CoMap: Proactive Provision for Crowdsourcing Map in Automotive Edge Computing
CoMap: Proactive Provision for Crowdsourcing Map in Automotive Edge Computing
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DOI：
10.1109/icc45041.2023.10278954
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Yongjie Xue;Yuru Zhang;Qian Liu;Dawei Chen;Kyungtae Han
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文献类型：
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作者：
Yongjie Xue;Yuru Zhang;Qian Liu;Dawei Chen;Kyungtae Han
来自互联和自动驾驶车辆（CAV）的众包数据是一种具有成本效益的方式，可以通过最新的瞬时道路信息来实现高清地图。然而，由于不可预测的资源竞争和分布式资源需求，实现具有确定性延迟性能的映射具有挑战性。在本文中，我们提出了CoMap，一个新的众包高清（HD）地图，以最大限度地减少网络资源使用的货币成本，同时满足百分之要求的端到端的延迟。我们设计了一种新的CROP算法来学习CAV卸载的资源需求，优化卸载决策，并以完全分布式的方式主动分配临时网络资源。特别是，我们创建了一个预测模型来估计基于贝叶斯神经网络的资源需求的不确定性，并开发了一个利用率平衡计划，以解决不平衡的资源利用在个别基础设施。我们在汽车边缘计算网络模拟器中进行了广泛的模拟，以评估CoMap的性能。结果表明，与现有解决方案相比，CoMap的平均资源使用量减少了80.4%。
Crowdsourcing data from connected and automated vehicles (CAVs) is a cost-efficient way to achieve high-definition maps with up-to-date transient road information. Achieving the map with deterministic latency performance is, however, challenging due to the unpredictable resource competition and distributional resource demands. In this paper, we propose CoMap, a new crowdsourcing high definition (HD) map to minimize the monetary cost of network resource usage while satisfying the percentile requirement of end-to-end latency. We design a novel CROP algorithm to learn the resource demands of CAV offloading, optimize offloading decisions, and proactively allocate temporal network resources in a fully distributed manner. In particular, we create a prediction model to estimate the uncertainty of resource demands based on Bayesian neural networks and develop a utilization balancing scheme to resolve the imbalanced resource utilization in individual infrastructures. We evaluate the performance of CoMap with extensive simulations in an automotive edge computing network simulator. The results show that CoMap reduces up to 80.4% average resource usage as compared to existing solutions.