An Optimized AMS Based Cloud Downloading Service with Advanced Caching and Intelligent Data Distribution Mechanism

The popularity of peer-to-peer video content downloading has surged due to diverse content availability and convenient sharing among users. However, scaling systems to accommodate the growing number of users and content items poses a challenge. This research aims to optimize video content downloading in peer-to-peer systems. The objective is to improve performance by developing advanced caching mechanisms, an intelligent data distribution algorithm, and efficient bandwidth resource management. The proposed approach involves implementing innovative caching mechanisms that store frequently accessed content closer to users, reducing download time. An intelligent data distribution algorithm minimizes bottlenecks and maximizes download speeds. Efficient bandwidth resource management ensures fair allocation. Results demonstrate significant enhancements in download time and overall system performance, leading to improved user experience. This research addresses the need for an optimized video content downloading system to handle increasing user and content volumes. The findings hold the potential to enhance user experiences, facilitate seamless video sharing, and advance peer-to-peer video content downloading

Towards Semantic Fast-Forward and Stabilized Egocentric Videos

The emergence of low-cost personal mobiles devices and wearable cameras and the increasing storage capacity of video-sharing websites have pushed forward a growing interest towards first-person videos. Since most of the recorded videos compose long-running streams with unedited content, they are tedious and unpleasant to watch. The fast-forward state-of-the-art methods are facing challenges of balancing the smoothness of the video and the emphasis in the relevant frames given a speed-up rate. In this work, we present a methodology capable of summarizing and stabilizing egocentric videos by extracting the semantic information from the frames. This paper also describes a dataset collection with several semantically labeled videos and introduces a new smoothness evaluation metric for egocentric videos that is used to test our method.Comment: Accepted for publication and presented in the First International Workshop on Egocentric Perception, Interaction and Computing at European Conference on Computer Vision (EPIC@ECCV) 201

Self-Sustaining Caching Stations: Towards Cost-Effective 5G-Enabled Vehicular Networks

In this article, we investigate the cost-effective 5G-enabled vehicular networks to support emerging vehicular applications, such as autonomous driving, in-car infotainment and location-based road services. To this end, self-sustaining caching stations (SCSs) are introduced to liberate on-road base stations from the constraints of power lines and wired backhauls. Specifically, the cache-enabled SCSs are powered by renewable energy and connected to core networks through wireless backhauls, which can realize "drop-and-play" deployment, green operation, and low-latency services. With SCSs integrated, a 5G-enabled heterogeneous vehicular networking architecture is further proposed, where SCSs are deployed along roadside for traffic offloading while conventional macro base stations (MBSs) provide ubiquitous coverage to vehicles. In addition, a hierarchical network management framework is designed to deal with high dynamics in vehicular traffic and renewable energy, where content caching, energy management and traffic steering are jointly investigated to optimize the service capability of SCSs with balanced power demand and supply in different time scales. Case studies are provided to illustrate SCS deployment and operation designs, and some open research issues are also discussed.Comment: IEEE Communications Magazine, to appea

Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data

The development of audio event recognition models requires labeled training data, which are generally hard to obtain. One promising source of recordings of audio events is the large amount of multimedia data on the web. In particular, if the audio content analysis must itself be performed on web audio, it is important to train the recognizers themselves from such data. Training from these web data, however, poses several challenges, the most important being the availability of labels : labels, if any, that may be obtained for the data are generally {\em weak}, and not of the kind conventionally required for training detectors or classifiers. We propose that learning algorithms that can exploit weak labels offer an effective method to learn from web data. We then propose a robust and efficient deep convolutional neural network (CNN) based framework to learn audio event recognizers from weakly labeled data. The proposed method can train from and analyze recordings of variable length in an efficient manner and outperforms a network trained with {\em strongly labeled} web data by a considerable margin

Establishing the design knowledge for emerging interaction platforms

While awaiting a variety of innovative interactive products and services to appear in the market in the near future such as interactive tabletops, interactive TVs, public multi-touch walls, and other embedded appliances, this paper calls for preparation for the arrival of such interactive platforms based on their interactivity. We advocate studying, understanding and establishing the foundation for interaction characteristics and affordances and design implications for these platforms which we know will soon emerge and penetrate our everyday lives. We review some of the archetypal interaction platform categories of the future and highlight the current status of the design knowledge-base accumulated to date and the current rate of growth for each of these. We use example designs illustrating design issues and considerations based on the authors’ 12-year experience in pioneering novel applications in various forms and styles

Editorial: Emerging techniques and applications for 5G networks and beyond

Editoria

Unmanned Aerial Vehicles (UAVs) as on-demand QoS enabler for Multimedia Applications in Smart Cities

The evolution of drones and similar small wingspan UAVs has resulted in their use in many commercial applications. This has allowed investigating the potential use of drones in the context of Internet of Things. In the recent past, there is ample evidence indicating the use of UAVs as a means to supplement mobile infrastructure to extend it for surveillance, monitoring, data collection and providing on-demand network access capabilities. This paper explores the potential of UAVs to act as on-demand QoS enablers for TCP-based applications within Smart Cities, particularly those applications that require low connection delays, reliability and high throughputs such as multimedia streaming.Many multimedia rich applications, such as live streaming, multi-player online gaming are mostly tied down to fixed-line broadband infrastructure. Mobile cloud technologies and Mobile Edge Computing (MEC) address the challenge by bringing the computing, storage and networking resources to the edge and integrating with the base station, thereby providing better content delivery. The paper presents a concept of UAV-based aerial MEC, which hosts a TCP-proxy that acts as an `On-Demand QoS' enabler to TCP-based applications in Smart Cities reducing the overall-connection delays and increasing the throughput thereby enhancing the end-user experience. With the technologies available in literature we demonstrate that a UAV-based aerial MEC with the capability to migrate QoS-enabling processes from the edge to the core and edge to the edge, to support mobile applications, is feasible