Route Planning Through Distributed Computing by Road Side Units
Route Planning Through Distributed Computing by Road Side Units
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DOI：
10.1109/access.2020.3026677
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发表时间：
2020-09
期刊：
影响因子：
3.9
通讯作者：
J. P. Talusan;Michael Wilbur;A. Dubey;K. Yasumoto
中科院分区：
文献类型：
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作者：
J. P. Talusan;Michael Wilbur;A. Dubey;K. Yasumoto
城市正在拥抱数据密集型应用程序，以最大限度地利用其有限的交通网络。谷歌等平台提供路线规划服务，以减轻交通拥堵的影响。这些使用需要互联网连接的远程服务器，这使得数据面临更大的网络故障和延迟问题风险。边缘计算是集中式架构的替代方案，在边缘提供可用于类似服务的计算能力。路边单元 (RSU) 是城市内的物联网 (IoT) 设备，提供了将计算卸载到边缘的机会。为了提供 RSU 上的处理环境，我们引入了 RSU-Edge，这是一种用于 RSU 的分布式边缘计算系统。我们通过 RSU-Edge 设计和开发分散式路线规划服务。在该服务中，城市被划分为网格并分配一个 RSU。用户向服务发送行程查询并获取路线。为了获得最大的准确性，必须将任务分配给最佳的 RSU。然而，这会使 RSU 过载，增加延迟。为了减少延迟，可以将任务从过载的 RSU 重新分配给其邻居。最佳分配与实际分配之间的距离会因数据陈旧而导致准确性损失。问题是确定最有效的任务分配，以便满足响应约束，同时保持可接受的准确性。我们创建了该系统，并对田纳西州纳什维尔的案例研究进行了分析，该分析显示了我们的算法在给定不同邻居级别的情况下对路线准确性和查询响应的影响。我们发现，与仅使用最优网格分配相比，我们的系统响应 1000 个查询的速度提高了 57.17%，模型精度仅损失 5.57% 至 7.25%。
Cities are embracing data-intensive applications to maximize their constrained transportation networks. Platforms such as Google offer route planning services to mitigate the effect of traffic congestion. These use remote servers that require an Internet connection, which exposes data to increased risk of network failures and latency issues. Edge computing, an alternative to centralized architectures, offers computational power at the edge that could be used for similar services. Road side units (RSU), Internet of Things (IoT) devices within a city, offer an opportunity to offload computation to the edge. To provide an environment for processing on RSUs, we introduce RSU-Edge, a distributed edge computing system for RSUs. We design and develop a decentralized route planning service over RSU-Edge. In the service, the city is divided into grids and assigned an RSU. Users send trip queries to the service and obtain routes. For maximum accuracy, tasks must be allocated to optimal RSUs. However, this overloads RSUs, increasing delay. To reduce delays, tasks may be reallocated from overloaded RSUs to its neighbors. The distance between the optimal and actual allocation causes accuracy loss due to stale data. The problem is identifying the most efficient allocation of tasks such that response constraints are met while maintaining acceptable accuracy. We created the system and present an analysis of a case study in Nashville, Tennessee that shows the effect of our algorithm on route accuracy and query response, given varying neighbor levels. We find that our system can respond to 1000 queries up to 57.17% faster, with only a model accuracy loss of 5.57% to 7.25% compared to using only optimal grid allocation.