7 research outputs found
Distributed interpolatory algorithms for set membership estimation
This work addresses the distributed estimation problem in a set membership
framework. The agents of a network collect measurements which are affected by
bounded errors, thus implying that the unknown parameters to be estimated
belong to a suitable feasible set. Two distributed algorithms are considered,
based on projections of the estimate of each agent onto its local feasible set.
The main contribution of the paper is to show that such algorithms are
asymptotic interpolatory estimators, i.e. they converge to an element of the
global feasible set, under the assumption that the feasible set associated to
each measurement is convex. The proposed techniques are demonstrated on a
distributed linear regression estimation problem
EDDA: An Efficient Distributed Data Replication Algorithm in VANETs
Efficient data dissemination in vehicular ad hoc networks (VANETs) is a challenging issue due to the dynamic nature of the network. To improve the performance of data dissemination, we study distributed data replication algorithms in VANETs for exchanging information and computing in an arbitrarily-connected network of vehicle nodes. To achieve low dissemination delay and improve the network performance, we control the number of message copies that can be disseminated in the network and then propose an efficient distributed data replication algorithm (EDDA). The key idea is to let the data carrier distribute the data dissemination tasks to multiple nodes to speed up the dissemination process. We calculate the number of communication stages for the network to enter into a balanced status and show that the proposed distributed algorithm can converge to a consensus in a small number of communication stages. Most of the theoretical results described in this paper are to study the complexity of network convergence. The lower bound and upper bound are also provided in the analysis of the algorithm. Simulation results show that the proposed EDDA can efficiently disseminate messages to vehicles in a specific area with low dissemination delay and system overhead
Opinion Dynamics and the Evolution of Social Power in Social Networks
A fundamental aspect of society is the exchange and discussion of
opinions between individuals, occurring in mediums and situations
as varied as company boardrooms, elementary school classrooms and
online social media. This thesis studies several mathematical
models of how an individual’s opinion(s) evolves via
interaction with others in a social network, developed to reflect
and capture different socio-psychological processes that occur
during the interactions.
In the first part, and inspired by Solomon E. Asch’s seminal
experiments on conformity, a novel discrete-time model of opinion
dynamics is proposed, with each individual having both an
expressed and a private opinion on the same topic. Crucially, an
individual’s expressed opinion is altered from the
individual’s private opinion due to pressures to conform to the
majority opinion of the social network. Exponential convergence
of the opinion dynamical system to a unique configuration is
established for general networks. Several conclusions are
established, including how differences between an individual’s
expressed and private opinions arise, and how to estimate
disagreement among the private opinions at equilibrium. Asch’s
experiments are revisited and re-examined, and then it is shown
that a few extremists can create “pluralistic ignorance”,
where people believe there is majority support for a position but
in fact the position is privately rejected by the majority of
individuals!
The second part builds on the recently proposed discrete-time
DeGroot–Friedkin model, which describes the evolution of an
individual’s self-confidence (termed social power) in his/her
opinion over the discussion of a sequence of issues. Using
nonlinear contraction analysis, exponential convergence to a
unique equilibrium is established for networks with constant
topology. Networks with issue-varying topology (which remain
constant for any given issue) are then studied; exponential
convergence to a unique limiting trajectory is established. In a
social context, this means that each individual forgets his/her
initial social power exponentially fast; in the limit, his/her
social power for a given issue depends only on the previously
occurring sequence of dynamic topology. Two further related works
are considered; a network modification problem, and a different
convergence proof based on Lefschetz Fixed Point Theory.
In the final part, a continuous-time model is proposed to capture
simultaneous discussion of logically interdependent topics; the
interdependence is captured by a “logic matrix”. When no
individual remains attached to his/her initial opinion, a
necessary and sufficient condition for the network to reach a
consensus of opinions is provided. This condition depends on the
interplay between the network interactions and the logic matrix;
if the network interactions are too strong when compared to the
logical couplings, instability can result. Last, when some
individuals remain attached to their initial opinions, sufficient
conditions are given for opinions to converge to a state of
persistent disagreement