Enabling intelligence in fog computing to achieve energy and latency reduction

Fog computing is an emerging architecture intended for alleviating the network burdens at the cloud and the core network by moving resource-intensive functionalities such as computation, communication, storage, and analytics closer to the End Users (EUs). In order to address the issues of energy efficiency and latency requirements for the time-critical Internet-of-Things (IoT) applications, fog computing systems could apply intelligence features in their operations to take advantage of the readily available data and computing resources. In this paper, we propose an approach that involves device-driven and human-driven intelligence as key enablers to reduce energy consumption and latency in fog computing via two case studies. The first one makes use of the machine learning to detect user behaviors and perform adaptive low-latency Medium Access Control (MAC)-layer scheduling among sensor devices. In the second case study on task offloading, we design an algorithm for an intelligent EU device to select its offloading decision in the presence of multiple fog nodes nearby, at the same time, minimize its own energy and latency objectives. Our results show a huge but untapped potential of intelligence in tackling the challenges of fog computing。

Maritime Communications in 5G and Beyond Networks

With the rapid development of marine activities,there has been an increasing use of Internet-of-Things(IoT) devices for maritime applications.This leads to a growing demand for high-speed and ultra-reliable maritime communications.Current maritime communication networks (MCNs) mainly rely on satellites and on-shore base stations (BSs).The former generally provides limited transmission rate while the latter lacks wide-area coverage capability.As a result,the development of current MCN lags far behind the terrestrial fifth-generation (5G) network.

Proposal for valleytronic materials:Ferrovalley metal and valley gapless semiconductor

Valleytronic materials can provide new degrees of freedom to future electronic devices.In this work,the concepts of the ferrovalley metal(FVM)and valley gapless semiconductor(VGS)are proposed,which can be achieved in valleytronic bilayer systems by electric field engineering.In valleytronic bilayer systems,the interaction between out-of-plane ferroelectricity and A-type antiferromagnetism can induce layer-polarized anomalous valley Hall(LP-AVH)effect.The K and−K valleys of FVM are both metallic,and electron and hole carriers simultaneously exist.In the extreme case,the FVM can become VGS by analogizing spin gapless semiconductor(SGS).Moreover,it is proposed that the valley splitting enhancement and valley polarization reversal can be achieved by electric field engineering in valleytronic bilayer systems.Taking the bilayer RuBr_(2)as an example,our proposal is confirmed by the first-principle calculations.The FVM and VGS can be achieved in bilayer RuBr_(2)by applying electric field.With appropriate electric field range,increasing electric field can enhance valley splitting,and the valley polarization can be reversed by flipping electric field direction.To effectively tune valley properties by electric field in bilayer systems,the parent monolayer should possess out-of-plane magnetization,and have large valley splitting.Our results shed light on the possible role of electric field in tuning valleytronic bilayer systems,and provide a way to design the ferrovalley-related material by electric field.

Physics of electron emission and injection in two-dimensional materials: Theory and simulation

Electrically contacting two-dimensional(2D)materials is an inevitable process in the fabrication of devices for both the study of fundamental nanoscale charge transport physics and the design of high-performance novel electronic and optoelectronic devices.The physics of electrical contact formation and interfacial charge injection critically underlies the performance,energyefficiency and the functionality of 2D-material-based devices,thus representing one of the key factors in determining whether 2D materials can be successfully implemented as a new material basis for the development of nextgeneration beyond-silicon solid-state device technology.In this review,the recent developments in the theory and the computational simulation of electron emission,interfacial charge injection and electrical contact formation in 2D material interfaces,heterostructures,and devices are reviewed.Focusing on thermionic charge injection phenomena which are omnipresent in 2Dmaterials-based metal/semiconductor Schottky contacts,we summarize various transport models and scaling laws recently developed for 2D materials.Recent progress on the first-principle density functional theory simulation of 2D-material-based electrical contacts are also reviewed.This review aims to provide a crystalized summary on the physics of charge injection in the 2D Flatlands for bridging the theoretical and the experimental research communities of 2D material device physics and technology.

Anomalously enhanced thermal stability of phosphorene via metal adatom doping: An experimental and first-principles study

Atomically thin black phosphorus, also known as phosphorene, is an emerging two-dimensional (2D) material, which has attracted increasing attention due to its unique electronic and optoelectronic properties. However, the reduced thermal stability of phosphorene limits its suitability for high-temperature fabrication processes, which could be detrimental for the performance of phosphorenebased devices. Here, we investigate the impact of doping by Al and Hf transition metal adatoms on the thermal stability of phosphorene. The formation of Al–P covalent bonds was found to significantly improve the thermal coefficients of the Ag1, B2g, and Ag2phonon modes to 0.00044, 0.00081, and 0.00012 cm–1·°C–1, respectively, which are two orders of magnitude lower than those observed for pristine P–P bonds (~0.01 cm–1·°C–1). First-principles calculations within the density functional theory framework reveal that the observed thermal stability enhancement in the Al-doped material reflects a significantly higher Al binding energy, due to the stronger Al–P bonds compared to the weak van der Waals interactions between adjacent P atoms in the undoped material. The present work thus paves the way towards phosphorene materials with improved structural stability, which could be promising candidates for potential nanoelectronic and optoelectronic applications. [Figure not available: see fulltext.] © 2016, Tsinghua University Press and Springer-Verlag Berlin Heidelberg.

Machine learning for intrusion detection in industrial control systems:challenges and lessons from experimental evaluation

Gradual increase in the number of successful attacks against Industrial Control Systems(ICS)has led to an urgent need to create defense mechanisms for accurate and timely detection of the resulting process anomalies.Towards this end,a class of anomaly detectors,created using data-centric approaches,are gaining attention.Using machine learning algorithms such approaches can automatically learn the process dynamics and control strategies deployed in an ICS.The use of these approaches leads to relatively easier and faster creation of anomaly detectors compared to the use of design-centric approaches that are based on plant physics and design.Despite the advantages,there exist significant challenges and implementation issues in the creation and deployment of detectors generated using machine learning for city-scale plants.In this work,we enumerate and discuss such challenges.Also presented is a series of lessons learned in our attempt to meet these challenges in an operational plant.

Empowering magnetic strong coupling and its application for nonlinear refractive index sensing

In the context of quantum strong coupling,the magnetic dipole(MD)emitters are largely overlooked due to the rarity of MD source and the non-magnetic nature of matters at high frequencies.Based on a semi-classic model,we theoretically demonstrate magnetic strong coupling between an MD cluster(Er^(3+):4 I13/2→4 I15/2 transition at 1,550 nm)and an antenna-in-cavity structure.It is found that placing the plasmonic diabolo/s-diabolo nanoantenna,which supports strong electric/magnetic dipole mode,inside a dielectric cavity could largely improve the strong coupling coefficient while suppressing the cavity loss rate compared to the bare nanoantenna counterparts,empowering the magnetic quantum strong coupling at a level of 104 emitters,which is remarkable considering the weak MD dipole momentum and small hotspot region at high frequency.Furthermore,the two Rabi resonance branches undergo highly asymmetrical changes upon a small variation on the environmental refractive index,which leads to an exotic exponential sensitivity profile by tracing the ratio between the two resonances widths.The proposed magnetic strong coupling for nonlinear refractive index sensing may add a new category to quantum plasmonic sensors.

A scheme for simulating multi-level phase change photonics materials

Chalcogenide phase change materials(PCMs)have been extensively applied in data storage,and they are now being proposed for high resolution displays,holographic displays,reprogrammable photonics,and all-optical neural networks.These wide-ranging applications all exploit the radical property contrast between the PCMs’different structural phases,extremely fast switching speed,long-term stability,and low energy consumption.Designing PCM photonic devices requires an accurate model to predict the response of the device during phase transitions.Here,we describe an approach that accurately predicts the microstructure and optical response of phase change materials during laser induced heating.The framework couples the Gillespie Cellular Automata approach for modelling phase transitions with effective medium theory and Fresnel equations.The accuracy of the approach is verified by comparing the PCM’s optical response and microstructure evolution with the results of nanosecond laser switching experiments.We anticipate that this approach to simulating the switching response of PCMs will become an important component for designing and simulating programmable photonics devices.The method is particularly important for predicting the multi-level optical response of PCMs,which is important for all-optical neural networks and PCM-programmable perceptrons.

Resistance modulation in Ge2Sb2Te5

Chalcogenide based phase change random access memory(PCRAM) holds great promise for high speed and large data storage applications.This memory is scalable,requires a low switching energy,has a high endurance,has fast switching speed,and is nonvolatile.However,decreasing the switching time whilst increasing the cycle endurance is a key challenge for this technology to replace dynamic random access memory.Here we demonstrate high speed and high endurance ultrafast transient switching in the SET state of a prototypical phase change memory cell.Volatile switching is modeled by electron-phonon and lattice scattering on short timescales and charge carrier excitation on long timescales.This volatile switching in phase change materials enables the design of hybrid memory modulators and ultrafast logic circuits.