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

Virtual Process Control Functions (VPFs) are a promising solution for replacing hardware controllers in industrial control processes in order to improve operational efficiency. With the introduction of Mobile Edge Computing (MEC) in 5G networks, VPFs could even be executed on cloud resources close to the mobile network edge to further improve operational efficiency. Nonetheless, for this to happen, it is fundamental to ensure that the placement of VPFs be resilient to potential cyber-attacks and component failures, besides being efficient. In this paper we address this problem by considering that VPF placement costs are incurred by reserving MEC resources, executing VPF instances, and by data transmission. We formulate the VPF placement problem as an integer programming (IP) problem, with resilience as a constraint. We propose a VPF placement algorithm based on generalized Benders decomposition and based on linear relaxation of the resulting sub-problems, which effectively reduces the number of integer variables to that of the number of MEC nodes. We evaluate the proposed solution with respect to operational cost, efficiency, and scalability, and compare it with a greedy baseline algorithm. Extensive simulations show that our algorithm performs well in realistic scenarios.

Resilient placement of virtual process control functions in mobile edge clouds

In this paper we propose an analytical model of a resilient, tree-based end-node multicast streaming architecture that employs path diversity and forward error correction for improved resilience to node churns and packet losses. Using the model and via simulations we study the performance of this architecture in the presence of packet losses and dynamic node behavior. We show that the overlay can distribute data to nodes arbitrarily far away from the root of the trees as long as the loss probability is lower than a certain threshold, but the probability of packet reception suddenly drops to zero once this threshold is exceeded. The value of the threshold depends on the ratio of redundancy and on the number of the distribution trees. Using the model and simulations we show that correlated and inhomogeneous losses slightly worsen the overlay’s performance. We apply the model to study the effects of dynamic node behavior and compare its results to simulations.

https://link.springer.com/content/pdf/10.1007/11753810_57.pdf

On the stability of end-point-based multimedia streaming

Workload characterization is important for understanding how systems and services are used in practice and to help identify design improvements. To better understand the longitudinal workload dynamics of chunk-based content delivery systems, this paper analyzes the BitTorrent usage as observed from two different vantage points. Using two simultaneously collected 48-week long traces, we analyze the differences in download characteristics and popularity dynamics observed locally at a university campus versus at a global scale. We find that campus users typically download larger files and are early adopters of new content, in the sense that they typically download files well before the time at which the global popularity of the files peak. The noticeable exception is music files, which the campus users are late to download. We also find that there typically is high churn in the set of files that are popular each week, both locally and globally, and that the most popular files peak significantly later than their release date. These findings provide insights that may improve the efficiency of content sharing locally, and thus increase the scalability of the global system.

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A longitudinal characterization of local and global bittorrent workload dynamics

Network slicing is a promising approach for enabling low latency computation offloading in edge computing systems. In this paper, we consider an edge computing system under network slicing in which ...

Joint Wireless and Edge Computing Resource Management with Dynamic Network Slice Selection

Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Visual sensor networks could be enabled to perform such tasks by allowing the camera nodes to offload their computational load to nearby processing nodes. In this paper, we address the problem of minimizing the completion time of multiple camera sensors that share the transmission and the processing resources of multiple processing nodes for computation offloading. We show that the problem is NP-hard, and propose a combination of central coordination and distributed optimization with limited signaling among the camera sensors as a solution. We analyze the existence of equilibrium allocations for the distributed algorithms, evaluate the effect of the network topology and of the video characteristics on the algorithms’ performance, and assess the benefits of central coordination. Our results demonstrate that with sufficient information available, distributed optimization can provide low completion times, moreover predictable and stable performance can be achieved with additional, sparse central coordination.

Gyorgy Dan

Papers

Resilient placement of virtual process control functions in mobile edge clouds

On the stability of end-point-based multimedia streaming

A longitudinal characterization of local and global bittorrent workload dynamics

Joint Wireless and Edge Computing Resource Management with Dynamic Network Slice Selection

Coordinating Distributed Algorithms for Feature Extraction Offloading in Multi-Camera Visual Sensor Networks