Thesis Proposal - Sensor Health & Edge Anomaly Detection for Industrial IoT

Sensors operating in harsh production environments face extreme conditions such as dirt, vibration, and mechanical wear, often

leading to subtle sensor drift and degraded data quality long before a complete failure occurs. In this thesis, we invite one or

two students to develop and evaluate lightweight anomaly detection algorithms that execute directly on resource-constrained

microcontrollers (Edge/TinyML). The goal is to analyze signal health patterns in real time at the sensor level, flagging data

quality issues before bad data reaches downstream systems.

The aim is to design, implement, and evaluate an edge-based self-diagnosing sensor system capable of automated drift and defect

detection. Within this scope, you will:

• Work with industrial time-series datasets (such as the NASA Turbofan degradation dataset) and model realistic sensor fault

profiles.

• Develop lightweight anomaly detection and signal-processing algorithms in Python and optimize them in C/C++ for microcontroller

deployment.

• Benchmark system performance in terms of detection accuracy, memory footprint, power efficiency, and the ability to distinguish

sensor faults from true physical process anomalies.

• RQ1: Which lightweight anomaly detection algorithms provide the best trade-off between detection rate and computational

resource efficiency when executing directly on a microcontroller?

• RQ2: How can edge-level algorithmic analysis effectively differentiate between true physical process anomalies and

sensor-related fault modes (e.g., signal noise, connection degradation, or calibration drift)?

We are looking for one or two students with an interest in embedded AI, signal processing, and industrial machine learning.

Proficiency in Python and C/C++, as well as foundational knowledge in signal processing, statistics, or machine learning, is

required. Experience with time-series data, sensor networks, or microcontroller development is a plus. Most importantly, you are

curious, motivated, and eager to apply TinyML to real-world hardware challenges. A requirement for the position is the ability to

communicate effectively in both Swedish and English, both verbally and in writing.

To give you the best possible support during your thesis, we’d like you to be able to come to the office connected to the project

and spend most of your time working from there.

We look forward to receiving your resume, and preferably, a personal letter in which you explain why you want to write your thesis

with Syntronic.