Autonomic networks play a major role in many self-organizing networking systems, ranging from computer networks (such as self-management, including self-configuration, self-optimization, self-healing, and self-protecting sensor networks, peer-to-peer networks, delay tolerant networks, robot networks, etc.) to social and even biological networks. Only very recently, researchers have started understanding the fundamental mechanisms underlying autonomic networks and developed mathematical models and designed dedicated efficient techniques (e.g., for fault and attack tolerance, delay tolerance, mobility tolerance, etc.).
The goal of this course is two-fold:
- First, we will introduce the fundamental models and methods used to reason about the correctness and performance of autonomic network algorithms. In particular, we will teach essential algorithmic and analytic techniques which, after attending the course, remain a useful toolbox and allow the students to develop and study their own algorithms.
- Second, we complement the theoretical lectures with practical case studies. That is we consider case studies in sensor networks, self-managed networks, and even robotics, to show the various application domains of autonomic network algorithms.
Internationally acclaimed academicians, researchers and practitioners with proven knowledge, experience, and demonstrable ability in teaching, consultancy, research, and training in the field of Distributed Computing will deliver lectures and discuss potential research problems in the course. The course is planned as per the norms set by Global Initiative of Academic Networks (GIAN), an initiative by Govt. of India for Higher Education.
Objectives :The primary objectives of the course are as follows:
- Introduce theoretical models for autonomic network algorithms.
- Introduce essential algorithmic techniques to devise efficient algorithms and analyze them theoretically and practically.
- Provide students with a set of tools to become independent researchers in the field.
- Highlight open research directions.
- Highlight interesting application domains (sensor networks, delay tolerant networks, mobility tolerance, mobility management/ robot networks, fault tolerance, Self-* properties, etc.)