Low-Power Wide-Area Networks in Intelligent Transportation: Review and Opportunities for Smart-Railways

1. Low-Power Wide-Area Networks
in Intelligent Transportation:
Review and Opportunities for Smart-Railway
Ruth Dirnfeld1, Francesco Flammini1,2, Stefano Marrone3,
Roberto Nardone4, Valeria Vittorini3
1 Linnaeus University, Växjö, Sweden
2 Mälardalen University, Västerås, Sweden
3 University of Naples Federico II, Italy
4 University Mediterranea of Reggio Calabria, Italy
speaker: Roberto Nardone - roberto.nardone@unirc.it
23rd IEEE International Conference on Intelligent Transportation Systems ITSC2020 –
September 20 – 23, 2020

2. Outline
1. Low-Power Wide-Area Networks (LPWAN)
2. The S2R RAILS project
3. LPWAN review
4. Results
5. Conclusions

3. Low-Power Wide-Area Networks
A LPWAN is a type of wireless telecommunication designed to allow long-range
communications at a low bit rate.
This communication is widely adopted among connected objects (things in IoT),
such as battery-supplied sensors.
In IoT, things are often too far from a power source and they could have
difficulties in connecting on the Internet continuously. LPWAN could reduce the
energy demand needed to connect to the Internet.

4. LPWAN in Intelligent Transportation
Systems (railway)
LPWAN-based systems are enabled to long-range communication over large
geographical areas with low-power requirements allowing:
 usage on track-side remote sites with no connection to the power grid (e.g.,
railway track routes in desert areas), or
 unpowered vehicles (e.g., rail freight cars).
Applications of LPWAN technologies appear as very promising for remote
surveillance, monitoring and control over large geographical areas, by
collecting data for several sensing applications (e.g., predictive condition-based
maintenance, security early warning and situation awareness, etc.) even in
situations where power supply is limited (e.g., where solar panels are employed)
or absent (e.g., installation on-board freight cars).

5. The RAILS project
Roadmaps for AI integration in the raiL Sector
Programme: Horizon 2020 - Grant Agreement n°: 881782
Types of action: S2R Research and Innovation (RIA)
Partners:
Start date: 01/12/2019 - End date: 30/11/2022
Consorzio Interuniversitario per
l’Informatica CINI
University of Leeds
Linnaeus University
Delft University of Technology

6. The RAILS project
RAILS aims at developing roadmaps for future research and fast uptake
of Artificial Intelligence in the railway sector by identifying effective
and suitable techniques and testing methods for AI, and assessing impacts
towards improving overall performance of the railway system as a whole.
RAILS will produce knowledge, ground breaking research and
experimental proof-of-concept for the adoption of AI in:
 rail automation,
 predictive maintenance and defect detection,
 traffic planning and capacity optimization.

7. LPWAN review
Research questions:
 Who has been working on this field, when and where?
 What are the most relevant communication technologies?
 What are the main implementations?
Search string:
(“smart” OR “intelligent”) AND
(“transport*” OR “transit”) AND
(“lora*” OR “lpwan”)
… in title, abstract, and keywords
Two reference databases: IEEE Xplore and Scopus

8. Analytical Results
 47 papers with authors from 21 countries in all continents, by 50 authors from
37 different institutions (24 universities, 9 research centers, 4 companies)
year
#ofpapers
 14.6% of authors from USA
 29.2% of papers from Asia
 25% of papers from Europe
 14.6% of papers from North America

9.  68 communication technologies
 58% of technologies appeared in
only two or fewer articles
 42% of technologies appeared in
three or more articles
Analytical Results

10. Revised papers

11. Application to ITS
 (smart public transport system) bus tracking, monitoring, or localization systems
using LoRaWAN
 Making the available city-bus system smarter by tracking, would make the
public vehicles more accessible
 Tests confirm that LoRaWAN-based systems can be implemented at a lower cost
than the existing bus location systems using cellular networks (e.g., 3G/LTE)
 (smart maritime network) tracking, monitoring and location systems for boats
 Drawbacks of cellular networks can be solved by tracking and monitoring
system based on LoRa, in areas closer to the shore
 Long-range connectivity is still a challenge

12. Conclusions
 Increasing attention on LPWAN for ITS from all over the world in the last 5 years
 The interest comes from universities, research centers, companies and private
research institutions
 It is essential to find the most appropriate trade-off considering the performance,
reliability and coverage requirements in the operating conditions of any specific ITS
application
 For example, ALOHA protocol for the implementation of bus communications has an high
packet loss rate when distance was higher than 1.8km
 Only 5 articles (<10% of all included articles) present implementations and
experiments in a real-life environment
 Results obtained in the area of public transportation and maritime applications may
prove useful for a comparison of operating conditions, expected issues for long
distances, power requirements, etc.

13. Thank you!
contacts:
roberto.nardone@unirc.it
rd222dv@student.lnu.se
francesco.flammini@lnu.se
stefano.marrone@unina.it
valeria.vittorini@unina.it
ACKNOWLEDGMENT: This work has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No
881782. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the
Shift2Rail JU members other than the Union.The information and views set out in this document are those of the authors
and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy
of the data included in this article. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the
use which may be made of the information contained therein.
23rd IEEE International Conference on Intelligent Transportation Systems ITSC2020 –
September 20 – 23, 2020