Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clients

Objective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance during streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose Moving QoE (MQoE, in short) models to measure QoE during periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.

Modeling of information support to optimize logistics tasks in transport sector using a programmable container transformer simulator

Introduction. The structure of the transport logistics system for the transportation of container transformers in an urbanized environment to optimize production costs with elements of intelligent urban mobility, as well as the simulation software for modeling and testing the developed system, are described. The basic principles of the interaction between elements of the system are presented through the behavioral modeling of containers and carriers.Software is created to simulate the operation of the logistics infrastructure for transformer containers using wireless technology and the Internet of Things; and services for the rapid information exchange between participants (objects and subjects) of this process are implemented. Materials and Methods. A general method of organizing a network with a web server and a mobile client, as well as the basic principle of interaction between the server and the client, is described. The basics of developing a simulator designed to simulate all possible states of a container transformer are specified.Results. A common system architecture and a simulator are created for the software debugging and testing under the organization of a single space to monitor and optimize cargo transportation using “smart” container transformers while providing transport services to the population and legal entities in an urban environment.Discussion and Conclusions. The developed simulator as part of the information system provides speeding up the creation, debugging and testing of the software for solving logistics problems in the transport sector.

Research and Practice of Autonomous Learning and Evaluation Mode Based on Mobile Client

This article focuses on the exploration and construction of a mobile online autonomous learning and evalua-tion model, and on the cultivation and guidance of students’ autonomous learning methods and abilities, to complete the construction and optimization of formative evaluation. The basic teaching of computer application in the freshman year is the experimental object and content, and the experimental and control groups with strict variables are built to carry out a comparative analysis, discussion, and in-depth exploration through experimental research on how to build a mo-bile and autonomous learning and evaluation model with perfect content and structure, to promote better online teaching of various majors in the school and improve autonomous learning and evaluation models.

Hyperledger Fabric Blockchain for data Security in IOT Devices

Data security for IOT devices is very import aspect these days as the world is moving towards digitalization. Consider a smart energy meter which provides a way to monitor the energy consumption at home, data security in such smart meter reading is very important. If the Power reading signals are tampered, then it may cause serious economic loss for the authorities. The personal information infringement of user can occur at the database and may fall in the hands of unethical persons. In order to address these issues in this paper we propose to use a permissioned blockchain network. Blockchain maintains time stamped ledger records that are very hard to tamper. Every transaction is recorded and distributed across many participant nodes, these records are immutable because they have blocks of data which are linked to each other with strong cryptographic hash. The blockchain network is built using hyperledger fabric, where all the participant nodes are registered and only registered nodes involve in consensus process of transaction. In fabric, MSP (membership service provider) identifies the identity of the participant nodes through X.509 digital certificates issued by certificate authority. Along with creation of blockchain network for the application, a mobile client, a web client, an Arduino client and web server is created. The Arduino client is the hardware module that has an energy meter (SDM120) measuring the energy consumption of the user and sends this information serially to NODEMCU. NODEMCU POSTs the read energy details to the web server at particular api, web server POSTs the details to the Blockchain Network, where transactions undergoes consensus to add this information to blockchain ledger. Now data is decentralized and every peer node has the local copy of ledger. The updated information can be queried and seen on the web Client and Mobile client user interfaces. Anonymity-enhanced blockchain has been implemented to avoid the disclosure of personal information or data. Also performance analysis of the application is carried out for number of sequential requests and concurrent requests from many users using different tools.

On Possible Cryptographic Optimization of Mobile Healthcare Application

The paper deals with a possible SOA based m-healthcare online system with secure mobile communication between patients and medical professionals with medical and insurance organizations. An example of an Android-based secure mobile client application is presented which can be used in the described secure m-healthcare model and it is experimentally evaluated. In the paper, we focus on possible optimization of cryptographic algorithms implemented in the secure Android mobile client application. The presented experimental results justify that security operations related to X.509v3 digital certificate generation and XML/WSS digital signature creation/verification are feasible on some current smart phones and justify the use of the proposed optimization techniques for implemented cryptographic algorithms.

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

<p class="0abstract">the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.</p>