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适用于动态物联网场景的 LoRaWAN 网关放置模型。

LoRaWAN Gateway Placement Model for Dynamic Internet of Things Scenarios.

机构信息

Computer Science Faculty, Federal University of Pará, Rua Augusto Corrêa 01, 66075-110 Belém, Brazil.

出版信息

Sensors (Basel). 2020 Aug 4;20(15):4336. doi: 10.3390/s20154336.

DOI:10.3390/s20154336
PMID:32759657
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7435864/
Abstract

Extended Range Wide Area Network (LoRaWAN) has recently gained a lot of attention from the industrial and research community for dynamic Internet of Things (IoT) applications. IoT devices broadcast messages for neighbor gateways that deliver the message to the application server through an IP network. Hence, it is required to deploy LoRaWAN gateways, i.e., network planning, and optimization, in an environment while considering Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) along with Quality of Service (QoS) requirements. In this article, we introduced a LoRaWAN gateway placement model for dynamic IoT applications called DPLACE. It divides the IoT devices into groups with some degree of similarity between them to allow for the placement of LoRaWAN gateways that can serve these devices in the best possible way. Specifically, DPLACE computes the number of LoRaWAN gateways based on the Gap statistics method. Afterward, DPLACE uses K-Means and Fuzzy C-means algorithms to calculate the LoRaWAN gateway placement. The simulations' results proved the benefits of DPLACE compared to state-of-the-art LoRaWAN gateway placement models in terms of OPEX, CAPEX, and QoS.

摘要

扩展范围广域网 (LoRaWAN) 最近引起了工业界和研究界的广泛关注，适用于动态物联网 (IoT) 应用。IoT 设备广播消息给相邻网关，这些网关通过 IP 网络将消息传递到应用服务器。因此，有必要在部署 LoRaWAN 网关时，考虑运营支出 (OPEX) 和资本支出 (CAPEX) 以及服务质量 (QoS) 要求，进行网络规划和优化。在本文中，我们为动态 IoT 应用引入了一种名为 DPLACE 的 LoRaWAN 网关放置模型。它将 IoT 设备分成具有一定相似性的组，以便可以放置 LoRaWAN 网关，从而以最佳方式为这些设备提供服务。具体来说，DPLACE 根据间隙统计方法计算 LoRaWAN 网关的数量。之后，DPLACE 使用 K-Means 和模糊 C 均值算法来计算 LoRaWAN 网关的放置。模拟结果证明，与最先进的 LoRaWAN 网关放置模型相比，DPLACE 在 OPEX、CAPEX 和 QoS 方面具有优势。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/d297a1796e79/sensors-20-04336-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/1ac892fd6d84/sensors-20-04336-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/ac85b60e561e/sensors-20-04336-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/294c01a5c905/sensors-20-04336-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/d297a1796e79/sensors-20-04336-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/1021d681ed94/sensors-20-04336-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/914b41cda482/sensors-20-04336-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/1bb351ffbeff/sensors-20-04336-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/4de3f4c05ece/sensors-20-04336-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/1ac892fd6d84/sensors-20-04336-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/ac85b60e561e/sensors-20-04336-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/294c01a5c905/sensors-20-04336-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fc/7435864/d297a1796e79/sensors-20-04336-g008.jpg

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