Real-time Cloud Research Articles

Development of Film Production Mobile Learning Application for Android Platform

Over the years, the film has changed as well as the pattern of how audiences watch movies. Youngsters nowadays are very fond of shooting videos and uploading them online. However, mobile learning applications for filmmaking are limited. Therefore, this paper presents the development of a mobile learning application named Film Geek for the Android platform. The objective of this project is to design and develop three main learning modules for filmmaking which are screenwriting, cinematography and directing. The methodology of this project consists of three phases which are requirement identification, system design and development and testing and evaluation which is adapted from the prototyping model. This mobile learning application has been developed using Microsoft Xamarin, a native cross-platform mobile application development framework. Furthermore, Firebase Real-Time cloud database was chosen for storing the application and user’s data. Functional and usability testing has been performed to evaluate the developed mobile learning application. The result of the testing shows that the functionality of the mobile learning application completely works (100%) as expected. Also, the usability test result of this application using SUS instrument achieved 86.3% which is within the acceptability ranges.

RT-TelSurg: Real Time Telesurgery Using SDN, Fog, and Cloud as Infrastructures

This paper proposes a novel and efficient real time network architecture, named RT-TelSurg, for one of the most appealing tactile Internet applications, i.e., Telesurgery. In telesurgery, the patient’s vital signs and status and the required robotic commands during the surgery should be received on time. Otherwise, the life of the patient or the safety of the operation is endangered. Hence, transmitted packets should meet their respective relative deadlines. Software-defined networking is a relatively new architecture for computer and telecommunications networks in which the network control plane is separated from the data plane. One way to achieve real time telesurgery is to employ cloud and fog networks using SDN as infrastructure. By using a real time cloud controller in the SDN as the core and a fog controller on the edge of the network on the master (physician) side, one may satisfy the acceptable level of the timing constraints of the telesurgery. Accordingly, based on the presented architecture, we develop the statistical performance model of our proposed approach, RT-TelSurg. This statistical queuing theory model is designed for critical and non-critical states of the network. Based on these states, network resources are allocated to telesurgery data in both modes, and a computational bottleneck is detected. RT-TelSurg is evaluated according to real time and surgery efficiency parameters. The results show that the average deadline hit ratio is 98.2% in different conditions, which is quite acceptable for telesurgery applications.

An analytical approach to real-time cloud services on IoT-based applications for smart city planning

This paper illustrates the cloud-based services on next generation smart living technology, first by providing the concept of smart technology of the next generation, followed by the wide area of application in this sector. Next, the current generation technological enhancement, IoT (Internet of Things) is brought into the smart living. The entire IoT-based Smart Application sector has then been divided into five categories: Power and Energy Sector, Transport Sector, Healthcare Sector, Retail Sector and Education Sector. Each sector has been analysed in detail with respect to possible real-time cloud services, which can be incorporated into the respective area. Consequently, existing cloud services, current trends, limitations and future scopes have been discussed followed by recommendations in each section. For example, for IoT based application in power and energy sector, contribution includes finding the limitations of cloud service have been found such as unoptimised communication scheme, data complexity, interoperability, cyber security risk and data integrity issue. To address these limitations respectively, a policy to ensure backward and horizontal compatibility, proposal to increase local processing, data compression and prediction, implementation of big data techniques, authentication and encryption, planning and redundancy have been included as recommendation. For other four categories, an intense analysis has been carried out in similar fashion. Finally, the recommendation has been added for each category for the next barrier scope of research.

An analytical approach to real-time cloud services on IoT-based applications for smart city planning

An analytical approach to real-time cloud services on IoT-based applications for smart city planning

A Hybrid Multi-level Statistical Load Balancer-Based Parameters Estimation Model in Realtime Cloud Computing Environment

As the size of the cloud-based applications and its tasks are increasing exponentially, it is necessary to estimate the load balancing metrics in the real-time cloud computing environments. Hybrid load balancing framework play a vital role in the cloud-based applications and tasks monitoring and resource allocation. Most of the conventional load balancing metrics are dependent on limited number of cloud metrics and type of virtual machines. Also, these models require high computational memory and time on large number of tasks. In this paper, an advanced multi-level statistical load balancer-based parameters estimation model is designed and implemented on the real-time cloud computing environment. In this model, a novel statistical load balancing data collector is used to find the best metrics for the load balance computation. In this model, different types of tasks are simulated under different virtual machine types such as small, medium and large instances. Experimental results show that the proposed multi-level based statistical load balancing collector has better efficiency than the conventional models in terms of memory utilization, CPU utilization, runtime and reliability are concerned.

Detection of DDoS Attacks in OpenStack-based Private Cloud Using Apache Spark

Security is a critical concern for cloud service providers. Distributed denial of service (DDoS) attacks are the most frequent of all cloud security threats, and the consequences of damage caused by DDoS are very serious. Thus, the design of an efficient DDoS detection system plays an important role in monitoring suspicious activity in the cloud. Real-time detection mechanisms operating in cloud environments and relying on machine learning algorithms and distributed processing are an important research issue. In this work, we propose a real-time detection of DDoS attacks using machine learning classifiers on a distributed processing platform. We evaluate the DDoS detection mechanism in an OpenStack-based cloud testbed using the Apache Spark framework. We compare the classification performance using benchmark and real-time cloud datasets. Results of the experiments reveal that the random forest method offers better classifier accuracy. Furthermore, we demonstrate the effectiveness of the proposed distributed approach in terms of training and detection time.

An Efficient Multi-Core Resource Allocation using the Multi-Level Objective Functions in Cloud Environment

Background: In recent years, the computational memory and energy conservation have become a major problem in cloud computing environment due to the increase in data size and computing resources. Since, most of the different cloud providers offer different cloud services and resources use limited number of user’s applications. Objective: The main objective of this work is to design and implement a cloud resource allocation and resources scheduling model in the cloud environment. Methods: In the proposed model, a novel cloud server to resource management technique is proposed on real-time cloud environment to minimize the cost and time. In this model different types of cloud resources and its services are scheduled using multi-level objective constraint programming. Proposed cloud server-based resource allocation model is based on optimization functions to minimize the resource allocation time and cost. Results: Experimental results proved that the proposed model has high computational resource allocation time and cost compared to the existing resource allocation models. Conclusion: This cloud service and resource optimization model is efficiently implemented and tested in real-time cloud instances with different types of services and resource sets.

An automated all-sky atmospheric monitoring camera for a next-generation ultrahigh-energy cosmic-ray observatory

We have developed a fully-automated instrument (FASCam—Fast All Sky Camera) designed to capture night all-sky images and analyse cloudiness and coarse atmospheric properties for a proposed next-generation ultrahigh energy cosmic ray observatory, called the Fluorescence detector Array of Single-pixel Telescopes (FAST). FASCam has a 360 × 90̂ (azimuth and elevation) field of view and is equipped with a series of photometric Johnson-Cousins filters (BVR) and an Ultra-violet (UV) filter. The camera is installed on the top of one of the three FAST prototype telescope buildings located in central Utah, U.S.A. . An online analysis of the images provides real time cloud coverage maps and coarse atmospheric extinction measurements and a night sky background estimate in the field of view of the FAST fluorescence telescopes. The result serves as an input parameter for the operation of the telescopes and avoids unnecessary data taking in the case of clouds covering the field of view of the telescopes, high atmospheric extinction, or high night sky background.

Green Communications for Future Vehicular Networks: Data Compression Approaches, Opportunities, and Challenges

Cellular vehicular networks constitute an important part of future vehicular communication systems. However, they pose challenges to fulfill vehicles with demands for the transmission rates of various network services while achieving satisfactory costs and energy consumption. The cloud radio access network architecture is considered as a green wireless access network framework that reduces the deployment costs of base stations while achieving resource sharing and dynamic scheduling to lower energy consumption, and thus will greatly improve spectrum efficiency. in this article, we first propose a cloud vehicular radio access network (C-VRAN) architecture to enable rapid network innovation for green vehicular communications. Specifically, we propose a vehicular network framework consisting of centralized processing, collaborative radio, real-time cloud computing, and data compression functions. Then, the design and the potential benefits of C-VRAN are presented in detail. Moreover, an effective data compression approach for the proposed architecture is proposed to meet low-distortion compression for cellular vehicular network data, thereby improving fiber resource utilization. Finally, we briefly discuss key challenges and solutions for future green vehicular communications.

EVALUATION OF MOBILE MAPPING SYSTEMS FOR INDOOR SURVEYS

Abstract. With their high recording rate of hundreds of thousands of points acquired per second, speed of execution and a remote acquisition mode, SLAM based mobile mapping systems (MMS) are a very powerful solution for capturing 3D point clouds in real time, simply by walking in the area of interest. Regarding indoor surveys, these MMS have been integrated in handheld or backpack solutions and become fast scanning sensors. Despite their advantages, the geometric accuracy of 3D point clouds guaranteed with these sensors is lower than the one reachable with static TLS. In this paper the effectiveness of two recent mobile mapping systems namely the GeoSLAM ZEB-REVO RT and the more recent GreenValley LiBackPack C50 is investigated for indoor surveys. In order to perform a reliable assessment study, several datasets produced with each sensor are compared to the high-cost georeferenced point cloud obtained with static laser scanning target-based technique.

An efficient management scheme of blockchain-based cloud user information using probabilistic weighting

As the number of users using cloud services has increased in recent years, the number of users leaving the group or changing their personal information is often the case. In this paper, a blockchain-based cloud user information management scheme is proposed using stochastic weights to confirm that the information of users receiving cloud services has not been tampered with. The proposed scheme uses color keys to reliably distinguish blockchains of user information in a cloud environment. The quality of service is improved by pasting user attribute information generated using color keys to the front of the blockchain. The proposed scheme uses different color key information and attribute information, so they have the advantage of effectively processing user information in different cloud environments. It also supports lower overhead and higher efficiency compared to traditional techniques that individually add authentication keys to users by authenticating information from multiple users who receive cloud services with color keys. In addition, the proposed scheme allows the provision of reliable real-time cloud services by creating a stochastic sum of weights for the first and last users that make up the cloud. As a result of the performance analysis, the proposed scheme reduced the time of blockchain generation by $$7.9\%$$ on average by linking user information to the probability value of user information. Efficiency in managing blockchain-based user information over overlay networks was up to $$11.5\%$$ better than using RSA alone, with an average $$7.7\%$$ lower overhead.

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle.

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

Hybrid encryption framework for securing big data storage in multi-cloud environment

In the present scenario, big data is facing many challenges regarding the data storage, data theft and unauthorized access. Many researchers are concentrated on developing the security mechanism for big data storage. To overcome the above issue, this paper concentrated on developing the encryption algorithm for storing big data in the multi cloud storage. The multi cloud storage environment permits the user to store the data in to different cloud storage services. This paper aims to develop the secure framework which restricts the insider attacks. The proposed framework contains data uploading, slicing, indexing, encryption, distribution, decryption, retrieval and merging process. The hybrid encryption algorithm was developed to provide the security to the big data before storing it in to the multi cloud. The Simulation analysis is carried with real time cloud storage environments. The proposed algorithm recorded around 2630 KB/S for the encryption process. The results prove the superiority of the proposed algorithm compared to the bench mark algorithms.

A Web Application Exhibiting Parking Guidance using Smart Sensor Networks

Smart parking is the digital approach to the modern-day parking problems. This project presents a new approach to the parking problems in our country in which we use ultrasonic sensors to detect the unoccupied places inside the parking area. This data is frequently captured and updated onto a real time cloud database. From that database we fetch the details of the parking area and present it to the end customer in the form of a web base application. This application will show the end customer where are the unoccupied slots are located and the path to that unoccupied space is presented onto the User Interface so that the user can park his car in a very limited amount of time.

Toward a robust and fast real-time point cloud registration with factor analysis and Student’s-t mixture model

Three-dimensional (3D) point cloud registration generally involves in unsatisfied situations like Gaussian white noise, data missing and disorder in affine. This paper proposes a robust and real-time point cloud registration, which combines the Student’s-t mixture model (SMM) with factor analysis. The proposed method extending the point cloud mathematical model to the orthogonal factor model and employs the SMM to fit the point cloud data, because the degree of freedom of Student’s t-distribution makes it more flexible in fitting the probability distribution of data. Since the Expectation Maximization (EM) algorithm has a stable estimation ability for the mixture model, the EM algorithm is used to estimate the factor load matrix. The filed data and experimental results show that the proposed algorithm can achieve accurate registration and fast convergence even in the case of point cloud disorder, data occlusion, incomplete loss and noise.

Ensemble-Based Data Assimilation of Volcanic Ash Clouds from Satellite Observations: Application to the 24 December 2018 Mt. Etna Explosive Eruption

Accurate tracking and forecasting of ash dispersal in the atmosphere and quantification of its uncertainty are of fundamental importance for volcanic risk mitigation. Numerical models and satellite sensors offer two complementary ways to monitor ash clouds in real time, but limits and uncertainties affect both techniques. Numerical forecasts of volcanic clouds can be improved by assimilating satellite observations of atmospheric ash mass load. In this paper, we present a data assimilation procedure aimed at improving the monitoring and forecasting of volcanic ash clouds produced by explosive eruptions. In particular, we applied the Local Ensemble Transform Kalman Filter (LETKF) to the results of the Volcanic Ash Transport and Dispersion model HYSPLIT. To properly simulate the release and atmospheric transport of volcanic ash particles, HYSPLIT has been initialized with the results of the eruptive column model PLUME-MoM. The assimilation procedure has been tested against SEVIRI measurements of the volcanic cloud produced during the explosive eruption occurred at Mt. Etna on 24 December 2018. The results show how the assimilation procedure significantly improves the representation of the current ash dispersal and its forecast. In addition, the numerical tests show that the use of the sequential Ensemble Kalman Filter does not require a precise initialization of the numerical model, being able to improve the forecasts as the assimilation cycles are performed.

AN IMPROVED HOMOMORPHIC BASED ENCRYPTION AND DECRYPTION PROCESS ON CLOUD TEXUAL DATA

Data security in cloud computing plays a vital role in the real-time applications. Most of the traditional homomorphic encryption models are difficult to encrypt large data in the rea-time applications. Also, these models are not application to large integers for large dataset. In this work, we have optimized the traditional homomorphic encryption model with large data in realtime cloud environment. Experimental results proved that the present model has better efficiency in term of runtime in realtime cloud environment.

An Energy Efficient Hybrid PSO Algorithm in Cloud Environment

Algorithms exist to schedule various tasks in real time cloud environment. Nowadays many researchers are trying to schedule heavily loaded situations in real time cloud environment using swarming technique. For such studies many parameters need to be considered like cost of the system, processor latency, number of tasks and so on. With the increase in the number of tasks in the set, processing time also increases. In this situation, processor latency is at peak as the number of tasks increases and system costs increase. So the above mentioned problem is handled by proposing a task scheduler that uses a PSO algorithm to remove the limitations of past studies in a heavily loaded situation. The Particle Swarm optimization (PSO) and Invasive Weed Optimization (IWO) are combined to propose a new technique called the HWO algorithm. The proposed algorithm is recommended for preventive tasks in the single-processor in real-time environment systems.

Accurate Fault Location using Deep Neural Evolution Network in Cloud Data Center Interconnection

Due to the threat of failure and the discrete distribution of data center users, the research of distributed cloud data center provides real-time cloud services with robustness, reliability and security. Faced with data center interconnection, network failures cause mass services delay and interruption, which do a great damage to cloud computing. Many researchers have studied fault location methods in data center interconnection, which are easy to trap in local optimum limited by search capability and reduce the accuracy of location, especially when confronted with large-scale alarm information. In this article, the deep neural evolution network is introduced to extract deep-hidden fault features from massive collected alarm information in cloud data center interconnection. It has the prominent capacity of global search without the constraint of gradient to realize the breakthrough of fault location accuracy. The fault location method based on deep neural evolution network (FL-DNEN) is applied which uses the alarm set and suspicious scope of fault getting from fault propagation model as input and export deterministic faults accurately. The emulations demonstrate that the proposed method dramatically improves the accuracy of fault location to 92 percent with large-scale alarm information, which improves the resilience of cloud data center interconnection dramatically.

An intelligent recognition model for dynamic air traffic decision-making

Air traffic flow management system (ATFMS) is becoming increasingly important due to the rapid growth of air traffic and serious flight delay nowadays. To aware the air traffic flow density and identify the heat airspace in terminal areas of large hub airports is essential for an ATFMS. Due to numerous parameters in air traffic flow, traditional methods based on one single parameter fail to reflect the true complexity relationship between these parameters. This study aims to develop an intelligent air traffic flow heat airspace recognition model using advanced data science technique for establishing a real-time cloud map in the terminal airspace of airports, which attempts to use machine learning models to represent the complex relationship among these parameters. In the proposed intelligent recognition model, high dimensions of parameters (basic parameters, additional parameters and time parameters) are processed to achieve a comprehensive and accurate situation awareness for support dynamic air traffic decision-making. An aircraft trajectories points clustering method is developed to generate a 4D heat airspace map. The basic parameters and time parameters are used to identify the heat airspaces; the changes of additional parameters which influence the heat airspaces are identified and analyzed by use of grid graphs of flight trajectories; probability fitting graphs are used to verify accuracy of 4D results in order to support air traffic decision-making. A case study on Beijing International Airport (PEK) is conducted to test our model and has obtained two main research findings: there are two areas of PEK that have the high density and there are hot peaks at two different heights; flight trajectories and speed of trajectories also effect on the heat airspace. The study realizes that the proposed 4D heat airspace model is better for detailed and accurate information construction, expression of spatial changes, and visualization of multiple parameters of temporal and spatial density and range. It can assist the decisions on airspace allocation, and also have a definite reference meaning on alleviating the contradiction between the current air traffic demand and airspace resource constraints.