92,107 research outputs found
Improving Diffusion-Based Molecular Communication with Unanchored Enzymes
In this paper, we propose adding enzymes to the propagation environment of a
diffusive molecular communication system as a strategy for mitigating
intersymbol interference. The enzymes form reaction intermediates with
information molecules and then degrade them so that they have a smaller chance
of interfering with future transmissions. We present the reaction-diffusion
dynamics of this proposed system and derive a lower bound expression for the
expected number of molecules observed at the receiver. We justify a
particle-based simulation framework, and present simulation results that show
both the accuracy of our expression and the potential for enzymes to improve
communication performance.Comment: 15 pages, 4 figures, presented at the 7th International Conference on
Bio-Inspired Models of Network, Information, and Computing Systems (BIONETICS
2012) in Lugano, Switzerlan
Exploring the physical channel of diffusion-based molecular communication by simulation
Diffusion-based molecular communication is a promising bio-inspired paradigm to implement nanonetworks, i.e., the interconnection of nanomachines. The peculiarities of the physical channel in diffusion-based molecular communication require the development of novel models, architectures and protocols for this new scenario, which need to be validated by simulation. With this purpose, we present N3Sim, a simulation framework for diffusion-based molecular communication. N3Sim allows to simulate scenarios where transmitters encode the information by releasing molecules into the medium, thus varying their local concentration. N3Sim models the movement of these molecules according to Brownian dynamics, and it also takes into account their inertia and the interactions among them. Receivers decode the information by sensing the particle concentration in their neighborhood. The benefits of N3Sim are multiple: the validation of channel models for molecular communication and the evaluation of novel modulation schemes are just a few examples.Peer ReviewedPostprint (author’s final draft
N3SIM : Simulator for diffusion-based molecular communication in Nanonetworks
English: Nanotechnology is enabling the development of devices in a scale ranging from one to a few hundred nanometers, known as nanomachines. How these nanomachines will communicate is still an open debate. Molecular communication is a promising paradigm that has been proposed to implement nanonetworks, i.e., the interconnection of nanomachines. The peculiarities of the physical channel in diffusion-based molecular communication require the development of novel models, architectures and protocols for this new scenario, which need to be validated by simulation. With this purpose, N3Sim, a simulator for diffusion-based molecular communication has been developed. N3Sim allows simulating scenarios where transmitters encode the information by releasing molecules into the medium, thus varying their local concentration. N3Sim models the movement of these molecules according to Brownian dynamics, and it also takes into account their inertia and the interactions among them. Receivers decode the information by sensing the particle concentration in their neighborhood. The benefits of N3Sim are multiple: the validation of channel models for molecular communication and the evaluation of novel modulation schemes are just a few examples
Excitable Media Seminar
The simulation data presented here, and the conceptual framework developed for their interpretation are, both, in need of substantial refinement and extension. However, granting that they are initial pointers of some merit, and elementary indicators of general principles, several implications follow: the activity patterns of neurons and their assemblies are\ud
interdependent with the extracellular milieu in which they are embedded, and to whose time varying composition they contribute. The complexity of this interdependence in the temporal dimension forecloses any time and context invariant relation between what the experimenter may consider stimulus input and its representation in neural activity. Hence, ideas of coding by (quasi)-digital neurons are called in question by the mutual interdependence of neurons and their\ud
humoral milieu. Instead, concepts of 'mass action' in the Nervous system gain a new perspective: this time augmented by including the chemical medium surrounding neurons as part of the dynamics of the system as a whole. Accordingly, a meaningful way to describe activity in a neuron assembly would be in terms of a state space in which it can move along an infinite number of trajectories.\u
Complexity, parallel computation and statistical physics
The intuition that a long history is required for the emergence of complexity
in natural systems is formalized using the notion of depth. The depth of a
system is defined in terms of the number of parallel computational steps needed
to simulate it. Depth provides an objective, irreducible measure of history
applicable to systems of the kind studied in statistical physics. It is argued
that physical complexity cannot occur in the absence of substantial depth and
that depth is a useful proxy for physical complexity. The ideas are illustrated
for a variety of systems in statistical physics.Comment: 21 pages, 7 figure
Controllability of Social Networks and the Strategic Use of Random Information
This work is aimed at studying realistic social control strategies for social
networks based on the introduction of random information into the state of
selected driver agents. Deliberately exposing selected agents to random
information is a technique already experimented in recommender systems or
search engines, and represents one of the few options for influencing the
behavior of a social context that could be accepted as ethical, could be fully
disclosed to members, and does not involve the use of force or of deception.
Our research is based on a model of knowledge diffusion applied to a
time-varying adaptive network, and considers two well-known strategies for
influencing social contexts. One is the selection of few influencers for
manipulating their actions in order to drive the whole network to a certain
behavior; the other, instead, drives the network behavior acting on the state
of a large subset of ordinary, scarcely influencing users. The two approaches
have been studied in terms of network and diffusion effects. The network effect
is analyzed through the changes induced on network average degree and
clustering coefficient, while the diffusion effect is based on two ad-hoc
metrics defined to measure the degree of knowledge diffusion and skill level,
as well as the polarization of agent interests. The results, obtained through
simulations on synthetic networks, show a rich dynamics and strong effects on
the communication structure and on the distribution of knowledge and skills,
supporting our hypothesis that the strategic use of random information could
represent a realistic approach to social network controllability, and that with
both strategies, in principle, the control effect could be remarkable
Adoption as a Social Marker: Innovation Diffusion with Outgroup Aversion
Social identities are among the key factors driving behavior in complex
societies. Signals of social identity are known to influence individual
behaviors in the adoption of innovations. Yet the population-level consequences
of identity signaling on the diffusion of innovations are largely unknown. Here
we use both analytical and agent-based modeling to consider the spread of a
beneficial innovation in a structured population in which there exist two
groups who are averse to being mistaken for each other. We investigate the
dynamics of adoption and consider the role of structural factors such as
demographic skew and communication scale on population-level outcomes. We find
that outgroup aversion can lead to adoption being delayed or suppressed in one
group, and that population-wide underadoption is common. Comparing the two
models, we find that differential adoption can arise due to structural
constraints on information flow even in the absence of intrinsic between-group
differences in adoption rates. Further, we find that patterns of polarization
in adoption at both local and global scales depend on the details of
demographic organization and the scale of communication. This research has
particular relevance to widely beneficial but identity-relevant products and
behaviors, such as green technologies, where overall levels of adoption
determine the positive benefits that accrue to society at large.Comment: 26 pages, 10 figure
Coordination of Decisions in a Spatial Agent Model
For a binary choice problem, the spatial coordination of decisions in an
agent community is investigated both analytically and by means of stochastic
computer simulations. The individual decisions are based on different local
information generated by the agents with a finite lifetime and disseminated in
the system with a finite velocity. We derive critical parameters for the
emergence of minorities and majorities of agents making opposite decisions and
investigate their spatial organization. We find that dependent on two essential
parameters describing the local impact and the spatial dissemination of
information, either a definite stable minority/majority relation
(single-attractor regime) or a broad range of possible values (multi-attractor
regime) occurs. In the latter case, the outcome of the decision process becomes
rather diverse and hard to predict, both with respect to the share of the
majority and their spatial distribution. We further investigate how a
dissemination of information on different time scales affects the outcome of
the decision process. We find that a more ``efficient'' information exchange
within a subpopulation provides a suitable way to stabilize their majority
status and to reduce ``diversity'' and uncertainty in the decision process.Comment: submitted for publication in Physica A (31 pages incl. 17 multi-part
figures
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