3,826 research outputs found
Resilience of Traffic Networks with Partially Controlled Routing
This paper investigates the use of Infrastructure-To-Vehicle (I2V)
communication to generate routing suggestions for drivers in transportation
systems, with the goal of optimizing a measure of overall network congestion.
We define link-wise levels of trust to tolerate the non-cooperative behavior of
part of the driver population, and we propose a real-time optimization
mechanism that adapts to the instantaneous network conditions and to sudden
changes in the levels of trust. Our framework allows us to quantify the
improvement in travel time in relation to the degree at which drivers follow
the routing suggestions. We then study the resilience of the system, measured
as the smallest change in routing choices that results in roads reaching their
maximum capacity. Interestingly, our findings suggest that fluctuations in the
extent to which drivers follow the provided routing suggestions can cause
failures of certain links. These results imply that the benefits of using
Infrastructure-To-Vehicle communication come at the cost of new fragilities,
that should be appropriately addressed in order to guarantee the reliable
operation of the infrastructure.Comment: Accepted for presentation at the IEEE 2019 American Control
Conferenc
Cooperation Enforcement for Packet Forwarding Optimization in Multi-hop Ad-hoc Networks
Ad-hoc networks are independent of any infrastructure. The nodes are
autonomous and make their own decisions. They also have limited energy
resources. Thus, a node tends to behave selfishly when it is asked to forward
the packets of other nodes. Indeed, it would rather choose to reject a
forwarding request in order to save its energy. To overcome this problem, the
nodes need to be motivated to cooperate. To this end, we propose a
self-learning repeated game framework to enforce cooperation between the nodes
of a network. This framework is inspired by the concept of "The Weakest Link"
TV game. Each node has a utility function whose value depends on its
cooperation in forwarding packets on a route as well as the cooperation of all
the nodes that form this same route. The more these nodes cooperate the higher
is their utility value. This would establish a cooperative spirit within the
nodes of the networks. All the nodes will then more or less equally participate
to the forwarding tasks which would then eventually guarantee a more efficient
packets forwarding from sources to respective destinations. Simulations are run
and the results show that the proposed framework efficiently enforces nodes to
cooperate and outperforms two other self-learning repeated game frameworks
which we are interested in.Comment: Published in the proceedings of the IEEE Wireless Communications and
Networking Conference (WCNC 2012), Paris, France, 201
Wardrop Equilibrium in Discrete-Time Selfish Routing with Time-Varying Bounded Delays
This paper presents a multi-commodity, discrete-
time, distributed and non-cooperative routing algorithm, which is
proved to converge to an equilibrium in the presence of
heterogeneous, unknown, time-varying but bounded delays.
Under mild assumptions on the latency functions which describe
the cost associated to the network paths, two algorithms are
proposed: the former assumes that each commodity relies only on
measurements of the latencies associated to its own paths; the
latter assumes that each commodity has (at least indirectly) access
to the measures of the latencies of all the network paths. Both
algorithms are proven to drive the system state to an invariant set
which approximates and contains the Wardrop equilibrium,
defined as a network state in which no traffic flow over the
network paths can improve its routing unilaterally, with the latter
achieving a better reconstruction of the Wardrop equilibrium.
Numerical simulations show the effectiveness of the proposed
approach
PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies
Many interactions between network users rely on trust, which is becoming
particularly important given the security breaches in the Internet today. These
problems are further exacerbated by the dynamics in wireless mobile networks.
In this paper we address the issue of trust advisory and establishment in
mobile networks, with application to ad hoc networks, including DTNs. We
utilize encounters in mobile societies in novel ways, noticing that mobility
provides opportunities to build proximity, location and similarity based trust.
Four new trust advisor filters are introduced - including encounter frequency,
duration, behavior vectors and behavior matrices - and evaluated over an
extensive set of real-world traces collected from a major university. Two sets
of statistical analyses are performed; the first examines the underlying
encounter relationships in mobile societies, and the second evaluates DTN
routing in mobile peer-to-peer networks using trust and selfishness models. We
find that for the analyzed trace, trust filters are stable in terms of growth
with time (3 filters have close to 90% overlap of users over a period of 9
weeks) and the results produced by different filters are noticeably different.
In our analysis for trust and selfishness model, our trust filters largely undo
the effect of selfishness on the unreachability in a network. Thus improving
the connectivity in a network with selfish nodes.
We hope that our initial promising results open the door for further research
on proximity-based trust
Boltzmann meets Nash: Energy-efficient routing in optical networks under uncertainty
Motivated by the massive deployment of power-hungry data centers for service
provisioning, we examine the problem of routing in optical networks with the
aim of minimizing traffic-driven power consumption. To tackle this issue,
routing must take into account energy efficiency as well as capacity
considerations; moreover, in rapidly-varying network environments, this must be
accomplished in a real-time, distributed manner that remains robust in the
presence of random disturbances and noise. In view of this, we derive a pricing
scheme whose Nash equilibria coincide with the network's socially optimum
states, and we propose a distributed learning method based on the Boltzmann
distribution of statistical mechanics. Using tools from stochastic calculus, we
show that the resulting Boltzmann routing scheme exhibits remarkable
convergence properties under uncertainty: specifically, the long-term average
of the network's power consumption converges within of its
minimum value in time which is at most ,
irrespective of the fluctuations' magnitude; additionally, if the network
admits a strict, non-mixing optimum state, the algorithm converges to it -
again, no matter the noise level. Our analysis is supplemented by extensive
numerical simulations which show that Boltzmann routing can lead to a
significant decrease in power consumption over basic, shortest-path routing
schemes in realistic network conditions.Comment: 24 pages, 4 figure
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