Stephan Otto

Emerging technologies and challenges for the Internet of Things

The Internet of Things (IoT) envisages a dynamic network of networks where everyday objects can interact with each other, sense the environment and potentially act on it. Although this seems to be a simple concept, the structural and technical challenges that arise are manifold. This paper gives an overview of some of these challenges and presents the related emerging technologies and research trends.

Virtual and augmented reality in sports: an overview and acceptance study

The interest in virtual and augmented reality exploded during the last two years. We propose the use of these systems in the field of sports by combining this technology with a local area radio-based localization technology. This allows for novel application scenarios using virtual environments in team-based sports, which are outlined in this work. We conducted an online survey among 227 athletes about the acceptance of virtual reality headsets for training in different kind of sport disciplines.

Decentralized evolutionary agents streamlining logistic network design

We propose a decentralized evolutionary approach for studying autonomous heterogeneous agents interacting in a supply chain. Such logistics networks can be seen as complex networks that need to adapt their internal structure (e.g. transport routes, interactions) as reaction to environmental changes, e.g. the market demand, supplier unavailability or route changes. We model such distributed supply chains as a decentralized multi-agent system in order to draw an analogy to real world scenarios. This paper describes a decentralized evolutionary optimization approach that differs in two ways from traditional EA. First the fitness calculation is replaced by an economic model. Second the entire agent population constructs only one solution. The connections in supply-chains can be seen as a complex network of coexisting but simple interdependent agent strategies producing together the necessary transportation network. We describe how our decentralized approach can be used to solve inherently distributed problems where no central optimization algorithm exist. The simulation results show the applicability of the approach to transport network optimization.