Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks

We present and evaluate a procedure that utilizes mobility and throughput prediction to prefetch video streaming data in integrated cellular and WiFi networks. The effective integration of such heterogeneous wireless technologies will be significant for supporting high performance and energy efficient video streaming in ubiquitous networking environments. Our evaluation is based on trace-driven simulation considering empirical measurements and shows how various system parameters influence the performance, in terms of the number of paused video frames and the energy consumption; these parameters include the number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and the number of WiFi hotspots. Also, we assess the procedure's robustness to time and throughput variability. Finally, we present our initial prototype that implements the proposed approach.Comment: 7 pages, 15 figure

MADServer: An Architecture for Opportunistic Mobile Advanced Delivery

Rapid increases in cellular data traffic demand creative alternative delivery vectors for data. Despite the conceptual attractiveness of mobile data offloading, no concrete web server architectures integrate intelligent offloading in a production-ready and easily deployable manner without relying on vast infrastructural changes to carriers’ networks. Delay-tolerant networking technology offers the means to do just this. We introduce MADServer, a novel DTN-based architecture for mobile data offloading that splits web con- tent among multiple independent delivery vectors based on user and data context. It enables intelligent data offload- ing, caching, and querying solutions which can be incorporated in a manner that still satisfies user expectations for timely delivery. At the same time, it allows for users who have poor or expensive connections to the cellular network to leverage multi-hop opportunistic routing to send and receive data. We also present a preliminary implementation of MADServer and provide real-world performance evaluations

TP-DS: A Heuristic Approach for Traffic Pattern Discovery System in MANET’s

As mobile ad hoc network (MANET) systems research has matured and several testbeds have been built to study MANETs, research has focused on developing new MANET applications such as collaborative games, collaborative computing, messaging systems, distributed security schemes, MANET middleware, peer-to-peer file sharing systems, voting systems, resource management and discovery, vehicular computing and collaborative education systems. Many techniques are proposed to enhance the anonymous communication in case of the mobile ad hoc networks (MANETs). However, MANETs are vulnerable under certain circumstances like passive attacks and traffic analysis attacks. Traffic analysis problem expose some of the methods and attacks that could infer MANETs are still weak under the passive attacks. In this Research, proposed ‘Traffic pattern Discovery System in MANET’s, aheuristic approach(TP-DS) , enables a passive global adversary to accurately infer the traffic pattern in an anonymous MANET without compromising any node. TP-DS works well on existing on-demand anonymous MANET routing protocols to determine the source node, destination node and the end-to-end communication path. Detailed simulations show that TP-DS can infer the hidden traffic pattern with accuracy as high than the TP-DS and gives the result with accuracy of 95%. DOI: 10.17762/ijritcc2321-8169.150310

Relieving the Wireless Infrastructure: When Opportunistic Networks Meet Guaranteed Delays

Major wireless operators are nowadays facing network capacity issues in striving to meet the growing demands of mobile users. At the same time, 3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g., Wi-Fi). In this context of hybrid connectivity, we propose Push-and-track, a content dissemination framework that harnesses ad hoc communication opportunities to minimize the load on the wireless infrastructure while guaranteeing tight delivery delays. It achieves this through a control loop that collects user-sent acknowledgements to determine if new copies need to be reinjected into the network through the 3G interface. Push-and-Track includes multiple strategies to determine how many copies of the content should be injected, when, and to whom. The short delay-tolerance of common content, such as news or road traffic updates, make them suitable for such a system. Based on a realistic large-scale vehicular dataset from the city of Bologna composed of more than 10,000 vehicles, we demonstrate that Push-and-Track consistently meets its delivery objectives while reducing the use of the 3G network by over 90%.Comment: Accepted at IEEE WoWMoM 2011 conferenc

Enriching Remote Control Applications with Fog Computing

Fog computing has emerged in the recent years as a paradigm tailored to serve geo-distributed applications requiring low latency. Remote Control (RC) applications allow a mobile device to control another device from remote. To enrich Quality of Experience (QoE) of RC applications, in this paper we investigate the use of fog computing as a viable platform to offload computation of tasks that would be expensive if performed locally on a mobile device. The proposed approach, supported with next 5G communication systems, will enable a Tactile Internet experience. In this paper we study and compare offload policies to accommodate tasks in the fog platform and analyze the requirements to minimize outages

Prediction Model for Offloading in Vehicular Wi-Fi Network

It cannot be denied that, the inescapable diffusion of smartphones, tablets and other vehicular network applications with diverse networking and multimedia capabilities, and the associated blooming of all kinds of data-hungry multimedia services that passengers normally used while traveling exert a big challenge to cellular infrastructure operators. Wireless fidelity (Wi-Fi) as well as fourth generation long term evolution advanced (4G LTE-A) network are widely available today, Wi-Fi could be used by the vehicle users to relieve 4G LTE-A networks. Though, using IEE802.11 Wi-Fi AP to offload 4G LTE-A network for moving vehicle is a challenging task since it only covers short distance and not well deployed to cover all the roads. Several studies have proposed the offloading techniques based on predicted available APs for making offload decision. However, most of the proposed prediction mechanisms are only based on historical connection pattern. This work proposed a prediction model which utilized historical connection pattern, vehicular movement and driver profile to predict the next available AP.  The proposed model is compared with the existing models to evaluate its practicability

On Optimal and Fair Service Allocation in Mobile Cloud Computing

This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models