Karim Jahed

Exploiting multiple wireless interfaces in smartphones for traffic offloading

Smartphones are evolving at a fast rate in terms of their computational, storage, and communications capabilities. A high-end smartphone is equipped with multiple wireless interfaces with varying bit rates, energy consumption requirements, and coverage ranges. The joint utilization of the existing wireless interfaces facilitates the development of advanced techniques to boost the performance of wireless networks and enhance the experience of mobile users. Among these techniques is device-to-device cooperation where a smartphone receives content from a base station on a given wireless interface and distributes it to other devices in its vicinity via another wireless interface. Another technique is traffic offloading in heterogeneous network scenarios where a smartphone downloads content using multiple wireless interfaces. In this paper, we study the readiness of high-end smartphones to utilize multiple wireless interfaces simultaneously focusing on capabilities and challenges. We adopt an experimental approach using a mobile cooperative video distribution testbed to obtain and evaluate performance results with focus on energy consumption. We consider various scenarios involving a combination of wireless technologies that include Bluetooth, WiFi, WiFi-Direct, and 3G.

An optimized approach to video traffic splitting in heterogeneous wireless networks with energy and QoE considerations

Abstract Due to the exploding traffic demands with the ubiquitous anticipated spread of 5G and Internet of Things, research has been active to devise mechanisms for meeting these demands while maintaining high quality user experience. In support of this direction, 3GPP is working towards cellular/WiFi interworking in heterogeneous networks to boost throughput, capacity, coverage and quality of experience. However, the continuous use of multiple wireless interfaces will increase the system performance but at the expense of more energy. As a result, there is a need for a dynamic use of multiple interfaces to provide a balance between energy consumption, throughput and user experience. Previous work in this field has considered improving throughput and reducing energy consumption, but did not consider simultaneously quality of experience as perceived by the end user. In this work, we aim at devising real-time traffic splitting strategies between WiFi and cellular networks to maximize user experience, reduce delay, and balance the needed energy consumption. We develop solutions for cellular/WiFi network resource management using Lyapunov drift-plus-penalty optimization approach. We evaluate the proposed approach using parameters determined via experimental measurements from mobile devices, and using our own test bed implementation to provide an evaluation under realistic operation conditions. Results show the performance effectiveness of the proposed traffic splitting approach in terms of throughput, delay, queue stability, energy consumption and quality of user experience by monitoring the frequency and lengths of video stalls.

Optimized group owner selection in WiFi direct networks

Device-to-Device communication is an essential component in the evolution towards the Internet of Things. The relatively new WiFi-Direct standard allows devices to communicate directly over the well established IEEE 802.11 protocol without the need for an intermediate access point. The increased range and bandwidth advantage of WiFi-Direct over existing short-range technologies such as Bluetooth and Zigbee allows a whole new class of applications to benefit from cooperation. Within a WiFi-Direct network, one device is elected as the group owner and acts as a central hub for all communications. The standard does not dictate a group owner selection strategy and leaves the decision to be taken at the application layer. In this work, we present an optimized strategy for group owner selection within WiFi-Direct networks that aims at maximizing the overall network performance in terms of increased throughput. Moreover, we propose a low complexity multi-device group owner negotiation protocol that runs at the application layer and extends the existing WiFi-Direct protocol, which limits group owner negotiation to two devices only. Simulation results demonstrate the effectiveness of the proposed selection strategy in achieving near-optimum results.

Energy measurements for mobile cooperative video streaming

In a classical mobile video streaming architecture, the server is responsible for processing each request from the mobile clients even if those requests are for the same content in the same geographical area. This tends to be resource exhaustive in terms of complexity, radio resources, and energy consumption especially when delivering high bit rate multimedia content. In this paper, we exploit cooperation between network technologies to reduce the load placed on a given multimedia server and reduce the overall energy drain of mobile devices. We consider a set of mobile devices that wish to receive a common video content from a designated video server. The mobile devices organize themselves into multiple Bluetooth piconets. The master in each piconet receives an H.264 encoded video content from the server via an IEEE 802.11 WLAN access point and relays it to its slave mobile devices using standard Bluetooth connections. A prototypical implementation of the proposed model in an experimental testbed is used to perform energy and video quality measurements in real conditions. Results demonstrate notable energy consumption gains while maintaining video quality in various scenarios.

Scalable Multimedia Streaming in Wireless Networks with Device-to-Device Cooperation

We present a scalable mobile multimedia streaming system with device-to-device cooperation that enables common content distribution in dense wireless networking environments. This is particularly applicable to use cases such as delivering real-time multimedia content to fans watching a soccer game in a stadium or to participants attending a major conference in a large auditorium. The key novel characteristics of our system include seamless neighbor discovery and link quality estimation, intelligent clustering and channel allocation algorithms based on constrained minimum spanning trees, robustness against device mobility, and device centric operation with no changes to existing wireless systems. We demonstrate the functionality of the proposed system on Android devices using heterogeneous networks (cellular/WiFi/WiFi-Direct) and show the formation of multiple clusters to allow for scalable operation. The gained insights will help bridge the gap between theoretical and simulation based research conducted in this area and practical operation taking into account the capabilities and limitations of existing wireless technologies and smartphones/tablets.

Optimized device centric aggregation mechanisms for mobile devices with multiple wireless interfaces

Abstract Wireless broadband technologies and services are witnessing exponential growth to meet the demands of mobile users. State-of-the-art wireless networks are evolving with enhancements spanning all protocol layers and all network components from radio access to core network nodes. This has been coupled with a tremendous transformation of end user mobile devices towards multi-purpose smartphones and tablets with multi-core processing power, extendable memory storage, large battery capacity, and support for a wide range of wireless connectivity options. A standard smartphone currently can support short range Bluetooth and WiFi-Direct connectivity, local area WiFi connectivity, and long range 2G/3G/4G mobile connectivity. This naturally provides opportunities for data aggregation utilizing multiple wireless interfaces simultaneously to enhance device and network performance. In this work, we present the design, implementation, and testing of optimized device-centric data aggregation mechanisms for both file downloading and video streaming applications. The main novelty of the proposed mechanisms is their device-centric design, which makes them practical and feasible without any changes to wireless standards; moreover, they are scalable to support any number of wireless interfaces whereas previous related work has dealt with devices having two interfaces only. To demonstrate the effectiveness of the proposed mechanisms in terms of performance gains and practical feasibility, we develop an experimental testbed using Android devices and perform extensive testing for several network scenarios.

Practical device-centric WiFi/cellular link aggregation mechanism for mobile devices

One key enhancement in recent 3GPP cellular standard releases is WiFi/cellular network inter-operation and integration with protocols and techniques that can optimize target performance metrics. 3GPP specifications focus on network- centric solutions that require the involvement of the cellular operator in the WiFi/cellular connection and mobility management procedures. Even though this provides notable gains due to the centralized control, its implementation requires network upgrades and is relatively complex which limits the possibility of quick market penetration. In this work, we present a practical and effective device-centric WiFi/cellular link aggregation mechanism that can boost the download bit rate and minimize the download delay. The proposed mechanism is self-adaptive and achieves optimized aggregated download bit rate without the need for link quality estimation information, changes to the WiFi or cellular standards, or proxy server configuration. Moreover, it can be customized to support different service classes as shown for file downloading and video streaming applications. We do extensive testing using an experimental implementation over Android smartphones in order to demonstrate the practical feasibility of the proposed mechanism and to quantify its performance gains under different operational conditions.

Failure recovery in wireless content distribution networks with device-to-device cooperation

Abstract Device-to-device cooperation has emerged as a prominent solution to a wide range of challenges in large-scale wireless networks. However, the ad hoc nature of cooperative networks and their proneness to failure are a major obstacle towards their real world deployment and wide adoption. In this work, we focus on failure recovery and scalability in wireless content distribution networks with device-to-device cooperation, where a number of mobile devices in a given geographical area are interested in downloading a common content from an application service provider. We present low complexity effective algorithms based on clustering and tree construction methods in order to address three different types of dynamic node behavior, namely new devices joining the network, existing devices leaving the network, and existing devices moving locally within the network. Moreover, we propose a constrained version of the minimum spanning tree algorithm with bounds on the height of the tree and the maximum degree per node, in order to capture practical operational constraints for device-to-device cooperation in wireless networks. We present results for various network scenarios using simulations and experimental test bed to demonstrate the effectiveness of the proposed algorithms in terms of performance efficiency, computational complexity, and practical implementation feasibility.

Social-Aware Device-to-Device Offloading Based on Experimental Mobility and Content Similarity Models

Device-to-device (D2D) offloading has been shown to be a highly effective technique to enhance the performance of wireless networks. Yet, for any two mobile users to share data efficiently and reliably via D2D links, they should be in close proximity for long enough period of time, share similar content interests, and have some level of incentive and trust to cooperate. In this work, we focus on the practical implementation aspects of D2D data sharing taking into account realistic operational conditions. To this end, we design and conduct an experimental study to collect location and neighbor discovery data from 38 mobile users in a university campus over several weeks using our own customized crowdsourcing Android mobile application. The collected data is then processed and utilized to empirically model mobility-related parameters that include contact frequency, contact duration, and inter-contact duration. The participating users did also fill a user interest survey in order to correlate mobility and connectivity patterns with content interests and social network relations. The obtained insights are then used to develop a practical implementation framework for designing effective D2D data sharing strategies. To test the proposed ideas under realistic operational constraints, we design and implement a social-aware D2D data sharing Android mobile application and demonstrate its functionality and effectiveness using an example case study scenario.

Highly Scalable Parallel Search-Tree Algorithms: The Virtual Topology Approach

Summary form only given. We introduce the notion of a virtual topology and explore the use of search-tree indexing to achieve highly scalable parallel search-tree algorithms for NP-hard problems. Vertex Cover and Cluster Editing are used as case studies.