New mobile network technologies are introduced about every ten years. Each technology is still designed to have a long lifetime that results in mobile operators running multiple radio technologies at the same time on their network. Currently, up to four technologies are operated simultaneously: 2G, 3G, 4G and 5G. This increases the network’s energy consumption and wastes radio resources because newer technologies provide more capacity on the same spectrum. For these reasons, many mobile operators have closed down their 3G network and even more will do so in the coming years. The hope is that the 3G sunset will have a positive impact, but there is no published analysis on this. In this paper, we provide a world-first analysis of the impact of closing 3G in a modern operational nation-wide mobile network. We show the impact on energy consumption and capacity over a five-month period and how the network behaves after 3G is closed and once 4G takes over the frequencies freed by 3G. We find that 4G can serve the frequency with the same power consumption while offering much higher capacity.

This paper investigates the fundamental traffic steering issue for cellular-enabled unmanned aerial vehicles (UAVs), where each UAV needs to select one from different Mobile Network Operators (MNOs) to steer its traffic for improving the Quality-of-Service (QoS). To this end, we first formulate the issue as an optimization problem aiming to minimize the maximum outage probabilities of the UAVs. This problem is non-convex and non-linear, which is generally difficult to be solved. We propose a solution based on the framework of deep reinforcement learning (DRL) to solve it, in which we define the environment and the agent elements. Furthermore, to avoid sharing the learned experiences by the UAV in this solution, we further propose a federated deep reinforcement learning (FDRL)-based solution. Specifically, each UAV serves as a distributed agent to train separate model, and is then communicated to a special agent (dubbed coordinator) to aggregate all training models. Moreover, to optimize the aggregation process, we also introduce a FDRL with DRL-based aggregation (DRL2A) approach, in which the coordinator implements a DRL algorithm to learn optimal parameters of the aggregation. We consider deep Q-learning (DQN) algorithm for the distributed agents and Advantage Actor-Critic (A2C) for the coordinator. Simulation results are presented to validate the effectiveness of the proposed approach.

This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach.

This paper considers the scenario of Unmanned Aerial Vehicles (UAVs) acting as flying base stations (UAV-BSs) to provide network connectivity to ground Internet of Things (IoT) devices. More precisely, we investigate the issue where a UAV-BS needs to be replaced by a new one in a seamless way. First, we formulate the issue as an optimization problem aiming to maximize the minimum transmission rate of the served IoT devices during the UAV-BS replacement process. This is translated into jointly optimizing the trajectory of the source UAV-BS (the one to be replaced) and the target UAV-BS (the replacing one), while pushing the IoT devices to seamlessly transfer their connections to the target UAV-BS. We therefore consider a target replacement zone where the UAV-BS replacement can happen, along with IoT connections transfer. Furthermore, we propose a solution based on Deep Reinforcement Learning (DRL). More precisely, we introduce a Multi-Heterogeneous Agent-based approach (MHA-DRL), where two types of agents are considered, namely the UAV-BS agents and the IoT agents. Each agent implements a DQN (Deep Q-Learning) algorithm, where UAV-BS agents learn optimal policies to perform replacement while IoT agents learn optimal policies to transfer their connections to the target UAV-BS. The conducted performance evaluations show that the proposed approach can achieve near optimal optimization.

The increasing trend of the traffic demand from mobile users and the presence of limited resources creates a challenge for network resource management. Understanding the data usage pattern and traffic demand of mobile users is a way forward to enable data-driven network resource management. However, due to the complex nature of mobile networks, understanding and characterizing data usage pattern of mobile users is a daunting task. In this work, we investigate and characterize data usage patterns and behavior of users in mobile networks. We leverage a dataset (~340 M records) collected through a crowd-based mobile network measurement platform - Netradar - across six countries. We elucidate different network factors and study how they affect the data usage patterns by taking mobile users in Finland as a use case. We perform a comparison on data usage patterns of mobile users across six countries by considering total data consumption, network access, the number of sessions created per user, throughput, and user satisfaction level on services. We show that data usage behavior of users over a mobile network is primarily driven by user mobility, the type of data subscription plan marketed by Mobile Network Operators (MNOs), network congestion, and network coverage. Besides, the data usage patterns over different network technologies (e.g., preferring cellular over WiFi) and the percentage of users accessing congested networks vary by country; mostly due to the market pricing strategy and radio coverage. However, the overall data consumption (cellular and WiFi) is comparatively similar in most of the countries we studied.

We contribute by quantifying the effect of network latency and battery consumption on mobile app performance and retention, i.e., user's decisions to continue or stop using apps. We perform our analysis by fusing two large-scale crowdsensed datasets collected by piggybacking on information captured by mobile apps. We find that app performance has an impact in its retention rate. Our results demonstrate that high energy consumption and high latency decrease the likelihood of retaining an app. Conversely, we show that reducing latency or energy consumption does not guarantee higher likelihood of retention as long as they are within reasonable standards of performance. However, we also demonstrate that what is considered reasonable depends on what users have been accustomed to, with device and network characteristics, and app category playing a role. As our second contribution, we develop a model for predicting retention based on performance metrics. We demonstrate the benefits of our model through empirical benchmarks which show that our model not only predicts retention accurately, but generalizes well across application categories, locations and other factors moderating the effect of performance.

Quality of experience (QoE) models quantify the relationship between user experience and network quality of service. With the exception of a few studies, most research on QoE has been conducted in laboratory conditions. Therefore, in order to validate and develop QoE models for the wild, researchers should carry out large scale field studies. This paper contributes data and observations from such a large-scale field study on mobile devices carried out in Finland with 292 users and 64,036 experience ratings. 74% of the ratings are associated with Wifi or LTE networks. We report descriptive statistics and classification results predicting normal vs. bad QoE in in-the-wild measurements. Our results illustrate a 20% improvement over baselines for standard classification metrics (G-Mean). Furthermore, both network features (such as delay) and non-network features (such as device memory) show importance in the models. The models’ performance suggests that mobile QoE prediction remains a difficult problem in field conditions. Our results help inform future modeling efforts and provide a baseline for such real-world mobile QoE prediction.

No abstract available.

The throughput of a cellular network depends on a number of factors such as radio technology, limitations of device hardware (e.g., chipsets, antennae), physical layer effects (interference, fading, etc.), node density and demand, user mobility, and the infrastructure of Mobile Network Operators (MNO). Therefore, understanding and identifying the key factors of cellular network performance that affect end-users experience is a challenging task. We use a dataset collected using netradar, a platform that measures cellular network performance crowd- sourced from mobile user devices. Using this dataset we develop a methodology (a classifier using a machine learning approach) for understanding cellular network performance. We examine key characteristics of cellular networks related to throughput from the perspective of mobile user activity, MNO, smartphone models, link stability, location and time of day. We perform a network-wide correlation and statistical analysis to obtain a basic understanding of the influence of individual factors. We use a machine learning approach to identify the important features and to understand the relationship between different ones. These features are then used to build a model to classify the stability of cellular network based on the data reception characteristics of the user. We show that it is possible to classify reasons for network instability using minimal cellular network metrics with up to 90% of accuracy.

There have been efforts taken by different research projects to understand the complexity and the performance of a mobile broadband network. Various mobile network measurement platforms are proposed to collect performance metrics for analysis. Data integration would provide more thorough data analyses as well as prediction and decision models from one dataset to another. The crucial part of the data integration is to find out, whether two datasets have corresponding features (performance metrics). However, finding common features across datasets is a challenging task. For example, features might: 1) have similar names but be different metrics, 2) have different names but be similar metrics, or 3) be same metrics but have differences in the underlying methodology. We designed a feature mapping methodology between two crowdsourced LTE measurement-based datasets. Our method is based on correlations between the features and the mapping algorithm is solving a maximum constraint satisfaction problem (CSP). We define our constraints as inequality patterns between the correlation coefficients of the measured features. Our results show that the method maps measurement features based on their correlation coefficients with high confidence scores (between 0.78 to 1.0 depending on the amount of features). We observe that mapping score increases as a function of the amount of features. Altogether, our results show that this methodology can be used as an automated tool in the measurement data integration.

Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterizing execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of the EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Ourframework, use cases, and tools are available as open source from GitHub.

In recent years, mobile access networks operating at millimeter wavelengths have received a great deal of attention, as they promise previously unattainably high mobile data rates. At these frequencies, mobile access links are expected to use highly directional beamforming antennas, which are also well suited to backhaul links. Therefore, access points can efficiently act as self-backhauled relays by using the same spectrum, circuits and antennas for mobile access and backhaul links, thus forming a multi-hop in-band backhaul network. The contributions of our paper are extensive simulations to investigate user level performance in such multi-hop networks. We specifically take into account the momentary data traffic of every link in order to calculate the interference. Results quantify the detrimental effect of interference on user level performance. Furthermore, the potential benefit of using the combination of in-band and dedicated backhaul links is evaluated. Additionally, this paper investigates the user level effects of the sudden loss of a link in the backhaul mesh network, and underlines the importance of effective rerouting algorithms. The feasibility of the in-band concept is demonstrated, and we can confirm that the user level experience will surpass the performance provided by previous generation mobile networks.

The quality of service of a mobile application is critical to ensure user satisfaction. Techniques have been proposed to accomplish adaptation of quality of service dynamically. However, there is still a limited understanding about how to provide a utility model for code execution. One key challenge is modeling the level of quality in the code execution that can be provisioned by the cloud. Since the allocation of cloud resources has a cost, it is important to optimize cloud usage. We propose a software-defined networking approach that allows modeling and controlling code acceleration of a mobile application deployed across multiple type of devices. By segregating the computational requirements of the mobile application into groups, we were able to define the acceleration needed by each group of devices. As the computational requirements of a device can change across time, a mobile device can be re-assigned to another group based on demand. Our SDN approach implements a model that allows the system to predict workload based on acceleration groups. Evaluating our system in a real testbed showed that it is possible to predict workload and allocate optimal resources to handle that workload with 87.5% accuracy.

Ethernet is the prevalent link layer mechanism in data communication networks. Today, it is used in enterprise and home networks, data center networks, telecommunication networks, and in various industrial deployments. In addition, more and more hosts are becoming mobile, connecting to Ethernet networks via Wi-Fi, or through virtualization solutions in data centers. Unfortunately, the Ethernet protocol suite itself has no generic support for host mobility. In this paper, we evaluate the effects of host mobility in Ethernet networks using real-time emulation. We compare routing bridges, an IETF-driven Ethernet frame forwarding protocol, with our DBridges design. DBridges is an evolution of the routing bridges standard, integrating a one-hop Distributed Hash Tables (DHT) scheme into the protocol. Our solution offers improved scalability characteristics in Ethernet networks, as well as enhanced support for host mobility. Our evaluation shows that while host mobility without any explicit signaling will remain a best-effort service in Ethernet networks, we can significantly improve its efficiency and reliability in certain use cases, while providing improved scalability and safety properties. We also show that while the host mobility support in DBridges suffers from transient forwarding loops, the problem is exceedingly rare in real networks and is mitigated by the base functionality in routing bridges.

Mobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always co-located to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user's participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users’ social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user's present need. As a result, we extended the widely used opportunistic model with a long-term perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild.

In this paper, we quantify reordering and connection interruption while downloading data to mobile phones in modern HSPA/HSPA+ mobile networks based on end-to-end measurements conducted in commercial operator networks. We show how these events are affected by phone model, operator, time of the day, user movement and signal strength. We find that phone model, time of day and user movement affect the number of connection interruptions, but not their length. Data loss during interruptions is influenced by operator and time of day. The number of reordering events is impacted by the operator and the time of day, the operator additionally affects the reordering extent. This improves the understanding of HSPA/HSPA+ networks and enables further research to improve the data transfer and methods for delay spike prevention.

Mobile short data services are commonly available on most mobile communication devices, but are mostly used for plain text messaging. In this paper we investigate the use of different mobile services for use with unified messaging platform. The services analyzed are the ubiquitous GSM/UMTS branch cellular mobile network, TETRA professional mobile radio (PMR) network and Iridium low earth orbit (LEO) satellite phone network. Individually each of these have various strengths and weaknesses, so the task is to optimize the system to concentrate on the strengths of each network. Contribution of this paper is a robust control scheme for mobile modems, a simple model for integrating them to a messaging platform and performance evaluation of GSM, TETRA and Iridium message services. This work can be used in emergency management in an always best connected manner.

Platforms for measuring cellular networks from mobile phones are very popular. Unfortunately, they all suffer from a simplistic approach and measurement errors, and questions how to interpret these measurements. One such error source is that it is easy to measure the signal strength but hard to draw exact conclusions from it. Our work focuses on understanding the measured signal strength and analyzing it's relationship to enduser service quality. To study interesting causalities, such as the above, between different parameters we developed a measurement platform called Netradar for gathering information from mobile devices. We show the correlation between signal strength and TCP goodput in graphs and geographical maps, and the behavior depending on time of the day. We also discuss grouping of measurements to enable estimating the quality of connectivity in an area.

Ethernet has become the de facto layer 2 transmission technology, partly due to ease of use and cost efficiency. The cores of most data centers around the world are based on Ethernet, and large access and core networks are built without IP routing. The inherent simplicity of Ethernet has several drawbacks, including the overhead of network-wide broadcasting and the use of the spanning tree protocol. The academia and the industry have presented numerous solutions to overcome the limitations of Ethernet. Many of these solutions have deployment constraints and often force a change of the whole network. In this paper, we present DBridges, an efficient solution for building large-scale Ethernets based on the IETF-driven RBridges and SEATTLE. DBridges provide loop-safety, dramatic decrease in broadcast traffic, and resiliency and graceful failover without affecting the plug-and-play vision of Ethernet. The key contributions of our solution are that we are backward compatible with RBridges and can support incremental deployment. We analyze and show these benefits in detail, and present various performance measurements using our proof-of-concept implementation. We estimate that with DBridges it is possible to increase the size of Ethernet broadcast domains at least tenfold.

The mobile Internet that was a huge hype ten years ago is finally here. We have a wealth of mobile devices that allow us to enjoy and consume Internet content from any number of web sites and services. With faster processors and radio links, our use of the Internet and the traffic mobile users generate grows at a tremendous pace. In this development, we still have a huge challenge to tackle: energy efficiency. When ten years ago one would recharge his personal mobile device once a week, we now do that daily; Smart phones today are not able to carry enough energy to allow us several days of usage time. The focus on this paper is to present and analyse one solution to help us in our daily lives. We implement and study a scheme where web content, a page, is delivered as a whole to a mobile device, instead of sending each individual object of the page separately. Combined with RRC state based header compression and selective content compression, our proposal allows the radio to keep in low power state for longer durations, and as a consequence brings huge energy savings. Download times also decrease, thus bringing increased Quality of Experience.

This paper introduces a serious security problem that people believe has been fixed, but which is still very much existing and evolving, namely evasions. We describe how protocols can still be misused to fool network security devices, such as intrusion prevention systems.

Software switching has been largely dismissed as an inefficient option compared to hardware switching, both in terms of cost and performance. Recent increases in processing power for commodity hardware has given reason to revisit software switching. The flexibility of a commodity hardware, software based platform allows us to rapidly create prototypes, while the increased performance of the hardware is starting to enable software based network devices to be used in production environments. Combining these two aspects of commodity hardware based switching has led us to create our own prototype implementation of a next generation frame forwarding protocol. In this paper, we detail the architecture of our implementation and compare the performance of Ethernet switching platforms to our implementation with a synthetic benchmark, designed to measure the maximum frame forwarding rate of the platforms. We explore the throughput characteristics of all platforms in single- and multi core configurations, while keeping the rest of the platform configuration as identical as possible.

High availability WAN connectivity has traditionally been implemented with a single service provider building links with high quality hardware, redundancy, and SLA guarantees. We have studied and implemented an alternative, a MIP-based overlay approach that is both economical and can be deployed in present-day Internet. In this paper, we investigate the performance of our implementation of a Redundant Array of Independent Internet Connections (RAIIC) and based on results, propose additional modifications to the approach.

This paper presents distributed and authenticated service discovery and session management schemes. The SLP and SIP protocols have been enhancedfor use in ad-hoc networks. We have coupled the protocols with the AODV ad-hoc routing protocol to make the signaling as efficient as possible. The design has been implemented and tried out in a real ad-hoc network testbed. Our results show that the system is feasible in practice, and very efficient both in terms of bandwidth consumption and computational load.

Various forms of ad-hoc networking are slowly emerging. However, a wider diffusion of ad-hoc networking in everyday life will only be possible if existing common middleware and applications are extended for use in ad-hoc networks. Many new Internet applications are based on sessions, e.g., instant messaging, VoIP and multimedia communications. The Session Initiation Protocol (SIP) is a popular protocol to manage sessions in IP networks. SIP is based on centralized servers, and application-level routing. SIP-based applications can not work in a distributed network of peer nodes, such as an ad-hoc network, due to the lack of the centralized servers. In this paper, we present a novel design that extends SIP for use in ad-hoc networks. Our enhancements allow existing applications to discover users within an ad-hoc network, make use of presence information, and set up VoIP and instant messaging sessions. These operations are performed in a secure way, as the framework provides integrity protection and verification of sender's authenticity. We discuss our software implementation of the scheme, and evaluate the performance, and applicability.

Network information theory states that novel methods, such as, cooperative communication and relaying can increase the overall network transport capacity. These methods are not utilized in current networking technologies. The 3G RAN will not scale up to meet the requirements set for telecommunication in the next decade. We need a novel fourth generation (4G) RAN design. This paper analyses challenges in wireless packet scheduling algorithms for 4G systems and proposes energy efficient scheduling and queue management solutions for the 4G wireless environment. Focus in our work is kept on a relay networking scenarios, which comprise of operator managed RAN including relays together with user controlled mobile routers

Multicast address allocation in the Internet has been the focus of much research and standardization. However, the multicast address allocation problem has been overlooked in ad hoc networking research. In this paper, we survey approaches to solving the multicast address allocation problem and analyze the security characteristics of the proposals. We show that using random assignment in IPv6 based ad hoc networks for multicast address allocation is more secure and bandwidth efficient than the previous proposals. We use passive listening to obtain network prefixes for autoconfiguring a global multicast address. The ideas presented in this paper are useful for practical deployment of ad hoc networks

Various forms of ad-hoc networking are slowly emerging. However, a wider diffusion of ad-hoc networking in everyday life will only be possible if existing common applications are adapted for use in ad-hoc networks. There has been some work on adapting Internet-based applications to function in ad-hoc networks. Yet, in order to make ad-hoc networking even more useful and interesting to people, applications must also be able to connect to the Internet. Many new Internet applications are based on sessions, e.g., instant messaging, VoIP and multimedia communications. The session initiation protocol (SIP) is a popular protocol to manage sessions in IP networks, based on centralized servers, and application-level routing. However, in order to establish SIP sessions between an ad-hoc network and the Internet, some logic must be built in the ad-hoc gateway node to make messages flow correctly to and from the ad-hoc network. In this paper, we present an intelligent SIP gateway that makes it possible to route messages properly between ad-hoc networks, and the Internet, even when the ad-hoc IP addresses are not globally routable

The Internet protocol is being deployed at an ever-increasing pace to offer connectivity to support a wide range of applications. Current research is targeting mobile hosts, and numerous research projects are considering the technologies needed to support the nomadic user. The IST BRAIN and MIND projects have studied the problems using a top-down approach, from user requirements through the application layer all the way to link layer specific issues. This paper presents the results of the study of quality of service provision at the IP layer, to offer service differentiation to application data flows, even over wireless networks.

In addition to conceptual work, the IST MIND project aims to verify practically a set of concepts related to the use of broadband, multimedia services by mobile users in an IP network. This paper presents an overview of the trials, and focuses on a few particular topics that are dedicated to the experimentation of adaptive applications within a wireless IP access network, including advanced QoS and mobility protocols. The novelty of the experimentation is underlined, and first results are presented.

Next generation IP networks and applications will have to address the increasingly important challenges of wireless access, mobility management, the provision of quality of service (QoS), and multimedia issues. These problems form the basis of the research within the EU financed BRAIN (Broadband Radio Access for IP based Networks) project. The project is developing a novel architecture that will be able to deal with the extreme QoS violations that are likely to occur during a running session that is exposed to the radio access environment. The core of this architecture supports different types of applications. It inherits and develops from the traditional Internet approach, but incorporates aspects of a modern flexible QoS middleware solution. The given problem is addressed in a comprehensive, modular, and open manner, by providing different APIs to different types of applications. It provides powerful functions to application programmers, but does not assume that lower level functionality must be hidden from the application programmer. It encompasses a variety of objects, APIs, end-system mechanisms and protocols to cope with the dynamic variation in mobility management and QoS. This solution will provide applications with more predictable services and allow applications to react in a pre-determined way to QoS violations.