30 hp – Efficient Vehicle-to-Cloud Telemetry over MQTT: Evaluating XCP-Based Data Transport and Predictive Compression
Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project.
TRATON GROUP is one of the world’s leading commercial vehicle manufacturers, with brands including Scania, MAN, International and Volkswagen Truck & Bus. Its portfolio covers light commercial vehicles, trucks and buses, complemented by financing, charging and digital logistics services. Through its global operations, production sites and extensive sales and service networks, TRATON has access to diverse vehicle platforms, real-world operational data and fleet deployment environments. This provides a strong foundation for developing and validating innovative solutions for sustainable and efficient transportation.
In the Cloud and Embedded Platform domain within TRATON, we develop new solutions for connected vehicles in our Internet of Things (IoT) platform, as part of TRATON’s increasing focus on communication, services and smart transport solutions.
Connected vehicles continuously generate measurement and diagnostic data that can support fleet services, vehicle development, maintenance, and cloud-based analytics. Transmitting this data from vehicles to cloud services over mobile networks introduces several challenges, including limited bandwidth, variable connectivity, communication cost, latency, and processing constraints in the vehicle.
A typical data path begins with signals collected inside the vehicle. These signals must be extracted, encoded, transported through an onboard communication client, transmitted over the air, and reconstructed by a cloud-side receiver. The efficiency of this complete pipeline depends not only on the compression algorithm, but also on the data representation, protocol overhead, message structure, reliability mechanisms, and computational cost.
Within the vehicle, standardization is important because the data originates from heterogeneous embedded systems. XCP is therefore a relevant candidate for representing and accessing measurement data on the vehicle side. However, the data must still be transported efficiently between the vehicle and the cloud. MQTT is a suitable candidate for this offboard communication because it is lightweight, widely supported, and designed for unreliable or bandwidth-constrained networks.
This thesis will investigate how an efficient vehicle-to-cloud telemetry model can be designed by combining a standardized onboard data representation with an MQTT-based over-the-air transport layer.
The aim of this thesis is to design and evaluate an efficient vehicle-to-cloud telemetry pipeline in which measurement data is collected using a standardized vehicle-side interface and transmitted over MQTT to a cloud-side receiver.
The thesis will investigate how different encoding, batching, compression, and prediction strategies affect the overall efficiency of the communication path. The evaluation will consider not only compression ratio, but also bandwidth usage, latency, computational cost, memory consumption, reliability, and preservation of data accuracy.
How can vehicle measurement data be transported from an onboard client to a cloud receiver over MQTT with minimal bandwidth and latency while maintaining acceptable computational complexity and data fidelity?
RQ1 – End-to-end efficiency
How do different data representations and transport strategies affect the total size and performance of vehicle-to-cloud telemetry over MQTT?
RQ2 – Role of standardization
How can XCP-based measurement data be integrated into an MQTT-based vehicle-to-cloud communication model, and what protocol overhead does this introduce?
RQ3 – Compression and prediction
Can signal-aware encoding, delta coding, or lightweight predictive models reduce bandwidth usage compared with conventional JSON, Protocol Buffers, and generic compression methods?
RQ4 – System trade-offs
What are the trade-offs between bandwidth reduction, latency, CPU usage, memory consumption, implementation complexity, and reconstruction accuracy?
If time permits:
RQ5 – Impact on cloud analytics
Does compression and reconstruction affect the performance of a downstream cloud analytics task, such as anomaly detection?
Masters programmes in Computer Science, Electrical Engineer, or equivalent.
Number of students: 1
Start date for the Thesis project: Spring 2027
Estimated timescale: 20 weeks
Tomas Sjögren, Solution Architect, tomas.sjogren@scania.com
Juan Carlos Andresen,Group Manager, 08-553 835 16, juan-carlos.andresen@scania.com
Your application should contain the following:
Until 2026-10-31. Applicants will be assessed on a continuous basis until the position is filled.
A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.
Scania is a world-leading provider of transport solutions committed to a better tomorrow. Our purpose is to drive the shift towards a sustainable transport system. In doing so, we are creating a world of mobility that’s better for business, society and our environment.
Employing more than 50,000 people in about 100 countries, Scania’s research and development is concentrated in Sweden, while production takes place in Europe and South America.
