Simulation 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.

A variety of applications and forwarding protocols have been proposed for opportunistic networks (OppNets) in the literature. However, the methodology of evaluation, testing and comparing these forwarding protocols are not standardized yet, which leads to large levels of ambiguity in performance evaluation studies. Performance results depend largely on the evaluation environment, and on the used parameters and models. More comparability in evaluation scenarios and methodologies would largely improve also the availability of protocols and the repeatability of studies, and thus would accelerate the development of this research topic. In this survey paper, we focus our attention on how various OppNets data forwarding protocols are evaluated rather than what they actually achieve. We explore the models, parameters and the evaluation environments and make observations about their scalability, realism and comparability. Finally, we deduce some best practices on how to achieve the largest impact of future evaluation studies of OppNets data dissemination/forwarding protocols.

In this paper, we present our experience with redesigning an introductory computer science course for (electrical) engineers with blended learning concepts. It is a large mandatory course with eight credit points over the course of two semesters, where first year electrical engineers learn how to program with Arduino, C and Python. Additionally, they need to cover basic computer science concepts such as binary numbers, Boolean algebra, encodings, finite state machines and object-oriented programming. We designed the course to cover the fields that are most relevant to our students’ future careers. With the new format, students declare they are much more engaged with the course, they drop the course less often and they actually achieve better exam results. We completely re-structured the course, introduced inverted classroom elements and hackathons and we continuously optimized and adapted the course. The main focus is on hands-on-experience and teamwork, which we mostly achieve by the use of hackathons. In this paper, we described the contents and teaching concepts of the course and we discussed the achieved results.

Infrastructure‐less networks connect communication devices end‐to‐end by managing links and routes independent of fixed networking facilities, relying on dedicated protocols running on end‐user devices. The large variety of infrastructure‐less concepts and related aspects can be confusing both for beginning Ph.D. students as well as experienced researchers who wish to get an overview of neighbouring areas to their own research foci. Frequently discussed topics such as different types of sensor‐, vehicular‐, or opportunistic networks are covered. The authors describe different networking concepts by looking at aspects such as the main properties, common applications, and ongoing research. Furthermore, the concepts by common characteristics such as node mobility, network density, or power consumption are compared. The authors also discuss network performance evaluation by describing commonly used metrics, different evaluation techniques, and software tools for simulation‐based evaluation. The references given in each section help obtain in‐depth information about the presented topics and give hints about open research questions, which can be a starting point for own investigations.

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.