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Gil Zussman

Gil Zussman appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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Networking and Internet ArchitectureSystems and ControlData Structures and AlgorithmsMachine LearningComputer VisionDistributed, Parallel, and Cluster Computingeess.SPeess.SYInformation Theorymath.ITmath.OCPerformanceArtificial IntelligenceComputational Complexitycs.CYDiscrete Mathematicseess.IVEmerging Technologiesmath.COphysics.soc-ph

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Published work

22 published item(s)

preprint2026arXiv

Multi-Pedestrian Safety Warning at Urban Intersections Use Case of Digital Twin

Digital twins (DTs) for urban transportation systems have gained increasing attention; however, their systematic evaluation in safety-critical scenarios remains limited. This paper presents a multi-pedestrian safety warning system at urban intersections enabled by a tightly coupled physical-digital twin framework. Built upon the COSMOS city-scale wireless testbed in New York City, the proposed system integrates camera and ultra-wideband (UWB), edge-cloud computing, predictive trajectory modeling, and MQTT-based communication to deliver real-time safety alerts to vulnerable road users (VRUs). The system is evaluated through both field deployment and virtual reality (VR) experiments. Results demonstrate high warning generation accuracy, localization accuracy, efficient end-to-end latency under different model configurations, and significant reductions in user response time when warnings are issued. The proposed DT framework provides a scalable, modular, and generalizable solution for real-time multi-pedestrian safety enhancement at complex urban intersections.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Birds Eye View Social Distancing Analysis System

Social distancing can reduce the infection rates in respiratory pandemics such as COVID-19. Traffic intersections are particularly suitable for monitoring and evaluation of social distancing behavior in metropolises. We propose and evaluate a privacy-preserving social distancing analysis system (B-SDA), which uses bird's-eye view video recordings of pedestrians who cross traffic intersections. We devise algorithms for video pre-processing, object detection and tracking which are rooted in the known computer-vision and deep learning techniques, but modified to address the problem of detecting very small objects/pedestrians captured by a highly elevated camera. We propose a method for incorporating pedestrian grouping for detection of social distancing violations. B-SDA is used to compare pedestrian behavior based on pre-pandemic and pandemic videos in a major metropolitan area. The accomplished pedestrian detection performance is $63.0\%$ $AP_{50}$ and the tracking performance is $47.6\%$ MOTA. The social distancing violation rate of $15.6\%$ during the pandemic is notably lower than $31.4\%$ pre-pandemic baseline, indicating that pedestrians followed CDC-prescribed social distancing recommendations. The proposed system is suitable for deployment in real-world applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Identification of Intraday False Data Injection Attack on DER Dispatch Signals

The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions provide economic and environmental value, they also impose increased risk of cyber attacks and operational challenges. This paper investigates the vulnerability of the power grids with high renewable penetration against an intraday false data injection (FDI) attack on DER dispatch signals and proposes a kernel support vector regression (SVR) based detection model as a countermeasure. The intraday FDI attack scenario and the detection model are demonstrated in a numerical experiment using the HCE 187-bus test system.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Outdoor-to-Indoor 28 GHz Wireless Measurements in Manhattan: Path Loss, Environmental Effects, and 90% Coverage

Outdoor-to-indoor (OtI) signal propagation further challenges the already tight link budgets at millimeter-wave (mmWave). To gain insight into OtI mmWave scenarios at 28 GHz, we conducted an extensive measurement campaign consisting of over 2,200 link measurements. In total, 43 OtI scenarios were measured in West Harlem, New York City, covering seven highly diverse buildings. The measured OtI path gain can vary by up to 40 dB for a given link distance, and the empirical path gain model for all data shows an average of 30 dB excess loss over free space at distances beyond 50 m, with an RMS fitting error of 11.7 dB. The type of glass is found to be the single dominant feature for OtI loss, with 20 dB observed difference between empirical path gain models for scenarios with low-loss and high-loss glass. The presence of scaffolding, tree foliage, or elevated subway tracks, as well as difference in floor height are each found to have an impact between 5-10 dB. We show that for urban buildings with high-loss glass, OtI coverage can support 500 Mbps for 90% of indoor user equipment (UEs) with a base station (BS) antenna placed up to 49 m away. For buildings with low-loss glass, such as our case study covering multiple classrooms of a public school, data rates over 2.5/1.2 Gbps are possible from a BS 68/175 m away from the school building, when a line-of-sight path is available. We expect these results to be useful for the deployment of mmWave networks in dense urban environments as well as the development of relevant scheduling and beam management algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Smart City Intersections: Intelligence Nodes for Future Metropolises

Traffic intersections are the most suitable locations for the deployment of computing, communications, and intelligence services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of the edge-computing paradigm which aligns well with physical intersections in metropolises. This paper focuses on high-bandwidth, low-latency applications, and in that context it describes: (i) system design considerations for smart city intersection intelligence nodes; (ii) key technological components including sensors, networking, edge computing, low latency design, and AI-based intelligence; and (iii) applications such as privacy preservation, cloud-connected vehicles, a real-time "radar-screen", traffic management, and monitoring of pedestrian behavior during pandemics. The results of the experimental studies performed on the COSMOS testbed located in New York City are illustrated. Future challenges in designing human-centered smart city intersections are summarized.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Switching in the Rain: Predictive Wireless x-haul Network Reconfiguration

Wireless x-haul networks rely on microwave and millimeter-wave links between 4G and/or 5G base-stations to support ultra-high data rate and ultra-low latency. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the base-stations sharing the network and preventing transient congestion that may be caused by re-routing. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Physics-Informed Deep Neural Network Method for Limited Observability State Estimation

The precise knowledge regarding the state of the power grid is important in order to ensure optimal and reliable grid operation. Specifically, knowing the state of the distribution grid becomes increasingly important as more renewable energy sources are connected directly into the distribution network, increasing the fluctuations of the injected power. In this paper, we consider the case when the distribution grid becomes partially observable, and the state estimation problem is under-determined. We present a new methodology that leverages a deep neural network (DNN) to estimate the grid state. The standard DNN training method is modified to explicitly incorporate the physical information of the grid topology and line/shunt admittance. We show that our method leads to a superior accuracy of the estimation when compared to the case when no physical information is provided. Finally, we compare the performance of our method to the standard state estimation approach, which is based on the weighted least squares with pseudo-measurements, and show that our method performs significantly better with respect to the estimation accuracy.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

A Fast Distributed Stateless Algorithm for $α$-Fair Packing Problems

Over the past two decades, fair resource allocation problems have received considerable attention in a variety of application areas. However, little progress has been made in the design of distributed algorithms with convergence guarantees for general and commonly used $α$-fair allocations. In this paper, we study weighted $α$-fair packing problems, that is, the problems of maximizing the objective functions (i) $\sum_j w_j x_j^{1-α}/(1-α)$ when $α> 0$, $α\neq 1$ and (ii) $\sum_j w_j \ln x_j$ when $α= 1$, over linear constraints $Ax \leq b$, $x\geq 0$, where $w_j$ are positive weights and $A$ and $b$ are non-negative. We consider the distributed computation model that was used for packing linear programs and network utility maximization problems. Under this model, we provide a distributed algorithm for general $α$ that converges to an $\varepsilon-$approximate solution in time (number of distributed iterations) that has an inverse polynomial dependence on the approximation parameter $\varepsilon$ and poly-logarithmic dependence on the problem size. This is the first distributed algorithm for weighted $α-$fair packing with poly-logarithmic convergence in the input size. The algorithm uses simple local update rules and is stateless (namely, it allows asynchronous updates, is self-stabilizing, and allows incremental and local adjustments). We also obtain a number of structural results that characterize $α-$fair allocations as the value of $α$ is varied. These results deepen our understanding of fairness guarantees in $α-$fair packing allocations, and also provide insight into the behavior of $α-$fair allocations in the asymptotic cases $α\rightarrow 0$, $α\rightarrow 1$, and $α\rightarrow \infty$.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

A study of cascading failures in real and synthetic power grid topologies using DC power flows

Using the linearized DC power flow model, we study cascading failures and their spatial and temporal properties in the US Western Interconnect (USWI) power grid. We also introduce the preferential Degree And Distance Attachment (DADA) model, with similar degree distributions, resistances, and currents to the USWI. We investigate the behavior of both grids resulting from the failure of a single line. We find that the DADA model and the USWI model react very similarly to that failure, and that their blackout characteristics resemble each other. In many cases, the failure of a single line can cause cascading failures, which impact the entire grid. We characterize the resilience of the grid by three parameters, the most important of which is tolerance $α$, which is the ratio of the maximal load a line can carry to its initial load. We characterize a blackout by its yield, which we define as the ratio of the final to the initial consumed currents. We find that if $α\leq2$, the probability of a large blackout occurring is very small. By contrast, in a broad range of $1 < α < 2$, the initial failure of a single line can result, with a high probability, in cascading failures leading to a massive blackout with final yield less than 80%. The yield has a bimodal distribution typical of a first-order transition, i.e., the failure of a randomly selected line leads either to an insignificant current reduction or to a major blackout. We find that there is a latent period in the development of major blackouts during which few lines are overloaded, and the yield remains high. The duration of this latent period is proportional to the tolerance. The existence of the latent period suggests that intervention during early time steps of a cascade can significantly reduce the risk of a major blackout.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Doubly Balanced Connected Graph Partitioning

We introduce and study the Doubly Balanced Connected graph Partitioning (DBCP) problem: Let $G=(V,E)$ be a connected graph with a weight (supply/demand) function $p:V\rightarrow \{-1,+1\}$ satisfying $p(V)=\sum_{j\in V} p(j)=0$. The objective is to partition $G$ into $(V_1,V_2)$ such that $G[V_1]$ and $G[V_2]$ are connected, $|p(V_1)|,|p(V_2)|\leq c_p$, and $\max\{\frac{|V_1|}{|V_2|},\frac{|V_2|}{|V_1|}\}\leq c_s$, for some constants $c_p$ and $c_s$. When $G$ is 2-connected, we show that a solution with $c_p=1$ and $c_s=3$ always exists and can be found in polynomial time. Moreover, when $G$ is 3-connected, we show that there is always a `perfect' solution (a partition with $p(V_1)=p(V_2)=0$ and $|V_1|=|V_2|$, if $|V|\equiv 0 (\mathrm{mod}~4)$), and it can be found in polynomial time. Our techniques can be extended, with similar results, to the case in which the weights are arbitrary (not necessarily $\pm 1$), and to the case that $p(V)\neq 0$ and the excess supply/demand should be split evenly. They also apply to the problem of partitioning a graph with two types of nodes into two large connected subgraphs that preserve approximately the proportion of the two types.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Experimental Evaluation of Large Scale WiFi Multicast Rate Control

WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

On the Capacity Regions of Single-Channel and Multi-Channel Full-Duplex Links

We study the achievable capacity regions of full-duplex links in the single- and multi-channel cases (in the latter case, the channels are assumed to be orthogonal -- e.g., OFDM). We present analytical results that characterize the uplink and downlink capacity region and efficient algorithms for computing rate pairs at the region's boundary. We also provide near-optimal and heuristic algorithms that "convexify" the capacity region when it is not convex. The convexified region corresponds to a combination of a few full-duplex rates (i.e., to time sharing between different operation modes). The algorithms can be used for theoretical characterization of the capacity region as well as for resource (time, power, and channel) allocation with the objective of maximizing the sum of the rates when one of them (uplink or downlink) must be guaranteed (e.g., due to QoS considerations). We numerically illustrate the capacity regions and the rate gains (compared to time division duplex) for various channel and cancellation scenarios. The analytical results provide insights into the properties of the full-duplex capacity region and are essential for future development of scheduling, channel allocation, and power control algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Panda: Neighbor Discovery on a Power Harvesting Budget

Object tracking applications are gaining popularity and will soon utilize Energy Harvesting (EH) low-power nodes that will consume power mostly for Neighbor Discovery (ND) (i.e., identifying nodes within communication range). Although ND protocols were developed for sensor networks, the challenges posed by emerging EH low-power transceivers were not addressed. Therefore, we design an ND protocol tailored for the characteristics of a representative EH prototype: the TI eZ430-RF2500-SEH. We present a generalized model of ND accounting for unique prototype characteristics (i.e., energy costs for transmission/reception, and transceiver state switching times/costs). Then, we present the Power Aware Neighbor Discovery Asynchronously (Panda) protocol in which nodes transition between the sleep, receive, and transmit states. We analyze \name and select its parameters to maximize the ND rate subject to a homogeneous power budget. We also present Panda-D, designed for non-homogeneous EH nodes. We perform extensive testbed evaluations using the prototypes and study various design tradeoffs. We demonstrate a small difference (less then 2%) between experimental and analytical results, thereby confirming the modeling assumptions. Moreover, we show that Panda improves the ND rate by up to 3x compared to related protocols. Finally, we show that Panda-D operates well under non-homogeneous power harvesting.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Resource Allocation and Rate Gains in Practical Full-Duplex Systems

Full-duplex communication has the potential to substantially increase the throughput in wireless networks. However, the benefits of full-duplex are still not well understood. In this paper, we characterize the full-duplex rate gains in both single-channel and multi-channel use cases. For the single-channel case, we quantify the rate gain as a function of the remaining self-interference and SNR values. We also provide a sufficient condition under which the sum of uplink and downlink rates on a full-duplex channel is concave in the transmission power levels. Building on these results, we consider the multi-channel case. For that case, we introduce a new realistic model of a small form-factor (e.g., smartphone) full-duplex receiver and demonstrate its accuracy via measurements. We study the problem of jointly allocating power levels to different channels and selecting the frequency of maximum self-interference suppression, where the objective is maximizing the sum of the rates over uplink and downlink OFDM channels. We develop a polynomial time algorithm which is nearly optimal in practice under very mild restrictions. To reduce the running time, we develop an efficient nearly-optimal algorithm under the high SINR approximation. Finally, we demonstrate via numerical evaluations the capacity gains in the different use cases and obtain insights into the impact of the remaining self-interference and wireless channel states on the performance.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Generation of Synthetic Spatially Embedded Power Grid Networks

The development of algorithms for enhancing the resilience and efficiency of the power grid requires performance evaluation with real topologies of power transmission networks. However, due to security reasons, such topologies and particularly the locations of the substations and the lines are usually not publicly available. Therefore, we study the structural properties of the North American grids and present an algorithm for generating synthetic spatially embedded networks with similar properties to a given grid. The algorithm uses the Gaussian Mixture Model (GMM) for density estimation of the node positions and generates a set of nodes with similar spatial distribution to the nodes in a given network. Then, it uses two procedures, which are inspired by the historical evolution of the grids, to connect the nodes. The algorithm has several tunable parameters that allow generating grids similar to any given grid. Particularly, we apply it to the Western Interconnection (WI) and to grids that operate under the SERC Reliability Corporation (SERC) and the Florida Reliability Coordinating Council (FRCC), and show that it generates grids with similar structural and spatial properties to these grids. To the best of our knowledge, this is the first attempt to consider the spatial distribution of the nodes and lines and its importance in generating synthetic power grids.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Joint Transmission in Cellular Networks with CoMP - Stability and Scheduling Algorithms

Due to the current trend towards smaller cells, an increasing number of users of cellular networks reside at the edge between two cells; these users typically receive poor service as a result of the relatively weak signal and strong interference. Coordinated Multi-Point (CoMP) with Joint Transmission (JT) is a cellular networking technique allowing multiple Base Stations (BSs) to jointly transmit to a single user. This improves the users' reception quality and facilitates better service to cell-edge users. We consider a CoMP-enabled network, comprised of multiple BSs interconnected via a backhaul network. We formulate the OFDMA Joint Scheduling (OJS) problem of determining a subframe schedule and deciding if and how to use JT in order to maximize some utility function. We show that the OJS problem is NP-hard. We develop optimal and approximation algorithms for specific and general topologies, respectively. We consider a time dimension and study a queueing model with packet arrivals in which the service rates for each subframe are obtained by solving the OJS problem. We prove that when the problem is formulated with a specific utility function and solved optimally in each subframe, the resulting scheduling policy is throughput-optimal. Via extensive simulations we show that the bulk of the gains from CoMP with JT can be achieved with low capacity backhaul. Moreover, our algorithms distribute the network resources evenly, increasing the inter-cell users' throughput at only a slight cost to the intra-cell users. This is the first step towards a rigorous, network-level understanding of the impact of cross-layer scheduling algorithms on CoMP networks.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Cascading Failures in Power Grids - Analysis and Algorithms

This paper focuses on cascading line failures in the transmission system of the power grid. Recent large-scale power outages demonstrated the limitations of percolation- and epid- emic-based tools in modeling cascades. Hence, we study cascades by using computational tools and a linearized power flow model. We first obtain results regarding the Moore-Penrose pseudo-inverse of the power grid admittance matrix. Based on these results, we study the impact of a single line failure on the flows on other lines. We also illustrate via simulation the impact of the distance and resistance distance on the flow increase following a failure, and discuss the difference from the epidemic models. We then study the cascade properties, considering metrics such as the distance between failures and the fraction of demand (load) satisfied after the cascade (yield). We use the pseudo-inverse of admittance matrix to develop an efficient algorithm to identify the cascading failure evolution, which can be a building block for cascade mitigation. Finally, we show that finding the set of lines whose removal has the most significant impact (under various metrics) is NP-Hard and introduce a simple heuristic for the minimum yield problem. Overall, the results demonstrate that using the resistance distance and the pseudo-inverse of admittance matrix provides important insights and can support the development of efficient algorithms.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Max-min Fair Rate Allocation and Routing in Energy Harvesting Networks: Algorithmic Analysis

This paper considers max-min fair rate allocation and routing in energy harvesting networks where fairness is required among both the nodes and the time slots. Unlike most previous work on fairness, we focus on multihop topologies and consider different routing methods. We assume a predictable energy profile and focus on the design of efficient and optimal algorithms that can serve as benchmarks for distributed and approximate algorithms. We first develop an algorithm that obtains a max-min fair rate assignment for any given (time-variable or time-invariable) unsplittable routing or a routing tree. For time-invariable unsplittable routing, we also develop an algorithm that finds routes that maximize the minimum rate assigned to any node in any slot. For fractional routing, we study the joint routing and rate assignment problem. We develop an algorithm for the time-invariable case with constant rates. We show that the time-variable case is at least as hard as the 2-commodity feasible flow problem and design an FPTAS to combat the high running time. Finally, we show that finding an unsplittable routing or a routing tree that provides lexicographically maximum rate assignment (i.e., that is the best in the max-min fairness terms) is NP-hard, even for a time horizon of a single slot. Our analysis provides insights into the problem structure and can be applied to other related fairness problems.

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preprint2014arXiv

Movers and Shakers: Kinetic Energy Harvesting for the Internet of Things

Numerous energy harvesting wireless devices that will serve as building blocks for the Internet of Things (IoT) are currently under development. However, there is still only limited understanding of the properties of various energy sources and their impact on energy harvesting adaptive algorithms. Hence, we focus on characterizing the kinetic (motion) energy that can be harvested by a wireless node with an IoT form factor and on developing energy allocation algorithms for such nodes. In this paper, we describe methods for estimating harvested energy from acceleration traces. To characterize the energy availability associated with specific human activities (e.g., relaxing, walking, cycling), we analyze a motion dataset with over 40 participants. Based on acceleration measurements that we collected for over 200 hours, we study energy generation processes associated with day-long human routines. We also briefly summarize our experiments with moving objects. We develop energy allocation algorithms that take into account practical IoT node design considerations, and evaluate the algorithms using the collected measurements. Our observations provide insights into the design of motion energy harvesters, IoT nodes, and energy harvesting adaptive algorithms.

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preprint2014arXiv

Project-based Learning within a Large-Scale Interdisciplinary Research Effort

The modern engineering landscape increasingly requires a range of skills to successfully integrate complex systems. Project-based learning is used to help students build professional skills. However, it is typically applied to small teams and small efforts. This paper describes an experience in engaging a large number of students in research projects within a multi-year interdisciplinary research effort. The projects expose the students to various disciplines in Computer Science (embedded systems, algorithm design, networking), Electrical Engineering (circuit design, wireless communications, hardware prototyping), and Applied Physics (thin-film battery design, solar cell fabrication). While a student project is usually focused on one discipline area, it requires interaction with at least two other areas. Over 5 years, 180 semester-long projects have been completed. The students were a diverse group of high school, undergraduate, and M.S. Computer Science, Computer Engineering, and Electrical Engineering students. Some of the approaches that were taken to facilitate student learning are real-world system development constraints, regular cross-group meetings, and extensive involvement of Ph.D. students in student mentorship and knowledge transfer. To assess the approaches, a survey was conducted among the participating students. The results demonstrate the effectiveness of the approaches. For example, 70% of the students surveyed indicated that working on their research project improved their ability to function on multidisciplinary teams more than coursework, internships, or any other activity.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Power Grid Vulnerability to Geographically Correlated Failures - Analysis and Control Implications

We consider power line outages in the transmission system of the power grid, and specifically those caused by a natural disaster or a large scale physical attack. In the transmission system, an outage of a line may lead to overload on other lines, thereby eventually leading to their outage. While such cascading failures have been studied before, our focus is on cascading failures that follow an outage of several lines in the same geographical area. We provide an analytical model of such failures, investigate the model's properties, and show that it differs from other models used to analyze cascades in the power grid (e.g., epidemic/percolation-based models). We then show how to identify the most vulnerable locations in the grid and perform extensive numerical experiments with real grid data to investigate the various effects of geographically correlated outages and the resulting cascades. These results allow us to gain insights into the relationships between various parameters and performance metrics, such as the size of the original event, the final number of connected components, and the fraction of demand (load) satisfied after the cascade. In particular, we focus on the timing and nature of optimal control actions used to reduce the impact of a cascade, in real time. We also compare results obtained by our model to the results of a real cascade that occurred during a major blackout in the San Diego area on Sept. 2011. The analysis and results presented in this paper will have implications both on the design of new power grids and on identifying the locations for shielding, strengthening, and monitoring efforts in grid upgrades.

0 citations0 reviewsNo code linkedOpen access
preprint2010arXiv

Channel Fragmentation in Dynamic Spectrum Access Systems - a Theoretical Study

Dynamic Spectrum Access systems exploit temporarily available spectrum (`white spaces') and can spread transmissions over a number of non-contiguous sub-channels. Such methods are highly beneficial in terms of spectrum utilization. However, excessive fragmentation degrades performance and hence off-sets the benefits. Thus, there is a need to study these processes so as to determine how to ensure acceptable levels of fragmentation. Hence, we present experimental and analytical results derived from a mathematical model. We model a system operating at capacity serving requests for bandwidth by assigning a collection of gaps (sub-channels) with no limitations on the fragment size. Our main theoretical result shows that even if fragments can be arbitrarily small, the system does not degrade with time. Namely, the average total number of fragments remains bounded. Within the very difficult class of dynamic fragmentation models (including models of storage fragmentation), this result appears to be the first of its kind. Extensive experimental results describe behavior, at times unexpected, of fragmentation under different algorithms. Our model also applies to dynamic linked-list storage allocation, and provides a novel analysis in that domain. We prove that, interestingly, the 50% rule of the classical (non-fragmented) allocation model carries over to our model. Overall, the paper provides insights into the potential behavior of practical fragmentation algorithms.

0 citations0 reviewsNo code linkedOpen access