Learning Graphs from Linear Measurements: Fundamental Trade-offs and Applications

We consider a specific graph learning task: reconstructing a symmetric matrix that represents an underlying graph using linear measurements. We present a sparsity characterization for distributions of random graphs (that are allowed to contain high-degree nodes), based on which we study fundamental trade-offs between the number of measurements, the complexity of the graph class, and the probability of error. We first derive a necessary condition on the number of measurements. Then, by considering a three-stage recovery scheme, we give a sufficient condition for recovery. Furthermore, assuming the measurements are Gaussian IID, we prove upper and lower bounds on the (worst-case) sample complexity for both noisy and noiseless recovery. In the special cases of the uniform distribution on trees with n nodes and the Erdős-Rényi (n,p) class, the fundamental trade-offs are tight up to multiplicative factors with noiseless measurements. In addition, for practical applications, we design and implement a polynomial-time (in n ) algorithm based on the three-stage recovery scheme. Experiments show that the heuristic algorithm outperforms basis pursuit on star graphs. We apply the heuristic algorithm to learn admittance matrices in electric grids. Simulations for several canonical graph classes and IEEE power system test cases demonstrate the effectiveness and robustness of the proposed algorithm for parameter reconstruction

Autonomic computing meets SCADA security

© 2017 IEEE. National assets such as transportation networks, large manufacturing, business and health facilities, power generation, and distribution networks are critical infrastructures. The cyber threats to these infrastructures have increasingly become more sophisticated, extensive and numerous. Cyber security conventional measures have proved useful in the past but increasing sophistication of attacks dictates the need for newer measures. The autonomic computing paradigm mimics the autonomic nervous system and is promising to meet the latest challenges in the cyber threat landscape. This paper provides a brief review of autonomic computing applications for SCADA systems and proposes architecture for cyber security

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

Smart Solutions: Smart Grid Demokit

Treball desenvolupat dins el marc del programa 'European Project Semester'.The purpose of this report is to justify the design choices of the smart grid demo kit. Something had to be designed to make a smart grid clear for people who have little knowledge about smart grids. The product had to be appealing and clear for people to understand. And eventually should be usable, for example, on an information market. The first part of the research consisted of looking how to shape the whole system. How the 'tiles' had to look to be interactive for users and what they should feature. One part of this was doing research to get to know more about the already existing knowledge amount users. Another research investigated what appeals the most to the users. After this, a concept was created in compliance with the group and the client. The concept consists of hexagonal tiles, each with a different function: houses, solar panels, wind turbines, factories and energy storages. These tiles are all different parts of a smart grid. When combining these tiles, it can be made clear to users how smart grids work. The tiles are fabricated using a combination of 3D printing and laser cutting. The tiles have laser cut symbols on top of them to show what part of the smart grid they are. Digital LED strips are on top of the tiles to show the direction of the energy flow, and the colors indicate if the tile is producing or consuming power from the grid. The tiles are connected to each other by the so called “grid blocks”. These blocks make up the central power grid and are also lighting up by LED strips. Each tile is equipped with a microcontroller which controls the LED strips and makes it possible for the different tiles to “talk” with each other. Using this, the central tile knows which tiles are connected to the system. The central tile controls all tiles and runs the simulation of the smart grid. For further development of the project, it can be investigated how to control and adjust the system from an external system, for example by a tablet. The final product consists of five tiles connected by seven grid blocks which show how a smart grid works