# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 1 Introduction  
Opportunistic networks (OppNets) are an emerging concept, which describes an infrastructure-less communication model, where devices exchange information when they are in proximity of each other. Different to traditional communications, where infrastructure is installed first and the quality of the communications is directly related to the capacity of this infrastructure, OppNets rely fully on the mobility of people who carry the devices.  
OppNets can be applied in various applications, mostly: (1) in disaster scenarios, where the infrastructure has been damaged; (2) for various delay-tolerant applications, such as dissemination of city or campus events and notifications, and crowd sensing; and (3) for strictly localised real-time applications, such as autonomous driving. In this paper, we mostly focus on the second family of applications, where a delay of several hours is acceptable.  
Furthermore, crowd sensing of various types of information, e.g. traffic, environmental properties (noise, air pollution, temperature, etc.), but also personal information like lifestyle, are currently gaining more and more interest. The main challenge of these applications is to preserve the privacy of the information gatherers and to motivate them to collect and spread this information. It is envisioned to makesuch 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 detailed description of the operation of the Keetchi forwarding protocol. Section 6 presents a set of use cases to demonstrate the differences between Keetchi and Epidemic and their performance. Section 7 details the simulation evaluation setup used to evaluate Keetchi against Epidemic while Section 8 provides the results of this performance evaluation. Finally, section 9 concludes the paper and sketches some future work directions.