Thesis topics
Pattern-Based Analysis of Sensor Networks and the Development of an Intelligent Monitoring System
title
Pattern-Based Analysis of Sensor Networks and the Development of an Intelligent Monitoring System
institution
doctoral_school
supervisor
discipline
description
Modern environmental monitoring increasingly rely on sensor networks that provide real-time
data on industrial processes. These networks, consisting of sensors measuring parameters enable
detailed spatial mapping of environmental dynamics and early detection of critical changes.
The proposed doctoral research aims to develop an intelligent, pattern-recognition-based
monitoring system capable of analyzing sensor data, identifying anomalies and critical trends, and
issuing automatic alerts. The core of the research lies in applying machine learning and artificial
intelligence techniques to process spatial and temporal data streams generated by sensor
networks. The goal is to detect and interpret characteristic data patterns that indicate specific
events or abnormal states before they escalate.
The research will include the design of a data acquisition architecture, the development of pattern
recognition and predictive algorithms, and the implementation of real-time alerting mechanisms.
Emphasis will be placed on the scalability and adaptability of the system, ensuring that it can be
applied across diverse domains.
The expected outcome is an adaptive, reliable, and real-time monitoring system capable of
automatically recognizing critical patterns in sensor data, predicting potential issues, and
providing timely support for human decision-making. This research will contribute to advancing
the practical integration of artificial intelligence in spatial sensor networks and data-driven
environmental monitoring, fostering the development of intelligent and sustainable technological
ecosystems.
data on industrial processes. These networks, consisting of sensors measuring parameters enable
detailed spatial mapping of environmental dynamics and early detection of critical changes.
The proposed doctoral research aims to develop an intelligent, pattern-recognition-based
monitoring system capable of analyzing sensor data, identifying anomalies and critical trends, and
issuing automatic alerts. The core of the research lies in applying machine learning and artificial
intelligence techniques to process spatial and temporal data streams generated by sensor
networks. The goal is to detect and interpret characteristic data patterns that indicate specific
events or abnormal states before they escalate.
The research will include the design of a data acquisition architecture, the development of pattern
recognition and predictive algorithms, and the implementation of real-time alerting mechanisms.
Emphasis will be placed on the scalability and adaptability of the system, ensuring that it can be
applied across diverse domains.
The expected outcome is an adaptive, reliable, and real-time monitoring system capable of
automatically recognizing critical patterns in sensor data, predicting potential issues, and
providing timely support for human decision-making. This research will contribute to advancing
the practical integration of artificial intelligence in spatial sensor networks and data-driven
environmental monitoring, fostering the development of intelligent and sustainable technological
ecosystems.
student count limit
2
location
Szombathely
deadline
2026-05-31