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Ultra-dense deployments in 5G, the next generation of cellular networks, are an alternative to provide ultra-high throughput by bringing the users closer to the base stations. On the other hand, 5G deployments must not incur a large increase in energy consumption in order to keep them cost-effective and most importantly to reduce the carbon footprint of cellular networks. We propose a reinforcement learning cell switching algorithm, to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed algorithm can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed algorithm can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex. Comment: 30 pages, 7 Figures, Submitted to IEEE Transactions on Green Communications and Networking (IEEETGCN)

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a $17$ node charging network equipped with Level $2$ chargers at a major North American University campus. The data is recorded for $166$ weeks starting from late $2011$. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities. Comment: Accepted by IEEE Energycon 2016

The recent advancements in hardware miniaturization capabilities have boosted the diffusion of systems based on Energy Harvesting (EH) technologies, as a means to power embedded wireless devices in a sustainable and low-cost fashion. Despite the undeniable management advantages, the intermittent availability of the energy source and the limited power supply has led to challenging system trade-offs, resulting in an increased attack surface and a general relaxation of the available security services. In this paper, we survey the security issues, applications, techniques, and challenges arising in wireless networks powered via EH technologies. We explore the vulnerabilities of EH networks, and we provide a comprehensive overview of the scientific literature, including attack vectors, cryptography techniques, physical-layer security schemes for data secrecy, and additional physical-layer countermeasures. For each of the identified macro-areas, we compare the scientific contributions across a range of shared features, indicating the pros and cons of the described techniques, the research challenges, and a few future directions. Finally, we also provide an overview of the emerging topics in the area, such as Non-Orthogonal Multiple Access (NOMA) and Rate-Splitting Multiple Access (RSMA) schemes, and Intelligent Reconfigurable Surfaces, that could trigger the interest of industry and academia and unleash the full potential of pervasive EH wireless networks. Comment: This is a personal copy of the authors. Not for redistribution. The final version of the paper is available through the IEEExplore Digital Library, at the link: https://ieeexplore.ieee.org/document/9170604, with the DOI: 10.1109/COMST.2020.3017665

Deep Neural Networks (DNNs) have been established as the state-of-the-art algorithm for advanced machine learning applications. Recently proposed by the Google Brain's team, the Capsule Networks (CapsNets) have improved the generalization ability, as compared to DNNs, due to their multi-dimensional capsules and preserving the spatial relationship between different objects. However, they pose significantly high computational and memory requirements, making their energy-efficient inference a challenging task. This paper provides, for the first time, an in-depth analysis to highlight the design and management related challenges for the (on-chip) memories deployed in hardware accelerators executing fast CapsNets inference. To enable an efficient design, we propose an application-specific memory hierarchy, which minimizes the off-chip memory accesses, while efficiently feeding the data to the hardware accelerator. We analyze the corresponding on-chip memory requirements and leverage it to propose a novel methodology to explore different scratchpad memory designs and their energy/area trade-offs. Afterwards, an application-specific power-gating technique is proposed to further reduce the energy consumption, depending upon the utilization across different operations of the CapsNets. Our results for a selected Pareto-optimal solution demonstrate no performance loss and an energy reduction of 79% for the complete accelerator, including computational units and memories, when compared to a state-of-the-art design executing Google's CapsNet model for the MNIST dataset. Comment: Accepted for publication at the IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

Abstract A simple model for simulating flows of active suspensions is investigated. The approach is based on dissipative particle dynamics. While the model is potentially applicable to a wide range of self-propelled particle systems, the specific class of self-motile bacterial suspensions is considered as a modeling scenario. To mimic the rod-like geometry of a bacterium, two dissipative particle dynamics particles are connected by a stiff harmonic spring to form an aggregate dissipative particle dynamics molecule. Bacterial motility is modeled through a constant self-propulsion force applied along the axis of each such aggregate molecule. The model accounts for hydrodynamic interactions between self-propelled agents through the pairwise dissipative interactions conventional to dissipative particle dynamics. Numerical simulations are performed using a customized version of the open-source software package LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) software package. Detailed studies of the influence of agent concentration, pairwise dissipative interactions, and Stokes friction on the statistics of the system are provided. The simulations are used to explore the influence of hydrodynamic interactions in active suspensions. For high agent concentrations in combination with dominating pairwise dissipative forces, strongly correlated motion patterns and a fluid-like spectral distributions of kinetic energy are found. In contrast, systems dominated by Stokes friction exhibit weaker spatial correlations of the velocity field. These results indicate that hydrodynamic interactions may play an important role in the formation of spatially extended structures in active suspensions.

Scalability and security problems of the centralized architecture models in cyberphysical systems have great potential to be solved by novel blockchain based distributed models.A decentralized energy trading system takes advantage of various sources and effectively coordinates the energy to ensure optimal utilization of the available resources. It achieves that goal by managing physical, social and business infrastructures using technologies such as Internet of Things (IoT), cloud computing and network systems. Addressing the importance of blockchain-enabled energy trading in the context of cyberphysical systems, this article provides a thorough overview of the P2P energy trading and the utilization of blockchain to enhance the efficiency and the overall performance including the degree of decentralization, scalability and the security of the systems. Three blockchain based energy trading models have been proposed to overcome the technical challenges and market barriers for better adoption of this disruptive technology. Comment: 6 pages, 3 Figures, IEEE Computer, Accepted with minor revision

The evolution of a pulsar wind nebula (PWN) depends on properties of the progenitor star, supernova, and surrounding environment. As some of these quantities are difficult to measure, reproducing the observed dynamical properties and spectral energy distribution (SED) with an evolutionary model is often the best approach in estimating their values. G21.5-0.9, powered by the pulsar J1833-1034, is a well observed PWN for which previous modeling efforts have struggled to reproduce the observed SED. In this study, we reanalyze archival infrared (IR; Herschel, Spitzer) and X-ray (Chandra, NuSTAR, Hitomi) observations. The similar morphology observed between IR line and continuum images of this source indicates that a significant portion of this emission is generated by surrounding dust and gas, and not synchrotron radiation from the PWN. Furthermore, we find the broadband X-ray spectrum of this source is best described by a series of power laws fit over distinct energy bands. For all X-ray detectors, we find significant softening and decreasing unabsorbed flux at higher energy bands. Our model for the evolution of a PWN is able to reproduce the properties of this source when the supernova ejecta has a low initial kinetic energy $E_{\mathrm{sn}} \approx 1.2 \times 10^{50}\,\mathrm{ergs}$ and the spectrum of particles injected into the PWN at the termination shock is softer at low energies. Lastly, our hydrodynamical modeling of the SNR can reproduce its morphology if there is a significant density increase of the ambient medium ${\sim} 1.8$ pc north of the explosion center. Comment: 24 pages, 15 figures, accepted for publication in The Astrophysical Journal (accepted manuscript); corrected typo in standard deviation equation of appendix