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

BitTorrent is a very scalable file sharing protocol that utilizes the upload bandwidth of peers to offload the original content source. With BitTorrent, each file is split into many small pieces, each of which may be downloaded from different peers. While BitTorrent allows peers to effectively share pieces in systems with sufficient participating peers, the performance can degrade if participation decreases. Using measurements of over 700 trackers, which collectively maintain state information of a combined total of 2.8 million unique torrents, we identify many torrents for which the system performance can be significantly improved by re-allocating peers among the trackers. We propose a light-weight distributed swarm management algorithm that manages the peer torrents while ensuring load fairness among the trackers. The algorithm achieves much of its performance improvements by identifying and merging small swarms, for which the performance is more sensitive to fluctuations in the peer participation, and allows load sharing for large torrents.

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Dynamic swarm management for improved BitTorrent performance

A vehicular cloud (VC) is a type of mobile ad hoc cloud in which multiple vehicles share their resources and perform collaborative jobs. In this article, we propose a software-defined (SD) VC (SDVC) architecture to achieve flexible VC control and efficient resource utilization in a centralized manner.

The Software-Defined Vehicular Cloud: A New Level of Sharing the Road

The power system state estimator is an important application used to calculate optimal power flows, to maintain the system in a secure state, and to detect faulty equipment. Its importance in the operation of the smart grid is expected to increase, and therefore its security is an important concern. Based on a realistic model of the communication infrastructure used to deliver measurement data from the substations to the state estimator, in this paper we investigate the vulnerability of the power system state estimator to attacks performed against the communication infrastructure. We define security metrics that quantify the importance of individual substations and the cost of attacking individual measurements. We provide efficient algorithms to calculate these metrics, and use the metrics to show how various network layer and application layer mitigation strategies can be used to decrease the vulnerability of the state estimator. We illustrate the efficiency of the algorithms on the IEEE 118 and 300 bus benchmark power systems.

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Network-layer protection schemes against stealth attacks on state estimators in power systems

We consider the problem of allocating wireless and computing resources to a set of autonomous wireless devices in an edge computing system. Devices in the system can decide whether or not to use edge computing resources for offloading computing tasks so as to minimize their completion time, while the edge cloud operator can allocate wireless and computing resources to the devices. We model the interaction between devices and the operator as a Stackelberg game, prove the existence of Stackelberg equilibria, and propose an efficient decentralized algorithm for computing equilibria. We provide a bound on the price of anarchy of the game, which also serves as an approximation ratio bound for the proposed algorithm. Our simulation results show that the joint allocation of wireless and computing resources by the operator can halve the completion times compared to a system with static resource allocation. At the same time, the convergence time of the proposed algorithm is approximately linear in the number of devices, and thus it could be effectively implemented for edge computing resource management.

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Wireless and Computing Resource Allocation for Selfish Computation Offloading in Edge Computing

In this paper we propose and analyze a generalized multiple-tree-based overlay architecture for peer-to-peer live streaming that employs multipath transmission and forward error correction. We give mathematical models to describe the stability properties of the overlay and evaluate the error recovery in the presence of node dynamics and packet losses. We show how the stability of the overlay improves with the proper allocation of the outgoing bandwidths of the peers among the trees without compromising its error correcting capability.

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Gyorgy Dan

Papers

Dynamic swarm management for improved BitTorrent performance

The Software-Defined Vehicular Cloud: A New Level of Sharing the Road

Network-layer protection schemes against stealth attacks on state estimators in power systems

Wireless and Computing Resource Allocation for Selfish Computation Offloading in Edge Computing

On the Performance of Multiple-Tree-Based Peer-to-Peer Live Streaming