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Item type:Publication, Access status: without files , Evaluation of cluster effect in mobile opportunistic networksThis paper analyses how data dissemination occurs in mobile Opportunistic Networks by evaluating the impact of different parameters such as density of neighbors, communication range, and speed. There exist several analytical models to evaluate the data dissemination time in OppNets. These models were developed based on a very strong assumption of uniform distribution of infected nodes. We prove that this assumption does not work for the whole spectrum of mobile OppNets. This paper shows our simulation results validated with our analytical model and discusses the impact of different parameters on data dissemination.Publicationtype: Conference Paper64 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Online teaching of project-based learning courses : issues, challenges and outcomes(ACM Special Interest Group on Data Communication (SIGCOMM), 2020-08-05); ; ; ; This paper aims to share the experiences during online teaching of two different project-based learning courses at Hamburg University of Technology (TUHH) during the COVID-19 (Corona) pandemic 2020. The success of project-based learning courses relies on how freely and openly students can discuss their task-oriented problems in small teams. The most effort-intensive part of the online teaching of project-based learning is to enhance collaboration among students. This paper highlights the challenges we faced, how we overcame some of these challenges, and our lessons learned. Additionally, the results from a student survey conducted during the semester show the students’ feedback on online teaching.Publicationtype: Conference Paper896 748 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , My smartphone tattles: considering popularity of messages in opportunistic data dissemination(Multidisciplinary Digital Publishing Institute (MDPI), 2019-01-29); ; ; ; Opportunistic networks have recently seen increasing interest in the networking community. They can serve a range of application scenarios, most of them being destination-less, i.e., without a-priori knowledge of who is the final destination of a message. In this paper, we explore the usage of data popularity for improving the efficiency of data forwarding in opportunistic networks. Whether a message will become popular or not is not known before disseminating it to users. Thus, popularity needs to be estimated in a distributed manner considering a local context. We propose KEETCHI, a data forwarding protocol based on Q-Learning to give more preference to popular data rather than less popular data. Our extensive simulation comparison between KEETCHI and the well known EPIDEMIC protocol shows that the network overhead of data forwarding can be significantly reduced while keeping the delivery rate the same.Publicationtype: Journal ArticleScopus© Citations 9 618 507 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Teaching Modelling and Analysis of Communication Networks using OMNeT++ Simulator(2018-10-23); ; ; ; At the Hamburg University of Technology, Germany, the modelling of communication networks using the OMNeT++ simulator and the INET framework is taught for Master students. Teaching the concepts of simulation and modelling while letting the students obtain a hands-on experience during a 14-week (4h/week) period (single semester) is a challenging task. The diversity of the pre-knowledge of the participating students and the duration of the course are the main challenges that need to be addressed when organising such a course. This paper discusses the structure of this course and the best practices followed. The course adopts a methodology where lectures on concepts are mixed with INET based exercises that begin with simple topics and gradually moving into advanced topics.Publicationtype: Conference Paper478 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Modeling of Data Dissemination in OppNets(IEEE, 2018-08); ; ; ; This paper focuses on modeling the charateristics of opportunistic networks (OppNets). The motivation of this work is to develop a model which is closer to the reality to represent the data dissemination in OppNets. This paper shows our preliminary results validating our model in OppNets scenarios which give an opportunity to exchange data among a group of people.Publicationtype: Conference Paper213 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Simulating Opportunistic Networks: Survey and Future DirectionsSimulation is one of the most powerful tools we have for evaluating the performance of Opportunistic Networks. In this survey, we focus on available tools and models, compare their performance and precision and experimentally show the scalability of different simulators. We also perform a gap analysis of state-of-the-art Opportunistic Network simulations and sketch out possible further development and lines of research. This survey is targeted at students starting work and research in this area while also serving as a valuable source of information for experienced researchers.Publicationtype: Journal Article251 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 311 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Parameterization of the SWIM Mobility Model Using Contact TracesOpportunistic networks (OppNets) are focused to exploit direct, localised communications which occur in a peer- to-peer manner mostly based on people?s movements and their contact durations. Therefore the use of realistic mobility models is critical to evaluate the data dissemination in OppNets. One of the mobility models that is available in OMNeT++ which can be used to mimic human movement patterns is Small Worlds in Motion (SWIM). The SWIM model is based on the intuition that humans often visit nearby locations and if the visited location is far away, then it is probably due to the popularity of the location. As an alternative to mobility of a node, pairwise contact probabilities are also used to evaluate the data dissemination in OppNets. Pairwise contact probabilities can be used to predict that a node will be met by a particular node. These probabilities can be derived in many ways. One of the ways is to calculate the average probability with which a node will meet another particular node at any point of time. Another way is to calculate the probability with which a node will meet another based on the time of day. The way of calculating pairwise contact probability depends on the scenario. In this work, the pairwise contact probabilities obtained from the real traces are used to tune the parameters of the SWIM mobility model. The traces and the SWIM model are compared in terms of contact durations, inter-contact times and, number of pairwise contacts. How to decide SWIM parameters using real contact traces are being addressed.Publicationtype: Conference Paper127