Literature DB >> 33917255

A Multi-Layer LoRaWAN Infrastructure for Smart Waste Management.

David Baldo1, Alessandro Mecocci1, Stefano Parrino1, Giacomo Peruzzi1, Alessandro Pozzebon1.   

Abstract

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.

Entities:  

Keywords:  IoT; LoRaWAN; edge computing; fire detection; smart bin; smart city; smart drop-off container; smart waste management

Year:  2021        PMID: 33917255     DOI: 10.3390/s21082600

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  3 in total

1.  An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation.

Authors:  Abdallah Namoun; Burhan Rashid Hussein; Ali Tufail; Ahmed Alrehaili; Toqeer Ali Syed; Oussama BenRhouma
Journal:  Sensors (Basel)       Date:  2022-05-05       Impact factor: 3.847

2.  A 5G-Enabled Smart Waste Management System for University Campus.

Authors:  Edoardo Longo; Fatih Alperen Sahin; Alessandro E C Redondi; Patrizia Bolzan; Massimo Bianchini; Stefano Maffei
Journal:  Sensors (Basel)       Date:  2021-12-10       Impact factor: 3.576

3.  An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People.

Authors:  Salam Dhou; Ahmad Alnabulsi; A R Al-Ali; Mariam Arshi; Fatima Darwish; Sara Almaazmi; Reem Alameeri
Journal:  Sensors (Basel)       Date:  2022-07-12       Impact factor: 3.847

  3 in total

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