Publications on self-organizing networked systems: Recent Episodes
Lakeside Labs
Lakeside Labs performs research on self-organizing systems and, in particular, their application in technology. Here you find some of our publications.
Abstract—We present and evaluate new ROS packages for
coordinated multi-robot exploration, namely communication,
global map construction, and exploration. The packages allow
completely distributed control and do not rely on (but allow)
central controllers. Their integration including application layer
protocols allows out of the box installation and execution. The
communication package enables reliable ad hoc communication
allowing to exchange local maps between robots which are
merged to a global map. Exploration uses the global map
to spatially spread robots and decrease exploration time. The
intention of the implementation is to offer basic functionality for
coordinated multi-robot systems and to enable other research
groups to experimentally work on multi-robot systems. The
packages are tested in real-world experiments using Turtlebot
and Pioneer robots. Further, we analyze their performance using
simulations and verify their correct working.
Major challenges for the transition of power sys- tems do not only tackle power electronics but also communication technology, power market economy and user acceptance studies. Simulation is an important research method therein, as it helps to avoid costly failures. A common smart grid simulation platform is still missing. We introduce a conceptual model of agents in multiple flow networks. Flow networks extend the depth of established power flow analysis through use of networks of information flow and financial transactions. We use this model as a basis for comparing different power system simulators. Furthermore, a quantitative comparison of simulators is done to facilitate the decision for a suitable tool in comprehensive smart grid simulation.
The idea of changing our energy system from a hierarchical design into a set of nearly inde- pendent microgrids becomes feasible with the availability of small renewable energy generators. The smart microgrid concept comes with several challenges in research and engineering tar- geting load balancing, pricing, consumer integration and home automation. In this paper we first provide an overview on these challenges and present approaches that target the problems identified. While there exist promising algorithms for the particular field, we see a missing integration which specifically targets smart microgrids. Therefore, we propose an architec- ture that integrates the presented approaches and defines interfaces between the identified components such as generators, storage, smart and “dumb” devices.
To improve the energy awareness of consumers, it is necessary to provide them with information about their en- ergy demand, not just on the household level. Non-intrusive load monitoring (NILM) gives the consumer the opportunity to disaggregate their consumed power on the appliance level. The consumer is provided with information about the energy de- mand of each individual appliances. In this paper we present an evolutionary optimization algorithm, applicable to NILM purposes. It can be used to detect appliances with a prob- abilistic power demand model. We show that the detection performance of the evolutionary algorithm can be improved if the single population approach of the evolutionary algorithm is replaced by a parallel population approach with individual exchange and by the introduction of application-oriented pre- processing and mutation methods. The proposed algorithm is tested with Matlab simulations and is evaluated according to the fitness reached and detection probability of the algorithm.
Households account for a significant fraction of
overall energy consumption. Energy usage can be reduced by
improving the efficiency of devices and optimizing their use as
well as by encouraging people to change their behaviour towards
a more sustainable lifestyle. In this study, we investigate patterns
of domestic energy use in Carinthia (Austria) and Friuli-Venezia
Giulia (Italy). In particular, we report the results of an online
survey about electrical devices and their use in households. We
outline typical scenarios in the two regions and discuss possible
strategies to reduce the consumption of energy in these regions.
Purpose: Content delivery in dynamic networks is a challenging task, because paths
may change during delivery and content might get lost. Replication is a typical
measure to increase robustness and performance.
Method: In previous work we proposed a hormone-based algorithm that delivers
content, and optimizes the distribution of replicas. Clients express demands by
creating hormones that will be released to the network. The corresponding resources
are attracted by this hormone and travel towards a higher hormone concentration. This
leads to a placement of content close to their most frequent requesters. In addition to
that the hormone-based delivery requires an appropriate replication and clean-up
strategy to balance the replicas throughout the network without exceeding the nodes’
storage limits or the networks communication capacity.
This paper describes the design and implementation of a distributed self-stabilizing clock synchronization algorithm based on the biological example of Asian Fireflies. Huge swarms of these fireflies use the principle of pulse coupled oscillators in order to synchronously emit light flashes to attract mating partners. When applying this algorithm to real sensor networks, typically,
nodes cannot receive messages while transmitting, which prevents the networked nodes from reaching synchronization. In order to counteract this deafness problem, we adopt a variant of the Reachback Firefly Algorithm to distribute the timing of light flashes in a given time window without affecting the quality of the synchronization. A case study implemented on 802.15.4 Zigbee nodes
presents the application of this approach for a time-triggered communication scheduling and coordinated duty cycling in order to enhance the battery lifetime of the nodes.
Typically, self-organizing systems comprise of a large number of individual agents whose behavior needs to be controlled by set parameters so that their interactions lead to the creation of the desired system. To be self-organizing, the system must mimic the evolutionary process. One way to do this is by use of an evolutionary algorithm. This mimics naturally-occurring genetic variation (mutation and recombination of genes). To fulfill this purpose, we have created a tool named FREVO (FRamework for EVOlutionary design), which separates the input needed into the following components: target problem evaluation, controller repre- sentation and the optimization method. FREVO provides well-defined interfaces for these components and supports a graphical user interface to simulate the evolutionary process. After obtaining the outcome for a simulation, it is possible to validate and evaluate the results within FREVO. FREVO has been successfully applied to various problems, from cooperative robotics to economics, pattern generation and wireless sensor networks. In this paper, we give an overview of the architecture of FREVO and introduce a case study involving smart grid networks.
Self-organizing systems obtain a global system behavior via typically simple local interactions among a number of components or agents, respectively. The emergent ser- vice often displays properties like adaptability, robust- ness, and scalability, which makes the self-organizing paradigm interesting for technical applications like coop- erative autonomous robots. The behavior for the local in- teractions is usually simple, but it is often difficult to de- fine the right set of interaction rules in order to achieve a desired global behavior. In this paper we describe a novel design approach using an evolutionary algorithm and ar- tificial neural networks to automatize the part of the de- sign process that requires most of the effort. A simulated robot soccer game was implemented to test and evaluate the proposed method. A new approach in evolving com- petitive behavior is also introduced using Swiss System instead of the full tournament to cut down the number of necessary simulations.
Self-organizing systems obtain a global system behavior via typically simple local interactions among a number of components or agents, respectively. The emergent ser- vice often displays properties like adaptability, robust- ness, and scalability, which makes the self-organizing paradigm interesting for technical applications like coop- erative autonomous robots. The behavior for the local in- teractions is usually simple, but it is often difficult to de- fine the right set of interaction rules in order to achieve a desired global behavior. In this paper we describe a novel design approach using an evolutionary algorithm and ar- tificial neural networks to automatize the part of the de- sign process that requires most of the effort. A simulated robot soccer game was implemented to test and evaluate the proposed method. A new approach in evolving com- petitive behavior is also introduced using Swiss System instead of the full tournament to cut down the number of necessary simulations.
The Video Browser Showdown (VBS) is a live video browsing competition where international researchers, working in the field of interactive video search, evaluate and demonstrate the efficiency of their tools in presence of the audience. The aim of the VBS is to evaluate video browsing tools for efficiency at known-item search (KIS) tasks with a well-defined data set in direct comparison to other tools. For each task the moderator presents a target clip on a shared screen that is visible to all participants.
The Video Browser Showdown (VBS) 2012 showed that certain Known-Item- Search (KIS) tasks can be performed effectively and efficiently with our AAU Video Browser[1]. It is solely based on intelligent interaction means and refrains from content analysis, but it takes use of the human abilities to recognize and classify items very fast. Therefore, scenes that are significantly different
from the other scenes in a video or scenes that are expected at certain locations of a video.
This paper describes our contribution to the social event detection (SED) task of the MediaEval Benchmark 2013. We present a robust unsupervised approach for the clustering of tagged photos and videos into social events. Results on the SED datasets show that the proposed approach yields an excellent generalization ability and state-of-the-art clustering performance.
Abstract—We present a novel interface for large-scale video archives that uses content-based filtering of search results. The interface has been used for the Interactive Known-Item Search (Interactive KIS) task of TRECVID 2012 and achieved good search performance. We found that for KIS tasks contentbased filtering as used in our interface is convenient and able to successfully narrow down interactive search for many of the TRECVID KIS queries used in
2012.
W. Elmenreich, R. D'Souza, C. Bettstetter, and H. de Meer. A survey of models and design methods for self-organizing networked systems. In Proceedings of the Fourth International Workshop on Self-Organizing Systems.
W. Elmenreich, T. Ibounig, and I. Fehérvári. Robustness versus performance in sorting and tournament algorithms. In Acta Polytechnica. Vol. 6, No. 5, pages 7–18, ISSN 1785-8860, 2009
I. Fehérvári and W. Elmenreich. Evolving neural network controllers for a team of self-organizing robots. In Journal of Robotics. Hindawi, 2010
A. Sobe, W. Elmenreich, and L. Böszörmenyi. Towards a Self-Organizing Replication Model for Non-Sequential Media Access. ACM Multimedia 2010 Workshop - Social, Adaptive and Personalized Multimedia Interaction and Access, SAPMIA 2010
W. Elmenreich and I. Fehérvári. Evolving self-organizing cellular automata based on neural network genotypes. In C. Bettstetter, C. Gershenson, editor, Proceedings of the Fifth International Workshop on Self-Organizing Systems.
A. Sobe, W. Elmenreich, and L. Böszörmenyi. Replication for bio-inspired delivery in unstructured peer-to-peer networks. In Proceedings of the Ninth International Workshop on Intelligent Solutions in Embedded Systems, Regensburg, Germany, July 201
Á. Pintér-Bartha, A. Sobe, and W. Elmenreich. Towards the light – Comparing evolved neural network controllers and finite state machine controllers. In Proceedings of the Tenth International Workshop on Intelligent Solutions in Embedded Systems, pages 83–87, Klagenfurt, Austria, July 2012
A. Sobe, I. Fehérvári, and W. Elmenreich. Frevo: A tool for evolving and evaluating self-organizing systems. In Proceedings of the 1st International Workshop on Evaluation for Self-Adaptive and Self-Organizing Systems, Lyon, France, September 2012
Tusch R., Pletzer F., Krätschmer A., Böszörmenyi L., Rinner B., Mariacher T., Harrer M.
Quaritsch M., Kuschnig R., Mersheeva V., Wischounig-Strucl D., Yahyanejad S., Yanmaz E., Friedrich G., Hellwagner H., Bettstetter Ch., Rinner B.
Quaritsch M., Stojanovski E., Bettstetter C., Friedrich G., Hellwagner H., Rinner B., Hofbauer M., Shah M.
Wilfried Elmenreich, N. Marchenko, H. Adam, C. Hofbauer, G. Brandner, C. Bettstetter, M. Huemer