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  • 1.
    Abrahamsson, Henrik
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ahlgren, Bengt
    RISE - Research Institutes of Sweden, ICT, SICS.
    Björkman, Mats
    Mälardalen University, Sweden.
    Brunstrom, Anna
    Karlstad University, Sweden.
    Marsh, Ian
    RISE - Research Institutes of Sweden, ICT, SICS.
    Selecting Operator in 3G/4G Networks for Time-Critical C-ITS Applications2018Conference paper (Refereed)
  • 2.
    Abrahamsson, Henrik
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ahlgren, Bengt
    RISE - Research Institutes of Sweden, ICT, SICS.
    Brunstrom, Anna
    Karlstad University, Sweden.
    Marsh, Ian
    RISE - Research Institutes of Sweden, ICT, SICS.
    Björkman, Mats
    Mälardalen University, Sweden.
    Connected Vehicles in Cellular Networks: Multi-access versus Single-access Performance2018Conference paper (Refereed)
    Abstract [en]

    Connected vehicles can make roads traffic safer andmore efficient, but require the mobile networks to handle timecriticalapplications. Using the MONROE mobile broadbandmeasurement testbed we conduct a multi-access measurementstudy on buses. The objective is to understand what networkperformance connected vehicles can expect in today’s mobilenetworks, in terms of transaction times and availability. The goalis also to understand to what extent access to several operatorsin parallel can improve communication performance.In our measurement experiments we repeatedly transfer warningmessages from moving buses to a stationary server. Wetriplicate the messages and always perform three transactionsin parallel over three different cellular operators. This creates adataset with which we can compare the operators in an objectiveway and with which we can study the potential for multi-access.In this paper we use the triple-access dataset to evaluate singleaccessselection strategies, where one operator is chosen for eachtransaction. We show that if we have access to three operatorsand for each transaction choose the operator with best accesstechnology and best signal quality then we can significantlyimprove availability and transaction times compared to theindividual operators. The median transaction time improves with6% compared to the best single operator and with 61% comparedto the worst single operator. The 90-percentile transaction timeimproves with 23% compared to the best single operator andwith 65% compared to the worst single operator.

  • 3.
    Ben Abdesslem, Fehmi
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Abrahamsson, Henrik
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ahlgren, Bengt
    RISE - Research Institutes of Sweden, ICT, SICS.
    Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications2018In: TMA 2018 - Proceedings of the 2nd Network Traffic Measurement and Analysis Conference, 2018Conference paper (Refereed)
    Abstract [en]

    Cooperative Intelligent Transport Systems (C-ITS) make road traffic safer and more efficient, but require the mobile networks to handle time-critical applications. While some applications may need new dedicated communications technologies such as IEEE 802.11p or 5G, other applications can use current cellular networks. This study evaluates the performance that connected vehicles can expect from existing networks, and estimates the potential gain of multi-access by simultaneously transmitting over several operators. We upload time-critical warning messages from buses in Sweden, and characterise transaction times and network availability. We conduct the experiments with different protocols: UDP, TCP, and HTTPS. Our results show that when using UDP, the median transaction time for sending a typical warning message is 135 ms. We also show that multi-access can bring this value down to 73 ms. For time-critical applications requiring transaction times under 200 ms, multi-access can increase availability of the network from to 57.4% to 92.0%.

  • 4.
    Ben Abdesslem, Fehmi
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS.
    Abrahamsson, Henrik
    RISE - Research Institutes of Sweden, ICT, SICS.
    Ahlgren, Bengt
    RISE - Research Institutes of Sweden, ICT, SICS.
    Measuring Mobile Network Multi-Access for Time-Critical C-ITS Applications2018Conference paper (Refereed)
    Abstract [en]

    Cooperative Intelligent Transport Systems (C-ITS)make road traffic safer and more efficient, but require the mobilenetworks to handle time-critical applications. While some applicationsmay need new dedicated communications technologiessuch as IEEE 802.11p or 5G, other applications can use currentcellular networks. This study evaluates the performance that connectedvehicles can expect from existing networks, and estimatesthe potential gain of multi-access by simultaneously transmittingover several operators. We upload time-critical warning messagesfrom buses in Sweden, and characterise transaction times andnetwork availability. We conduct the experiments with differentprotocols: UDP, TCP, and HTTPS. Our results show that whenusing UDP, the median transaction time for sending a typicalwarning message is 135 ms. We also show that multi-access canbring this value down to 73 ms. For time-critical applicationsrequiring transaction times under 200 ms, multi-access canincrease availability of the network from to 57.4% to 92.0%.

  • 5.
    Ben Abdesslem, Fehmi
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Lindgren, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Cacheability of YouTube Videos in Cellular Networks2014Conference paper (Refereed)
    Abstract [en]

    Video traffic now represents a growing proportion of the traffic on cellular networks, causing capacity problems for operators and increased delays for users. Studies have shown that deploying caches at the network level reduces the delay for the end-user and the overall traffic volume for the telecom operator. In this paper, we analyse a large nationwide dataset of real-life video requests sent by mobile users to a popular video streaming website. This analysis is the first to rely on such a large dataset, and sheds light on the optimal cacheability of video content with caches distributed in the cellular network, and how efficient some existing cache replacement algorithms are at reducing the number of requests sent to the video provider. We show that depending on the cache size and algorithm parameters, up to 20.33% of the requests can be served by a local cache.

  • 6.
    Ben Abdesslem, Fehmi
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Lindgren, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Demo: Mobile Opportunistic System for Experience Sharing (MOSES) in Indoor Exhibitions2014Conference paper (Refereed)
    Abstract [en]

    Information-Centric Networking (ICN) is an alternative architecture for computer networks, where the communication is focused on the data being transferred instead of the communicating hosts. This paper describes a demo of an experience sharing application for mobile phones built on an ICN platform designed for devices with intermittent connectivity. In particular, we detail how this application will be showcased in an indoor exhibition where experience is shared with media content that is geo-tagged using Bluetooth beacons and spread opportunistically to other users. 

  • 7.
    Ben Abdesslem, Fehmi
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Lindgren, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Large Scale Characterisation of YouTube Requests in a Cellular Network2014Conference paper (Refereed)
    Abstract [en]

    Traffic from wireless and mobile devices is expected to soon exceed traffic from fixed devices. Understanding the behaviour of users on mobile devices is important in order to improve the offered services and the provision of the underlying network. Globally, more than 60% of consumer Internet traffic is estimated to be video traffic, and the most popular video website, YouTube, estimates that mobile access makes up nearly 40% of the global watch time. This paper presents the first work to study the characteristics of YouTube user requests on a nationwide cellular network. This study is based on the analysis of a large dataset generated by 3 million users and collected by a major telecom operator. We show for instance that 20% of the users generate 78% of the requests, and that over 80% of the requests target only 20% of the distinct videos accessed during the data collection period. Our results provide a comprehensive insight into the way people use YouTube on mobile devices, and show a very high potential for video cacheability on the cellular network.

  • 8.
    Ben Abdesslem, Fehmi
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Lindgren, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    The Pursuit of 'Appiness: Exploring Android Market Download Behaviour in a Nationwide Cellular Network2014Conference paper (Refereed)
    Abstract [en]

    Mobile devices are now part of our everyday lives, and the emergence of online application marketplaces allow a rapid spread of new mobile applications to a large user base. Such user-installed mobile applications constitute a large part of our daily interaction with the devices. With more than one million available applications, Android Market, the online catalog for Android devices allows users to choose and download a large selection of disparate applications. Analysing and characterising the application marketplace download patterns provides a better insight on the needs and behaviour of users In this paper, we explore a large dataset collected by a major European telecom operator to study the downloads of Android applications on a nationwide scale. Our findings include that more than 43% of the application data downloaded is for games, and that a set of only 10 GB of applications is responsible for 88% of the 45 TB downloaded in total by all the users. 

  • 9.
    Ben Abdesslem, Fehmi
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Lindgren, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Hess, Andrea
    RISE, Swedish ICT, SICS.
    Understanding usage and activity in cellular networks by investigating HTTP requests2015In: 2015 12th Annual IEEE Consumer Communications and Networking Conference (CCNC), 2015, 11, p. 570-575, article id 7158036Conference paper (Refereed)
    Abstract [en]

    The number of mobile devices is estimated to now exceed the world’s population, using more and more cloud services, and hence generating more and more traffic. Smartphones generate 95% of the total global handset traffic, and while approximately half of this traffic is sent to cellular networks, other handsets such as tablets are also using increasingly the cellular networks. This paper provides a closer look at the traffic generated on cellular networks by exploring billions of HTTP requests sent by millions of users to a nation-wide cellular network during 41 days. We confirm that - as in many other contexts - 20% of the users are responsible for more than 80% of the requests and provide a deeper analysis of the cellular network usage. Furthermore, we characterise the activity of users on their mobile device and which cloud services they use. For instance, almost 30% of the users use the cellular network frequently, mainly using search services and social networks, but 20% of their requests are sent to advertisement and tracking systems.

  • 10.
    Boman, Magnus
    et al.
    RISE - Research Institutes of Sweden, ICT, SICS. KTH Royal Institute of Technology, Sweden.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Forsell, Erik
    Karolinska Institute, Sweden; Stockholm County Council, Sweden.
    Gillblad, Daniel
    RISE - Research Institutes of Sweden, ICT, SICS.
    Görnerup, Olof
    RISE - Research Institutes of Sweden, ICT, SICS.
    Isacsson, Nils
    Karolinska Institute, Sweden; Stockholm County Council, Sweden.
    Sahlgren, Magnus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Kaldo, Viktor
    Karolinska Institute, Sweden; Stockholm County Council, Sweden; Linnaeus University, Sweden.
    Learning machines in Internet-delivered psychological treatment2019In: Progress in Artificial Intelligence, ISSN 2192-6352Article in journal (Refereed)
    Abstract [en]

    A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.

  • 11.
    Gong, Qingyuan
    et al.
    Fudan University, China.
    Chen, Yang
    Fudan University, China.
    Yu, Xiaolong
    Fudan University, China.
    Xu, Chao
    Fudan University, China.
    Guo, Zhichun
    Fudan University, China.
    Xiao, Yu
    Aalto University, Finland.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Wang, Xin
    Fudan University, China.
    Hui, Pan
    University of Helsinki, Finland; Hong Kong University of Science and Technology, Hong Kong.
    Exploring the power of social hub services2018In: World wide web (Bussum), ISSN 1386-145X, E-ISSN 1573-1413Article in journal (Refereed)
    Abstract [en]

    Given the diverse focuses of emerging online social networks (OSNs), it is common that a user has signed up on multiple OSNs. Social hub services, a.k.a., social directory services, help each user manage and exhibit her OSN accounts on one webpage. In this work, we conduct a data-driven study by crawling over one million user profiles from about.me, a representative online social hub service. Our study aims at gaining insights on cross-OSN social influence from the crawled data. We first analyze the composition of the social hub users. For each user, we collect her social accounts from her social hub webpage, and aggregate the content generated by these accounts on different OSNs to gain a comprehensive view of this user. According to our analysis, there is a high probability that a user would provide consistent information on different OSNs. We then explore the correlation between user activities on different OSNs, based on which we propose a cross-OSN social influence prediction model. With the model, we can accurately predict a user’s social influence on emerging OSNs, such as Instagram, Foursquare, and Flickr, based on her data published on well-established OSNs like Twitter.

  • 12.
    Li, Fei
    et al.
    Fudan University, China.
    Chen, Yang
    Fudan University, China.
    Xie, Rong
    Fudan University, China.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Lindgren, Anders
    RISE - Research Institutes of Sweden, ICT, SICS.
    Understanding Service Integration of Online Social Networks: A Data-Driven Study2018In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018, 2018, p. 848-853Conference paper (Refereed)
    Abstract [en]

    The cross-site linking function is widely adopted by online social networks (OSNs). This function allows a user to link her account on one OSN to her accounts on other OSNs. Thus, users are able to sign in with the linked accounts, share contents among these accounts and import friends from them. It leads to the service integration of different OSNs. This integration not only provides convenience for users to manage accounts of different OSNs, but also introduces usefulness to OSNs that adopt the cross-site linking function. In this paper, we investigate this usefulness based on users' data collected from a popular OSN called Medium. We conduct a thorough analysis on its social graph, and find that the service integration brought by the crosssite linking function is able to change Medium's social graph structure and attract a large number of new users. However, almost none of the new users would become high PageRank users (PageRank is used to measure a user's influence in an OSN). To solve this problem, we build a machine-learning-based model to predict high PageRank users in Medium based on their Twitter data only. This model achieves a high F1-score of 0.942 and a high area under the curve (AUC) of 0.986. Based on it, we design a system to assist new OSNs to identify and attract high PageRank users from other well-established OSNs through the cross-site linking function.

  • 13.
    Lindgren, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ben Abdesslem, Fehmi
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ahlgren, Bengt
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Schelén, Olov
    Malik, Adeel
    Applicability and Tradeoffs of Information-Centric Networking for Efficient IoT2015Other (Other academic)
  • 14.
    Lindgren, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ben Abdesslem, Fehmi
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ahlgren, Bengt
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Schelén, Olov
    Luleå University of Technology, Sweden.
    Malik, Adeel Mohammad
    Ericsson, Sweden.
    Design Choices for the IoT in Information-Centric Networks2016In: : 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2016, 10, p. 882-888Conference paper (Refereed)
    Abstract [en]

    This paper outlines the tradeoffs involved in utilizing Information-Centric Networking (ICN) for Internet of Things (IoT) scenarios. It describes contexts and applications where the IoT would benefit from ICN, and where a hostcentric approach would be better. Requirements imposed by the heterogeneous nature of IoT networks are discussed in terms of connectivity, power availability, computational and storage capacity. Design choices are then proposed for an IoT architecture to handle these requirements, while providing efficiency and scalability. An objective is to not require any IoT specific changes of the ICN architecture per se, but we do indicate some potential modifications of ICN that would improve efficiency and scalability for IoT and other applications.

  • 15.
    Lindgren, Anders
    et al.
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ben Abdesslem, Fehmi
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Ahlgren, Bengt
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Schelén, Olov
    Malik, Adeel Mohammad
    Proposed Design Choices for IoT over Information Centric Networking2015Other (Other academic)
    Abstract [en]

    This document discusses and describes design choices made in order to utilize Information Centric Networking (ICN) for the Internet of Things (IoT). Based on requirements and challenges identified in draft-zhang-icnrg-iotchallenges-00, we propose design choices for an IoT architecture to handle these requirements, while providing efficiency and scalability. An objective is to, as far as possible, not require IoT specific changes of the ICN architectures per se, but we do indicate some potential modifications of ICN that would improve efficiency and scalability for IoT and other applications. Furthermore, the document starts outlining how to map the proposed design choices to existing ICN architectures, in a first instance shown for CCN1.0.

  • 16.
    Lv, Qiujian
    et al.
    Chinese Academy of Sciences, China.
    Qiao, Yuanyuan
    Beijing University of Posts and Telecommunications, China.
    Zhang, Yi
    Beijing University of Posts and Telecommunications, China.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Lin, Wenhui
    Aisino Corporation, China.
    Yang, Jie
    Beijing University of Posts and Telecommunications, China.
    Measuring Geospatial Properties: Relating Online Content Browsing Behaviors to Users’ Points of Interest2018In: Wireless personal communications, ISSN 0929-6212, E-ISSN 1572-834X, Vol. 101, no 3, p. 1469-1498Article in journal (Refereed)
    Abstract [en]

    With the growth of the Mobile Internet, people have become active in both the online and offline worlds. Investigating the relationships between users’ online and offline behaviors is critical for personalization and content caching, as well as improving urban planning. Although some studies have measured the spatial properties of online social relationships, there have been few in-depth investigations of the relationships between users’ online content browsing behaviors and their real-life locations. This paper provides the first insight into the geospatial properties of online content browsing behaviors from the perspectives of both geographical regions and individual users. We first analyze the online browsing patterns across geographical regions. Then, a multilayer-network-based model is presented to discover how inter-user distances affect the distributions of users with similar online browsing interests. Drawing upon results from a comprehensive study of users of three popular online content services in a metropolitan city in China, we achieve a broad understanding of the general and specific geospatial properties of users’ various preferences. Specifically, users with similar online browsing interests exhibit, to a large extent, strong geographic correlations, and different services exhibit distinct geospatial properties in terms of their usage patterns. The results of this work can potentially be exploited to improve a vast number of applications. 

  • 17.
    Qiao, Yuanyuan
    et al.
    Beijing University of Posts and Telecommunications, China.
    Si, Zhongwei
    Beijing University of Posts and Telecommunications, China.
    Zhang, Yanting
    Beijing University of Posts and Telecommunications, China.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Zhang, Xinyu
    Beijing University of Posts and Telecommunications, China.
    Yang, Jie
    Beijing University of Posts and Telecommunications, China.
    A hybrid Markov-based model for human mobility prediction2018In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 278, no SI, p. 99-109Article in journal (Refereed)
    Abstract [en]

    Human mobility behavior is far from random, and its indicators follow non-Gaussian distributions. Predicting human mobility has the potential to enhance location-based services, intelligent transportation systems, urban computing, and so forth. In this paper, we focus on improving the prediction accuracy of non-Gaussian mobility data by constructing a hybrid Markov-based model, which takes the non-Gaussian and spatio-temporal characteristics of real human mobility data into account. More specifically, we (1) estimate the order of the Markov chain predictor by adapting it to the length of frequent individual mobility patterns, instead of using a fixed order, (2) consider the time distribution of mobility patterns occurrences when calculating the transition probability for the next location, and (3) employ the prediction results of users with similar trajectories if the recent context has not been previously seen. We have conducted extensive experiments on real human trajectories collected during 21 days from 3474 individuals in an urban Long Term Evolution (LTE) network, and the results demonstrate that the proposed model for non-Gaussian mobility data can help predicting people’s future movements with more than 56% accuracy. 

  • 18.
    Valerio, Lorenzo
    et al.
    CNR Consiglio Nazionale delle Ricerche, Italy.
    Ben Abdesslem, Fehmi
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Lindgren, Anders
    RISE, Swedish ICT, SICS, Decisions, Networks and Analytics lab.
    Bruno, Rafaele
    CNR Consiglio Nazionale delle Ricerche, Italy.
    Passarella, Andrea
    CNR Consiglio Nazionale delle Ricerche, Italy.
    Luoto, Markus
    VTT Technical Research Centre of Finland, Finland.
    Offloading Cellular Traffic with Opportunistic Networks: A Feasibility Study2015In: 2015 14th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET), 2015, 17, article id 7173296Conference paper (Refereed)
    Abstract [en]

    The widespread diffusion of powerful mobile devices with diverse networking and multimedia capabilities, and the associated blossoming of content-centric multimedia services is contributing to the exponential increase of data traffic in cellular networks. Mobile data offloading is a promising technique to cope with these problems, which allows to deliver data originally targeted for cellular networks to complementary networking technologies. Among the various forms of mobile data offloading in this study we focus on offloading through opportunistic networks. Differently from previous studies in this field we evaluate the efficiency of opportunistic offloading schemes by using a real cellular traffic dataset collected in a large metropolitan area over a period of one month. We focus our analysis on video requests for popular video providers, and we evaluate the potential benefits of using an opportunistic data dissemination scheme to request this videos from local users instead of using the cellular network. As a benchmark, we compare the performance of such system with a simple caching mechanism. We show that a simple opportunistic offloading scheme can improve the performance of the caching system even if only 10% of the users participate in the opportunistic dissemination. This means that operators could offload their network efficiently without needing to deploy additional caching infrastructure.

  • 19.
    Zhang, Zhiqiang
    et al.
    Tongji University, China.
    Rao, Weixiong
    Tongji University, China.
    Di, Xiaolei
    Tongji University, China.
    Zhao, Peng
    Tongji University, China.
    Xu, Xiaofeng
    Tongji University, China.
    Ben Abdesslem, Fehmi
    RISE - Research Institutes of Sweden, ICT, SICS.
    Demo Abstract: Frequent Pattern-based Trajectory Completion2018In: Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems, 2018, p. 311-312Conference paper (Other academic)
    Abstract [en]

    GPS sensors have been widely used to track people's everyday life trajectories, generating massive trajectory datasets. The trajectory data typically contains sparse GPS points, and completing trajectories is often necessary. State-of-the-art methods [3, 4] essentially complete the entire route by using a single metric, e.g., either the shortest distance or the fastest driving/walking time. Unfortunately, using a single metric may not always work in real life due to the diversity of mobility patterns. In this demo abstract, we propose a frequent pattern (FP)-based trajectory completion approach, and demonstrate a system prototype to showcase the advantages of our approach over four previous works, in terms of accuracy and running time.

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