Using Triggers for Emulation of Opportunistic Networking

Opportunistic networks do not require the availability of an end-to-end path, but may instead take advantage of tem- porary connectivity opportunities. Opportunistic networks pose a challenge for network emulation as the traditional em- ulation setup where application/transport endpoints send/ receive packets from the network following a black box approach is no longer applicable. Instead opportunistic networking protocols and applications need to react to the dynamics of the underlying network beyond what is conveyed through the exchange of packets. In order to support emulation evaluations for such challenging applications we in this paper introduce the concept of emulation triggers that can emulate arbitrary cross-layer feedback and that are synchronized with the emulated scenario. The design and implementation of triggers in the KauNet emulator are described. The use of triggers in the context of opportunistic networking is brie y sketched

Emulating opportunistic networks with KauNet Triggers

In opportunistic networks the availability of an end-to-end path is no longer required. Instead opportunistic networks may take advantage of temporary connectivity opportunities. Opportunistic networks present a demanding environment for network emulation as the traditional emulation setup, where application/transport endpoints only send and receive packets from the network following a black box approach, is no longer applicable. Opportunistic networking protocols and applications additionally need to react to the dynamics of the underlying network beyond what is conveyed through the exchange of packets. In order to support IP-level emulation evaluations of applications and protocols that react to lower layer events, we have proposed the use of emulation triggers. Emulation triggers can emulate arbitrary cross-layer feedback and can be synchronized with other emulation effects. After introducing the design and implementation of triggers in the KauNet emulator, we describe the integration of triggers with the DTN2 reference implementation and illustrate how the functionality can be used to emulate a classical DTN data-mule scenario

On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks

We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data diffusion in ad-hoc networks, Ad Hoc Netw. (2011

Evaluating Mobility Pattern Space Routing for DTNs

Because a delay tolerant network (DTN) can often be partitioned, the problem of routing is very challenging. However, routing benefits considerably if one can take advantage of knowledge concerning node mobility. This paper addresses this problem with a generic algorithm based on the use of a high-dimensional Euclidean space, that we call MobySpace, constructed upon nodes' mobility patterns. We provide here an analysis and the large scale evaluation of this routing scheme in the context of ambient networking by replaying real mobility traces. The specific MobySpace evaluated is based on the frequency of visit of nodes for each possible location. We show that the MobySpace can achieve good performance compared to that of the other algorithms we implemented, especially when we perform routing on the nodes that have a high connection time. We determine that the degree of homogeneity of mobility patterns of nodes has a high impact on routing. And finally, we study the ability of nodes to learn their own mobility patterns.Comment: IEEE INFOCOM 2006 preprin