A New Forgery Image Dataset and its Subjective Evaluation

The aim of this research paper is to present a new forgery image dataset with a thorough subjective evaluation in detecting manipulated images, considering various parameters. The original images were obtained from public sources, and meaningful forgeries were produced using an image editing plat- form with three techniques: cut-paste, copy-move, and erase-fill. Both pre-processing and post-processing methods were used to generate fake images. The subjective evaluation revealed that the accuracy of manipulated image detection was affected by various factors, such as user type, image quantity, tampering method, and image resolution, which were analyzed using quantitative data

Predicting Quality Of Experience For Online Video Systems Using Machine Learning

As the expansion of the online video broadcasting continues in every area of the modern connected world, the need for measuring and predicting the Quality of Experience for content delivery has never been this important. This demo paper has designed and developed a real-time and continuously trained machine learning model in order to predict QoE for online video systems. For this purpose, a platform has been developed where video content is unicasted to a cluster of users simultaneously while objective video metrics are collected into a database. At the end of each video, each user is queried with a subjective survey about their experience. Both quantitative statistics (video metrics) and qualitative information (user surveys) are used continuously as training data to machine learning model. The overall results show that proposed QoE estimation system provides an average Mean Opinion Score (MOS) precision with an error rate ranging from 12% to 15%. This methodology can efficiently answer the problem of predicting user experience for any online video delivery system, while overcoming the problematic interpretation of subjective consumer experience in terms of quantitative metrics

V2I Applications in Highways: How RSU Dimensioning Can Improve Service Delivery

This paper investigates the performance of Vehicle-to-Infrastructure (V2I) services over Vehicular Networks (VANETs) that are assisted by Road Side Units (RSU). More specifically, an analytical study of RSU dimensioning and a respective module is designed and developed in a simulated VANET environment. Two V2I application scenarios (e.g. car crash, spot weather) are considered in order to evaluate the impact of RSUs, vehicles’ size and speed and car crash start time and duration on applications’ performance. It is shown that the VANET network metrics (Packet Loss and Packet Delivery Ratio) are affected by the available MAC Bit rates and application scenarios. Mobility model metrics (Total Busy Time and Total CO2 Emissions) are also affected by the different application scenarios, number and type of vehicles

Energy-Aware IP Routing over SDN

The routing protocols play a vital role in saving energy, especially by minimizing the time a packet takes to travel from source to destination. The aim of energy-aware routing protocols is to select a route that engages routers in such a way that the overall energy consumption is minimized. In this paper, a relationship between resource utilization and energy consumption is stated, further, a resource-aware dynamic routing algorithm for SDN is proposed. The contribution of this paper is a queuing theory-based approach that measures the average waiting time of nodes and links based on their utilization and finds a path that costs the least time. The paper also proposes a framework for implementing routing algorithm over an SDN. Performance of the algorithm is verified using a GNS3 based implementation with an Opendaylight controller. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

On the Modelling of CDNaaS Deployment

With the increasing demand for over the top media content, understanding user perception and Quality of Experience (QoE) estimation have become a major business necessity for service providers. Online video broadcasting is a multifaceted procedure and calculation of performance for the components that build up a streaming platform requires an overall understanding of the Content Delivery Network as a service (CDNaaS) concept. Therefore, to evaluate delivery quality and predicting user perception while considering NFV (Network Function Virtualization) and limited cloud resources, a relationship between these concepts is required. In this paper, a generalized mathematical model to calculate the success rate of different tiers of online video delivery system is presented. Furthermore, an algorithm that indicates the correct moment to switch between CDNs is provided to improve throughput efficiency while maintaining QoE and keeping the cloud hosting costs as lowest possible

Load-Balancing for Edge QoE-Based VNF Placement for OTT Video Streaming

© 2018 IEEE. Over The Top (OTT) service providers require platforms to support distributed, complex, cloud-oriented, scalable, micro-service based systems. Such systems require on-the-fly placement of Virtual Network Functions (VNF) to support streaming and transcoding of content based on QoE feedback provided by the end-user. This paper proposes a QoE Scheme to support on-the-fly virtual network functions deployment for OTT video streaming and transcoding. The QoE feedback considers limited cloud resources, transcoding requirements, throughput and latency. Both horizontal and vertical scaling strategies (including VM migration) are discussed to cover up availability and reliability of intermediate and edge Content Delivery Network (CDN) cache nodes

Edu-Cloud: On-the-fly Employability Skills as a Service

21st Century global job market competition requires Science, Technology, Engineering and Mathematics (STEM) university curricula to support both state-of-the-art technical and soft skills learning to improve graduate employment. This necessitates the transformation of the current teaching and learning methodology powered by a social and col- laborative platform to provide a social co-learning environment. This social co-learning will provide students with opportunities for self-enrichment while supporting their technical skills and hands-on needs. The platform must also provide the required lab infrastructure for hands-on experimentation. This paper proposes the design and implementation of a cloud based platform called Edu-Cloud. The Edu-Cloud has been designed to provide automated resource provisioning and perform on-the-fly deployment of scalable virtual network functions to stream multimedia content closer to the global learners. This would help to meet the specific learning needs of a group of global interconnected students with similar learning skills and abilities. The benchmarking performance results show that the proposed framework works efficiently while reducing primary network traffic by deploying resources closer to the users and support scalability for a global deployment scenario

IoTA: IoT Automated SIP-based Emergency Call Triggering System for general eHealth purposes

The expansion of Internet of Things (IoT) and the evolution in communication technologies have enabled homes, cars even whole cities to be network connected. However, during an emergency incident, IoT devices have not been used to trigger emergency calls directly to healthcare providers mainly due to their constrained capabilities and lack of support session-oriented communications. Moreover, emergency services are currently offered by public safety stakeholders that do not support call triggering by IoT devices. This paper proposes IoTA framework which enables IoT devices to generate automatically emergency calls and support bi-directional communication sessions between healthcare providers and end users. The IoTA framework incorporates intelligent algorithms for processing and evaluating emergency events from various devices and performs emergency calls immediately after the occurrence of an event. The healthcare providers can interact with the IoTA framework requesting continuous real-time sensor data. A prototype implementation and initial evaluation results are presented as a proof of concept for people suffering from diverse chronic diseases. Experimental results have shown that the proposed framework can be considered as a promising solution for detecting, reporting emergency events, eliminating the hoax calls and responding swiftly saving lives

Multi-Agent Learning Approach for UAVs Enabled Wireless Networks

The unmanned aerial vehicle (UAV) technology provides a potential solution to scalable wireless edge networks. This paper uses two UAVs, with accelerated motions and fixed altitudes, to realize a wireless edge network, where one UAV forwards the downlink signal to user terminals (UTs) distributed over an area where another UAV collects uplink data. Both downlink and uplink transmissions consider the active user probability and the queue structure as well as the hovering times of UAVs. Specifically, we develop a novel joint Q-Learning multi-agent (JQ-LMA) algorithm to maximize the overall energy efficiency of the edge networks, through optimizing the UAVs trajectories, transmit powers, and the resistant distance between UAVs. The simulation results demonstrate that the proposed algorithm achieves much higher energy efficiency than other benchmark schemes

Wireless Information-Theoretic Security: Theoretical analysis & experimental measurements with multiple eavesdroppers in an outdoor obstacle-dense MANET

Wireless Information-Theoretic Security (WITS) has been suggested as a robust security scheme, especially for infrastructure-less networks. Based on the physical layer, WITS considers quasi-static Rayleigh fading instead of the classic Gaussian wiretap scenario. In this paper, they key parameters of WITS are investigated by implementing an 802.11n ad-hoc network in an outdoor obstacle-dense topology. Measurements performed throughout the topology allow for a realistic evaluation of a scenario with multiple moving eavesdroppers. Low speed user movement has been considered, so that Doppler spread can be discarded. A set of discrete field test trials have been conducted, based on simulation of human mobility throughout an obstacle-constrained environment. Average Signal-to-Noise Ratio (SNR) values have been measured for all moving nodes, and the Probability of Non-Zero Secrecy Capacity has been calculated for different eavesdropping cooperative schemes (Selection Combining and Maximal-Ratio Combining). In addition, the Outage Probability has been estimated with regard to a nonzero target Secrecy Rate for both techniques. The results have been compared with the respective values of WITS key parameters derived from theoretical analysis