Our paper on the resilient coordination of (artificial) birds to fly in a V-formation has been accepted for presentation at the Fifteenth International Symposium on Automated Technology for Verification and Analysis. The paper is entitled Attacking the V: On the Resiliency of Adaptive-Horizon MPC and has been written jointly by Ashish Tiwari (SRI), Scott A. Smolka (SUNY Stony Brook), Lukas Esterle, Anna Lukina (TU Wien), Junxing Yang (SUNY Stony Brook) and Radu Grosu (TU WIEN).
Our paper entitled “Online Multi-object k-coverage with Mobile Smart Cameras” has been accepted for presentation at the International Conference on Distributed Smart Cameras. The paper discusses mobile smart cameras and how to coordinated them in a distributed fashion in order to ensure objects to be covered by at least k cameras at the same time. This extends the well known Cooperative Multi-Robot Observation of Multiple Moving Targets problem (CMOMMT). The paper has been written in cooperation with Peter Lewis.
The talks were based on extended Abstracts. The first talk was on the challenges in CPS we might be able to overcome when introducing self-awareness into our CPS.
The second talk was on Mobile Smart Cameras and the challenges we need to address as well as their interplay with self-awareness challenges.
After joining a Dagstuhl Seminar in January 2015 on ‘Model-driven Algorithms and Architectures for Self-Aware Computing Systems’, we started working on a book on self-aware computing systems. Almost 2 years later, this book has finally been released!
The book is entitled Self-Aware Computing Systems has just been released with Springer and gives a definition of self-aware computing systems, discusses individual as well as collective self-aware computing systems, what is needed to achieve them and how we can transform current computing systems towards self-aware ones.
This book provides formal and informal definitions and taxonomies for self-aware computing systems, and explains how self-aware computing relates to many existing subfields of computer science, especially software engineering. It describes architectures and algorithms for self-aware systems as well as the benefits and pitfalls of self-awareness, and reviews much of the latest relevant research across a wide array of disciplines, including open research challenges.
The chapters of this book are organized into five parts: Introduction, System Architectures, Methods and Algorithms, Applications and Case Studies, and Outlook. Part I offers an introduction that defines self-aware computing systems from multiple perspectives, and establishes a formal definition, a taxonomy and a set of reference scenarios that help to unify the remaining chapters. Next, Part II explores architectures for self-aware computing systems, such as generic concepts and notations that allow a wide range of self-aware system architectures to be described and compared with both isolated and interacting systems. It also reviews the current state of reference architectures, architectural frameworks, and languages for self-aware systems. Part III focuses on methods and algorithms for self-aware computing systems by addressing issues pertaining to system design, like modeling, synthesis and verification. It also examines topics such as adaptation, benchmarks and metrics. Part IV then presents applications and case studies in various domains including cloud computing, data centers, cyber-physical systems, and the degree to which self-aware computing approaches have been adopted within those domains. Lastly, Part V surveys open challenges and future research directions for self-aware computing systems.
It can be used as a handbook for professionals and researchers working in areas related to self-aware computing, and can also serve as an advanced textbook for lecturers and postgraduate students studying subjects like advanced software engineering, autonomic computing, self-adaptive systems, and data-center resource management. Each chapter is largely self-contained, and offers plenty of references for anyone wishing to pursue the topic more deeply.
Our paper entitled ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans has been accepted for publication at the ETAPS 23rd International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS).
The work was done in collaboration with Anna Lukina, Christian Hirsch, Ezio Bartocci, and Radu Grosu from Technische Universität Wien, Austria, Junxing Yang and Scott A. Smolka from Stony Brook University, New York, and Ashish Tiwari from SRI.
We introduce ARES, an efficient approximation algorithm for generating optimal plans (action sequences) that take an initial state of a Markov Decision Process (MDP) to a state whose cost is below a specified (convergence) threshold. ARES uses Particle Swarm Optimization, with adaptive sizing for both the receding horizon and the particle swarm. Inspired by Importance Splitting, the length of the horizon and the number of particles are chosen such that at least one particle reaches a next-level state, that is, a state where the cost decreases by a required delta from the previous-level state. The level relation on states and the plans constructed by ARES implicitly define a Lyapunov function and an optimal policy, respectively, both of which could be explicitly generated by applying ARES to all states of the MDP, up to some topological equivalence relation. We also assess the effectiveness of ARES by statistically evaluating its rate of success in generating optimal plans. The ARES algorithm resulted from our desire to clarify if flying in V-formation is a flocking policy that optimizes energy conservation, clear view, and velocity alignment. That is, we were interested to see if one could find optimal plans that bring a flock from an arbitrary initial state to a state exhibiting a single connected V-formation. For flocks with 7 birds, ARES is able to generate a plan that leads to a V-formation in 95% of the 8,000 random initial configurations within 63 seconds, on average. ARES can also be easily customized into a model-predictive controller (MPC) with an adaptive receding horizon and statistical guarantees of convergence. To the best of our knowledge, our adaptive-sizing approach is the first to provide convergence guarantees in receding-horizon techniques.
Today we received fantastic news that our project entitled “CPS/IoT Ecosystem” has been accepted for funding from the Federal Ministry for Science, Research and Economy.
The project aims to setup a cyber-physical system with roughly 5000 sensor nodes at three main locations in and around Vienna. In addition to local communication capabilities, we also plan to introduce real-time communication networks and cloud services to this system. This allows us to work on current and future challenges in CPS.
The CPS/IoT Ecosystem project has a budget of 3.6 Million Euro and is a collaboration effort between the Vienna University of Technology, Institute of Science and Technology, the Austrian Institute of Technology, and TTTech.
Ramin Hasani, Radu Grosu, and I wrote an extended abstract on our work modelling the nervous system of Caenorhabditis elegans, potential application of artificial neural circuits and extracting prospective learning techniques based on the behaviour of this little worm.
The title of this abstract is Investigations on the Nervous System of Caenorhabditis elegans
We have been invited to submit an article for the journal Elektrotechnik und Informationstechnik (e&i). The article has been written in collaboration with Prof. Radu Grosu and is entitled Cyber-Physical Systems: Challenge of the 21st Century should be published soon.
Here is the abstract:
Cyber-physical systems and the Internet-of-Things will be omnipresent in the near future. These systems will be tightly integrated in and interacting with our environment to support us in our daily tasks and in achieving our personal goals. However, to achieve this vision, we have to tackle various challenges.
For the last one and a half years I contributed and worked on one of the first books on self-aware computing systems. And it has finally been released! The book, entitled Self-aware Computing Systems – An Engineering Approach, discusses self-awaren computing systems from an engineering perspective and covers the work of the European FP7 project ‘EPiCS – Engineering Proprioception in Computing Systems’. I contributed to several chapters throughout this book.
Thank you and congratulations to every single person helping to make this book!
Taking inspiration from self-awareness in humans, this book introduces the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems. The basic ability of such self-aware computing systems is to collect information about their state and progress, learning and maintaining models containing knowledge that enables them to reason about theirbehaviour. Self-aware computing systems will have the ability to utilise this knowledge to effectively and autonomously adapt and explain their behaviour, in changing conditions.
This book addresses these fundamental concepts from an engineering perspective, aiming at developing primitives for building systems and applications. It will be of value to researchers, professionals and graduate students in computer science and engineering.
Our paper, entitled Self-organising Zooms for Decentralised Redundancy Management in Visual Sensor Networks by Lukas Esterle, Bernhard Rinner and Peter R. Lewis, has been accepted for publication as full paper at this years International Conference on Self-adatpive and Self-organising Systems (SASO).
The conference will be in Boston this year and I am looking forward to present our work there!