Towards LoRaWAN Communications Adapted to the VANET Context

The Research Institute in Computer Science, Mathematics, Automation, and Signal Processing (IRIMAS) is a research team (EA 7499) at the University of Haute-Alsace (UHA).

This interdisciplinary institute brings together all research activities related to the fields of mathematics, computer science, electronics, electrical engineering, automation, and signal and image processing at the University of Haute-Alsace.

IRIMAS is affiliated with the Doctoral School 269: Mathematics, Information Sciences, and Engineering (MSII) of the University of Haute-Alsace and the University of Strasbourg.

Environment:
The intern will be supervised by members of the RT and OMEGA teams at the IRIMAS laboratory, UHA, Mulhouse. IRIMAS provides a high-level scientific environment, hosting numerous researchers working across various fields, ranging from artificial intelligence and optimization to communication theory.

LPWANs (Low Power Wide Area Networks) [1] are gaining significant attention both from industry, due to their technological and economic appeal, and the scientific community, given the challenges that remain for this new technology. This dual interest stems primarily from the ability of these networks to provide connectivity to devices spread over large geographic areas with very low energy consumption and, therefore, low cost. In this context, LoRa/LoRaWAN stands out as one of the leading solutions in the market and is utilized in various application domains. Among the less-explored applications where LoRa/LoRaWAN could be highly beneficial are cooperative-intelligent transport systems (C-ITS), particularly V2X (Vehicle-to-Everything) and V2I (Vehicle-to-Infrastructure) communications in the field of VANETs (Vehicular Ad-hoc Networks).

In such networks, each component (vehicles, buses, trams, road infrastructure, motorcycles, bicycles, pedestrians, etc.) cooperates with others to facilitate citizens' mobility, improve the efficiency of transport systems, and help reduce their costs and environmental impact. This internship aims to study a network architecture for V2X/V2I communications using LoRa/LoRaWAN.

Initially, the intern's task will be to study the impact of mobility on LoRa/LoRaWAN nodes. This solution relies on various transmission settings (physical layer) [2,3] that influence network characteristics, facing several limitations [4,5,6], including those linked to the imperfect orthogonality of spreading factors (SF). These limitations can result from co-SF interference (i.e., interference from transmissions using the same SF) or cross-SF interference (i.e., interference from transmissions using different SFs). For an information packet sent with a given SF to be correctly decoded, the signal-to-interference-plus-noise ratio (SNR) margins for all other SFs must be respected [7]. This issue will likely be more pronounced in a mobility context, necessitating this study.

Secondly, an analysis of latency, throughput, range, and reliability specifications for such communications must be conducted, as well as an evaluation of LoRa/LoRaWAN’s compatibility with these requirements and an identification of potential limitations.

Internship Tasks:

• Literature review on LoRa/LoRaWAN networks and their use in VANETs.
• Identification of current limitations: imperfect SF orthogonality, Doppler effect, latency, and interference in V2X (V2V, V2I) communications.
• Familiarization with the NS3 simulator and its LoRa/LoRaWAN module [8].
• Based on the literature review, select some proposals for implementation in the simulator and compare them.
• Propose a solution to improve LoRa performance in VANETs by adjusting physical parameters (SF, CR, BW) with an implementation that meets the internship's objective. The proposed solution should leverage recent advances in Artificial Intelligence (AI) and Machine Learning (ML) to support optimization algorithms.
• Analyze the results and write the internship report.
• Conclude the work with a scientific publication.

Adjustments to the tasks may occur depending on the progress of the internship.

We are looking for a Master’s student in Computer Networks (or equivalent) with the following qualities:

• Strong foundation in computer and wireless networks.
• Programming experience (Python, C, C++).
• Excellent knowledge of optimization techniques (metaheuristics) and/or machine learning (AI-based) techniques is highly desirable.
• Familiarity with the Linux environment.
• Good writing skills.
• A good command of English.
• A curious and inventive mindset.