89 research outputs found
Modelling Data Dissemination in Opportunistic Networks
In opportunistic networks data dissemination is an impor- tant, although not widely explored, topic. Since oppor- tunistic networks topologies are very challenged and un- stable, data-centric approaches are an interesting direction to pursue. Data should be proactively and cooperatively disseminated from sources towards possibly interested re- ceivers, as sources and receivers might not be aware of each other, and never get in touch directly. In this paper we con- sider a utility-based cooperative data dissemination system in which the utility of data is defined based on the social relationships between users. Specifically, we study the per- formance of this system through an analytical model. Our model allows us to completely characterise the data dissem- ination process, as it describes both its stationary and tran- sient regimes. After validating the model, we study the sys- tem\u27s behaviour with respect to key parameters such as the definition of the data utility function, the initial data allo- cation on nodes, the number of users in the system, and the data popularity
Heterogeneous epidemic model for assessing data dissemination in opportunistic networks
In this paper we investigate a susceptible-infected-susceptible (SIS)
epidemic model describing data dissemination in opportunistic networks with
heterogeneous setting of transmission parameters. We obtained the estimation of
the final epidemic size assuming that amount of data transferred between
network nodes possesses a Pareto distribution, implying scale-free properties.
In this context, more heterogeneity in susceptibility means the less severe
epidemic progression, and, on the contrary, more heterogeneity in infectivity
leads to more severe epidemics -- assuming that the other parameter (either
heterogeneity or susceptibility) stays fixed. The results are general enough
and can be useful in general epidemic theory for estimating the epidemic
progression for diseases with no significant acquired immunity -- in the cases
where Pareto distribution holds
Modeling and Measuring Performance of Data Dissemination in Opportunistic Networks
In this thesis we focus on understanding, measuring and describing the performance of Opportunistic Networks (ONs) and their applications. An “opportunistic network” is a term introduced to describe a sparse, wireless, ad hoc network with highly mobile nodes. The opportunistic networking paradigm deviates from the traditional end-to-end connectivity concept: Forwarding is based on intermittent connectivity between mobile nodes (typically, users with wireless devices); complete routes between sources and destinations rarely exist. Due to this unique property of spontaneous link establishment, the challenges that exist in ONs are specific. The unstructured nature of these networks makes it difficult to give any performance guarantees on data dissemination. For this reason, in Part I of this thesis we explore the dynamics that affect the performance of opportunistic networks. We choose a number of meaningful scenarios where our models and algorithms can be validated using large and credible data sets. We show that a drift and jump model that takes a spatial approach succeeds in capturing the impact of infrastructure and mobile-to-mobile exchanges on an opportunistic content update system. We describe the effects of these dynamics by using the age distribution of a dynamic piece of data (i.e., information updates) as the performance measure. The model also succeeds in capturing a strong bias in user mobility and reveals the existence of regions, whose statistics play a critical role in the performance perceived in the network. We exploit these findings to design an application for greedy infrastructure placement, which relies on the model approximation for a large number of nodes. Another great challenge of opportunistic networking lies in the fact that the bandwidth available on wireless links, coupled with ad hoc networking, failed to rival the capacity of backbones and to establish opportunistic networks as an alternative to infrastructure-based networks. For this reason, we never study ONs in an isolated context. Instead, we consider the applications that leverage interconnection between opportunistic networks and legacy networks and we study the benefits this synergy brings to both. Following this approach, we use a large operator-provided data set to show that opportunistic networks (based on Wi-Fi) are capable of offloading a significant amount of traffic from 3G networks. At the same time, the offloading algorithms we propose reduce the amount of energy consumed by mobiles, while requiring Wi-Fi coverage that is several times smaller than in the case of real-time offloading. Again we confirm and reuse the fact that user mobility is biased towards certain regions of the network. In Part II of this thesis, we treat another issue that is essential for the acceptance and evolution of opportunistic networks and their applications. Namely, we address the absence of experimental results that would support the findings of simulation based studies. Although the techniques such as contact-based simulations should intuitively be able to capture the performance of opportunistic applications, this intuition has little evidence in practice. For this reason, we design and deploy an experiment with real users who use an opportunistic Twitter application, in a way that allows them to maintain communication with legacy networks (i.e., cellular networks, the Internet). The experiment gives us a unique insight into certain performance aspects that are typically hidden or misinterpreted when the usual evaluation techniques (such as simulation) are used. We show that, due to the commonly ignored factors (such as the limited transmission bandwidth), contact-based simulations significantly overestimate delivery ratio and obtain delays that are several times lower than those experimentally acquired. In addition to this, our results unanimously show that the common practice of assuming infinite cache sizes in simulation studies, leads to a misinterpretation of the effects of a backbone on an opportunistic network. Such simulations typically overestimate the performance of the opportunistic component, while underestimating the utility of the backbone. Given the discovered deficiencies of the contact-based simulations, we consider an alternative statistical treatment of contact traces that uses the weighted contact graph. We show that this approach offers a better interpretation of the impact of a backbone on an opportunistic network and results in a closer match when it comes to modeling certain aspects of performance (namely, delivery ratio). Finally, the security requirements for the opportunistic applications that involve an interconnection with legacy networks are also highly specific. They cannot be fully addressed by the solutions proposed in the context of autonomous opportunistic (or ad hoc) networks, nor by the security frameworks used for securing the applications with continuous connectivity. Thus, in Part III of this thesis, we put together a security framework that fits the networks and applications that we target (i.e., the opportunistic networks and applications with occasional Internet connectivity). We then focus on the impact of security print on network performance and design a scheme for the protection of optimal relaying capacity in an opportunistic multihop network. We fine-tune the parameters of our scheme by using a game-theoretic approach and we demonstrate the substantial performance gains provided by the scheme
Design and Performance Evaluation of Data Dissemination Systems for Opportunistic Networks Based on Cognitive Heuristics
It is often argued that the Future Internet will be a very large scale content-centric network. Scalability issues will stem even more from the amount of content nodes will gen- erate, share and consume. In order to let users become aware and retrieve the content they really need, these nodes will be required to swiftly react to stimuli and assert the rele- vance of discovered data under uncertainty and only partial information. The human brain performs the task of infor- mation ltering and selection using the so-called cognitive heuristics, i.e. simple, rapid, low-resource demanding, yet very eective schemes that can be modeled using a func- tional approach. In this paper we propose a solution based on one such heuristics, namely the recognition heuristic, for dealing with data dissemination in opportunistic networks. We show how to implement an algorithm that exploits the environmental information in order to implement an eec- tive dissemination of data based on the recognition heuristic, and provide a performance evaluation of such a solution via simulation
Social-aware Forwarding in Opportunistic Wireless Networks: Content Awareness or Obliviousness?
With the current host-based Internet architecture, networking faces
limitations in dynamic scenarios, due mostly to host mobility. The ICN paradigm
mitigates such problems by releasing the need to have an end-to-end transport
session established during the life time of the data transfer. Moreover, the
ICN concept solves the mismatch between the Internet architecture and the way
users would like to use it: currently a user needs to know the topological
location of the hosts involved in the communication when he/she just wants to
get the data, independently of its location. Most of the research efforts aim
to come up with a stable ICN architecture in fixed networks, with few examples
in ad-hoc and vehicular networks. However, the Internet is becoming more
pervasive with powerful personal mobile devices that allow users to form
dynamic networks in which content may be exchanged at all times and with low
cost. Such pervasive wireless networks suffer with different levels of
disruption given user mobility, physical obstacles, lack of cooperation,
intermittent connectivity, among others. This paper discusses the combination
of content knowledge (e.g., type and interested parties) and social awareness
within opportunistic networking as to drive the deployment of ICN solutions in
disruptive networking scenarios. With this goal in mind, we go over few
examples of social-aware content-based opportunistic networking proposals that
consider social awareness to allow content dissemination independently of the
level of network disruption. To show how much content knowledge can improve
social-based solutions, we illustrate by means of simulation some
content-oblivious/oriented proposals in scenarios based on synthetic mobility
patterns and real human traces.Comment: 7 pages, 6 figure
Content Discovery in Mobile Networks Using thePublish and Subscribe Paradigm
Articolo presentato alla riunione annuale dell'Associazione Gruppo Telecomunicazioni e Tecnologie dell'Informazione (GTTI) 200
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
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