Applications of research on human mobility for mobile computing have been mostly evolving around the opportunities for data dissemination and opportunistic routing. Common metrics for the characterization of human mobility are: i) Inter-contact time (ICT), measuring the time between two consecutive Inter-contacts of two de-vices; ii) Jump size, indicating the distance between two points where the device has stopped; and iii) Pause time, that represents the time spent in the same place (Kim et al., 2006; Lee et al., 2009; Song et al., 2010a; Karamshuk et al., 2011).
The work described in (Chaintreau et al., 2007), reports a study on the ICTs of two distinct datasets. One is based on records collected from the access logs of WiFi networks. The second, named direct contact, contains records captured directly by devices, either produced specifically to be carried by users, or by exploiting the Blue-tooth connectivity of off-the-shelf devices. Authors observed that the distribution of inter-contact times follows a power law for ICTs smaller than 1 day with the remaining presenting an exponential decay. Another research that supports the same findings can be found in (Karagiannis et al., 2010)). To improve the human mobility characteriza-tion, the authors of Hagle (Su et al., 2007) also consider the contact duration.
(Pietil¨anen and Diot, 2012) goes beyond ICTs and addresses temporal communi-ties (clusters of devices that are in range for a given time) and their relations. Authors extracted temporal communities from four distinct datasets, the largest of which con-sidering the observation of 97 nodes over 9 months. To improve the study, authors obtained the social relations between users in some datasets, either by knowing the af-filiation (on conference related traces) or by Facebook friendship graphs. This knowl-edge was used to establish social communities among users. In spite of the small scale
3.3. CONTACT PREDICTION 31
and duration of the study, authors presented two interesting conclusions. On one side, that the establishment of social communities has direct implications on temporal com-munities. On the other, that one particular class of devices, those with a high contact rate that are rarely seen in temporal communities, contribute significantly for the effi-cient content dissemination in opportunistic social networks.
Social communities are equally the focus of SocialCast (Costa et al., 2008). This work exploits the knowledge that humans tend to share interests and locations to de-velop an efficient routing protocol for publish-subscribe on Delay-Tolerant Networks.
SocialCast uses Kalman filters for forecasting future contacts, based on previous obser-vations of co-location between publisher and subscribers.
An innovative approach at predicting contacts, is presented in (Orlinski and Filer, 2013), where authors added a duration variable to communities detection, thus cre-ating spatio-temporal communities. The community relevance is increased propor-tionally to its duration, which improves the efficiency of cluster based data delivery in Pocket Switched Networks (networks formed by encounters between devices car-ried by humans). It was shown that spatio-temporal communities can contribute to improve the efficiency of information dissemination in these opportunistic networks.
Simulation experiments were conducted in the same datasets used by the Haggle project.
Contact prediction has also led to the development of routing algorithms. These constrain the number of retransmissions, in order to reduce network congestion due to spurious flooding. Prophet (Lindgren et al., 2003) uses this strategy on MANETs, by restricting message dissemination to devices that have a higher probability of con-tacting the destination. Prophet uses an history of previous events in order to predict future encounters by calculating the probability of a device being useful in a nearby future for packet routing.
In (Huang et al., 2015) authors propose PreKR, a framework that optimizes the forwarding on opportunistic networks by using a kernel regression based estimation for link pattern prediction. Using historical observations of network maps on three
datasets (one of them being the dataset of the Haggle project), PreKR determines the probability of a recurrence of a link between two devices. Authors determined that PreKR outperforms all other prediction methods, including Prophet. The distinguish-ing factor was the use of kernel regression, that allowed PreKR to achieve an accuracy of more than 90%.
Bubble Rap (Hui et al., 2011), is a socially influenced routing protocol, that lever-aged on the mobility traces of the Haggle project to infer temporal communities. Au-thors used the K-Clique (Palla et al., 2005) and weighted network analysis (WNA) algo-rithms (Newman, 2004), two forms of centralized algoalgo-rithms, to extract communities from mobile traces. The two algorithms were chosen for their features. K-Clique de-tects overlapping communities, but requires as a complex configuration process before being used. WNA is easier to set up, but is incapable of detecting overlapping com-munities. These algorithms are specially useful for forwarding applications, where a path needs to be predicted. However, for applications where a prediction of temporal communities is enough, K-Clique and WNA proved to be highly complex.
In (Song et al., 2010b), authors evaluated the limits of predictability of human mo-bility by analysing the movement patterns of mobile phone users and found that the observed mobility was highly predictable, where most users are localized in a finite neighbourhood. Authors estimated that there is a potential 93% average predictability in user mobility.
In (Foell et al., 2014), authors also predict human mobility, but limit it to a user being present on a bus/bus stop. Authors detail a number of possible prediction al-gorithms for this scenario and were able to predict future bus stops using historical data. These predictions would also suit the C3 environment, if we consider that all bus riders are in range and participate on the C3.
The study of the related work suggests that research on contact prediction is still at an embryonary stage. Only a few of the works address the problem of estimating the moment of which future contact will happen, as well as estimation of the number of devices in transmission range. In contrast, research has been focused on the prediction