Mobility: a double-edged sword for HSPA networks

This paper presents an empirical study on the performance of mobile High Speed Packet Access (HSPA, a 3.5G cellular standard) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large scale empirical study on the performance of commercial mobile HSPA networks

Resource-efficient strategies for mobile ad-hoc networking

The ubiquity and widespread availability of wireless mobile devices with ever increasing inter-connectivity (e. g. by means of Bluetooth, WiFi or UWB) have led to new and emerging next generation mobile communication paradigms, such as the Mobile Ad-hoc NETworks (MANETs). MANETs are differentiated from traditional mobile systems by their unique properties, e. g. unpredictable nodal location, unstable topology and multi-hop packet relay. The success of on-going research in communications involving MANETs has encouraged their applications in areas with stringent performance requirements such as the e-healthcare, e. g. to connect them with existing systems to deliver e-healthcare services anytime anywhere. However, given that the capacity of mobile devices is restricted by their resource constraints (e. g. computing power, energy supply and bandwidth), a fundamental challenge in MANETs is how to realize the crucial performance/Quality of Service (QoS) expectations of communications in a network of high dynamism without overusing the limited resources. A variety of networking technologies (e. g. routing, mobility estimation and connectivity prediction) have been developed to overcome the topological instability and unpredictability and to enable communications in MANETs with satisfactory performance or QoS. However, these technologies often feature a high consumption of power and/or bandwidth, which makes them unsuitable for resource constrained handheld or embedded mobile devices. In particular, existing strategies of routing and mobility characterization are shown to achieve fairly good performance but at the expense of excessive traffic overhead or energy consumption. For instance, existing hybrid routing protocols in dense MANETs are based in two-dimensional organizations that produce heavy proactive traffic. In sparse MANETs, existing packet delivery strategy often replicates too many copies of a packet for a QoS target. In addition, existing tools for measuring nodal mobility are based on either the GPS or GPS-free positioning systems, which incur intensive communications/computations that are costly for battery-powered terminals. There is a need to develop economical networking strategies (in terms of resource utilization) in delivering the desired performance/soft QoS targets. The main goal of this project is to develop new networking strategies (in particular, for routing and mobility characterization) that are efficient in terms of resource consumptions while being effective in realizing performance expectations for communication services (e. g. in the scenario of e-healthcare emergency) with critical QoS requirements in resource-constrained MANETs. The main contributions of the thesis are threefold: (1) In order to tackle the inefficient bandwidth utilization of hybrid service/routing discovery in dense MANETs, a novel "track-based" scheme is developed. The scheme deploys a one-dimensional track-like structure for hybrid routing and service discovery. In comparison with existing hybrid routing/service discovery protocols that are based on two-dimensional structures, the track-based scheme is more efficient in terms of traffic overhead (e. g. about 60% less in low mobility scenarios as shown in Fig. 3.4). Due to the way "provocative tracks" are established, the scheme has also the capability to adapt to the network traffic and mobility for a better performance. (2) To minimize the resource utilization of packet delivery in sparse MANETs where wireless links are intermittently connected, a store-and-forward based scheme, "adaptive multicopy routing", was developed for packet delivery in sparse mobile ad-hoc networks. Instead of relying on the source to control the delivery overhead as in the conventional multi-copy protocols, the scheme allows each intermediate node to independently decide whether to forward a packet according to the soft QoS target and local network conditions. Therefore, the scheme can adapt to varying networking situations that cannot be anticipated in conventional source-defined strategies and deliver packets for a specific QoS targets using minimum traffic overhead. ii (3) The important issue of mobility measurement that imposes heavy communication/computation burdens on a mobile is addressed with a set of resource-efficient "GPS-free" soluti ons, which provide mobility characterization with minimal resource utilization for ranging and signalling by making use of the information of the time-varying ranges between neighbouring mobile nodes (or groups of mobile nodes). The range-based solutions for mobility characterization consist of a new mobility metric for network-wide performance measurement, two velocity estimators for approximating the inter-node relative speeds, and a new scheme for characterizing the nodal mobility. The new metric and its variants are capable of capturing the mobility of a network as well as predicting the performance. The velocity estimators are used to measure the speed and orientation of a mobile relative to its neighbours, given the presence of a departing node. Based on the velocity estimators, the new scheme for mobility characterization is capable of characterizing the mobility of a node that are associated with topological stability, i. e. the node's speeds, orientations relative to its neighbouring nodes and its past epoch time. iiiBIOPATTERN EU Network of Excellence (EU Contract 508803

Multitask Learning for Network Traffic Classification

Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training such models requires a large amount of labeled data. Labeling data is often the most difficult and time-consuming process in building a classifier. To solve this challenge, we reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class. The motivation of this approach is twofold: First, bandwidth requirement and duration are useful in many applications, including routing, resource allocation, and QoS provisioning. Second, these two values can be obtained from each flow easily without the need for human labeling or capturing flows in a controlled and isolated environment. We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy. We conduct two experiment with ISCX and QUIC public datasets and show the efficacy of our approach