Abstract-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.

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.2 of 25 such data publicly available, as is already the case for a project in Canada [1]. In such cases, OppNets offer a good alternative, as they are free in terms of costs and accessible to everyone with minimal equipment (e.g., a smartphone).Much research effort has been invested recently in designing and evaluating data dissemination protocols for OppNets [2,3]. On the one hand, these protocols need to leverage at best, the mobility of people and the available communication opportunities (contacts) and, on the other hand, they need to minimise the overhead of communications. In general, there are two main communication scenarios for OppNets: destination-less and destination-oriented. The first scenario assumes that either all network participants should receive the message (e.g., storm warning or a big city event) or that the receivers are not known a-priori (e.g., all firefighters on duty). The second scenario assumes that the destinations are well known ahead. Below, we discuss that the first scenario is by far more important in OppNets, even though most of the research efforts have been invested in the second.The contributions of this paper are two-fold. Firstly, we introduce the concept of message popularity to data forwarding, where popular data are given higher preference when forwarding than less popular data. This is different from using priority, as popularity cannot be assessed before the data are received and evaluated (liked) by the people. Secondly, we introduce a novel data dissemination protocol called KEETCHI that exploits the popularity of data in destination-less scenarios by estimating it with Q-Learning. Furthermore, we demonstrate the efficiency of KEETCHI through extensive simulations against EPIDEMIC [4], the best known protocol for destination-less OppNets.This paper is organised as follows. Section 2 provides background information in terms of application scenarios and implementation challenges. Section 3 is a review of existing destination-less OppNets forwarding protocols and the usage of popularity in a broader context. Section 4 defines popularity of messages. Section 5 provides a...

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