Department of Information Engineering and Mathematics, University of Siena, 53100 Siena, Italy.
Sensors (Basel). 2021 Apr 7;21(8):2600. doi: 10.3390/s21082600.
Long Range Wide Area Network (LoRaWAN) has rapidly become one of the key enabling technologies for the development of Internet of Things (IoT) architectures. A wide range of different solutions relying on this communication technology can be found in the literature: nevertheless, the most part of these architectures focus on single task systems. Conversely, the aim of this paper is to present the architecture of a LoRaWAN infrastructure gathering under the same network different typologies of services within one of the most significant sub-systems of the Smart City ecosystem (i.e., the Smart Waste Management). The proposed architecture exploits the whole range of different LoRaWAN classes, integrating nodes of growing complexity according to the different functions. The lowest level of this architecture is occupied by smart bins that simply collect data about their status. Moving on to upper levels, smart drop-off containers allow the interaction with users as well as the implementation of asynchronous downlink queries. At the top level, Video Surveillance Units (VSUs) are provided with machine learning capabilities for the detection of the presence of fire nearby bins or drop-off containers, thus fully implementing the Edge Computing paradigm. The proposed network infrastructure and its subsystems have been tested in a laboratory and in the field. This study has enhanced the readiness level of the proposed technology to Technology Readiness Level (TRL) 3.
远距离广域网 (LoRaWAN) 已迅速成为物联网 (IoT) 架构发展的关键使能技术之一。在文献中可以找到广泛依赖于这种通信技术的不同解决方案:然而,这些架构的大部分都专注于单一任务系统。相反,本文的目的是介绍一种 LoRaWAN 基础设施架构,该架构在智慧城市生态系统的一个最重要的子系统(即智能废物管理)内,将不同类型的服务汇集在同一个网络中。所提出的架构利用了不同 LoRaWAN 类别的全部范围,根据不同的功能集成了越来越复杂的节点。该架构的最低层由智能垃圾桶组成,这些垃圾桶只能收集有关其状态的数据。再向上一层,智能投放容器允许与用户进行交互,并实现异步下行链路查询。在最高层,视频监控单元 (VSUs) 配备了机器学习功能,用于检测附近垃圾桶或投放容器是否存在火灾,从而完全实现边缘计算范例。所提出的网络基础设施及其子系统已经在实验室和现场进行了测试。这项研究提高了所提出技术的准备水平,达到了技术准备水平 (TRL) 3。