Showing papers by "Nicola Bui published in 2015"

Staying Alive: System Design for Self-Sufficient Sensor Networks

Abstract: Self-sustainability is a crucial step for modern sensor networks. Here, we offer an original and comprehensive framework for autonomous sensor networks powered by renewable energy sources. We decompose our design into two nested optimization steps: the inner step characterizes the optimal network operating point subject to an average energy consumption constraint, while the outer step provides online energy management policies that make the system energetically self-sufficient in the presence of unpredictable and intermittent energy sources. Our framework sheds new light into the design of pragmatic schemes for the control of energy-harvesting sensor networks and permits to gauge the impact of key sensor network parameters, such as the battery capacity, the harvester size, the information transmission rate, and the radio duty cycle. We analyze the robustness of the obtained energy management policies in the cases where the nodes have differing energy inflow statistics and where topology changes may occur, devising effective heuristics. Our energy management policies are finally evaluated considering real solar radiation traces, validating them against state-of-the-art solutions, and describing the impact of relevant design choices in terms of achievable network throughput and battery-level dynamics.

Mobile network resource optimization under imperfect prediction

Abstract: A highly interesting trend in mobile network optimization is to exploit knowledge of future network capacity to allow mobile terminals to prefetch data when signal quality is high and to refrain from communication when signal quality is low. While this approach offers remarkable benefits, it relies on the availability of a reliable forecast of system conditions. This paper focuses on the reliability of simple prediction techniques and their impact on resource allocation algorithms. In addition, we propose ICARO, a resource allocation technique that is robust to prediction uncertainties. The algorithm combines autoregressive filtering and statistical models for short, medium, and long term forecasting. We validate our approach by means of an extensive simulation campaign based on real measurement data collected in Berlin. We show that our solution performs close to an omniscient optimizer and outperforms a limited horizon omniscient optimizer by 10 – 15%. Our solution provides up to 30% saving of system resources compared to a simple solution that always maintains a full buffer and is close to optimal in terms of buffer under-run time.

Anticipatory Admission Control and Resource Allocation for Media Streaming in Mobile Networks

Abstract: The exponential growth of media streaming traffic will have a strong impact on the bandwidth consumption of the future wireless infrastructure. One key challenge is to deliver services taking into account the stringent requirements of mobile video streaming, e.g., the users' expected Quality-of-Service. Admission control and resource allocation can strongly benefit from the use of anticipatory information such as the prediction of future user's demand and expected channel gain. In this paper, we use this information to formulate an optimal admission control scheme that maximizes the number of accepted users into the system with the constraint that not only the current but also the expected demand of all users must be satisfied. Together with the optimal set of accepted users, the optimal resource scheduling is derived. In order to have a solution that can be computed in a reasonable time, we propose a low complexity heuristic. Numerical results show the performance of the proposed scheme with respect to the state of the art.

Anticipatory quality-resource allocation for multi-user mobile video streaming

Abstract: Mobile video delivery forms the largest part of the traffic in cellular networks. Thus optimizing the resource allocation to satisfy a user's quality of experience is becoming paramount in modern communications. This paper belongs to the line of research known as anticipatory networking that makes use of prediction of wireless capacity to improve communication performance. In particular, we focus on the problem of optimal resource allocation for steady video delivery under maximum average quality constraints for multiple users. We formulate the problem as a piecewise linear program and provide a heuristic algorithm, which solution is close to optimal. Based on our formulation we are now able to trade off minimum video quality, average quality and offered network capacity.

Lightweight mobile bandwidth availability measurement

Abstract: Mobile data traffic is increasing rapidly and wireless spectrum is becoming a more and more scarce resource This makes it highly important to operate the mobile network efficiently In this paper we are proposing a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones Our main idea leverages an original packet dispersion based, technique to estimate both per user capacity and asymptotic dispersion rate This allows passive measurements using only existing mobile traffic Our technique is able to efficiently filter outliers introduced by mobile network schedulers In order to verify the feasibility of our measurement technique, we run a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day The campaign demonstrates that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity

Behind the NAT — A measurement based evaluation of cellular service quality

Abstract: Mobile applications such as VoIP, (live) gaming, or video streaming have diverse QoS requirements ranging from low delay to high throughput. The optimization of the network quality experienced by end-users requires detailed knowledge of the expected network performance. Also, the achieved service quality is affected by a number of factors, including network operator and available technologies. However, most studies focusing on measuring the cellular network do not consider the performance implications of network configuration and management. To this end, this paper reports about an extensive data set of cellular network measurements, focused on analyzing root causes of mobile network performance variability. Measurements conducted over four weeks in a 4G cellular network in Germany show that management and configuration decisions have a substantial impact on the performance. Specifically, it is observed that the association of mobile devices to a Point of Presence (PoP) within the operator's network can influence the end-to-end RTT by a large extent. Given the collected data a model predicting the PoP assignment and its resulting RTT leveraging Markov Chain and machine learning approaches is developed. RTT increases of 58% to 73% compared to the optimum performance are observed in more than 57% of the measurements.

Media download optimization through prefetching and resource allocation in mobile networks

Abstract: Mobile network operators are expected to face significant traffic increase in the upcoming years. One alternative method is to intelligently move transmissions to times of network underutilization, either on 3G/4G or by offloading to WiFi. Video content, predicted by Cisco to constitute 69% of mobile traffic, offers the greatest potential for offloading. To this end, the demonstrated app strives to relieve the mobile network in a two ways. First, long-term prefetching of promising videos based on posts from the user's Online Social Network feed is performed. The knowledge about which video is likely being requested in the near future offers the opportunity to schedule the transmission according to its probability of being watched. Second, the approach is complemented with short-term prefetching, which is used whenever a content could not be downloaded by long-term prefetching. In this case, resources are optimized so as to maximize the communication efficiency while preserving the quality of service. The demonstrated app considers the smartphone's observed cellular network history to optimize the mobile throughput. A customized video player implements both the long-term and short-term prefetching. It reduces both the load on mobile networks, decreases playback pausing events and hereby achieves a high QoE. Thus, the player addresses both the operators' and the users' needs.