Wagner Meira, João F. M. SarubbiAbstract:In this article, we describe a strategy for planning the Roadside Infrastructure for vehicular networks based on the global behavior of drivers. Instead of relying on the trajectories of all vehicles, our proposal relies on the migration ratios of vehicles between urban regions in order to infer the better locations for deploying the Roadside units. By relying on the global behavior of drivers, our strategy does not incur in privacy concerns. Given a set of α available Roadside units, our goal is to select those α-better locations for placing the Roadside units in order to maximize the number of distinct vehicles experiencing at least one V2I contact opportunity. Our results demonstrate that full knowledge of the vehicle trajectories are not mandatory for achieving a close-to-optimal deployment performance when we intend to maximize the number of distinct vehicles experiencing (at least one) V2I contact opportunities.
Wagner Meira - One of the best experts on this subject based on the ideXlab platform.
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Non-Intrusive Planning the Roadside Infrastructure for Vehicular Networks
IEEE Transactions on Intelligent Transportation Systems, 2016Co-Authors: Cristiano M Silva, Wagner Meira, João F. M. SarubbiAbstract:In this article, we describe a strategy for planning the Roadside Infrastructure for vehicular networks based on the global behavior of drivers. Instead of relying on the trajectories of all vehicles, our proposal relies on the migration ratios of vehicles between urban regions in order to infer the better locations for deploying the Roadside units. By relying on the global behavior of drivers, our strategy does not incur in privacy concerns. Given a set of α available Roadside units, our goal is to select those α-better locations for placing the Roadside units in order to maximize the number of distinct vehicles experiencing at least one V2I contact opportunity. Our results demonstrate that full knowledge of the vehicle trajectories are not mandatory for achieving a close-to-optimal deployment performance when we intend to maximize the number of distinct vehicles experiencing (at least one) V2I contact opportunities.
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design of Roadside communication Infrastructure with qos guarantees
International Symposium on Computers and Communications, 2015Co-Authors: Cristiano M Silva, Wagner MeiraAbstract:There are several kinds of envisioned vehicular applications: video delivery, accidents detection, dissemination of traffic announcements, and so forth. Such applications demand minimal (and possibly distinct) QoS guarantees that must couple the vehicular network. Given that vehicular networks will soon become reality, we demand strategies for planning and managing such networks. In this work we propose Delta (A), a QoS-based strategy for planning the Roadside Infrastructure supporting a vehicular network. Thus, the network provider may employ our strategy to design a new network, compare the performance of distinct vehicular networks, and even evaluate the adherence between vehicular applications and the network. Delta is based on two metrics: i) connectivity duration, and ii) percentage of vehicles presenting such connectivity duration. For instance, if a given vehicular application requires that 20% of the vehicles are connected during 30% of the trip, we say that such application requires a deployment Delta (0.3, 0.2). Complementary, we also present Delta-g, a greedy heuristic for solving Delta. A deployment Delta (0.1, 0.1) requires the coverage of 0.09% of the road network, while a deployment Delta (0.9, 0.9) requires 21.67% of coverage.
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design of Roadside Infrastructure for information dissemination in vehicular networks
Network Operations and Management Symposium, 2014Co-Authors: Cristiano M Silva, Andre L L Aquino, Wagner MeiraAbstract:This work presents a probabilistic constructive heuristic to design the Roadside Infrastructure for information dissemination in vehicular networks. We formulate the problem as a Probabilistic Maximum Coverage Problem (PMCP) and we use them to maximize the number of vehicles in contact with the Infrastructure. We compare our approach to a non-probabilistic MCP in simulated urban areas considering Manhattan-style topology with variable traffic conditions. The results reveal that our approach (Probabilistic MCP) increases the number of contacts between vehicles and dissemination points, optimizes the allocation of dissemination points, distributes the dissemination points in a layout that better fits the traffic flow and provides more regularity in the number of contacts experienced by vehicles.
Terence D. Todd - One of the best experts on this subject based on the ideXlab platform.
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capacity augmentation in energy efficient vehicular Roadside Infrastructure
Ubiquitous Computing, 2017Co-Authors: Naby Nikookaran, Terence D. Todd, George KarakostasAbstract:This paper considers the problem of capacity augmentation in energy efficient road-side unit (RSU) deployments. RSU placements for a road network, and a set of vehicular traffic flow design traces are used as inputs. The objective is to find an RSU radio capacity assignment that minimizes the long-term operating expenditure costs subject to meeting packet deadline constraints with a given packet loss rate target. A procedure, referred to as the capacity augmentation (CA) algorithm, is proposed that iterates over the RSUs, selecting candidates for capacity augmentation based on their packet loss rate sensitivities. A variety of results are presented that characterize and compare the performance of the CA Algorithm using a greedy online packet scheduler. In particular, we show how to counterbalance the lack of causality in designing the RSU network when it is used for the online scheduling of incoming transmission requests. The comparisons include those using energy-optimal offline scheduling obtained by solving an integer linear program (ILP) formulation. It is shown that the CA Algorithm is an efficient way to assign RSU radio capacity that achieves the desired performance objectives.
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Vehicle-to-Vehicle Forwarding in Green Roadside Infrastructure
IEEE Transactions on Vehicular Technology, 2016Co-Authors: Morteza Azimifar, Amir Khezrian, Terence D. Todd, George KarakostasAbstract: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.
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energy efficient scheduling in green vehicular Infrastructure with multiple Roadside units
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Amir Khezrian, Terence D. Todd, George Karakostas, Morteza AzimifarAbstract:In this paper, we propose low-complexity algorithms for downlink traffic scheduling in green vehicular Roadside Infrastructure. In multiple Roadside unit (RSU) deployments, the energy provisioning of the RSUs may differ, and it is therefore desirable to balance RSU usage from a normalized min-max energy viewpoint. This paper considers both splittable RSU assignment (SRA) and unsplittable RSU asssignment (URA) scheduling. An offline integer linear programming bound is first derived for normalized min-max RSU energy usage. We then show that in the SRA case, there is a polynomial complexity 2-approximation bound for the normalized min-max energy schedule. This paper then proposes several online scheduling algorithms. The first is a greedy online algorithm that makes simple RSU selections, followed by minimum-energy time slot assignments. A normalized min-max algorithm is then proposed [2-approximation online algorithm (TOAA)], which is an online version of the 2-approximation bound. Two algorithms are then introduced based on a potential function scheduling approach. The 1-objective algorithm uses an objective based on normalized min-max energy, and we show that it has an upper bounded worst-case competitive ratio performance. The 2-objective algorithm uses the same approach but incorporates a total-energy secondary objective as well. Results from a variety of experiments show that the proposed scheduling algorithms perform well. In particular, we find that in the SRA case, the TOAA algorithm performs very close to the lower bound but at the expense of having to reassign time slots whenever a new vehicle arrives. In the URA case, our low-complexity 1-objective algorithm performs better than the others over a wide range of traffic conditions.
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on off sleep scheduling in energy efficient vehicular Roadside Infrastructure
International Conference on Communications, 2013Co-Authors: Shokouh Mostofi, Terence D. Todd, Abdulla Hammad, George KarakostasAbstract:Smart downlink scheduling can be used to reduce Infrastructure-to-vehicle energy costs in delay tolerant Roadside networks. In this paper we incorporate this type of scheduling into ON/OFF Roadside unit sleep activity, to further reduce Infrastructure power consumption. To achieve significant power savings however, the OFF-to-ON sleep transitions may be very lengthy, and this overhead must be taken into account when performing the ON state scheduling. We first incorporate the OFF/ON sleep transitions into a lower bound on energy usage that can be computed for given input sample functions. An online scheduling algorithm referred to as the Flow Graph Sleep Scheduler (FGS) is then introduced, which makes locally optimum decisions about when to initiate new ON/OFF cycles. This is done by computing an estimate of the energy needed to fulfill known vehicle communication requirements with and without the OFF period. This calculation is efficiently done using a novel minimum flow graph formulation. Results from a variety of experiments show that the proposed scheduling algorithm performs well when compared to the energy lower bound. It is especially attractive in situations where vehicle demands and arrival rates are such that the energy costs permit frequent ON/OFF cycling.
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downlink traffic scheduling in green vehicular Roadside Infrastructure
IEEE Transactions on Vehicular Technology, 2013Co-Authors: Abdulla Hammad, Terence D. Todd, George Karakostas, Dongmei ZhaoAbstract:In this paper we consider the problem of scheduling for energy-efficient Roadside Infrastructure. In certain scenarios, vehicle locations can be predicted with a high degree of accuracy, and this information can be used to reduce downlink Infrastructure-to-vehicle energy communication costs. Offline scheduling results are first presented that provide lower bounds on the energy needed to satisfy arriving vehicular communication requirements. We show that the packet-based scheduling case can be formulated as a generalization of the classical single-machine job scheduling problem with a tardiness penalty, which is referred to as α-Earliness-Tardiness. A proof is given that shows that even under a simple distance-dependent exponential radio path loss assumption, the problem is NP-complete. The remainder of the paper then focuses on timeslot-based scheduling. We formulate this problem as a Mixed-Integer Linear Program (MILP) that is shown to be solvable in polynomial time using a proposed minimum cost flow graph construction. Three energy-efficient online traffic scheduling algorithms are then introduced for common vehicular scenarios where vehicle position is strongly deterministic. The first, i.e., Greedy Minimum Cost Flow (GMCF), is motivated by our minimum cost flow graph formulation. The other two algorithms have reduced complexity compared with GMCF. The Nearest Fastest Set (NFS) scheduler uses vehicle location and velocity inputs to dynamically schedule communication activity. The Static Scheduler (SS) performs the same task using a simple position-based weighting function. Results from a variety of experiments show that the proposed scheduling algorithms perform well when compared with the energy lower bounds in vehicular situations where path loss has a dominant deterministic component so that energy costs can be estimated. Our results also show that near-optimal results are possible but come with increased computation times compared with our heuristic algorithms.
