Analyzing temporal scale behaviour of connectivity properties of node encounters

Nowadays the growing popularity of wireless networks, combined with a wide availability of personal wireless devices, make the role of human mobility modeling more prominent in wireless networks, particularly in infrastructure-less networks such as Delay Tolerant Networks and Opportunistic Networks. The knowledge about encounters’ patterns among mobile nodes will be helpful for understanding the role and potential of mobile devices as relaying nodes. Data about the usage of Wi-Fi networks can be exploited to analyze the patterns of encounters between pairs of mobile devices and then be extrapolated for other contexts. Since human mobility occurs in different spatial and temporal scales, the role of scale in mobility modeling is crucial. Although spatial properties of mobility have been studied in different scales, by our knowledge there is no fundamental perspective about human mobility properties at different temporal scales. In this paper we evaluate the connectivity properties of node encounters at different temporal durations. We observed that connectivity properties of node encounters follow almost the same trends in different time intervals, although slopes and exponential decaying rates may be different. Our observations illustrate that networks formed from encounters of nodes extracted from Wi-Fi traces do not exhibit a scale free behaviour.Fundação para a Ciência e a Tecnologi

Applications of Temporal Graph Metrics to Real-World Networks

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.Comment: 25 page

Multi-scale Population and Mobility Estimation with Geo-tagged Tweets

Recent outbreaks of Ebola and Dengue viruses have again elevated the significance of the capability to quickly predict disease spread in an emergent situation. However, existing approaches usually rely heavily on the time-consuming census processes, or the privacy-sensitive call logs, leading to their unresponsive nature when facing the abruptly changing dynamics in the event of an outbreak. In this paper we study the feasibility of using large-scale Twitter data as a proxy of human mobility to model and predict disease spread. We report that for Australia, Twitter users' distribution correlates well the census-based population distribution, and that the Twitter users' travel patterns appear to loosely follow the gravity law at multiple scales of geographic distances, i.e. national level, state level and metropolitan level. The radiation model is also evaluated on this dataset though it has shown inferior fitness as a result of Australia's sparse population and large landmass. The outcomes of the study form the cornerstones for future work towards a model-based, responsive prediction method from Twitter data for disease spread.Comment: 1st International Workshop on Big Data Analytics for Biosecurity (BioBAD2015), 4 page

Using Neighborhood Beyond One Hop in Disruption-Tolerant Networks

Most disruption-tolerant networking (DTN) protocols available in the literature have focused on mere contact and intercontact characteristics to make forwarding decisions. Nevertheless, there is a world behind contacts: just because one node is not in contact with some potential destination, it does not mean that this node is alone. There may be interesting end-to-end transmission opportunities through other nearby nodes. Existing protocols miss such possibilities by maintaining a simple contact-based view of the network. In this paper, we investigate how the vicinity of a node evolves through time and whether such information can be useful when routing data. We observe a clear tradeoff between routing performance and the cost for monitoring the neighborhood. Our analyses suggest that limiting a node's neighborhood view to three or four hops is more than enough to significantly improve forwarding efficiency without incurring prohibitive overhead.Comment: 5 pages, 5 figures, 1 tabl

STEPS - an approach for human mobility modeling

In this paper we introduce Spatio-TEmporal Parametric Stepping (STEPS) - a simple parametric mobility model which can cover a large spectrum of human mobility patterns. STEPS makes abstraction of spatio-temporal preferences in human mobility by using a power law to rule the nodes movement. Nodes in STEPS have preferential attachment to favorite locations where they spend most of their time. Via simulations, we show that STEPS is able, not only to express the peer to peer properties such as inter-ontact/contact time and to reflect accurately realistic routing performance, but also to express the structural properties of the underlying interaction graph such as small-world phenomenon. Moreover, STEPS is easy to implement, exible to configure and also theoretically tractable