S. Bosse, Unified Distributed Sensor and Environmental Information Processing with Multi-Agent Systems: Models, Platforms, and Technological Aspects,
ISBN XXX; epubli, in press Sample: Intro
This work addresses the challenge of unified and distributed computing in strong heterogeneous environments ranging from Sensor Networks to Internet Clouds by using Mobile Multi-Agent Systems. A unified agent behaviour model, agent processing platform architecture, and synthesis framework should close the operational gap between low-resource data processing units, for example, single microchips embedded in materials, mobile devices, and generic computers including servers. Robustness, Scalability, Self organization, Reconfiguration and Adaptivity including Learning are major cornerstones. The range of fields of application is not limited: Sensor Data Processing, Load monitoring of technical structures, Structural Health Monitoring, Energy Management, Distributed Computing, Distributed Databases and Search, Automated Design, Cloud-based Manufacturing, and many more. This work touches various topics to reach the ambitious goal of unified smart and distributed computing and contributing to the design of intelligent sensing systems: Multi-Agent Systems, Agent Processing Platforms, System-on-Chip Designs, Architectural and Algorithmic Scaling, High-level Synthesis, Agent Programming Models and Languages, Self-organizing Systems, Numerical and AI Algorithms, Energy Management, Distributed Sensor Networks, and multi-domain simulation techniques. None of these topics may be considered standalone. Only a balanced composition of all topics can meet the requirements in future computing networks, for example, the Internet-of-Things with billions of heterogeneous devices. Smart can be defined on different operational and processing levels and having different goals in mind. One aspect is the adaptivity and reliability in the presence of sensor, communication, node, and network failures that should not compromise the trust and quality of the computed information, for example, the output of a Structural Health Monitoring System. A Smart System can be considered on node, network, and network of network level. Another aspect of "smartness" is information processing with inaccurate or incomplete models (mechanical, technical, physical) requiring machine learning approaches, either supervised with training at design-time or unsupervised based on reward learning at run-time. Some examples of Self-organizing and Adaptive Systems are given in this work, for example, distributed feature recognition and event-based sensor processing.
DOI: 10.4018/IJDST.2017100103 View PDFView PUB
Ubiquitous computing and The Internet-of-Things (IoT) emerge rapidly in today’s life and evolve to Self-organizing systems (SoS). A unified and scalable information processing and communication methodology is required. In this work mobile agents are used to merge the IoT with Mobile and Cloud environments seamlessly. A portable and scalable Agent Processing Platform (APP) provides an enabling technology that is central for the deployment of Multi-agent Systems (MAS) in strongly heterogeneous networks including the Internet. A large-scale use-case deploying Multi-agent systems in a distributed heterogeneous seismic sensor and geodetic network is used to demonstrate the suitability of the MAS and platform approach. The MAS is used for earthquake monitoring based on a new incremental distributed learning algorithm applied to regions of sensor data from stations of a seismic network with global ensemble voting. This network environment can be extended by ubiquitous sensing devices like smart phones. Different (mobile) agents perform sensor sensing, aggregation, local learning and prediction, global voting and decision making, and the application. The incremental distributed learning algorithm outperforms a prior developed non-incremental algorithm (Distributed Interval Decision Tree learner) and can be efficiently used in low-resource platform networks.
D. Lehmhus, S. Bosse, M. Busse, Autonomous Property Change in Adaptive Composites: A Simulation-based study on Multi-Agent-Systems Approaches, DGM Verbundwerkstoffe Congress, 21. Symposium,
5. - 7. July 2017, Bremen, Germany Conference
Load-bearing structures are typically designed towards relevant load cases assuming static shape and fixed sets of materials properties decided upon during design and materials selection. Structures that could change local properties in service in response to load change could raise additional weight saving potentials , thus supporting lightweight design and sustainability. Materials with such capabilities must necessarily be composite in the sense of a heterogeneous build-up, exhibiting e. g. an architecture consisting of numerous active cells with sensing, signal and data processing and actuation/stimulation capability. One concern regarding active smart cellular structures is correlated control of cells’ responses, and the underlying informational organization providing robustness and real-time capabilities. We suggest a two-stage approach which combines machine learning with mobile and reactive Multi-agent Systems (MAS). In it, the MAS’ task is to analyze loading situations based on sensor data and negotiate matching spatial redistributions of material properties like elastic modulus to achieve higher-level optimization aims like a minimum of the total strain energy within the structure, or a reduction of peak stress levels. The associated machine learning approach would be employed to recognize loading situations already encountered in the past for which optimized solutions exist and in such cases bypass the MAS system to directly enforce the respective property distribution. In the present study, a proof of concept of the approach is presented which combines finite element method (FEM) and MAS simulation, with the former primarily taking the place of the physical structure. In addition, FEM simulations are used for off-line training of the MAS prior to its deployment in the real or simulated structure. The classification models learned this way represent a starting point which is constantly being updated at run-time during the service life of the structure using incremental learning techniques.
S. Bosse, E. Pournaras, An Ubiquitous Multi-Agent Mobile Platform for Distributed Crowd Sensing and
Social Mining, FiCloud 2017: The 5th International Conference on Future Internet of Things and Cloud, Aug 21, 2017 - Aug 23, 2017,
Prague, Czech Republic View PDFConference
S. Bosse, D. Lehmhus, Towards Large-scale Material-integrated Computing: Self-Adaptive Materials and Agents, IEEE 2nd International Workshops on Foundations and Applications of Self Systems (FASW), DOI: 10.1109/FAS-W.2017.123,
18-22 September 2017, University of Arizona, Tucson, AZ View PDFConference
In the past decades there was an exponential growth of computer networks and computing devices, connecting computers with a size in the m3 range. The Internet-of-Things (IoT) emerges connecting everything, demanding for new distributed computing and communication approaches. Currently, the IoT connects devices with a size in the cm3 range. But new technologies enable the integration of computing in materials and technical structures with sensor and actor networks connecting devices in the mm3 range. This work investigates issues in large-scale computer networks related to the deployment of low- and very-low resource miniaturized nodes integrated within materials. These networks operate under harsh conditions with possibility of technical failures requiring robustness. Despite sensor networks used for structural monitoring, self-adaptive materials can profit from self-organizing and autonomous distributed data processing using Multi-agent systems, demonstrated in this paper. Self-adaptive materials are able to adapt the material or mechanical structure properties based on their environmental interaction (load/stress) to minimize the risk of overloading. A structure that could change its local properties in service based on the identified loading situation could thus potentially raise additional weight saving potentials and thus supporting lightweight design, and in consequence, sustainability.
D. Lehmhus, S. Bosse, A. Gemilang, A Multi-Agent System based approach for Adaptive Property Control in Smart Load-Bearning Structures, European Congress and Exhibition on Advanced Materials and Processes, EUROMAT (2017), Symposium E6, Modeling, Simulation
17-22 September, 2017, Thessaloniki, Greek Conference
S. Bosse, D. Schmidt, M. Koerdt, Robust and Adaptive Signal Segmentation for Structural Monitoring Using Autonomous Agents, In Proceedings of the 4th Int. Electron. Conf. Sens. Appl., 15–30 November 2017; doi:10.3390/ecsa-4-04917
Monitoring of mechanical structures is a Big Data challenge and includes Structural Health Monitoring (SHM) and Non-destructive Testing (NDT). The sensor data produced by common measuring techniques, e.g., guided wave propagation analysis, is characterized by a high dimensionality in the temporal and spatial domain. There are off- and on-line methods applied at maintenance- or run-time, respectively. On-line methods (SHM) usually are constrained by low-resource processing platforms, sensor noise, unreliability, and real-time operation requiring advanced and efficient sensor data processing. Commonly, structural monitoring is a task that maps high-dimensional input data on low-dimensional output data (information, that is feature extraction), e.g., in the simplest case a Boolean output variable “Damaged”. Machine Learning (ML), e.g., supervised learning, can be used to derive such a mapping function. But ML quality and performance depends strongly on the input data size. Therefore, adaptive and reliable input data reduction (that is feature selection) is required at the first layer of an automatic structural monitoring system. Assuming some kind of two-dimensional sensor data (or n-dimensional data in general), image segmentation can be used to identify Regions of Interest (ROI), e.g., of wave propagation fields. Wave propagation in materials underlie reflections that must be distinguished, especially in hybrid materials (e.g., combining metal and fibre-plastic composites) there are complex wave propagation fields. The image segmentation is one of the most crucial part of image processing (Mishra, 2011). Major difficulties in image segmentation are noise and the differing homogeneity (fuzziness and signal gradients) of regions, complicating the definition of suitable threshold conditions for the edge detection or region splitting/clustering. Many traditional image segmentation algorithms are constrained by this issue. Artificial Intelligence can aid to overcome this limitation by using autonomous agents as an adaptive and self-organizing software architecture, presented in this work. Using a collection of co-operating agents decomposes a large and complex problem in smaller and simpler problems with a Divide-and-Conquer approach. Related to the image segmentation scenario, agents are working mostly autonomous (de-coupled) on dynamic bounded data from different regions of an image (i.e., distributed with simulated mobility), adapted to the locality, being reliable and less sensitive to noisy sensor data. In this work, different agent behaviour and segmentation approaches are introduced and evaluated with measured high-dimensional data from piezo-electric acusto-ultrasonic sensors recording wave propagation in plate-like structures. Commonly, SHM deploys only a small set of sensors and actuators at static positions delivering only a few temporal resolved sensor signals (1D), whereas NDT methods additionally can use spatial scanning to create images of wave signals (2D). Both one-dimensional temporal and two-dimensional spatial segmentation is considered to find characteristic ROIs.