Sahra Sedigh Sarvestani

Analysis of Reliability and Resilience for Smart Grids

Smart grids, where cyber infrastructure is used to make power distribution more dependable and efficient, are prime examples of modern infrastructure systems. The cyber infrastructure provides monitoring and decision support intended to increase the dependability and efficiency of the system. This comes at the cost of vulnerability to accidental failures and malicious attacks, due to the greater extent of virtual and physical interconnection. Any failure can propagate more quickly and extensively, and as such, the net result could be lowered reliability. In this paper, we describe metrics for assessment of two phases of smart grid operation: the duration before a failure occurs, and the recovery phase after an inevitable failure. The former is characterized by reliability, which we determine based on information about cascading failures. The latter is quantified using resilience, which can in turn facilitate comparison of recovery strategies. We illustrate the application of these metrics to a smart grid based on the IEEE 9-bus test system.

Survivability Evaluation of Gas, Water and Electricity Infrastructures

The infrastructures used in cities to supply power, water and gas are consistently becoming more automated. As society depends critically on these cyber-physical infrastructures, their survivability assessment deserves more attention. In this overview, we first touch upon a taxonomy on survivability of cyber-physical infrastructures, before we focus on three classes of infrastructures (gas, water and electricity) and discuss recent modelling and evaluation approaches and challenges.

Towards Comprehensive Modeling of Reliability for Smart Grids: Requirements and Challenges

Smart grids utilize computation and communication to improve the efficacy and dependability of power generation, transmission, and distribution. As such, they are among the most critical and complex cyber-physical systems. The success of smart grids in achieving their stated goals is yet to be rigorously proven. In this paper, our focus is on improvements (or lack thereof) in reliability. We discuss vulnerabilities in the smart grid and their potential impact on its reliability, both generally and for the specific example of the IEEE-14 bus system. We conclude the paper by presenting a preliminary Markov imbedded systems model for reliability of smart grids and describe how it can be evolved to capture the vulnerabilities discussed.

Influence-Aware Predictive Density Queries Under Road-Network Constraints

Density query is a very useful query type that informs users about highly concentrated/dense regions, such as a traffic jam, so as to reschedule their travel plans to save time. However, existing products and research work on density queries still have several limitations which, if can be resolved, will bring more significant benefits to our society. For example, we identify an important problem that has never been studied before. That is none of the existing works on traffic prediction consider the influence of the predicted dense regions on the subsequent traffic flow. Specifically, if road A is estimated to be congested at timestamp \(t_1\), the prediction of the condition on other roads after \(t_1\) should consider the traffic blocked by road A. In this paper, we formally model such influence between multiple density queries and propose an efficient query algorithm. We conducted extensive experiments and the results demonstrate both the effectiveness and efficiency of our approach.

Chapter Two - A Survey of Research on Data Corruption in Cyber-Physical Critical Infrastructure Systems.

Abstract Computer systems are present in every aspect of modern life. In many of these systems, corruption of data is unavoidable as a result of both intentional and unintentional means. In many systems, this erroneous data can result in severe consequences including financial loss, injury, or death. Critical infrastructure cyber–physical systems utilize intelligent control to improve performance; however, they are heavily data dependent. These systems have the potential to propagate corrupted data, leading to failure. This chapter presents a survey of work related to the propagation of corrupted data within critical infrastructure cyber–physical systems, including the sources of corrupted data and the structure of critical infrastructure cyber–physical systems. In addition, it presents a comparative analysis of various data corruption detection and mitigation techniques. Additionally, we discuss a number of studies on the negative effects of system execution on corrupted data. These key topics are essential to understanding how undetected corrupted data propagates through a critical infrastructure cyber–physical system.

Algorithms and Techniques for Proactive Search

While search engines have demonstrated improvements in both speed and accuracy, their response time is prohibitively long for applications that require immediate and accurate responses to search queries. Examples include identification of multimedia resources related to the subject matter of a particular class, as it is in session. This paper begins with a survey of collaborative recommendation and prediction algorithms, each of which applies a different method to predict future search engine usage based on the past history of a search engine user. To address the short- comings identified in existing techniques, we propose a proactive search approach that identifies resources likely to be of interest to the user without requiring a query. The approach is contingent on accurate determination of similarity, which we achieve with local alignment and output-based refinement of similarity neighborhoods. We demonstrate our proposed approach with trials on real-world search engine data. The results support our hypothesis that a majority of users exhibit search engine usage behavior that is predictable, allowing a proactive search engine to bypass the common query-response model and immediately deliver a list of resources of interest to the user.

Guest Editorial: Special Issue on Cyber-Physical Systems and Services

The papers in this special section focus on cyber-physical systems and applications for their use. A cyber-physical system (CPS) integrates a vast variety of static and mobile resources, including sensor and actuator networks, swarms of robots, remote-controlled vehicles, critical infrastructures, control and decision software, static data and just-in-time information from sensors, knowledge, data analytics and fusion software, event driven supply chains, and humans, and offers great potential for achieving tasks that are far beyond the capabilities of existing systems [1]. Individual users, organizations, and various communities can transform the vast space of cyberphysical entities into capabilities that no single entity can achieve alone. However, these capabilities do not come easily. Intelligence is needed for just-in-time composition of resources into services. Associated challenges include how to manage the vast number and diverse varieties of static and mobile physical entities, how to describe the capabilities of the cyber-physical entities, how to decompose high level goals into low-level control commands for the individual entities, how to achieve intelligent coordination and manage information flow among the entities.

A Survey of Data Cleansing Techniques for Cyber-Physical Critical Infrastructure Systems

Abstract Critical infrastructure cyber-physical systems heavily depend on accurate data in order to facilitate intelligent control and improve performance. Corruption of data in these systems is unavoidable, resulting from both intentional and unintentional means. The consequence of making control decisions based on erroneous or corrupted data can be severe including financial loss, injury, or death. This makes employing a mechanism to detect and mitigate corrupted data crucial. Many techniques have been developed to detect and mitigate corrupted data. However, these techniques vary greatly in their capability to detect certain anomalies and required computing resources. This chapter presents a survey of data cleansing techniques and their applicability to various control levels in a critical infrastructure cyber-physical systems.

A Multi-stage Approach to Personalized Course Selection and Scheduling

Recommender systems that utilize pertinent and available contextual information are applicable to and useful in a broad range of domains. This paper utilizes context-aware recommendation to facilitate personalized education and assist students in selecting courses (or in non-traditional curricula, learning artifacts) that meet curricular requirements, leverage their skills and background, and are relevant to their interests. The research contribution described in this paper is a methodology that generates a schedule of courses (and associated course content) that takes into consideration a students profile, while meeting curricular and prerequisite requirements and aiming to reduce attributes such as cost and time-to-degree. The optimization problem - multiple integer linear programming problems and a single scheduling problem - is solved in stages using a known linear solver as well as graph-based heuristics. The efficacy of the algorithm is demonstrated through a case study.

Models, metamodels, and model transformation for cyber-physical systems

One approach to increasing the sustainability of critical systems is to fortify them with cyber infrastructure that monitors the system, enables early diagnosis of faults, and provides decision support that facilitates greater efficacy. Modeling and analysis of the resulting cyber-physical systems is a significant challenge, as the physical and cyber infrastructures can be very different in time scales, complexity, and architecture. Model composition is a potential solution. Metamodeling seeks to facilitate model composition by providing methods for composing models of different types, including performance and dependability models, as well as methods for transforming one type of model to another. This paper describes the application of metamodeling to cyber-physical systems. Our proposed approach to model transformation is based on abstract interpretation, a program analysis technique. Models exist for disparate attributes of cyber-physical systems, but these models are typically domain-specific. We seek to map models from one physical domain to another, or to extract a model of one system attribute from a model of another attribute. This ability would considerably increase the utility of the existing body of knowledge on modeling of cyber-physicals systems.