Alexander Kroller received his Diploma in Mathematics at Prof. Grotschel's group at TU Berlin in 2003. He then joined the group of Prof. Fekete at the Braunschweig Institute of Technology, where he received his PhD in 2007. He specializes in wireless sensor networks with a focus on distributed algorithms, and has published a dozen of reviewed papers and journal articles. In addition, he was the driving force behind the development and implementation of the simulation testbed Shawn, which will be crucial for our WP4. Throughout this work, he has been in close contact to Stefan Fischer's group.
In Pervasive Adaptation, Dr. Kroller continues his work on distributed geometric algorithm with abstract location knowledge, extending it to scenarios where the environment dynamically changes. He researches strategies by which partially mobile sensor networks can adapt to changing environments. Furthermore, he drives development for experiments and testing of adaption in dynamic environments, which is partially based on the simulator Shawn, which he co-authored.

Dennis Pfisterer is a postdoc at the Institute of Telematics, University of Lubeck, Germany (http://www.itm.uni-luebeck.de). After his studies of Information Technology at the University of Applied Sciences in Mannheim, he worked as a research assistant at the European Media Laboratory (http://www.eml.org) in Heidelberg in the area of resource adaptive systems. In 2003, he joined Prof. Fischer's group at the Braunschweig Institute of Technology (http://www.ibr.cs.tu-bs.de), and followed him in 2005 to Lubeck to continue his research on sensor networks at the Institute of Telematics. After his PhD, he broadened his research interests and now works on sensor networks in general, their integration with the Future Internet as well as Service Oriented Architectures (SOA). He has published more than thirty reviewed publications in his area of expertise.
In Pervasive Adaptation, he is especially interested in investigating and applying self-adaptation properties of sensor networks. The research goal is to identify the required technologies, protocols and algorithms to turn the vision of zero-configuration in sensor networks into reality.

Andrea Vitaletti is Assistant professor (Ricercatore) in computer science at Dipartimento di Informatica e Sistemistica "Antonio Ruberti'' - SAPIENZA UNIVERSITA' DI ROMA. PhD in computer science (ingegneria informatica) at SAPIENZA UNIVERSITA' DI ROMA with a thesis with title: "Scheduling Algorithms and Localization Tools for Wireless Network". During his Ph.D and the following years he visited some of the main international research centers such as ETHZ and AT&T Research Labs.
Andrea Vitaletti's main research interests are: 1) Design and analysis of efficient algorithms for resource constrained networks, 2) Location Based Services, 3) Wireless Sensor Networks. In 2002 he founded the WLAB (http://www.w-lab.it/) and he is currently the CTO. He has participated to several EU projects (AEOLUS, FRONTS and PERADA are still active) and national projects (MOTUS - industria 2015). He has published about 25 paper in international journals and conferences (h-index 7: Harzing's Publish or Perish, key A. Vitaletti). He has been reviewer for several international conferences and journals and TPC of Pervasive and MobileHCI. He leads e research group of 3 Ph.D students and 5 master students in the WSN lab at DIS. He is member of the joint-lab on security research. Since 2002 he teaches on network topics at SAPIENZA and he was the tutor of more than 80 master thesis.
In pervasive adaptation he is investigating the effectiveness of recommendation techniques based on collaborative filtering for use in fully decentralized, pervasive systems of small devices with limited communication and computational capabilities. In particular, he assumes that each item of interest is tagged with such a device, which stores aggregate information over the histories of users that visited the item in the past. Information exchange among nodes is thus mediated by users’ collective and unpredictable navigation patterns. In this scenario, Stigmergy is the primary interaction mechanism. Stigmergy is a form of self-organization where traces left in the environment by actions of agents stimulate the execution of a subsequent action, by the same or a different agent thus allowing spontaneous and indirect coordination between agents. The produced algorithms do not require any explicit interaction among users and can be easily and efficiently implemented. He analyzes the theoretical behaviour of our recommendation strategies and assess their performance in practice, both by simulation using synthetic data and on real, publicly available datasets.

Ioannis Chatzigiannakis obtained his B.Eng. from the Dept. of Electronics Engineering and Dept. of Computer Science of the University of Kent at Canterbury. He obtained his PhD in Computer Science and Engineering from the Dept. of Computer Engineering & Informatics of the University of Patras. He is currently Adjunct Faculty at the Computer Engineering & Informatics Department, Polytechnic School, Patras University, Greece (since October 2005). He is the Director of the Research Unit 1 "Foundations of Computer Science, Relevant Technologies and Applications" of the Research Academic Computer Technology Institute (since July 2007). He has co-authored over 70 publications that have appeared in international journals and refereed international conferences of the ACM, IEEE and EATCS, as well as four chapters in books by major publishers. His main research interests include distributed and mobile computing, wireless sensor networks, algorithm engineering and software systems. He has participated in R&D projects funded by the European Union and the Greek government. He has also served as a consultant to major Greek computing industries. He is the Secretary of the European Association for Theoretical Computer Science since July 2008.
In pervasive adaptation he explores the computational limits of adaptive networked populations of tiny artifacts. The basic assumption is that, at any point in time, every node participating in the computation can only store a small, constant, fraction of the overall data, a necessary constraint in order to address the crucial issue of scalability. It has been proven that, although such systems consist of extremely limited, cheap and bulk-produced hardware devices, they are still capable of carrying out very useful nontrivial computations. In parallel to the theoretical analysis of the computational limit, he also looks into experimental evaluation through simulations, testbed development and testing in real sensor populations.