Enabling Opportunistic Low-cost Smart Cities By Using Tactical Edge Node Placement
Enabling Opportunistic Low-cost Smart Cities By Using Tactical Edge Node Placement
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DOI：
10.23919/wons51326.2021.9415579
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Oluwashina Madamori;Esther Max-Onakpoya;Gregory D. Erhardt;C. Baker
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文献类型：
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作者：
Oluwashina Madamori;Esther Max-Onakpoya;Gregory D. Erhardt;C. Baker
智慧城市项目旨在通过部署物联网（IoT）设备，使政府实体能够有效地监测、控制和维护基础设施，从而加强城市基础设施的管理。然而，与智慧城市项目相关的财务负担不利于未来的智慧城市。影响智慧城市项目成本和可持续性的一个值得注意的因素是为物联网设备提供蜂窝互联网连接。针对这一问题，本文探讨了使用公共交通网络节点和骡子（例如将公交车站用作公共汽车）来促进设备到设备通信的连接，以降低智慧城市内的蜂窝连接成本。数据骡子将非紧急数据从物联网设备传输到边缘计算硬件，在边缘计算硬件中可以处理数据或将数据发送到云端。因此，本文重点关注智慧城市中的边缘节点放置，这些节点机会性地利用公共交通网络来减少对蜂窝连接的依赖，从而降低成本。我们引入了一种算法，该算法选择一组提供最大传感器覆盖范围的边缘节点，并探索另一种算法，该算法选择一组在预算内提供最小传输延迟的边缘节点。这些算法针对两个公共交通网络数据集进行了评估：北卡罗来纳州教堂山和肯塔基州路易斯维尔。结果表明，我们的算法始终优于依赖传统中心性指标（介数和内度中心性）的边缘节点放置策略，覆盖预算减少了 77% 以上，延迟减少了 20 分钟以上。
Smart city projects aim to enhance the management of city infrastructure by enabling government entities to monitor, control and maintain infrastructure efficiently through the deployment of Internet-of-things (IoT) devices. However, the financial burden associated with smart city projects is a detriment to prospective smart cities. A noteworthy factor that impacts the cost and sustainability of smart city projects is providing cellular Internet connectivity to IoT devices. In response to this problem, this paper explores the use of public transportation network nodes and mules, such as bus-stops as buses, to facilitate connectivity via device-to-device communication in order to reduce cellular connectivity costs within a smart city. The data mules convey non-urgent data from IoT devices to edge computing hardware, where data can be processed or sent to the cloud. Consequently, this paper focuses on edge node placement in smart cities that opportunistically leverage public transit networks for reducing reliance on and thus costs of cellular connectivity. We introduce an algorithm that selects a set of edge nodes that provides maximal sensor coverage and explore another that selects a set of edge nodes that provide minimal delivery delay within a budget. The algorithms are evaluated for two public transit network data-sets: Chapel Hill, North Carolina and Louisville, Kentucky. Results show that our algorithms consistently outperform edge node placement strategies that rely on traditional centrality metrics (betweenness and in-degree centrality) by over 77% reduction in coverage budget and over 20 minutes reduction in latency.