13,206 research outputs found
Analyzing Linear Communication Networks using the Ribosome Flow Model
The Ribosome Flow Model (RFM) describes the unidirectional movement of
interacting particles along a one-dimensional chain of sites. As a site becomes
fuller, the effective entry rate into this site decreases. The RFM has been
used to model and analyze mRNA translation, a biological process in which
ribosomes (the particles) move along the mRNA molecule (the chain), and decode
the genetic information into proteins.
Here we propose the RFM as an analytical framework for modeling and analyzing
linear communication networks. In this context, the moving particles are
data-packets, the chain of sites is a one dimensional set of ordered buffers,
and the decreasing entry rate to a fuller buffer represents a kind of
decentralized backpressure flow control. For an RFM with homogeneous link
capacities, we provide closed-form expressions for important network metrics
including the throughput and end-to-end delay. We use these results to analyze
the hop length and the transmission probability (in a contention access mode)
that minimize the end-to-end delay in a multihop linear network, and provide
closed-form expressions for the optimal parameter values
A contrasting look at self-organization in the Internet and next-generation communication networks
This article examines contrasting notions of self-organization in the Internet and next-generation communication networks, by reviewing in some detail recent evidence regarding several of the more popular attempts to explain prominent features of Internet structure and behavior as "emergent phenomena." In these examples, what might appear to the nonexpert as "emergent self-organization" in the Internet actually results from well conceived (albeit perhaps ad hoc) design, with explanations that are mathematically rigorous, in agreement with engineering reality, and fully consistent with network measurements. These examples serve as concrete starting points from which networking researchers can assess whether or not explanations involving self-organization are relevant or appropriate in the context of next-generation communication networks, while also highlighting the main differences between approaches to self-organization that are rooted in engineering design vs. those inspired by statistical physics
Resilient networking in wireless sensor networks
This report deals with security in wireless sensor networks (WSNs),
especially in network layer. Multiple secure routing protocols have been
proposed in the literature. However, they often use the cryptography to secure
routing functionalities. The cryptography alone is not enough to defend against
multiple attacks due to the node compromise. Therefore, we need more
algorithmic solutions. In this report, we focus on the behavior of routing
protocols to determine which properties make them more resilient to attacks.
Our aim is to find some answers to the following questions. Are there any
existing protocols, not designed initially for security, but which already
contain some inherently resilient properties against attacks under which some
portion of the network nodes is compromised? If yes, which specific behaviors
are making these protocols more resilient? We propose in this report an
overview of security strategies for WSNs in general, including existing attacks
and defensive measures. In this report we focus at the network layer in
particular, and an analysis of the behavior of four particular routing
protocols is provided to determine their inherent resiliency to insider
attacks. The protocols considered are: Dynamic Source Routing (DSR),
Gradient-Based Routing (GBR), Greedy Forwarding (GF) and Random Walk Routing
(RWR)
Activity driven modeling of time varying networks
Network modeling plays a critical role in identifying statistical
regularities and structural principles common to many systems. The large
majority of recent modeling approaches are connectivity driven. The structural
patterns of the network are at the basis of the mechanisms ruling the network
formation. Connectivity driven models necessarily provide a time-aggregated
representation that may fail to describe the instantaneous and fluctuating
dynamics of many networks. We address this challenge by defining the activity
potential, a time invariant function characterizing the agents' interactions
and constructing an activity driven model capable of encoding the instantaneous
time description of the network dynamics. The model provides an explanation of
structural features such as the presence of hubs, which simply originate from
the heterogeneous activity of agents. Within this framework, highly dynamical
networks can be described analytically, allowing a quantitative discussion of
the biases induced by the time-aggregated representations in the analysis of
dynamical processes.Comment: 10 pages, 4 figure
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