Downlink Traffic Scheduling in Green Vehicular Roadside Infrastructure

Hammad, Abdulla; Todd, T.D.; Karakostas, George; Zhao, Dongmei

doi:10.1109/tvt.2012.2227071

Cited by 34 publications

(39 citation statements)

References 45 publications

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“…The comparisons are similar for the max RSU energy results. As one would expect, the introduction of random components into the input data results in poorer scheduling decisions, as was noted in (Hammad et al, 2013). However, other than creating a greater split between the performance of the algorithms, it can be seen that the relative algorithm performance is the same as it was previously.…”

Section: Performance Evaluationmentioning

confidence: 79%

“…Reference (Ning et al, 2005) Based on this framework, a sub-carrier assignment policy is proposed, which aims to minimize power consumption while guaranteeing delay QoS requirements. Reference (Hammad et al, 2013) also studied the problem of scheduling for energy-efficient roadside infrastructure. It proposed three online scheduling algorithms all of which attempt to reduce downlink infrastructure-to-vehicle energy communication costs.…”

Section: Energy Efficiency In Wireless Networkmentioning

confidence: 99%

“…RSU scheduling was considered from an energy consumption viewpoint in reference (Hammad et al, 2013). This study focused on minimizing the downlink energy costs in the common case where passing vehicle communication requirements are delay tolerant.…”

Section: Related Workmentioning

confidence: 99%

“…To use this option however, requires that there is sufficient packet delay tolerance, and we assume that any vehicle can be served at any time throughout its transition time in the RSU coverage area (Hammad et al, 2013).…”

Section: System Model and Scheduling Definitionsmentioning

confidence: 99%

“…The theoretical bound for minmax RSU energy consumption as derived in Section 3.3 is referred to as Bound in the graphs and is compared to the online algorithms discussed in Section 3.5. The input data to the schedulers is taken from a highway environment where vehicles are assumed to maintain constant speed throughout the RSU coverage areas (Khabazian and Ali, 2008) (Hammad et al, 2013). It has been shown that in this type of scenario, good estimates of energy costs can be readily made (Wang, 2005)(C. Sommer and Dressler, 2011).…”

Section: Performance Evaluationmentioning

confidence: 99%

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Scheduling in green vehicular infrastructure with multiple roadside units

Khezrian

Hammad

Todd

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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In this thesis we consider low complexity downlink traffic scheduling for 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 energy viewpoint. We consider both splittable and unsplittable RSU assignment scheduling (SRA and URA). We first derive an offline integer linear programming bound for the normalized min-max RSU energy usage, which can be solved for a given input sample function. We then show that in the SRA case there is a polynomial complexity 2-approximation bound for the normalized min-max energy schedule. These bounds are used for comparisons with several proposed online scheduling algorithms. The first scheduler is a low complexity Greedy Online Algorithm (GOA) that makes greedy RSU selections followed by minimum energy time slot assignments. A normalized min-max online algorithm is then proposed (TOAA) which is an online version of the 2-approximation bound for SRA scheduling.Then, the Greedy Flow Graph Algorithm (GFGA), which makes greedy RSU selections followed by time slots reassignment whenever a new vehicle is assigned to the same RSU. This is done using a locally optimum integer linear program that can be efficiently solved using a minimum cost flow graph. Two low complexity algorithms iv are then introduced based on a potential function scheduling approach. The OneObjective algorithm, uses a primary objective based on normalized min-max energy.The second, the Bi-Objective algorithm, uses the same primary objective combined with a total energy secondary objective. These algorithms have provable performance guarantees, in that their worse-case competitive ratio performance is upper bounded.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 and GFGA algorithms perform 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 One-Objective algorithm performs better than the others over a wide range of traffic conditions.

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Section: Performance Evaluationmentioning

confidence: 79%

Section: Energy Efficiency In Wireless Networkmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: System Model and Scheduling Definitionsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Scheduling in green vehicular infrastructure with multiple roadside units

Khezrian

Hammad

Todd

et al. 2013

2013 IEEE International Conference on Communications (ICC)

Self Cite

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Energy Harvesting Networks

Singh¹,

Ku²,

Flanagan³

et al. 2020

Wiley 5G Ref

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In the era of big data, the rapid growth of data services is pushing forward the persistent evolution of wireless network designs to resolve new challenges such as spectrum efficiency, power consumption, quality‐of‐service (QoS), mobility. In particular, energy consumption issues become critical in many emerging wireless applications, for example, in Internet‐of‐Things (IoT), and motivate researchers to revisit the network designs which take the energy efficiency into consideration. Energy harvesting (EH) has recently been advocated as an alternative to perpetually power wireless nodes in the next‐generation wireless networks, especially for the networks that are confined by limited battery to affect the network lifetime or in the hazardous environments that are inconvenient to frequently replace batteries. The characteristics of various ambient energy sources are introduced with the focus of their impact on wireless communication designs, as compared with the conventional grid power supply. The design issues are then elaborated for various modern wireless networks, including cooperative networks, cognitive radio networks, vehicular networks, underwater networks, and unmanned aerial vehicle (UAV) networks. In particular, two examples regarding with the cooperative communications are provided to get more insight into the methodology for designing EH networks. The design challenges of the incorporation of EH into these wireless networks are highlighted in different EH environments from the underwater, ground to air.

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Multiple RSUs Scheduling for Energy Efficiency in Vehicular Ad Hoc Networks

Wang

Zhang

2019

Wireless Pers Commun

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Downlink Traffic Scheduling in Green Vehicular Roadside Infrastructure

Cited by 34 publications

References 45 publications

Scheduling in green vehicular infrastructure with multiple roadside units

Scheduling in green vehicular infrastructure with multiple roadside units

Energy Harvesting Networks

Multiple RSUs Scheduling for Energy Efficiency in Vehicular Ad Hoc Networks

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