An official website of the United States government
Future wireless networks need to support the increasing demands for high data rates and improved coverage. One promising solution is sectorization, where an infrastructure node (e.g., a base station) is equipped with multiple sectors employing directional communication. Although the concept of sectorization is not new, it is critical to fully understand the potential of sectorized networks, such as the rate gain achieved when multiple sectors can be simultaneously activated. In this paper, we focus on sectorized wireless networks, where sectorized infrastructure nodes with beam-steering capabilities form a multi-hop mesh network for data forwarding and routing. We present a sectorized node model and characterize the capacity region of these sectorized networks. We define the flow extension ratio and the corresponding sectorization gain, which quantitatively measure the performance gain introduced by node sectorization as a function of the network flow. Our objective is to find the optimal sectorization of each node that achieves the maximum flow extension ratio, and thus the sectorization gain. Towards this goal, we formulate the corresponding optimization problem and develop an efficient distributed algorithm that obtains the node sectorization under a given network flow with an approximation ratio of 2/3. Through extensive simulations, we evaluate the sectorization gain and the performance of the proposed algorithm in various network scenarios with varying network flows. The simulation results show that the approximate sectorization gain increases sublinearly as a function of the number of sectors per node.
more »
« less
Finding influential nodes in networks using pinning control: Centrality measures confirmed with electrochemical oscillators
The spatiotemporal organization of networks of dynamical units can break down resulting in diseases (e.g., in the brain) or large-scale malfunctions (e.g., power grid blackouts). Re-establishment of function then requires identification of the optimal intervention site from which the network behavior is most efficiently re-stabilized. Here, we consider one such scenario with a network of units with oscillatory dynamics, which can be suppressed by sufficiently strong coupling and stabilizing a single unit, i.e., pinning control. We analyze the stability of the network with hyperbolas in the control gain vs coupling strength state space and identify the most influential node (MIN) as the node that requires the weakest coupling to stabilize the network in the limit of very strong control gain. A computationally efficient method, based on the Moore–Penrose pseudoinverse of the network Laplacian matrix, was found to be efficient in identifying the MIN. In addition, we have found that in some networks, the MIN relocates when the control gain is changed, and thus, different nodes are the most influential ones for weakly and strongly coupled networks. A control theoretic measure is proposed to identify networks with unique or relocating MINs. We have identified real-world networks with relocating MINs, such as social and power grid networks. The results were confirmed in experiments with networks of chemical reactions, where oscillations in the networks were effectively suppressed through the pinning of a single reaction site determined by the computational method.
more »
« less
- Award ID(s):
- 1900011
- PAR ID:
- 10557367
- Publisher / Repository:
- AIP
- Date Published:
- Journal Name:
- Chaos: An Interdisciplinary Journal of Nonlinear Science
- Volume:
- 33
- Issue:
- 9
- ISSN:
- 1054-1500
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
While significant efforts have been attempted in the design, control, and optimization of complex networks, most existing works assume the network structure is known or readily available. However, the network topology can be radically recast after an adversarial attack and may remain unknown for subsequent analysis. In this work, we propose a novel Bayesian sequential learning approach to reconstruct network connectivity adaptively: A sparse Spike and Slab prior is placed on connectivity for all edges, and the connectivity learned from reconstructed nodes will be used to select the next node and update the prior knowledge. Central to our approach is that most realistic networks are sparse, in that the connectivity degree of each node is much smaller compared to the number of nodes in the network. Sequential selection of the most informative nodes is realized via the between-node expected improvement. We corroborate this sequential Bayesian approach in connectivity recovery for a synthetic ultimatum game network and the IEEE-118 power grid system. Results indicate that only a fraction (∼50%) of the nodes need to be interrogated to reveal the network topology.more » « less
-
Abstract The increased complexity of infrastructure systems has resulted in critical interdependencies between multiple networks—communication systems require electricity, while the normal functioning of the power grid relies on communication systems. These interdependencies have inspired an extensive literature on coupled multilayer networks, assuming a hard interdependence, where a component failure in one network causes failures in the other network, resulting in a cascade of failures across multiple systems. While empirical evidence of such hard failures is limited, the repair and recovery of a network requires resources typically supplied by other networks, resulting in documented interdependencies induced by the recovery process. In this work, we explore recovery coupling, capturing the dependence of the recovery of one system on the instantaneous functional state of another system. If the support networks are not functional, recovery will be slowed. Here we collected data on the recovery time of millions of power grid failures, finding evidence of universal nonlinear behavior in recovery following large perturbations. We develop a theoretical framework to address recovery coupling, predicting quantitative signatures different from the multilayer cascading failures. We then rely on controlled natural experiments to separate the role of recovery coupling from other effects like resource limitations, offering direct evidence of how recovery coupling affects a system’s functionality.more » « less
-
Electromigration (EM) is a major failure effect for on-chip power grid networks of deep submicron VLSI circuits. EM degradation of metal grid lines can lead to excessive voltage drops (IR drops) before the target lifetime. In this paper, we propose a fast data-driven EM-induced IR drop analysis framework for power grid networks, named {\it GridNet}, based on the conditional generative adversarial networks (CGAN). It aims to accelerate the incremental full-chip EM-induced IR drop analysis, as well as IR drop violation fixing during the power grid design and optimization. More importantly, {\it GridNet} can naturally leverage the differentiable feature of deep neural networks (DNN) to {\it obtain the sensitivity information of node voltage with respect to the wire resistance (or width) with marginal cost}. {\it GridNet} treats continuous time and the given electrical features as input conditions, and the EM-induced time-varying voltage of power grid networks as the conditional outputs, which are represented as data series images. We show that {\it GridNet} is able to learn the temporal dynamics of the aging process in continuous time domain. Besides, we can take advantage of the sensitivity information provided by {\it GridNet} to perform efficient localized IR drop violation fixing in the late stage design and optimization. Numerical results on 36000 synthesized power grid network samples demonstrate that the new method can lead to $$10^5\times$$ speedup over the recently proposed full-chip coupled EM and IR drop analysis tool. We further show that localized IR drop violation fix for the same set of power grid networks can be performed remarkably efficiently using the cheap sensitivity computation from {\it GridNet}.more » « less
-
The Digital Power Network (DPN) is an energy-on-demand approach. In terms of Internet of Things (IoT), it treats the energy itself as a `thing' to be manipulated (in contrast to energy as the `thing's enabler'). The approach is mostly appropriate for energy starving micro-grids with limited capacity, such as a generator for the home while the power grid is down. The process starts with a request of a user (such as, appliance) for energy. Each appliance, energy source or energy storage has an address which is able to communicate its status. A network server, collects all requests and optimizes the energy dissemination based on priority and availability. Energy is then routed in discrete units to each particular address (say air-condition, or, A/C unit). Contrary to packets of data over a computer network whose data bits are characterized by well-behaved voltage and current values at high frequencies, here we deal with energy demands at highvoltage, low-frequency and fluctuating current. For example, turning a motor ON requires 8 times more power than the level needed to maintain a steady states operation. Our approach is seamlessly integrating all energy resources (including alternative sources), energy storage units and the loads since they are but addresses in the network. Optimization of energy requests and the analysis of satisfying these requests is the topic of this paper. Under energy constraints and unlike the current power grid, for example, some energy requests are queued and granted later. While the ultimate goal is to fuse information and energy together through energy digitization, in its simplest form, this micro-grid can be realized by overlaying an auxiliary (communication) network of controllers on top of an energy delivery network and coupling the two through an array of addressable digital power switches.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1063/5.0163899
