Department of Mobile Systems and Analytics

Mobile networks underpin numerous vital operations of the modern society and are arguably becoming the most important piece of the modern communications infrastructure. The use of mobile networks has exploded over the last few years due to the immense popularity of mobile devices such as smartphones and tablets, combined with the availability of high-capacity Wifi and 4G/LTE networks. While today’s mobile networks do a reasonably good job connecting people, they face a number of challenges when it comes to supporting the diverse demands of emerging applications, simply because they were not designed with their requirements in mind. The applications and services of the future have quite different requirements in terms of capacity, latency, reliability, and efficiency. For example, while self-driving cars require very low delay with a very high reliability, for future media applications (e.g., ultra HD, 3D video) very high capacity is required. Therefore, the vision for future mobile (e.g. 5G networks) networks is not only providing higher capacity and an improved perceived user experience for different applications and services but also enabling application designers and network architects to build end-to-end virtual networks tailored to their applications’ requirements. The promise of great flexibility to support many different services and applications, however, comes at the cost of increased complexity. Real-world network measurements are essential in many network investigations, especially for performance and reliability analysis of complex networks and, applications and services that are running on these networks, since they provide an environment that is hard to mimic in models and simulators.
The main goal of Mobile Systems and Analytics (MOSAIC) is to empirically analyze mobile systems in order to design and validate novel protocols and applications for future mobile systems. To achieve its goal, MOSAIC follows a “hands-on” approach using open platforms such as MONROE to identify the bottlenecks in the current mobile networks (e.g. 3G/4G/5G, Wifi and satellite) and leverage on this knowledge to design and validate novel approaches for future mobile networks. To improve our understanding of mobile systems and the performance of applications running on them, MOSAIC focuses on merging the domain knowledge with machine learning methodologies. Building on the application-specific measurements and metrics, MOSAIC designs and validates application-specific protocol and system enhancements for mobile systems. More specifically, MOSAIC leverages on the experimental evaluation and characterization of mobile networks to design: (i) novel protocol extensions (e.g. TCP/MPTCP, QUIC/MPQUIC) and (ii) robust multimedia (e.g. DASH and HEVC) applications over mobile networks, especially considering challenging mobility scenarios such as the ones experienced on trains and drones.
People at Department of Mobile Systems and Analytics
Who we are?
Simula Metropolitan employees are researchers, postdoctoral fellows, PhD students, engineers and administrative people. We are from all over the world, ranging from newly educated to experienced researchers, all working on making research in digital engineering at the highest international level possible.
Publications at Department of Mobile Systems and Analytics
Journal Article
NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements
IEEE Internet of Things Journal (2021).Status: Published
NB-IoT Random Access: Data-driven Analysis and ML-based Enhancements
In the context of massive Machine Type Communications (mMTC), the Narrowband Internet of Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored Random Access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This paper presents the first data-driven analysis of NB-IoT RA, exploiting a large scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators’ configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. Comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a Machine Learning (ML) approach, and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in a RA enhanced scheme, and show that optimized configurations enable a power consumption reduction of at least 50%. We also make our dataset available for further exploration, toward the discovery of new insights and research perspectives.
Afilliation | Communication Systems, Machine Learning |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | IEEE Internet of Things Journal |
Date Published | 01/2021 |
Publisher | IEEE |
Keywords | Cellular Internet of Things, Empirical Analysis, massive Machine Type Communications, Narrowband Internet of Things, Random Access |
Notes | Supplementary Materials, Results, and Dataset available at https://mosaic-simulamet.com/nbiot-randomaccess/ |
URL | https://ieeexplore.ieee.org/document/9324758 |
DOI | 10.1109/JIOT.2021.3051755 |
Journal Article
A Modular Experimentation Methodology for 5G Deployments: The 5GENESIS Approach
Sensors 20 (2020).Status: Published
A Modular Experimentation Methodology for 5G Deployments: The 5GENESIS Approach
The high heterogeneity of 5G use cases requires the extension of the traditional per-component testing procedures provided by certification organizations, in order to devise and incorporate methodologies that cover the testing requirements from vertical applications and services. In this paper, we introduce an experimentation methodology that is defined in the context of the 5GENESIS project, which aims at enabling both the testing of network components and validation of E2E KPIs. The most important contributions of this methodology are its modularity and flexibility, as well as the open-source software that was developed for its application, which enable lightweight adoption of the methodology in any 5G testbed. We also demonstrate how the methodology can be used, by executing and analyzing different experiments in a 5G Non-Standalone (NSA) deployment at the University of Malaga. The key findings of the paper are an initial 5G performance assessment and KPI analysis and the detection of under-performance issues at the application level. Those findings highlight the need for reliable testing and validation procedures towards a fair benchmarking of generic 5G services and applications.
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | Sensors |
Volume | 20 |
Number | 6652 |
Date Published | 11/2020 |
Publisher | MDPI |
Keywords | 5G, Experimentation, methodology, Testbeds |
URL | https://www.mdpi.com/1424-8220/20/22/6652 |
DOI | 10.3390/s20226652 |
Estimating an Additive Path Cost with Explicit Congestion Notification
IEEE Transactions on Control of Network Systems (2020).Status: Accepted
Estimating an Additive Path Cost with Explicit Congestion Notification
Network Utility Maximization (NUM) is a well accepted theoretical framework that describes how congestion controls cooperate to achieve an ideal sending rate allocation, for given utility functions of senders and constraints of the network. These network constraints are expressed as a "cost'' in the framework. In practice, most congestion control mechanisms obtain feedback that is different from the framework's "cost''. This paper focuses on Explicit Congestion Notification (ECN), which has been shown to be advantageous when it is available, e.g. with the popular Datacenter TCP (DCTCP) mechanism. However, different from the framework's cost, ECN marks are not additive. We present a practical solution to this problem. Our solution changes how end hosts interpret the ECN signal, while for the router side, a special configuration of RED is used.
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE Transactions on Control of Network Systems |
Publisher | IEEE |
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
IEEE Communications Magazine 58, no. 9 (2020).Status: Published
Coverage and Deployment Analysis of Narrowband Internet of Things in the Wild
Narrowband Internet of Things (NB-IoT) is gaining momentum as a promising technology for massive Machine Type Communication (mMTC). Given that its deployment is rapidly progressing worldwide, measurement campaigns and performance analyses are needed to better understand the system and move toward its enhancement. With this aim, this paper presents a large scale measurement campaign and empirical analysis of NB-IoT on operational networks, and discloses valuable insights in terms of deployment strategies and radio coverage performance. The reported results also serve as examples showing the potential usage of the collected dataset, which we make open- source along with a lightweight data visualization platform.
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE Communications Magazine |
Volume | 58 |
Issue | 9 |
Number | 39-45 |
Date Published | 09/2020 |
Publisher | IEEE |
Notes | Preprint available here: https://arxiv.org/abs/2005.02341 |
URL | https://ieeexplore.ieee.org/document/9214385 |
DOI | 10.1109/MCOM.001.2000131 |
Online Identification of Groups of Flows Sharing a Network Bottleneck
IEEE/ACM Transactions on Networking 28 (2020): 2229-2242.Status: Published
Online Identification of Groups of Flows Sharing a Network Bottleneck
Abstract—Most Internet hosts today support multiple access technologies and network interfaces. Multipath transport protocols, like MPTCP, are being deployed (e.g., in smartphones), allowing transparent simultaneous use of multiple links. Besides providing increased resilience to link failures, multipath trans- ports may better exploit available (aggregate) capacity across all interfaces. The safest way to ensure fairness is to assume that any subflows of a multipath end-to-end connection may share bottleneck links, but knowledge of non-shared bottlenecks could allow multipath senders to exploit more capacity without being unfair to other flows. The problem of reliably detecting the existence of (non)-shared bottlenecks is not trivial and is compounded by the fact that bottlenecks may change due to traffic dynamics. In this paper we focus on practical methods to reliably group flows that share, possibly dynamic, bottlenecks online and in a passive manner (i.e., without injecting measurement traffic). We introduce a novel dynamic clustering algorithm that we apply to update our previous shared bottleneck flow grouping (SBFG) method standardized by the IETF, based on delay statistics. We also adapt an offline SBFG method based on wavelet filters to enable it for online operation. These SBFG methods are evaluated by a simple testbed, rigorous simulation and real-world Internet experiments in a testbed comprised of multihomed hosts. Our results suggest that there is no clear winner, and selection of the “best” SBFG method will have to consider tradeoffs regarding accuracy, lag, and application requirements.
Afilliation | Communication Systems |
Project(s) | RITE: Reducing Internet Transport Latency, Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE/ACM Transactions on Networking |
Volume | 28 |
Number | 5 |
Pagination | 2229--2242 |
Publisher | IEEE/ACM |
ISSN | Print ISSN: 1063-6692, Electronic ISSN: 1558-2566 |
Keywords | dynamic clustering, Internet congestion control, multipath congestion control., shared bottleneck detection |
Notes | Published in the Early Access area on IEEE Xplore. The content is final as presented with the exception of pagination and |
DOI | 10.1109/TNET.2020.3007346 |
Peekaboo: Learning-based Multipath Scheduling for Dynamic Heterogeneous Environments
IEEE Journal on Selected Areas in Communications (JSAC) Early access (2020).Status: Published
Peekaboo: Learning-based Multipath Scheduling for Dynamic Heterogeneous Environments
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics, Simula Metropolitan Center for Digital Engineering |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | IEEE Journal on Selected Areas in Communications (JSAC) |
Volume | Early access |
Publisher | IEEE |
DOI | 10.1109/JSAC.2020.3000365 |
Proceedings, non-refereed
A Survey on Universal Design for Fitness Wearable Devices
In arXiv, 2020.Status: Published
A Survey on Universal Design for Fitness Wearable Devices
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics, Simula Metropolitan Center for Digital Engineering |
Publication Type | Proceedings, non-refereed |
Year of Publication | 2020 |
Conference Name | arXiv |
Accession Number | 2006.00823 |
Journal Article
ThingsLocate: A ThingSpeak-Based Indoor Positioning Platform for Academic Research on Location-Aware Internet of Things
Technologies 7, no. 3 (2019).Status: Published
ThingsLocate: A ThingSpeak-Based Indoor Positioning Platform for Academic Research on Location-Aware Internet of Things
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2019 |
Journal | Technologies |
Volume | 7 |
Issue | 3 |
Number | 50 |
Date Published | 07/2019 |
Publisher | MDPI |
URL | https://doi.org/10.3390/technologies7030050 |
ViFi: Virtual Fingerprinting WiFi-based Indoor Positioning via Multi-Wall Multi-Floor Propagation Model
IEEE Transactions on Mobile Computing Early access (2019).Status: Published
ViFi: Virtual Fingerprinting WiFi-based Indoor Positioning via Multi-Wall Multi-Floor Propagation Model
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics |
Publication Type | Journal Article |
Year of Publication | 2019 |
Journal | IEEE Transactions on Mobile Computing |
Volume | Early access |
Date Published | 04/2019 |
Publisher | IEEE |
ISSN | 1558-0660 |
URL | https://ieeexplore.ieee.org/abstract/document/8680659 |
DOI | 10.1109/TMC.2019.2908865 |
Co-designing Smart Lighting and Communication for Visible Light Networks
IEEE Transactions on Mobile Computing (TMC) Early access (2019).Status: Published
Co-designing Smart Lighting and Communication for Visible Light Networks
Afilliation | Communication Systems |
Project(s) | Department of Mobile Systems and Analytics, Simula Metropolitan Center for Digital Engineering |
Publication Type | Journal Article |
Year of Publication | 2019 |
Journal | IEEE Transactions on Mobile Computing (TMC) |
Volume | Early access |
Publisher | IEEE |
DOI | 10.1109/TMC.2019.2915220 |