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An Optimal Image Storage Strategy for Container-Based Edge Computing in Smart Factory

Edge computing provides efficient and low-latency computing services for Internet of Things applications. Container virtualization technology is widely used as an indispensable key technology in edge computing. However, the creation of the container requires reading the corresponding image file. If the image file is not stored locally, it will take a lot of time to download, which increases the user’s extremely high service delay. Aiming at decreasing the download time of image files, we develop a two-stage optimization storage strategy of image files to decrease its download time based on edge computing. This strategy optimizes the image file placement in the initialization stage and the runtime stage, respectively. In the initialization stage, we propose a pseudo-polynomial time algorithm to filter all image files and select the image file combination, which best meets the capacity of the edge node for placement. In the runtime stage, we continue to optimize the local image repository based on the historical access records of the edge node. This operation can reduce the number of downloads of image files, thereby further reducing the user’s service delay. In addition, we created a real data set according to the service requirements and the structure of image files on the smart factory and the access records on the DockerHub. A large number of experiments are carried out based on the data set. Experimental results show that the two-stage optimization storage strategy can greatly reduce the download time of image files, thus reducing the service delay of edge nodes and improving the service quality of edge nodes.

Deep Reinforcement Learning for Reactive Content Caching With Predicted Content Popularity in Three-Tier Wireless Networks

With the explosive growth of micro-video applications, the mobile traffic generated by retrieving a few user-generated micro-videos has brought a massive burden to backhaul links and backbone networks. Unlike other types of videos, user-generated micro-videos are typically requested by a large number of users within an extremely short period after their release. Therefore, it is crucial to predict the content popularity and make caching decision for the newly requested content timely. To predict content popularity in different locations, the request probabilities of the content in different locations are translated into rating scores. Then, a recommendation system-based prediction model is designed to predict rating scores of the newly requested content in different locations. To increase caching diversity and realize vertically collaboration in a three-tier wireless network, a deep reinforcement learning-based reactive content caching strategy is proposed to make caching decision for the newly requested content. The main goal is to obtain a higher caching gain at a lower caching space cost. To evaluate the caching performance, a new metric, cache benefit rate, is defined as the download latency reduction brought by each bit of cache. Compared with matrix factorization, the performance of prediction model can be increased by 23.98% and 12.44% in terms of mean absolute error and root mean square error, respectively. Extensive simulations demonstrate that the proposed reactive caching strategy outperforms other caching strategies under different system parameters in terms of the cache hit rate, average download time, and cache benefit rate.

Federated Learning-Based Secure Electronic Health Record Sharing Scheme in Medical Informatics.

Medical Cyber-Physical Systems support the mobility of electronic health records data for clinical research to accelerate new scientific discoveries. Artificial Intelligence improves medical informatics, but current centralized data training and insecure data storage management techniques expose private medical data to unauthorized foreign entities. In this paper, a Federated Learning-based Electronic Health Record sharing scheme is proposed for Medical Informatics to preserve patient data privacy. A decentralized Federated Learning-based Convolutional Neural Network model trains data locally in the hospital and stores results in a private InterPlanetary File System. A secondary global model is trained at the research center using the local models. Private IPFS secures all medical data stored locally in the hospital. The novelty of this study resides in securing valuable hospital biomedical data useful for clinical research organizations. Blockchain and smart contracts enable patients to negotiate with external entities for rewards in exchange for their data. Evaluation results demonstrate that the decentralized CNN model performs better in accuracy, sensitivity, and specificity, similar to the traditional centralized model. The performance of the Private IPFS exceeds the Blockchain-based IPFS based on file upload and download time. The scheme is suitable for promoting a secure and privacy-friendly environment for sharing data with clinical research centers for biomedical research.

Enhanced Cloud Storage Encryption Standard for Security in Distributed Environments

With the growing number of cloud users, shared data auditing is becoming increasingly important. However, these schemes have issues with the certificate management. Although there is a certificate-shared auditing scheme, it is ineffective in dealing with dynamic data and protecting data privacy. The verifier cannot access the data content to ensure data integrity due to security concerns. This paper proposes a novel technique to ensure the integrity and improve the access control. A novel enhanced storage retrieval mechanism is used to improve the performance of the cloud’s storage and retrieval mechanisms to achieve this. The technique is evaluated in concern of the upload, download, encryption, and decryption time. As the file size grows, so does the time it takes to upload it. Similarly, the time taken to encrypt files of various formats and sizes evidenced that it depends on the file size and format. Thus, the encryption time increases as the file sizes increases, demonstrating the performance of the proposed system.

The worst II – eight days a week

The worst II – eight days a week

Assessment of App Store Description and Privacy Policy to Explore Ethical and Safety Concerns Associated with the Use of Mental Health Apps for Depression.

Mental Health apps (MH apps) could help address the huge unmet mental health care need of developing countries. This study aimed to explore potential ethical, data safety, and privacy issues associated with using MH apps for depression. A cross-sectional assessment of the top 50 MH apps (by Google Play store search result ordering) for depression available in India was conducted in November 2021. Most apps were listed under the category of health and fitness (54%). The median number of total and dangerous permissions requested at the time of download was nine and three, respectively. Privacy policy in English was available for 76%. The average length of the privacy policy was 2171 words, and the mean Flesch-Kincaid reading grade level was 12 (much higher than the recommended cut-off of eight). Important features relevant to safeguarding consumer confidentiality, including names of third parties with which user data could be shared (42%), explicit consent before sharing data with third parties (16%), and assurance regarding the collection of de-identifiable data (11%), were missing from the majority of privacy policies. There is an urgent need to improve the accessibility and usability of privacy policies by app developers, with the active involvement of other stakeholders.

PIRAP: Chaotic Fuzzy Encryption (CFE) Technique and Greedy Chemical Reaction Optimization (GCRO) Algorithm-Based Secured Mobi-Cloud Framework

This paper uses the chaotic fuzzy encryption (CFE) technique and a greedy chemical reaction optimization algorithm based on mobile cloud computing to protect data from any assault. Mobile users who use protected mobile cloud computing (Mobi-Cloud) must trust the cloud service provider to keep the data sent from their mobile devices safe when mobile users express strong reservations about storing personal information in the public cloud. The Greedy Chemical Reaction Optimization (G-CRO) algorithm reduces critical limitations by determining the offloading end with motion at run time, i.e. the task’s operating time, CPU usage, memory utilization, and energy usage. The experimental results show that the proposed method performs better in terms of file uploading time, file downloading time, memory usage in file uploading, memory usage in file downloading, encryption time, decryption time, memory usage on file encryption, memory usage on file decryption, security levels, and key generation time. Compared to the Proxy Re-Encryption (PRE) algorithm, the suggested method consumes less time for key creation (26.04).

Modified ECC for Secure Data Transfer in Multi-Tenant Cloud Computing

Cloud computing technologies comprise various kinds of significant desirable constrains such as security, liability, government surveillance, telecommunications capacity, anonymity and privacy. The usage of cipher text technology is considered as a desired technique for performing the process of encryption in order to solve the issue of granting security to the data that are shared in the cloud. Similarly, the architecture of multi-tenant in the cloud computing system grants benefits to both the service providers and end-users which shares a common cloud platform to multiple tenants (i.e.) users and suitable resources are also computed by implementing proposed architecture. Therefore in this research work, the concept of cipher text multi-tenant are integrated for providing enhanced security to the data shared in the cloud environment. Hence a Modified Elliptic Curve Cryptography (MECC) based on Diffee Hellman algorithm is proposed in this research paper which provides enhanced security using alternate key generation. The encryption, decryption, upload and download time are calculated and it is concluded that the algorithm that is proposed in this research paper consumes less time for all these measures when comparing with other existing algorithms. Characteristics like less memory, high operational performance, small sized keys and rapid key generation process, and effective resource savings enable the modified ECC to obtain high efficiency. The encryption time of the proposed MECC system was 51ms with the key length 4096 bits, whereas existing method had 92ms as encryption time. Likewise With the key length of 4096 bits, the decryption time for the proposed model was 159ms. Accordingly the proposed system has reduced cipher text key size of 836KB when compared with the existing AES, Blow Fish, and Two Fish. Additionally, the key generation time (35 s) also seems to be considerably reduced when compared with the existing methods. These statements reveal that the proposed system outshine the state of art methods in terms of key generation time, encryption and decryption time and computational complexity.

A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers

PurposeThis study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the system. Methods and MaterialsA cloud-based consultation and collaboration system for radiotherapy, OncoEvidance®, was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was deployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiotherapy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated remotely. ResultsAll the passing rates of per-beam dose verification are > 94% and all the passing rates of composite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification. ConclusionThe cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.

KARAJ: An Efficient Adaptive Multi-Processor Tool to Streamline Genomic and Transcriptomic Sequence Data Acquisition.

Here we developed KARAJ, a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs or various types of accession numbers to automate four tasks as follows; firstly, it provides a summary list of accessible datasets generated by or used in these scientific articles, enabling users to select appropriate datasets; secondly, KARAJ calculates the size of files that users want to download and confirms the availability of adequate space on the local disk; thirdly, it generates a metadata table containing sample information and the experimental design of the corresponding study; and lastly, it enables users to download supplementary data tables attached to publications. Further, KARAJ provides a parallel downloading framework powered by Aspera connect which reduces the downloading time significantly.

Cloud Detection Autonomous System Based on Machine Learning and COTS Components On-Board Small Satellites

One of the main applications of small satellites is Earth observation. CubeSats and different kinds of nanosatellites usually form constellations that obtain images mainly using an optical payload. There is a massive amount of data generated by these satellites and a limited capacity of download due to volume and mass constraints that make it difficult to use high-speed communication systems and high-power systems. For this reason, it is important to develop satellites with the autonomy to process data on board. In this way, the limited communication channel can be used efficiently to download relevant images containing the required information. In this paper, a system for the satellite on-board processing of RGB images is proposed, which automatically detects the cloud coverage level to prioritize the images and effectively uses the download time and the mission operation center. The system implements a Convolutional Neural Network (CNN) on a Commercial off-the-Shelf (COTS) microcontroller that receives the image and returns the cloud level (priority). After training, the system was tested on a dataset of 100 images with an accuracy of 0.9 and it was also evaluated with CubeSat images to evaluate the performance of a different image sensor. This implementation contributes to the development of autonomous satellites with processing on board.

Global shockwaves of the Hunga Tonga-Hunga Ha’apai volcano eruption measured at ground stations

The eruption of the Tonga volcano created globally propagating spherical shockwaves in the atmosphere. Analyses are done to data from two southern U.S. stations of the author sampling at 3-21s intervals and 189 weather stations at 1-5min intervals. The shockwaves arrived from two routes in the atmosphere: the shortest spherical arc and the longer spherical arc through the antipole. In most stations, signals up to the 6th path of shockwaves were recorded as the waves traveled around the globe multiple times. The speed of shockwaves is estimated to be 309.5± 2.9m/s, consistent with the speed of sound at the top of the troposphere where a waveguide exists. Discussion is made on the post-shockwave ringing of 4-8min as higher amplitude oscillations above the level of pre-shockwaves background noise. A theoretical wave dispersion is derived which verifies that the spherical shockwave's phase speed is the same as the speed of sound.

Usability testing of two Libyan banks' websites using the Dareboost tool (Bank of Commerce & Development, Wahda Bank)

Internet banking is constantly expanding in terms of content and functionality, and it has a significant impact on society. One goal of the Digital Agenda for Libya is to make Internet banking services available to all, reducing cash handling. Internet banking is a critical service that financial institutions provide to their customers in order to facilitate easier and faster access to financial services and transactions. Banks currently spend vast sums of money on the development and maintenance of websites and backend systems that provide clients with Internet banking services. Given the low accessibility of bank websites, evaluation and monitoring of the efficiency of those banking sites that offer the service are needed. The effect of moderating factors on online banking usability assessment in Libya is discussed here. As the Internet grows in popularity, banks are adopting technologies to create their own Web sites. The design of the website is an important factor in determining whether or not visitors will return to the site. Thus, the purpose of this study is to investigate the usability of the Internet banking Web sites of two main banks in Libya. The evaluation procedure is used in the World Wide Web environment to measure real data and obtain response time. To get results on the performance of a Web site, some data from the calculation of page size, composition, structure, and download time has been measured. In this study, a website performance evaluation tool, Dareboost, was used to evaluate the Bank of Commerce & Development website and Wahda Bank website. Dareboost has a- modern graphical user interface that is very easy and intuitive to use and gives a recommendations on how to improve the performance as the result. Based on the observation results, the Bank of Commerce & Development website received the highest score in most metrics. Therefore, the Bank of Commerce & Development website is the best in terms of usability.

Reinforcement Learning Based Multipath QUIC Scheduler for Multimedia Streaming.

With the recent advances in computing devices such as smartphones and laptops, most devices are equipped with multiple network interfaces such as cellular, Wi-Fi, and Ethernet. Multipath TCP (MPTCP) has been the de facto standard for utilizing multipaths, and Multipath QUIC (MPQUIC), which is an extension of the Quick UDP Internet Connections (QUIC) protocol, has become a promising replacement due to its various advantages. The multipath scheduler, which determines the path to which each packet should be transmitted, is a key function that affects the multipath transport performance. For example, the default minRTT scheduler typically achieves good throughput, while the redundant scheduler gains low latency. While the legacy schedulers may generally give a desirable performance in some environments, however, each application renders different requirements. For example, Web applications target low latency, while video streaming applications require low jitter and high video quality. In this paper, we propose a novel MPQUIC scheduler based on deep reinforcement learning using the Deep Q-Network (DQN) that enhances the quality of multimedia streaming. Our proposal first takes into account both delay and throughput as a reward for reinforcement learning to achieve a low video chunk download time. Second, we propose a chunk manager that informs the scheduler of the video chunk information, and we also tune the learning parameters to explore new random actions adequately. Finally, we implement our new scheduler on the Linux kernel and give results using the Mininet experiments. The evaluation results show that our proposal outperforms legacy schedulers by at least 20%.

Evaluation of R Tools for Downloading MODIS Images and Their Use in Urban Growth Analysis of the City of Tarija (Bolivia)

The aim of this study was to compare the available tools in R for downloading and processing Moderate Resolution Imaging Spectroradiometer (MODIS) data, specifically the Enhanced Vegetation Index (EVI) product. The R tools evaluated were the MODIS package, RGISTools, MODISTools, R Google Earth Engine (RGEE) package, MODIStsp, and the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) application. Each tool was used to download the same product (EVI) corresponding to the same day (3 December 2015), and downloaded data were used to analyze the urban growth of Tarija (Bolivia) as an interesting application. The following features were analyzed: download time and memory used during the download, additional post-processing time, local memory occupied on the computer, and downloaded file formats. Results showed that the most efficient R tools were those that work directly in the “cloud” or use text queries (RGEE and AppEEARS, respectively) and provide, as a final product, a cropped.tif image according to the area of interest.

VICAL: Global Calculator to Estimate Vegetation Indices for Agricultural Areas with Landsat and Sentinel-2 Data

The vegetation indices (VIs) estimated from remotely sensed data are simple and based on effective algorithms for quantitative and qualitative evaluations of the dynamics of biophysical crop variables such as vegetation cover, leaf area, vigor and development, and many others. Over the last decade, many VIs have been proposed and validated to enhance the vegetation signal by reducing the noise from effects produced either by the soil or by vegetation such as brightness, shadows, color, etc. VIs are commonly calculated from satellite images such as ones from Landsat and Sentinel-2 because of their medium resolution and free availability. However, despite the VIs being fairly simple algorithms, it can take hours to calculate them for an established agricultural area, mainly due to the pre-processing of the images (including atmospheric corrections, the detection of clouds and shadows), size and download time of the images, and the capacity of the computer equipment used. Time increases as the number of images increases. In this sense, the free to use Google Earth Engine (GEE) platform was here used to develop an application called VICAL to calculate 23 VIs map (VIs commonly used in agricultural applications) and time series of any agricultural area in the world with images (cloud-free) from Landsat and Sentinel-2 data. It was found that VICAL can calculate these 23 VIs accurately, and shows the potential of the GEE cloud-based tools using multispectral dataset to assess many spectral VIs. This tool is very beneficial for researchers with poor access to satellite data or in institutions with a lack of computational infrastructure to handle the large volumes of satellite datasets, since it is not necessary for the user writing a single line of code. The VICAL is open-access image analysis platform that can be modified to carry out more complex analysis or adapt it to a specific VI application.

Xatu

The performance of Adaptive Bitrate (ABR) algorithms for video streaming depends on accurately predicting the download time of video chunks. Existing prediction approaches (i) assume chunk download times are dominated by network throughput; and (ii) apriori cluster sessions (e.g., based on ISP and CDN) and only learn from sessions in the same cluster. We make three contributions. First, through analysis of data from real-world video streaming sessions, we show (i) apriori clustering prevents learning from related clusters; and (ii) factors such as the Time to First Byte (TTFB) are key components of chunk download times but not easily incorporated into existing prediction approaches. Second, we propose Xatu, a new prediction approach that jointly learns a neural network sequence model with an interpretable automatic session clustering method. Xatu learns clustering rules across all sessions it deems relevant, and models sequences with multiple chunk-dependent features (e.g., TTFB) rather than just throughput. Third, evaluations using the above datasets and emulation experiments show that Xatu significantly improves prediction accuracies by 23.8% relative to CS2P (a state-of-the-art predictor). We show Xatu provides substantial performance benefits when integrated with multiple ABR algorithms including MPC (a well studied ABR algorithm), and FuguABR (a recent algorithm using stochastic control) relative to their default predictors (CS2P and a fully connected neural network respectively). Further, Xatu combined with MPC outperforms Pensieve, an ABR based on deep reinforcement learning.

Latency Optimal Storage and Scheduling of Replicated Fragments for Memory Constrained Servers

We consider the setting of a distributed storage system where a single file is subdivided into smaller fragments of same size which are then replicated with a common replication factor across servers of identical cache size. An incoming file download request is sent to all the servers, and the download is completed whenever the request gathers all the fragments. At each server, we are interested in determining the set of fragments to be stored, and the sequence in which fragments should be accessed, such that the mean file download time for a request is minimized. We model the fragment download time as an exponential random variable independent and identically distributed for all fragments across all servers, and show that the mean file download time can be lower bounded in terms of the expected number of useful servers summed over all distinct fragment downloads. We present deterministic storage schemes that attempt to maximize the number of useful servers. We show that finding the optimal sequence of accessing the fragments is a Markov decision problem, whose complexity grows exponentially with the number of fragments. We propose heuristic algorithms that determine the sequence of access to the fragments which are empirically shown to perform well.

Data Dissemination Performance in P2P-Based Vehicular Communications for Smart City Environments

Peer-to-Peer (P2P) networks, as well as vehicular networks, have been extensively studied in the literature. However, the statistical characteristics of connection times among vehicles on the performance of a P2P network to allow Vehicle-to-Vehicle (V2V) communications have been largely overlooked. Specifically, the periods that vehicles remain connected to neighboring vehicles to directly communicate among them in different road characteristics (urban or highway) and road traffic conditions (low, medium, or high) are of main importance for the adequate performance of a P2P system. Indeed, these connection times establish the average number of peers and hence the bandwidth available in the communication system. These times have not been studied nor characterized before for the purpose of calculating the download probability and average download times, which are calculated in this work using a teletraffic analysis. We also present simulation results considering the CSMA/CA random access scheme established in the IEEE 802.11p communication protocol for V2V communications to investigate the effect of the random access protocol on the system performance.

Ideological and Political Teaching Resource Sharing Method Based on Edge Computing

In the process of ideological and political teaching resource sharing, the sharing time is long due to the interference of redundant data. Therefore, an ideological and political teaching resource sharing method based on edge computing is proposed. Based on the sharing model of ideological and political teaching resources of edge computing, the information entropy suppression method is used to eliminate the redundant data in the ideological and political teaching resources, calculate the sinking of the edge node of the resource after removing the redundancy, assign the task to the edge node, and finally build a location index for the ideological and political teaching resources to realize the sharing of ideological and political teaching resources. The results show that when using the research method to share ideological and political teaching resources, the completion time, average waiting delay, and resource download time of resource sharing are shorter.