论文标题
使用交叉技术通信降低LPWAN路边传感器的运行成本
Reducing Operation Cost of LPWAN Roadside Sensors Using Cross Technology Communication
论文作者
论文摘要
低功率广阔区域网络（LPWAN）是物联网（IoT）的新兴通信标准（IoT），具有支持大量路边传感器的连通性，具有极长的通信范围。但是，管理这样大规模的路边传感器网络的高运营成本仍然是一个重大挑战。在本文中，我们提出了低操作成本LPWAN（LOC-LPWAN），这是一个新颖的优化框架，旨在使用跨技术通信（CTC）降低操作成本。 LOC-LPWAN允许路边传感器将传感器数据卸载到传递车辆，然后使用CTC将数据转向LPWAN服务器，以降低数据订阅成本。 LOC-LPWAN找到传感器和车辆之间的最佳通信时间表，以最大程度地提高给定预算的吞吐量。此外，LOC-LPWAN通过防止某些传感器主导数据传输通道来优化传感器之间的公平性。还可以配置LOC-LPWAN，以确保在特定时间内接收数据包。用40辆具有24小时轨迹的车辆组成的现实出租车数据进行的广泛数值分析表明，LOC-LPWAN与没有使用车辆用于中继数据包的基线方法相比，LOC-LPWAN降低了50％。结果还表明，LOC-LPWAN将吞吐量提高了72.6％，将公平性提高了65.7％，并且与贪婪的算法相比，在相同的预算中，延迟将延迟减少28.8％。
Low-Power Wide-Area Network (LPWAN) is an emerging communication standard for Internet of Things (IoT) that has strong potential to support connectivity of a large number of roadside sensors with an extremely long communication range. However, the high operation cost to manage such a large-scale roadside sensor network remains as a significant challenge. In this article, we propose Low Operation-Cost LPWAN (LOC-LPWAN), a novel optimization framework that is designed to reduce the operation cost using the cross-technology communication (CTC). LOC-LPWAN allows roadside sensors to offload sensor data to passing vehicles that in turn forward the data to a LPWAN server using CTC aiming to reduce the data subscription cost. LOC-LPWAN finds the optimal communication schedule between sensors and vehicles to maximize the throughput given an available budget. Furthermore, LOC-LPWAN optimizes the fairness among sensors by preventing certain sensors from dominating the channel for data transmission. LOC-LPWAN can also be configured to ensure that data packets are received within a specific time bound. Extensive numerical analysis performed with real-world taxi data consisting of 40 vehicles with 24-hour trajectories demonstrate that LOC-LPWAN reduces the cost by 50% compared with the baseline approach where no vehicle is used to relay packets. The results also show that LOC-LPWAN improves the throughput by 72.6%, enhances the fairness by 65.7%, and reduces the delay by 28.8% compared with a greedy algorithm given the same amount of budget.
