- Reserach Profile
- Curriculum Vitae
Today sensor data processing and information mining become more and more complex concerning the amount of sensor data to be processed, the data dimension, the data quality, and the relationship between derived information and input data.
This is the case especially in large-scale sensing and measuring processes embedded in Cloud environments. Measuring uncertainties, calibration errors, and unreliability of sensors have a significant impact on the derivation quality of suitable information. In the technical and industrial context the raising complexity and distribution of data processing is a special issue.
Commonly, information is derived from raw input data by using some kind of mathematical model and functions, but often being incomplete or unknown. If reasoning of statements is primarily desired, Machine Learning can be an alternative.
Traditionally, sensor data is acquired and delivered to and processed by a central processing unit. The deployment of distributed Machine Learning using mobile Agents forming self-organizing and self-adaptive systems (self-X) pose the benefit for the enhancement of the sensor and data processing in technical and industrial systems.
This also addresses the quality of the computed statements, e.g., an accurate prediction of run-time parameters like mechanical loads or health conditions, the efficiency, and the reliability in the presence of partial system failures.
Agent-based methods are established for modelling and studying of complex dynamic systems and for implementing distributed intelligent data processing systems. Therefore, agent-based methods can be divided into three main classes:
Union of Agent-based Computing (ABC), Agent-based Modelling (ABM), Agent-based Simulation (ABS), and Agent-based Modelling and Simulation (ABMS)
Agent-based simulation is suitable for modelling complex social systems with respect to interaction between individual entities, manipulation of the world, spatial movement, and emergence effects of groups of entities. The main advantage is the bottom-up modelling approach composing large-scale complex systems by simple entity models. The main disadvantage of ABM is the (over-) simplified entity behaviour and simplification of the world the entities acting in.
Commonly, simulation bases on synthetic data or data retrieved by field studies. Many simulations and models lacking of diversity existing in real world. Commonly, sensor and model data (parameters) used in simulations (virtual world) is retrieved from experiments or field studies (real world). But there is neither a feedback from the virtual to the real world nor an interaction of the real world with the virtual world.
Mobile agents as mobile computational software processes can close this gap and provide sensor and information mining in distributed and mobile environments, including crowd sensing.
Mobile devices like smart phones are valuable sources for social data [E1], either by participatory crowd sensing with explicit participation of users providing first class data (e.g.,, performing surveys or polls) or implicitly by opportunistic crowd sensing collecting secondary class data, i.e., traces of device sensor data delivering, e.g., actual position, ambient conditions, network connectivity, digital media interaction, and so on.
Crowd sensing and Social Data Mining as a data source contribute more and more to investigations of digital traces in large-scale machine-human environments characterised by complex interactions and causalities between perception and action (decision making).
Mobile multi-agent systems are used for robust and adaptive information processing in highly heterogeneous environments. A central research topic is interaction and the implementation of self- * properties: self-organization, self-adaptivity, self-configuration. The core idea is the composition of complex systems consisting of simple loosely coupled unit cells. Efficient and scalable agent platforms (including hardware implementations) with ML modules are a key technology.
Crowd and things sensing with data mining of inherent sensor data through scalable and efficient information processing. Special focus on distribution, algorithms, communication, and platforms for limited embedded / mobile computing systems (in terms of computing power, storage capacity, energy) represent an important field of research that is addressed and solved with the agent paradigma.