Algorithms for Low-Power Wide-Area Network (LPWAN)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (1 February 2022) | Viewed by 3012

Special Issue Editor

University Ecclesiastical Academy of Vella, 450 01 Ioannina, Greece
Interests: computer networks; telematics; multimedia transmission; IoT; LPWANs

Special Issue Information

Dear Colleagues,

In the last few years, we have been facing an emerging development of the Internet of Things (IoT) concept and its applications. Many IoT devices are expected to be connected to the Internet, and these IoT devices have different communication needs compared to personal communication devices. Therefore, new communication technologies must be deployed by network operators in order to serve IoT devices, taking into account the specific requirements of IoT networking such as a long range, low power and low cost. Low-power wide-area networks (LPWANs) are appealing technologies for the implementation of IoT network infrastructure. Technologies such as LoRa, NB-IoT, SigFox or other sub-GHz technologies are employed in many IoT scenarios.

This Special Issue invites original contributions on topics regarding algorithmic issues related to LPWAN technologies, including, but not limited to, the following topics:

  • LPWAN technologies and architectures;
  • LPWAN-based sensor networks;
  • LPWAN infrastructures for smart buildings;
  • LPWAN infrastructures for smart cities;
  • LPWAN architectures for the Internet of Things;
  • Sub-GHz technologies and protocols;
  • LoRa technology and applications;
  • SigFox technology and applications;
  • NB-IoT technology and applications;
  • The integration of different LPWAN technologies;
  • 5G technologies for the Internet of Things.

Dr. Apostolos Gkamas
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • LPWAN 
  • IoT 
  • LoRa 
  • SigFox 
  • NB-IoT 
  • Wireless sensor networks
 

Published Papers (1 paper)

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Research

13 pages, 5199 KiB  
Article
Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks
by Winfred Ingabire, Hadi Larijani, Ryan M. Gibson and Ayyaz-UI-Haq Qureshi
Algorithms 2021, 14(11), 307; https://0-doi-org.brum.beds.ac.uk/10.3390/a14110307 - 23 Oct 2021
Cited by 15 | Viewed by 2346
Abstract
Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor [...] Read more.
Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m. Full article
(This article belongs to the Special Issue Algorithms for Low-Power Wide-Area Network (LPWAN))
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