Massive Data Transfer Research Articles

Spin‐Transfer‐Torque Magnetic Tunnel Junction Nonlinear In‐Sensor Computing Synapse for Improving the Performance of the Feedforward Neural Network

In‐sensor computing architecture has a great advantage especially in massive data sampling, transfer, and processing compared with the separated intelligent sensor systems. However, most of the in‐sensor computing device is proposed based on the traditional neural network model, where the synapse performs linear multiplication of input and weight. This approach fails to make the most use of the nonlinearity of in‐sensor computing devices. Therefore, in this article, first a modified feedforward neural network model with the nonlinear in‐sensor computing synapse (NSCS) located at the input layer is presented, and the backpropagation (BP) algorithm is modified to train the network. Then, the nonlinear characteristics of the NSCS composed of the spin‐transfer‐torque magnetic tunnel junction (STT–MTJ) devices and simple complementary metal‐oxide‐semiconductor (CMOS) circuit are analyzed. Based on the nonlinear response of STT–MTJ NSCS, the small‐scale network with NSCS synapse is experimented on the Modified National Institute of Standards and Technology dataset and compared with the traditional network of the same network size. In the simulation result, it is shown that better performance can be achieved with the STT–MTJ NSCS, including a 2–15 times improvement in convergence speed and a 2.5%–5.1% increase in accuracy.

A futuristic green service computing approach for smart city: A fog layered intelligent service management model for smart transport system

With the widespread adoption of technologies like the Internet of Things (IoT), context awareness, and decentralized computing at the edge of the network, service delivery in smart city parlance has been rapidly evolved. Management of Information and Communication Technologies (ICT) infrastructure for such dynamic environment at scale brings about new challenges. Existing fog layer resource management involves context-sharing and migration for real-time vertical and cross-vertical services. However, improper context migration might affect performance negatively. In this paper, the authors have envisioned improving the Quality of Service (QoS) of smart transportation while employing context-aware computing and Artificial Intelligence, which helps alleviate massive data transfers among Fog nodes and intelligent vehicles in real-time. The proposed Context-Aware Intelligent Transportation System (CAITS) manages the services of intelligent vehicles and manages the road traffic of traditional vehicles in an effective manner, with a three-layered learning model that accounts for on-vehicle, on-co-vehicle, and on-fog-and-vehicle learning by means of a platoon control algorithm as well as federated learning at the Fog level. Simulations are carried out on CloudSim simulator under different scenarios and the results demonstrate that the proposed scheme improves prediction efficacy of contexts at the Fog layer by approximately 8%–24% than existing models which in turn reflects in reduced service time and energy consumption of EVs and reduces the CO2 of environment.

A New Fog-Based Transmission Scheduler on the Internet of Multimedia Things Using a Fuzzy-Based Quantum Genetic Algorithm

The Internet of Multimedia Things (IoMT) has recently experienced a considerable surge in multimedia-based services. Due to the fast proliferation and transfer of massive data, the IoMT has service quality challenges. This paper proposes a novel fog-based multimedia transmission scheme for IoMT using the Sugano interference system with a quantum genetic optimization algorithm. The fuzzy system devises a mathematically organized strategy for generating fuzzy rules from input and output variables. The Quantum Genetic Algorithm (QGA) is a metaheuristic algorithm that combines genetic algorithms and quantum computing theory. It combines many critical elements of quantum computing, such as quantum superposition and entanglement. This provides a robust representation of population diversity and the capacity to achieve rapid convergence and high accuracy. As a result of the simulations and computational analysis, the proposed fuzzy-based QGA scheme improves packet delivery ratio and throughput by reducing end-to-end latency and delay when compared to traditional algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Heterogeneous Earliest-Finish-Time (HEFT) and Ant Colony Optimization (ACO). Consequently, it provides a more efficient scheme for multimedia transmission in IoMT.

Towards progressive geospatial information processing on web systems: a case study for watershed analysis in Iowa

Geographic Information Systems (GIS) are available as stand-alone desktop applications as well as web platforms for vector- and raster-based geospatial data processing and visualization. While each approach offers certain advantages, limitations exist that motivate the development of hybrid systems that will increase the productivity of users for performing interactive data analytics using multidimensional gridded data. Web-based applications are platform-independent, however, require the internet to communicate with servers for data management and processing which raises issues for performance, data integrity, handling, and transfer of massive multidimensional raster data. On the other hand, stand-alone desktop applications can usually function without relying on the internet, however, they are platform-dependent, making distribution and maintenance of these systems difficult. This paper presents RasterJS, a hybrid client-side web library for geospatial data processing that is built on the Progressive Web Application (PWA) architecture to operate seamlessly in both Online and Offline modes. A packaged version of this system is also presented with the help of Web Bundles API for offline access and distribution. RasterJS entails the use of latest web technologies that are supported by modern web browsers, including Service Workers API, Cache API, IndexedDB API, Notifications API, Push API, and Web Workers API, in order to bring geospatial analytics capabilities to large-scale raster data for client-side processing. Each of these technologies acts as a component in the RasterJS to collectively provide a similar experience to users in both Online and Offline modes in terms of performing geospatial analysis activities such as flow direction calculation with hydro-conditioning, raindrop flow tracking, and watershed delineation. A large-scale case study is included in the study for watershed analysis to demonstrate the capabilities and limitations of the library. The framework further presents the potential to be utilized for other use cases that rely on raster processing, including land use, agriculture, soil erosion, transportation, and population studies.

SAW Resonators and Filters Based on Sc0.43Al0.57N on Single Crystal and Polycrystalline Diamond.

The massive data transfer rates of nowadays mobile communication technologies demand devices not only with outstanding electric performances but with example stability in a wide range of conditions. Surface acoustic wave (SAW) devices provide a high Q-factor and properties inherent to the employed materials: thermal and chemical stability or low propagation losses. SAW resonators and filters based on synthetized by reactive magnetron sputtering on single crystal and polycrystalline diamond substrates were fabricated and evaluated. Our SAW resonators showed high electromechanical coupling coefficients for Rayleigh and Sezawa modes, propagating at 1.2 GHz and 2.3 GHz, respectively. Finally, SAW filters were fabricated on /diamond heterostructures, with working frequencies above 4.7 GHz and ~200 MHz bandwidths, confirming that these devices are promising candidates in developing 5G technology.

QoS and Privacy-Aware Routing for 5G-Enabled Industrial Internet of Things: A Federated Reinforcement Learning Approach

The development and maturity of the fifth-generation (5G) wireless communication technology provides the industrial Internet of Things (IIoT) with ultra-reliable and low-latency communications and massive machine-type communications, and forms a novel IIoT architecture, 5G-IIoT. However, massive data transfer between interconnecting industrial devices also brings new challenges for the 5G-IIoT routing process in terms of latency, load balancing, and data privacy, which affect the development of 5G-IIoT applications. Moreover, the existing research works on IIoT routing mostly focus on the latency and the reliability of the routing, disregarding the privacy security in the routing process. To solve these problems, in this article, we propose a quality of service (QoS) and data privacy-aware routing protocol, named QoSPR, for 5G-IIoT. Specifically, we improve the community detection algorithm info-map to divide the routing area into optimal subdomains, based on which the deep reinforcement learning algorithm is applied to build the gateway deployment model for latency reduction and load-balancing improvement. To eliminate areal differences, while considering the privacy preservation of the routing data, the federated reinforcement learning is applied to obtain the universal gateway deployment model. Then, based on the gateway deployment, the QoS and data privacy-aware routing is accomplished by establishing communications along the load-balancing routes of the minimum latencies. The validation experiment is conducted on real datasets. The experiment results show that as a data privacy-aware routing protocol, the QoSPR can significantly reduce both average latency and maximum latency, while maintaining excellent load balancing in 5G-IIoT.

Computing-in-memory with thin-filmtransistors: challenges and opportunities

Thin-film transistors (TFTs) have attracted significant interest recently fortheir great potential in a wide range of edge computing applications, due to their advantages such as large-area low-cost flexible fabrications, and well integration with sensors and displays. With the support of in situ processing of sensor data, TFT-based edge systems show their advantages in large-scale dense sensing with real-time energy-efficient processing and interaction, and more excitingly, they provide the opportunity to eliminate the massive data transfer to the cloud servers. However, the design of high-performance processing modules based on TFT is difficult, due to large device variation, poor stability, and low mobility. Computing-in-memory (CiM), which has been proposed recently as a high-efficiency high-parallelism computing approach, is expected to improve the capacity of TFT-based edge computing systems. In thispaper, various recent works on TFT-based CiM have been summarized, showing the superiority to conventional processing flow by efficient in-memory analog computation with mitigation of data transfer, and reduced analog-to-digital converter usage for sensor data. With both opportunities and challenges, the design space and trend of TFT-based CiM to be explored are then described. Finally, further development and co-optimization from device to system are discussed for the flourishing of the next-generation intelligent TFT-based edge system.

Computing-in-Memory with Thin-Film-Transistors: Challenges and Opportunities

Abstract Thin-film transistor (TFT) has attracted enormous interests recently for its great potential in a wide range of edge computing applications, benefitting from its large-area low-cost flexible fabrications, and well integration with sensors and displays. With the support of in-situ processing of sensor data, TFT-based edge systems show their advantages in large-scale dense sensing with real-time energy-efficient processing and interaction, and more excitingly, it provides the opportunity of eliminating the massive data transfer to the cloud servers. However, the design of high-performance processing modules based on TFT is difficult, due to large device variation, poor stability, and low mobility. Computing-in-memory (CiM), which has been proposed recently as a high-efficiency high-parallelism computing approach, is expected to improve the capacity of TFT-based edge computing systems. In this survey, various recent works on TFT-based CiM have been summarized, showing the superiority to conventional processing flow by efficient in-memory analog computation with mitigation of data transfer, and reduced ADC usage for sensor data. With both opportunities and challenges, the design space and trend of TFT-based CiM to be explored are then described. Finally, further development and co-optimization from device to system are discussed for flourishing of the next-generation intelligent TFT-based edge system.

Enhanced remote astronomical archive system based on the file-level Unlimited Sliding-Window technique

Data archiving is one of the most critical issues for modern astronomical observations. With the development of a new generation of radio telescopes, the transfer and archiving of massive remote data have become urgent problems to be solved. Herein, we present a practical and robust file-level flow-control approach, called the Unlimited Sliding-Window (USW), by referring to the classic flow-control method in the TCP protocol. Based on the USW and the Next Generation Archive System (NGAS) developed for the Murchison Widefield Array telescope, we further implemented an enhanced archive system (ENGAS) using ZeroMQ middleware. The ENGAS substantially improves the transfer performance and ensures the integrity of transferred files. In the tests, the ENGAS is approximately three to twelve times faster than the NGAS and can fully utilize the bandwidth of network links. Thus, for archiving radio observation data, the ENGAS reduces the communication time, improves the bandwidth utilization, and solves the remote synchronous archiving of data from observatories such as Mingantu spectral radioheliograph. It also provides a better reference for the future construction of the Square Kilometer Array (SKA) Science Regional Center.

Massive Data Transfer From and to Aircraft on Ground: Feasibility and Challenges

The analysis of large amount of sensor data can enhance aircraft operations and maintenance. In contrast to other use cases, aircraft sensor data are accumulated on-board during the flight. The data need to be transferred in a short time while the aircraft is on the ground due to limited and expensive connectivity in the air. This leads to a massive transfer of data in a short time. Therefore, we analyze the data rate demand per aircraft in terms of generated data per flight hour. We determine the influence of different parameters on the needed data rate per aircraft. We conclude that the needed data rates are feasible if the full day is used for transmitting. We show that multiple terabyte of generated data per flight hour can be supported by 5G data rates. Furthermore, we highlight challenges for the deployment of an aircraft data transfer system.

Intelligent Mobile Edge Caching for Popular Contents in Vehicular Cloud Toward 6G

Although vehicles are the most commonly used means of transportation in our daily lives, their computational power and memory resources are often ignored. The new paradigm, Internet of Vehicles, has turned on-road vehicles into intelligent service providers, making full use of on-board processing and storage capabilities. With the advent of 6 G technology, e.g., millimeter wave and terahertz, fast and massive data transfers become possible between moving objects. In this paper, we consider a scenario where vehicles act as mobile edge caching nodes and form a local cloud, to provide popular contents for nearby users via 6 G communication. To maximize the efficiency of content allocation under the limitation of short communication range, we first predict the driving trajectories of vehicles with a Markov chain model and estimate the popularity level of contents in each urban area. Next, we design a scoring system based on the above predictions to compare users' satisfaction and computational efficiency of different algorithms. Finally, we find a subset of vehicles with appropriate content to serve the demand in the area. The simulation experiments demonstrate that the proposed scheme outperforms the conventional methods by providing a cost-efficient content allocation solution with lower delay.

On the necessity of explicit cross-layer data formats in near-data processing systems

Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-Data processing (NDP) and a shift to code-to-data designs may represent a viable solution as packaging combinations of storage and compute elements on the same device has become feasible. The shift towards NDP system architectures calls for revision of established principles. ions such as data formats and layouts typically spread multiple layers in traditional DBMS, the way they are processed is encapsulated within these layers of abstraction. The NDP-style processing requires an explicit definition of cross-layer data formats and accessors to ensure in-situ executions optimally utilizing the properties of the underlying NDP storage and compute elements. In this paper, we make the case for such data format definitions and investigate the performance benefits under RocksDB and the COSMOS hardware platform.

Real-time Optimisation for Industrial Internet of Things (IIoT): Overview, Challenges and Opportunities

Industrial Internet-of-Things (IIoT) with massive data transfers and huge numbers of connected devices, incombination with the high demand for greater quality-of-services, signal processing is no longer producing small data sets but rather, very large ones (measured in gigabytes or terabytes), or even higher. This has posed critical challenges in the context of optimisation. Communication scenarios such as online applications come with the need for real-time optimisation. In such scenarios, often under a dynamic environment, a strict real-time deadline is the most important requirement to be met. To this end, embedded convex optimisation, which can be redesigned and updated within a fast time-scale given sufficient computing power, is a candidate to deal with the challenges in real-time optimisation applications. Real-time optimisation is now becoming a reality in signal processing and wireless networks of IIoT. Research into new technologies to meet future demands is receiving urgent attention on a global scale, especially when 5G networks are expected to be in place in 2020. This work addresses the fundamentals, technologies and practically relevant questions related to the many challenges arising from real-time optimisation communications for industrial IoT.

Cross-MapReduce: Data transfer reduction in geo-distributed MapReduce

The MapReduce model is widely used to store and process big data in a distributed manner. MapReduce was originally developed for a single tightly coupled cluster of computers. Approaches such as Hierarchical and Geo-Hadoop are designed to address geo-distributed MapReduce processing. However, these methods still suffer from high inter-cluster data transfer over the Internet, which is prohibitive for processing today’s globally big data. In line with our thinking that there is no need to transfer the entire intermediate results to a single global reducer, we propose Cross-MapReduce, a framework for geo-distributed MapReduce processing. Before any massive data transfer, our proposed method finds a set of best global reducers to minimize transferred data volumes. We propose a graph called Global Reduction Graph (GRG) to determine the number and the locations of the global reducers. We conducted extensive experimental evaluations using a real testbed to demonstrate the effectiveness of Cross-MapReduce. The experimental results show that Cross-MapReduce significantly outperforms the Hierarchical and Geo-Hadoop approaches and reduces the amount of data transfer over the Internet by 40%.

<u>n</u>KV in action

Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability. Near-data processing (NDP) designs represent a feasible solution, which although not new, has yet to see widespread use. In this paper we demonstrate various NDP alternatives in <u>n</u>KV, which is a key/value store utilizing <u>n</u>ative computational storage and <u>n</u>ear-data processing. We showcase the execution of classical operations ( GET, SCAN ) and complex graph-processing algorithms ( Betweenness Centrality ) in-situ, with 1.4x-2.7x better performance due to NDP. <u>n</u>KV runs on real hardware - the COSMOS+ platform.

Stream cipher designs: a review

Stream cipher is an important branch of symmetric cryptosystems, which takes obvious advantagesin speed and scale of hardware implementation. It is suitable for using in the cases of massive data transfer or resource constraints, and has always been a hot and central research topic in cryptography.With the rapid development of network and communication technology, cipher algorithms play more and more crucial role in information security. Simultaneously, the application environment of cipher algorithms is increasingly complex, which challenges the existing cipher algorithms and calls for novel suitable designs.To accommodate new strict requirements and provide systematic scientific basis for future designs, this paper reviews the development history of stream ciphers, classifies and summarizes the design principles of typical stream ciphers in groups, briefly discusses the advantages and weakness of various stream ciphers in terms of security and implementation. Finally, it tries to foresee the prospective design directions of stream ciphers.

Bandwidth allocation for communicating virtual machines in cloud data centers

High-performance computing in a cloud environment may require massive data transfer among some of the virtual machines (VMs). These VMs are deployed in physical machines (hosts) of a data center. The data transfer among the communicating VMs may use the same shared communication links of the data center. Hence, it is important to have efficient bandwidth allocation policies for different data transfer requests (DTRs) which result in better utilization of bandwidth and fair allocation among the DTRs. In this paper, a few bandwidth allocation policies are proposed and their performances are analyzed. While designing these policies, the objective is the maximization of throughput and bandwidth utilization while minimizing the service time and turnaround time. Some of the policies are based on integer linear programming (ILP) which runs in exponential time while others are based on polynomial-time heuristics. Experimental results show that the performances of heuristic-based policies are comparable to those given by ILP-based exponential time policies.

Enhanced sensitivity‐based decentralised framework for real‐time transient stability assessment in bulk power grids with renewable energy resources

With the increasing trend in the integration and deployment of distributed energy resources, the real-time assessment of power system transient stability is becoming more and more challenging. This study presents an enhanced sensitivity-based decentralised transient stability assessment scheme, which first virtually decomposes the large-scale power network into several smaller areas. Then, a new sensitivity-based scheme is developed to derive and quantify the kinetic energy-based stability assessment index. This index allows taking into account the effects of both AC generators and power electronic-based generator units. In addition, thanks to the new sensitivity-based scheme, the transient stability can be assessed in a decentralised manner using only the boundary buses in each segmented area, which relaxes the need for massive data availability and transfer across the network and enables its applicability for online applications in large-scale power systems. Simulation results on the 48-machine IEEE 140-bus and 544-machine 2000-bus synthetic power grid test systems demonstrate the efficacy of the proposed method.

Angle Characterization Radiation Detection of Microstrip Antenna for Short Range Terahertz Communication System

Communication technologies are crucial components to develop smart city systems related to low latency and massive data transfer. The near field communication (NFC) technology has been becoming more popular for close-range data transmissions, such as multimedia data transfer and non-contact payment, and still increasing in term of capacity and data transfer. Increasing carrier frequencies is a solution to fulfill those demands. In wireless communication technology, an antenna is a crucial element to convert electrical signals into electromagnetic waves or vice versa. Theoretically, an antenna dimension is comparable to the considered wavelength. This condition indicates that higher working frequency systems have a smaller antenna size due to its wavelength shorten. Terahertz (THz) spectrum band is promising to apply higher carrier frequencies. However, the antenna design and fabrication are becoming issues because of the tiny size, electronic components availability, and practical complexities. We have designed a microstrip antenna for working frequency of 0.35 THz by combining ground modification to cover distance and data rates up to 10 cm and more than 20 Gbps, respectively. By considering the previous design, this paper discusses different distances and rotation angles to analyze the effectiveness of wave transmission by simulation using CST Microwave Studio. Analysis use received power parameters to identify accepted signals detection possibility. The results show that the received power exceeded the sensitivity boundary from -50° to 40°, which show that the angles also limit the allowable accepted power level. Therefore, THz NFC systems should consider the antenna placement for an optimum condition application.

A Study on the Performance Monitoring and Optimization of a High Speed Network for the Transfer of Massive VLBI Data

A Study on the Performance Monitoring and Optimization of a High Speed Network for the Transfer of Massive VLBI Data