Paweł Foremski

Energy-Efficient Crowdsensing of Human Mobility and Signal Levels in Cellular Networks.

The paper presents a practical application of the crowdsensing idea to measure human mobility and signal coverage in cellular networks. Currently, virtually everyone is carrying a mobile phone, which may be used as a sensor to gather research data by measuring, e.g., human mobility and radio signal levels. However, many users are unwilling to participate in crowdsensing experiments. This work begins with the analysis of the barriers for engaging people in crowdsensing. A survey showed that people who agree to participate in crowdsensing expect a minimum impact on their battery lifetime and phone usage habits. To address these requirements, this paper proposes an application for measuring the location and signal strength data based on energy-efficient GPS tracking, which allows one to perform the measurements of human mobility and radio signal levels with minimum energy utilization and without any engagement of the user. The method described combines measurements from the accelerometer with effective management of the GPS to monitor the user mobility with the decrease in battery lifetime by approximately 20%. To show the applicability of the proposed platform, the sample results of signal level distribution and coverage maps gathered for an LTE network and representing human mobility are shown.

Waterfall: Rapid Identification of IP Flows Using Cascade Classification

In the last years network traffic classification has attracted much research effort, given that it represents the foundation of many Internet functionalities such as Quality of Service (QoS) enforcement, monitoring, and security. Nonetheless, the proposed works are not able to satisfactorily solve the problem, usually being suitable for only addressing a given portion of the whole network traffic and thus none of them can be considered an ultimate solution for network classification.

DNS-Class: immediate classification of IP flows using DNS

SUMMARY Nowadays we see a tremendous growth of the Internet, especially in terms of the amont of data being transmitted and new network protocols being introduced. This poses a challenge for network administrators, who need adequate tools for network management. Recent findings show that DNS can contribute valuable information on IP flows and improve traffic visibility in a computer network. In this paper, we apply these findings on DNS to propose a novel traffic classification algorithm with interesting features. We experimentally show that the information carried in domain names and port numbers is sufficient for immediate classification of a highly significant portion of the traffic. We present DNS-Class: an innovative, fast and reliable flow-based traffic classification algorithm, which on average yields 99.8% of true positives and < 0.1% of false positives on real traffic traces. The algorithm can work as a major element of a modular system in a cascade architecture. Additionally, we provide an analysis on how various network protocols depend on DNS in terms of flows, packets and bytes. We release the complete source code implementing the presented system as open source. Copyright

LTE or WiFi? Client-Side Internet Link Selection for Smartphones

Current mobile phones and tablets are equipped with two technologies for accessing the Internet: WiFi and Cellular. Deciding which of these two interfaces provides faster data transfer is often non-trivial, but most of the currently used devices use a simple priority scheme that prefers WiFi to Cellular.

Source Model of TCP Traffic in LTE Networks

We propose a generator that represents traffic transmitted in mobile wireless networks. It is based on measurements of IP flows in a real, large-scale LTE network. The proposed tool generates TCP flow sizes and durations. We verify the generator by comparison with the traces available in the literature, and we propose application in discrete event simulators.

Waterfall Traffic Identification: Optimizing Classification Cascades

The Internet transports data generated by programs which cause various phenomena in IP flows. By means of machine learning techniques, we can automatically discern between flows generated by different traffic sources and gain a more informed view of the Internet.

Demo: Wireless Link Selection on Smartphone: Throughput vs Battery Drain

The method for automatic selection of wireless interface on smart phones is presented. We consider the selection between WiFi and LTE / 3G radios to maximize the throughput of the data transmission and minimize the amount of energy used to scan and measure the throughput of the network. The proposed selection algorithm defines when to measure and how to estimate the network throughput to balance between the accuracy of the selection and the battery drain caused by the measurements. The link speed estimation method optimized for LTE and WiFi networks is also demonstrated