A Latency-Aware Task Offloading in Mobile Edge Computing Network for Distributed Elevated LiDAR

Lucic, Michael C.; Ghazzai, Hakim; Alsharoa, Ahmad; Massoud, Yehia

doi:10.1109/iscas45731.2020.9180527

Cited by 12 publications

(7 citation statements)

References 14 publications

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“…Several researchers [4], [5], [19], [20], propose to offload part of the heavy computation to the edge. Some studies [21]- [24] even proposed to move the LiDARs out of the vehicle and place them in other elevated positions such as side of the buildings or lamp posts and move the GPUs at the edge by connecting them with edge servers so that they can be shared among other AVs. However, it remains largely unclear what are the consequences of edge-resource deployment choices on AVs.…”

Section: Related Workmentioning

confidence: 99%

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

Atik¹,

Brocanelli²,

Grosu³

2022

Preprint

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<p>Future private and public transportation will be dominated by Autonomous Vehicles (AV), which are potentially safer than regular vehicles. However, ensuring good performance for the autonomous features requires fast processing of heavy computational tasks. Providing each AV with powerful enough computing resources is certainly a practical solution but may result in increased AV cost and decreased driving range. An alternative solution being explored in research is to install low-power computing hardware on each AV and offload the heavy tasks to powerful nearby edge servers. In this case, the AV's reaction time depends on how quickly the navigation tasks are completed in the edge server. To reduce task completion latency, the edge servers must be equipped with enough network and computing resources to handle the vehicle demands. However, this demand shows large spatio-temporal variations. Thus, deploying the same amount of resources in different locations may lead to unnecessary resource over-provisioning.</p> <p>Taking these challenges into consideration, in this paper, we discuss the implications of deploying different amounts of resources in different city areas based on real traffic data to sustain peak versus average demand. Because deploying edge resources to handle the average demand leads to lower deployment costs and better system utilization, we then investigate how peak-hour demand affect the safe travel time of AVs and whether current turn-by-turn navigation apps would still provide the fastest travel route. The insights and findings of this paper will inspire new research that can considerably speed up the deployment of edge-assisted AVs in our society.</p>

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Section: Related Workmentioning

confidence: 99%

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

Atik¹,

Brocanelli²,

Grosu³

2022

Preprint

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show abstract

“…LiDAR (Light Detection and Ranging) has now become an essential sensor for many autonomous driving experimental platforms [4], [5]. It is an efficient source of generating point clouds of the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Neural Network for Autonomous Steering using LiDAR Point Cloud Data

Ghazzai

Massoud

2022

2022 IEEE 65th International Midwest Symposium on Circuits and Systems (MWSCAS)

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Although numerous studies on end-to-end autonomous driving systems based on deep learning have been conducted, many of them used shallow feedforward neural networks, which are unsuitable for extracting useful information from complicated contexts and are mainly focused on video frames. This study investigates a LiDAR point cloud-based endto-end autonomous steering problem in structured roads. The control command to the vehicle is focused on the steering angle of the wheel, which is discretized into continuous integers as the direction category. The problem is then converted into a classification task, which is a mapping connection between the original point cloud data and the driving direction category. On the basis of the PointNet++ framework, we propose using Kmeans, KNN, and weighted sampling, to perform the steering decision making. Using the CARLA simulation environment, we have shown that the proposed approach is performing effective autonomous decision making with a rate strictly higher than 91% while requiring less inference speed compared to benchmarks.

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“…STPV systems were proposed for the first time by Swanson in 1979 [45-48]. STPV systems make use of a wavelength selective solar absorber to transform incident solar energy into heat energy, which is subsequently emitted as photons through a thermal transmitter [49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Tungsten Supported Plasmonic Nanoscale Absorber for Solar Thermal Photovoltaics Applications

Rana¹,

Khan²,

Batool³

et al. 2022

Preprint

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Current and modern photovoltaics energy systems require miniaturized, ultrathin, compact and high efficiency absorbing and optical devices. So, by keeping these challenges in mind and inspired by the artificially engineered materials, called metamaterials, a high bandwidth optical nanostructured based absorbing device is presented in this report. The design geometry of the proposed nano-absorber comprises an array of nano-circular antennas, etched on a dielectric spacer, aluminium nitride (AlN) which is coated by a thick reflecting layer of silver from the backside. Surprisingly, the design absorber offers high absorption value of above 90% across the large operational wavelengths starting from 400 to 1500 nm. The constituting nano-antennas induces plasmonic resonance features, which enables the localized surface plasmon and results in an enhanced absorption. The nanoscale absorber is also investigated under the excitation of polarization and oblique incident angles of the incoming optical light. Finally, the plasmonic metal, tungsten (W) offers high melting point so that it can sustain under the high-energy photon particles. As compared to previous published work related to plasmonic absorbers, this work provide ultra-compact geometry, easily manufactured design, super bandwidth and polarization insensitivity. So, this can be used for the applications of thermophotovoltaics and energy harvesting etc.

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A Latency-Aware Task Offloading in Mobile Edge Computing Network for Distributed Elevated LiDAR

Cited by 12 publications

References 14 publications

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

Are Turn-by-Turn Navigation Systems of Regular Vehicles Ready for Edge-Assisted Autonomous Vehicles?

End-to-End Neural Network for Autonomous Steering using LiDAR Point Cloud Data

Tungsten Supported Plasmonic Nanoscale Absorber for Solar Thermal Photovoltaics Applications

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