475 research outputs found
Not Always Sparse: Flooding Time in Partially Connected Mobile Ad Hoc Networks
In this paper we study mobile ad hoc wireless networks using the notion of
evolving connectivity graphs. In such systems, the connectivity changes over
time due to the intermittent contacts of mobile terminals. In particular, we
are interested in studying the expected flooding time when full connectivity
cannot be ensured at each point in time. Even in this case, due to finite
contact times durations, connected components may appear in the connectivity
graph. Hence, this represents the intermediate case between extreme cases of
fully mobile ad hoc networks and fully static ad hoc networks. By using a
generalization of edge-Markovian graphs, we extend the existing models based on
sparse scenarios to this intermediate case and calculate the expected flooding
time. We also propose bounds that have reduced computational complexity.
Finally, numerical results validate our models
Dynamic control of Coding in Delay Tolerant Networks
Delay tolerant Networks (DTNs) leverage the mobility of relay nodes to
compensate for lack of permanent connectivity and thus enable communication
between nodes that are out of range of each other. To decrease message delivery
delay, the information to be transmitted is replicated in the network. We study
replication mechanisms that include Reed-Solomon type codes as well as network
coding in order to improve the probability of successful delivery within a
given time limit. We propose an analytical approach that allows us to compute
the probability of successful delivery. We study the effect of coding on the
performance of the network while optimizing parameters that govern routing
Differential Games of Competition in Online Content Diffusion
Access to online contents represents a large share of the Internet traffic.
Most such contents are multimedia items which are user-generated, i.e., posted
online by the contents' owners. In this paper we focus on how those who provide
contents can leverage online platforms in order to profit from their large base
of potential viewers.
Actually, platforms like Vimeo or YouTube provide tools to accelerate the
dissemination of contents, i.e., recommendation lists and other re-ranking
mechanisms. Hence, the popularity of a content can be increased by paying a
cost for advertisement: doing so, it will appear with some priority in the
recommendation lists and will be accessed more frequently by the platform
users.
Ultimately, such acceleration mechanism engenders a competition among online
contents to gain popularity. In this context, our focus is on the structure of
the acceleration strategies which a content provider should use in order to
optimally promote a content given a certain daily budget. Such a best response
indeed depends on the strategies adopted by competing content providers. Also,
it is a function of the potential popularity of a content and the fee paid for
the platform advertisement service.
We formulate the problem as a differential game and we solve it for the
infinite horizon case by deriving the structure of certain Nash equilibria of
the game
Optimal curing policy for epidemic spreading over a community network with heterogeneous population
The design of an efficient curing policy, able to stem an epidemic process at
an affordable cost, has to account for the structure of the population contact
network supporting the contagious process. Thus, we tackle the problem of
allocating recovery resources among the population, at the lowest cost possible
to prevent the epidemic from persisting indefinitely in the network.
Specifically, we analyze a susceptible-infected-susceptible epidemic process
spreading over a weighted graph, by means of a first-order mean-field
approximation. First, we describe the influence of the contact network on the
dynamics of the epidemics among a heterogeneous population, that is possibly
divided into communities. For the case of a community network, our
investigation relies on the graph-theoretical notion of equitable partition; we
show that the epidemic threshold, a key measure of the network robustness
against epidemic spreading, can be determined using a lower-dimensional
dynamical system. Exploiting the computation of the epidemic threshold, we
determine a cost-optimal curing policy by solving a convex minimization
problem, which possesses a reduced dimension in the case of a community
network. Lastly, we consider a two-level optimal curing problem, for which an
algorithm is designed with a polynomial time complexity in the network size.Comment: to be published on Journal of Complex Network
Forward correction and fountain codes in delay tolerant networks
Abstract—Delay tolerant Ad-hoc Networks make use of mobility of relay nodes to compensate for lack of permanent connectivity and thus enable communication between nodes that are out of range of each other. To decrease delivery delay, the information that needs to be delivered is replicated in the network. Our objective in this paper is to study replication mechanisms that include coding in order to improve the probability of successful delivery within a given time limit. We propose an analytical approach that allows to quantify tradeoffs between resources and performance measures (energy and delay). We study the effect of coding on the performance of the network while optimizing parameters that govern routing. Our results, based on fluid approximations, are compared to simulations which validate the model 1. Index Terms—Forward correction, fountain codes, delay tolerant networks I
Emergence of Equilibria from Individual Strategies in Online Content Diffusion
Social scientists have observed that human behavior in society can often be
modeled as corresponding to a threshold type policy. A new behavior would
propagate by a procedure in which an individual adopts the new behavior if the
fraction of his neighbors or friends having adopted the new behavior exceeds
some threshold. In this paper we study the question of whether the emergence of
threshold policies may be modeled as a result of some rational process which
would describe the behavior of non-cooperative rational members of some social
network. We focus on situations in which individuals take the decision whether
to access or not some content, based on the number of views that the content
has. Our analysis aims at understanding not only the behavior of individuals,
but also the way in which information about the quality of a given content can
be deduced from view counts when only part of the viewers that access the
content are informed about its quality. In this paper we present a game
formulation for the behavior of individuals using a meanfield model: the number
of individuals is approximated by a continuum of atomless players and for which
the Wardrop equilibrium is the solution concept. We derive conditions on the
problem's parameters that result indeed in the emergence of threshold
equilibria policies. But we also identify some parameters in which other
structures are obtained for the equilibrium behavior of individuals
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