5,380 research outputs found
Design and analysis of adaptive hierarchical low-power long-range networks
A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications
Consensus for quantum networks: from symmetry to gossip iterations
This paper extends the consensus framework, widely studied in the literature on distributed computing and control algorithms, to networks of quantum systems. We define consensus situations on the basis of invariance and symmetry properties, finding four different generalizations of classical consensus states. This new viewpoint can be directly used to study consensus for probability distributions, as these can be seen as a particular case of quantum statistical states: in this light, our analysis is also relevant for classical problems. We then extend the gossip consensus algorithm to the quantum setting and prove it converges to symmetric states while preserving the expectation of permutation-invariant global observables. Applications of the framework and the algorithms to estimation and control problems on quantum networks are discussed
An Infrared Divergence Problem in the cosmological measure theory and the anthropic reasoning
An anthropic principle has made it possible to answer the difficult question
of why the observable value of cosmological constant (
GeV) is so disconcertingly tiny compared to predicted value of vacuum
energy density GeV. Unfortunately, there is a
darker side to this argument, as it consequently leads to another absurd
prediction: that the probability to observe the value for randomly
selected observer exactly equals to 1. We'll call this controversy an infrared
divergence problem. It is shown that the IRD prediction can be avoided with the
help of a Linde-Vanchurin {\em singular runaway measure} coupled with the
calculation of relative Bayesian probabilities by the means of the {\em
doomsday argument}. Moreover, it is shown that while the IRD problem occurs for
the {\em prediction stage} of value of , it disappears at the {\em
explanatory stage} when has already been measured by the observer.Comment: 9 pages, RevTe
On the Nature and Shape of Tubulin Trails: Implications on Microtubule Self-Organization
Microtubules, major elements of the cell skeleton are, most of the time, well
organized in vivo, but they can also show self-organizing behaviors in time
and/or space in purified solutions in vitro. Theoretical studies and models
based on the concepts of collective dynamics in complex systems,
reaction-diffusion processes and emergent phenomena were proposed to explain
some of these behaviors. In the particular case of microtubule spatial
self-organization, it has been advanced that microtubules could behave like
ants, self-organizing by 'talking to each other' by way of hypothetic (because
never observed) concentrated chemical trails of tubulin that are expected to be
released by their disassembling ends. Deterministic models based on this idea
yielded indeed like-looking spatio-temporal self-organizing behaviors.
Nevertheless the question remains of whether microscopic tubulin trails
produced by individual or bundles of several microtubules are intense enough to
allow microtubule self-organization at a macroscopic level. In the present
work, by simulating the diffusion of tubulin in microtubule solutions at the
microscopic scale, we measure the shape and intensity of tubulin trails and
discuss about the assumption of microtubule self-organization due to the
production of chemical trails by disassembling microtubules. We show that the
tubulin trails produced by individual microtubules or small microtubule arrays
are very weak and not elongated even at very high reactive rates. Although the
variations of concentration due to such trails are not significant compared to
natural fluctuations of the concentration of tubuline in the chemical
environment, the study shows that heterogeneities of biochemical composition
can form due to microtubule disassembly. They could become significant when
produced by numerous microtubule ends located in the same place. Their possible
formation could play a role in certain conditions of reaction. In particular,
it gives a mesoscopic basis to explain the collective dynamics observed in
excitable microtubule solutions showing the propagation of concentration waves
of microtubules at the millimeter scale, although we doubt that individual
microtubules or bundles can behave like molecular ants
Cooperative Control for Target Tracking with Onboard Sensing
Abstract We consider the cooperative control of a team of robots to estimate the position of a moving target using onboard sensing. In particular, we do not as-sume that the robot positions are known, but estimate their positions using relative onboard sensing. Our probabilistic localization and control method takes into ac-count the motion and sensing capabilities of the individual robots to minimize the expected future uncertainty of the target position. It reasons about multiple possi-ble sensing topologies and incorporates an efficient topology switching technique to generate locally optimal controls in polynomial time complexity. Simulations show the performance of our approach and prove its flexibility to find suitable sensing topologies depending on the limited sensing capabilities of the robots and the movements of the target. Furthermore, we demonstrate the applicability of our method in various experiments with single and multiple quadrotor robots tracking a ground vehicle in an indoor environment
The Self-Organization of Meaning and the Reflexive Communication of Information
Following a suggestion of Warren Weaver, we extend the Shannon model of
communication piecemeal into a complex systems model in which communication is
differentiated both vertically and horizontally. This model enables us to
bridge the divide between Niklas Luhmann's theory of the self-organization of
meaning in communications and empirical research using information theory.
First, we distinguish between communication relations and correlations among
patterns of relations. The correlations span a vector space in which relations
are positioned and can be provided with meaning. Second, positions provide
reflexive perspectives. Whereas the different meanings are integrated locally,
each instantiation opens global perspectives--"horizons of meaning"--along
eigenvectors of the communication matrix. These next-order codifications of
meaning can be expected to generate redundancies when interacting in
instantiations. Increases in redundancy indicate new options and can be
measured as local reduction of prevailing uncertainty (in bits). The systemic
generation of new options can be considered as a hallmark of the
knowledge-based economy.Comment: accepted for publication in Social Science Information, March 21,
201
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