Publications on self-organizing networked systems Lakeside Labs

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.