Fast Deployment Research Articles

Fast Deployment of Reliable Distributed Control Planes With Performance Guarantees

Current trends strongly indicate a transition towards large-scale programmable networks with virtual network functions. In such a setting, deployment of distributed control planes will be vital for ...

An Implementation of Electronic Passport Scheme Using Encrypted Security Along with Multiple Biometrics

Within the next year, travellers from dozens of nations may be carrying a new form of a passport. Electronic passports have known a wide and fast deployment all around the world since the International Civil Aviation Organization the world has adopted standards whereby passports can store biometric identifiers.

AND THE MILITARY BASES DURING THE MOVEMENT, DURING THE OPERATION OF INCORPORATED FORCES

The article analyzes the factors that influence the organization of technical support during the movement to take into account the experience of conducting an anti-terrorist operation (the operation of the combined forces) in the Donetsk and Luhansk regions. Changes in the nature of the armed struggle, the development of operations (combat operations), forms and methods of performing combat missions of the Armed Forces of Ukraine, and the further development of a comprehensive supply system necessitate the further development and improvement of the Armed Forces technical support system, rational distribution and use of forces, the purpose of timely restoration of samples of weapons and military equipment, uninterrupted supply of missiles and ammunition, replenishment of military technical facilities and, consequently, obtaining the benefits of the actions of units in a short time. It is known that the movement of troops (forces) is carried out for the purpose of fast and rapid deployment, the creation of appropriate groups of troops (forces) in designated areas (areas) to perform combat missions. Accordingly, the technical support system is entrusted with a number of heterogeneous tasks, the performance of which ensures the success of the movement of troops (forces) and the creation of appropriate groups. In recent times, the issue of technical support for the movement of troops (forces) has received little attention during various kinds of research, and the problems that arise during the organization and maintenance are considered only superficially. It is these issues that have become most urgent in our time, during the conduct of the anti-terrorist operation (the operation of the combined forces) in the territory of Donetsk and Luhansk regions. Maintenance issues, such as maintenance, restoration and maintenance of military-technical assets, are crucial, and the imperfection of management and failures in the organization of the entire process have a negative impact on the level of combat readiness of the troops, and sometimes lead to unjustified human casualties. The analysis of the consequences of organizational and staffing activities carried out in recent years on the reduction of technical support units and their numbers, personal experience of participation in ATO /OCF in the Donetsk and Luhansk regions shows that the technical support system does not fully allow for a full range of relevant measures, to which they rely.

Global Climate Data Processing and Mapping of Degradation Mechanisms and Degradation Rates of PV Modules

Photovoltaic (PV) systems are the cheapest source of electricity in sunny locations and nearly all European countries. However, the fast deployment of PV systems around the world is bringing uncertainty to the PV community in terms of the reliability and long-term performance of PV modules under different climatic stresses, such as irradiation, temperature changes, and humidity. Methodologies and models to estimate the annual degradation rates of PV modules have been studied in the past, yet, an evaluation of the issue at global scale has not been addressed so far. Hereby, we process the ERA5 climate re-analysis dataset to extract and model the climatic stresses necessary for the calculation of degradation rates. These stresses are then applied to evaluate three degradation mechanisms (hydrolysis-degradation, thermomechanical-degradation, and photo- degradation) and the total degradation rate of PV modules due to the combination of temperature, humidity, and ultraviolet irradiation. Further on, spatial distribution of the degradation rates worldwide is computed and discussed proving direct correlation with the Köppen-Geiger-Photovoltaic climate zones, showing that the typical value considered for the degradation rate on PV design and manufacturer warranties (i.e., 0.5%/a) can vary ± 0.3%/a in the temperate zones of Europe and rise up to 1.5%/a globally. The mapping of degradation mechanisms and total degradation rates is provided for a monocrystalline silicon PV module. Additionally, we analyze the temporal evolution of degradation rates, where a global degradation rate is introduced and its dependence on global ambient temperature demonstrated. Finally, the categorization of degradation rates is made for Europe and worldwide to facilitate the understanding of the climatic stresses.

Fast antenna deployment method for multistatic radar with multiple dynamic surveillance regions

In this paper, we consider an optimal antenna deployment problem for distributed multistatic radar under the situation of multiple surveillance regions in a dynamic environment. The problem is solved by proposing a dynamic multi-objective particle swarm optimization (DMOPSO) with the assistance of the proposed Kalman filtering prediction strategy. Firstly, we construct a dynamic multi-objective optimization problem for antenna deployment by choosing the effective coverage rates of different non-fixed surveillance regions as objective functions. Then, to avoid the unnecessary computational load for solving optimization problem and improve the real-time property of antenna deployment, a DMOPSO algorithm based on the Kalman filtering prediction strategy is proposed by extracting the relationships among adjacent optimization time segments. Finally, numerical results are provided to verify the validity of the proposed DMOPSO algorithm.

Self-supervised monocular image depth learning and confidence estimation

We present a novel self-supervised framework for monocular image depth learning and confidence estimation. Our framework reduces the amount of ground truth annotation data required for training Convolutional Neural Networks (CNNs), which is often a challenging problem for the fast deployment of CNNs in many computer vision tasks. Our DepthNet adopts a novel fully differential patch-based cost function through the Zero-Mean Normalized Cross Correlation (ZNCC) to take multi-scale patches as matching and learning strategies. This approach greatly increases the accuracy and robustness of the depth learning. Whilst the proposed patch-based cost function naturally provides a 0-to-1 confidence, it is then used to self-supervise the training of a parallel network for confidence map learning and estimation by exploiting the fact that ZNCC is a normalized measure of similarity which can be approximated as the confidence of the depth estimation. Therefore, the proposed corresponding confidence map learning and estimation operate in a self-supervised manner and is a parallel network to the DepthNet. Evaluation on the KITTI depth prediction evaluation dataset and Make3D dataset show that our method outperforms the state-of-the-art results.

Abstract 208: A Comprehensive Simulation-Based Training for E-CPR Improves the Neurological Outcome in Patients With Refractory Out-Of-Hospital Cardiac Arrest

Background: Prolonged conventional cardiopulmonary resuscitation (C-CPR) is associated with a poor prognosis in out-of-hospital cardiac arrest (OHCA) patients. Extracorporeal cardiopulmonary resuscitation (E-CPR) has been utilized as a rescue strategy for patients with cardiac arrest unresponsive to C-CPR. However, the indication and optimal duration to switch from C-CPR to E-CPR are not well established. In addition, the opportunities to develop teamwork skills and expertise to mitigate risks are few. We thus developed the implementation protocol for the E-CPR simulation program, and investigated whether the faster deployment of extracorporeal membrane oxygenation (ECMO) improves the neurological outcome in patients with refractory OHCA. Methods: A total of 42 consecutive patients (age 58±16 years, male ratio 90%, and initial shockable rhythm 64%) received E-CPR (3% of OHCA) during the study period. Among them, 32 (76%) were deployed ECMO during the pre-intervention time period (Pre: from January 2012 to September 2017), whereas 10 (24%) were deployed during the post-intervention time period (Post: October 2017 to May 2019). We compared the door to E-CPR time, collapse to E-CPR time, 30-day mortality, and favorable neurological outcome (Cerebral Performance Categories 1, 2) between the two periods. Results: There was no significant difference in age, the rates of male sex and shockable rhythm, and the time form collapse to emergency room admission between the two periods. The door to E-CPR time and the collapse to E-CPR time were significantly shorter in the post-intervention period compared to the pre-intervention period (Pre: 39 min [IQR; 30-50] vs. Post: 29 min [IQR; 22-31]; P=0.007, Pre: 76 min [IQR; 58-87] vs. Post: 59 min [IQR; 44-68]; P=0.02, respectively). The 30-day mortality was similar between the two periods (Pre: 88% vs. Post: 80%; P=0.6). In contrast, the rate of favorable neurological outcome at the time of discharge was significantly higher in post-intervention period (Pre: 0% vs. Post: 20%; P=0.01) compared to the pre-intervention period. Conclusion: A comprehensive simulation-based training for E-CPR seems to improve the neurological outcome in patients with refractory OHCA patients.

The Need for and Path to Harmonized Life Cycle Assessment and Techno‐Economic Assessment for Carbon Dioxide Capture and Utilization

The use of carbon dioxide as a feedstock for a broad range of products can help mitigate the effects of climate change through long‐term removal of carbon or as part of a circular carbon economy. Research on capture and conversion technologies has intensified in recent years, and the interest in deploying these technologies is growing fast. However, sound understanding of the environmental and economic impacts of these technologies is required to drive fast deployment and avoid unintended consequences. Life cycle assessments (LCAs) and techno‐economic assessments (TEAs) are useful tools to quantify environmental and economic metrics; however, these tools can be very flexible in how they are applied, with the potential to produce significantly different results depending on how the boundaries and assumptions are defined. Built on ISO standards for generic LCAs, several guidance documents have emerged recently from the Global CO2 Initiative, the National Energy Technology Laboratory, and the National Renewable Energy Laboratory that further define assessment specifications for carbon capture and utilization. Overall agreement in the approaches is noted with differences largely based on the intended use cases. However, further guidance is needed for assessments of early‐stage technologies, reporting details, and reporting for policymakers and nontechnical decision‐makers.

Constrained Kalman Filter for Identification of Semiphysical Building Thermal Models

Model-based energy management of buildings through a model predictive control framework proved to be the promising solution for improving the energy efficiency of the building sector. The keystone for further improvements and real implementation is to have reliable and accurate mathematical models that are simple enough to be used in real-time control. In the work presented, a general identification framework for acquiring a control-oriented semiphysical thermal model of a building based on a modified constrained unscented Kalman filter algorithm is proposed. Experiments performed on a living-lab skyscraper building show that the adopted algorithm applied to short-term operation data outperforms the standard Kalman filter forms, both in convergence rate and numerical stability. The proposed method thus paves the way to the fast deployment of model-based energy management strategies in buildings.

Join trajectory optimization and communication design for UAV-enabled OFDM networks

Due to the advantages of high mobility, flexible maneuverability and fast deployment, Unmanned Aerial Vehicle (UAV), which can be usually served as aerial communication platform, not only supports information transmission in the Internet of things (IoT), but also provides reliable power supplement for low-power wireless devices. Considering an UAV-enabled Orthogonal Frequency Division Multiplexing (OFDM) network where subcarriers are divided into two groups for information transmission and energy harvesting, respectively, a joint UAV trajectory optimization and communication design scheme is proposed based on Simultaneous Wireless Information and Power Transfer (SWIPT) technology. Under the given average harvested energy for users, the objective of the scheme is to maximize the average achievable rate for all users by jointly optimizing UAV trajectory, user scheduling, subcarrier and power allocation. To solve the formulated optimization problem, it is transformed into two subproblems to optimize resource allocation and UAV trajectory, respectively. The former is a mixed integer non-convex optimization problem. To solve it, a three-variable alternative iteration algorithm is proposed to obtain the optimal user scheduling, subcarrier and power allocation. Since the latter is non-convex, it can be transformed into convex optimization problem by relaxing objective function and constraint to obtain the optimal trajectory. Simulation results demonstrate that the proposed algorithm has good convergence and the proposed UAV-enabled OFDM network has better performance.

Performance Analysis of Microservice Design Patterns

Microservice-based solutions are currently gaining momentum because they do not have the disadvantages of traditional monolithic architectures. Business interest in microservices is increasing since the microservice architecture brings a lightweight, independent, reuse-oriented, and fast service deployment approach that minimizes infrastructural risks. This approach is at an early stage of its development, and in view of this, it is important to understand the performance of its design patterns. In this article, we obtained performance results related to query response time, efficient hardware usage, hosting costs, and packet-loss rate, for three microservice design patterns practiced in the software industry.

Optimal container resource allocation in cloud architecture: A new hybrid model

A huge variety of fields and industries depend upon cloud computing based microservice due to its high-performance capability. Also, the merit of container usage is enormous; it enable larger portability, easier and faster deployment and restricted overheads. However, the rapid evolution causes issues in terms of container automation and management, Till now, a number of research works has concentrated on solving the open issues in container automation and management. In fact, container resource allocation is the major key hole for cloud providers since it directly influences the resource consumption and system performance. In this manner, this paper introduces a new optimized container resource allocation model by proposing a new optimization concept. To make the possibility of optimal container resource allocation, a new hybridized algorithm is implanted; namely, Whale Random update assisted Lion Algorithm (WR-LA), which is the hybrid form of Lion Algorithm (LA) and Whale Optimization Algorithm (WOA) is introduced. Moreover, the solution of optimized resource allocation is made by considering objectives like Threshold Distance, Balanced Cluster Use, System Failure, and Total Network Distance, respectively. Finally, the performance of the proposed model is compared over other conventional models and proves its superiority.

N2SkyC: User Friendly and Efficient Neural Network Simulation Fostering Cloud Containers

Sky computing is a new computing paradigm leveraging resources of multiple Cloud providers to create a large scale distributed infrastructure. N2Sky is a research initiative promising a framework for the utilization of Neural Networks as services across many Clouds. This involves a number of challenges ranging from the provision, discovery and utilization of services to the management, monitoring, metering and accounting of the infrastructure. Cloud Container technology offers fast deployment, good portability, and high resource efficiency to run large-scale and distributed systems. In recent years, container-based virtualization for applications has gained immense popularity. This paper presents the new N2SkyC system, a framework for the utilization of Neural Networks as services, aiming for higher flexibility, portability, dynamic orchestration, and performance by fostering microservices and Cloud container technology.

Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds

With their fast development and deployment, a large number of cloud services provided by distributed cloud data centers have become the most important part of Internet services. In spite of numerous benefits, their providers face some challenging issues, e.g., dynamic resource scaling and power consumption. Workload prediction plays a crucial role in addressing them. Accuracy and fast learning are the key performances. Its consistent efforts have been made for their improvement. This paper proposes an integrated prediction method that combines the Savitzky–Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In this approach, a task time series is first smoothed by the SG filter, and the smoothed one is then decomposed into multiple components via wavelet decomposition. Based on them, an integrated model is, for the first time, established and can well characterize the statistical features of both trend and detailed components. Experimental results demonstrate that it achieves better prediction results and faster learning speed than some representative prediction methods. Note to Practitioners —Workload prediction plays an important role in constructing scalable and green distributed cloud data centers. This paper presents a novel and fundamental methodology to achieve accuracy and fast learning for workload prediction. It develops an integrated prediction approach that combines the Savitzky–Golay filter and wavelet decomposition with stochastic configuration networks to predict workload at the next time slot. In order to establish a fine prediction model for the obtained information while achieving better prediction results and faster learning speed, this paper proposes an integrated method, SGW-S, to build a prediction model of a task time series and determine its optimal model parameters. The experimental results in the real-world data set show that the proposed method outperforms baseline methods in predicting the large-scale task time series. The proposed approach can aid the design and optimization of industrial cloud data centers and practitioners’ prediction of different types of task time series.

Industrial Automation using Zigbee Communication Protocol

If we compare wireless communication over wired communication, wireless communication offer more advantages when compared to wired communication such as lower cost, fast deployment, higher flexibility & scalability and mobile nature of system communicated wirelessly. In Industrial automation, industrial communication has very challenging requirements like packet deadline, low transmission jitter, etc. In some places wired communication is only accepted and it cannot be replaced by wireless communication. Industrial applications also run more flexible requirements applications such as email, Video content or any other application. Those services are known as Best Effort (BE) services. In order to do both the industrial application and BE services we have proposed Zigbee communication together with the IEEE 802.11 standard in this article along with comparison between the two standards using physical layer solutions. This Zigbee communication is performed using a industrial automation design and it leads to less power consumption. Result and analysis in terms of real time services is left as a future work in this paper. It is proposed that through Zigbee solution it is possible to obtain better result in certain cases than those achieved using IEEE 802.11 standards.

A Methodology to Alleviation of Security Attack in Software Defined Network

Computer Network is a collaboration platform for digital gadgets. Virtualization is abstracting layers from underlying Architecture. Virtualization platform provides flexibility, green resource usage and fast deployment of the carrier. Integration of Computer Network and Virtualization Platform Create Network Virtualization Platform. Software-Defined Network (SDN) Provide dynamic and scalable networking offerings to Business Environments Cloud Network and Data Centers. SDN is controlling network infrastructure with the control plane. Security is a challenging problem for the network. SDN comes with Network Programmability and automation. SDN has an advantage, but it contains a new safety issue. SDN is focused on network safety data plane and control. Security Challenges in SDN is Control Plane, Data Plane Attacks. In Controller Layer Attacker at controller Layer, an routing, visitors filtering denying, or allowing unnecessary site visitors. Control plane assault used to drop or lack of manage over traffic policy and Quality of carrier control. Data Plane Attack Attacker can create DOS Denial of Service.

EHealth Cloud Security Challenges: A Survey.

Cloud computing is a promising technology that is expected to transform the healthcare industry. Cloud computing has many benefits like flexibility, cost and energy savings, resource sharing, and fast deployment. In this paper, we study the use of cloud computing in the healthcare industry and different cloud security and privacy challenges. The centralization of data on the cloud raises many security and privacy concerns for individuals and healthcare providers. This centralization of data (1) provides attackers with one-stop honey-pot to steal data and intercept data in-motion and (2) moves data ownership to the cloud service providers; therefore, the individuals and healthcare providers lose control over sensitive data. As a result, security, privacy, efficiency, and scalability concerns are hindering the wide adoption of the cloud technology. In this work, we found that the state-of-the art solutions address only a subset of those concerns. Thus, there is an immediate need for a holistic solution that balances all the contradicting requirements.

Formula omitted]-Hub: Large-scale video learning, storage, and retrieval on heterogeneous hardware platforms

The burgeoning of Internet of Things (IoT) and camera-equipped mobile devices contributes a tremendous amount of video data generated at the edge of the network. At the same time, we have witnessed the fast deployment of many video-based application services, such as plate recognition for public safety, intelligent transportation, Industry 4.0 and so on. The success of these services, in turn, requires large-scale video data being learned, stored, and retrieved in a more efficient way. A generic software and hardware framework for large-scale IoT video analysis and service support is still missing. To address this challenge, we present π-Hub, PerceptIn’s robotic cloud solution which supports large-scale video data analysis, storage, and query by implementing the learn-store-retrieve paradigm. Interestingly, we found that among the learning, storage, and retrieval services each of them stresses one type of resources on heterogeneous computing servers, i.e., GPU, CPU, and Memory, respectively, therefore it is extremely cost-efficient to co-locate these services together to fully utilize the resources. In addition, several optimization techniques for data writing, reading, and data reduction are proposed and evaluated. The evaluation results show that these techniques improve the performance of the learning, storage and retrieval services significantly as well as notably reduce the cost of the system. We also verify π-Hub’s scalability by reliably running a 1000-machine deployment to support up to one million users. Finally, we conclude the paper by discussing several lessons learned from this study and future work.

Triple bottom line analysis of the Spanish solar photovoltaic sector: A footprint assessment

Sustainable development, in its wider sense, i.e., economic, social and environmental, has emerged as one of the key challenges for humankind in the 21st century. Solar photovoltaic (PV) emerges as a key technology to meet not only the climate targets but also those related to social progress and economic growth. This paper's main objective is to capture the momentum and potentialities of the Spanish Solar PV sector using a Triple Bottom Line (TBL) analysis from a broad economic, social and environmental footprint perspective in an MRIO context in 2016.Regarding the economic impact, we find that the Spanish Solar PV sector accounts for 0.19% and 0.31% of GDP in direct and in total terms, respectively; besides, 60% of indirect GDP is created within Spanish economy and 40% is generated abroad, while 86% of induced GDP is generated within domestic economy. By activity, PV manufactures is the only one to generate most of its indirect GDP abroad (68%). In social terms, Solar PV activities are responsible for 5904 direct jobs and 18,377 in total in Spain in 2016, and the most important activity is energy production and distribution (50.2% of total generated employment). In environmental terms, the footprint of the whole Solar PV industry accounts for only 734 ktCO2, which helps to avoid a great volume of direct emissions in electricity generation, with more than 69% generated beyond Spain's borders.With regard to the potentialities of the Spanish Solar PV sector, and given the forecasted growing deployment of Solar PV in Spain by 2030 and 2050, this industry is set to be one of the leading sectors of the Spanish sustainability transition in the near future. Current policy requires the fast deployment of Solar PV throughout the country to ensure more sustainability-friendly economic, social and environmental development.

Support for biogas in the EU electricity sector – A comparative analysis

This paper aims to present a comparative analysis of biogas electricity deployment in EU countries over the period of 2010–2017. The analysis shows that the deployment of biogas electricity is mainly linked to the moment of the shift in the support framework, the maturity of tenders and the feasibility of long-term objectives. With 200 incentives in place the EU has created favourable conditions to support biogas electricity. Manure and waste are the main feedstocks of biogas electricity in the EU that receive the highest incentives. However, the recent shift towards capacity market mechanism has had an adverse effect in the deployment of biogas electricity. The way in which the biogas is currently valorised, has not been very effective in some EU countries. We found that the EU average growth rate of biogas electricity over 2016–2017 dropped 4 times compared with 2014–2016. Plans/targets have been very supportive in the fast deployment of biogas electricity. Nevertheless, a clear post 2020 picture for renewables is still missing in several EU countries that are risking lagging in biogas electricity deployment. The future of biogas is been seen promising on the upgrade to biomethane as a sustainable input for environment and economy.