The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

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Smart Grid — The New and Improved Power Grid: A Survey

Smart Grid - The New and Improved Power Grid:

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.

False data injection attacks against state estimation in electric power grids

A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including the interacting bad measurements introduced by arbitrary, non-random causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.In this paper, we present a new class of attacks, called false data injection attacks, against state estimation in electric power grids. We show that an attacker can exploit the configuration of a power system to launch such attacks to successfully introduce arbitrary errors into certain state variables while bypassing existing techniques for bad measurement detection. Moreover, we look at two realistic attack scenarios, in which the attacker is either constrained to some specific meters (due to the physical protection of the meters), or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios, which can not only change the results of state estimation, but also modify the results in arbitrary ways. We demonstrate the success of these attacks through simulation using IEEE test systems. Our results indicate that security protection of the electric power grid must be revisited when there are potentially malicious attacks.

Cyber-physical systems are ubiquitous in power systems, transportation networks, industrial control processes, and critical infrastructures. These systems need to operate reliably in the face of unforeseen failures and external malicious attacks. In this paper: (i) we propose a mathematical framework for cyber-physical systems, attacks, and monitors; (ii) we characterize fundamental monitoring limitations from system-theoretic and graph-theoretic perspectives; and (ii) we design centralized and distributed attack detection and identification monitors. Finally, we validate our findings through compelling examples.

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Attack Detection and Identification in Cyber-Physical Systems

Freshness of information is of critical importance for a variety of applications based on wireless camera networks where multi-view image processing is required. In this study, we propose to jointly optimize the use of communication and computing resources such that information from multiple views is delivered is obtained in a timely fashion. To this end, we extend the concept of age of information to capture packets carrying correlated data. We consider the joint optimization of processing node assignment and camera transmission policy, so as to minimize the maximum peak age of information from all sources. We formulate the multi-view age minimization problem (MVAM) and prove that it is NP-hard. We provide fundamental results including tractable cases and optimality conditions. To solve the MVAM efficiently, we develop a modular optimization algorithm following a decomposition approach. Numerical results show that, by employing our approach, the maximum peak age is significantly reduced in comparison to a traditional centralized solution with minimum-time scheduling.

Minimizing age of correlated information for wireless camera networks

The success of peer-to-peer overlays for live multicast streaming depends on their ability to maintain low delays and a low ratio of information loss end-to-end. However, data distribution over an overlay consisting of unreliable peers is inherently subject to disturbances. Resilience is thus inevitably a key requirement for peer-to-peer live-streaming architectures. In this article, we present a survey of the media distribution methods, overlay structures, and error-control solutions proposed for peer-to-peer live streaming. We discuss the trade off between resilience and overhead and argue that efficient architectures can be defined only through thorough performance analysis.

Resilience in live peer-to-peer streaming [Peer-to-Peer Multimedia Streaming]

Motivated by improved models for content workloadprediction, in this paper we consider the problem of dynamiccontent allocation for a hybrid content delivery system thatcombines cloud-based storage with low cost dedicated serversthat have limited storage and unmetered upload bandwidth. Weformulate the problem of allocating contents to the dedicatedstorage as a finite horizon dynamic decision problem, and showthat a discrete time decision problem is a good approximation forpiecewise stationary workloads. We provide an exact solution tothe discrete time decision problem in the form of a mixed integerlinear programming problem, propose computationally feasibleapproximations, and give bounds on their approximation ratios.Finally, we evaluate the algorithms using synthetic and measuredtraces from a commercial music on-demand service and giveinsight into their performance as a function of the workload characteristics.

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Dynamic content allocation for cloud-assisted service of periodic workloads

Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in wireline and wireless networks, but is known to be challenging due to non-stationarity and due to its burstiness and self-similar nature. The prediction of network traffic at the user level is particularly challenging, because the traffic characteristics emerge from a complex interaction of user level and application protocol behavior. In this work we address the problem of predicting the network traffic at the user level over a short horizon, motivated by its applications in cellular scheduling. Motivated by recent works on robust adversarial learning, we treat the prediction problem for non-stationary traffic in an adversarial context, and propose a meta-learning scheme that consists of a set of predictors, each optimized to predict a particular kind of traffic, and of a master policy that is trained for choosing the best fit predictor dynamically based on recent prediction performance, using deep reinforcement learning. We evaluate the proposed meta-learning scheme on a variety of traffic traces consisting of video and non-video traffic. Our results show that it consistently outperforms state-of-the-art predictors, and can adapt to before unseen traffic without the need for retraining the individual predictors.

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A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction

Smart-grid applications based on synchrophasor measurements have recently been shown to be vulnerable to timing attacks. A fundamental question is whether timing attacks could remain undetected by bad-data detection algorithms used in conjunction with state-of-the-art situational-awareness state estimators. In this paper, we analyze the detectability of timing attacks on linear state-estimation. We show that it is possible to forge delay attacks that are undetectable. We give a closed form for an undetectable attack; it imposes two phase offsets to two or more synchrophasor-based measurement units that can be translated to synchrophasors’ time delays. We also propose different methods for combining two-delays attacks to produce a larger impact. We simulate the attacks on a benchmark power-transmission grid, we show that they are successful and can lead to physical grid damage. To prove undetectability, we use classic bad-data detection techniques such as the largest normalized residual and the ${\chi ^{2}}$ -test.

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Gyorgy Dan

Papers

Minimizing age of correlated information for wireless camera networks

Resilience in live peer-to-peer streaming [Peer-to-Peer Multimedia Streaming]

Dynamic content allocation for cloud-assisted service of periodic workloads

A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction

Undetectable Timing-Attack on Linear State-Estimation by Using Rank-1 Approximation