scholarly journals Construction of Trusted Routing Based on Trust Computation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Bei Gong ◽  
Jingxuan Zhu ◽  
Yubo Wang
Keyword(s):  
Comprehensive Evaluation ◽  
Transmission Rate ◽  
Research Result ◽  
The Self ◽  
Base Stations ◽  
Main Research ◽  
Self Organized ◽  
Rate Transmission ◽  
Evaluation Of Data ◽  
Self Organizing

In the field of applied IoT, a large number of wireless sensor devices are tasked with data production and collection, providing IoT subjects with a large amount of basic data to support top-level IoT applications. However, there is a considerable risk of being attacked on such sensor networks that are organized in a wireless form. These relatively independent network devices have extremely limited performance and lifetime, a problem that can be supplemented in a centralized network with base stations by relying on the performance of the core nodes of the network, but in a decentralized self-organizing network, they can have a serious adverse impact on the implementation of security solutions. Considering the fundamental nature of the data generated by such end devices in IoT application services, the protection of their security is also directly related to the quality of upper layer services provided. The main research result of this paper is the design of a trust routing scheme for self-organizing networks. The scheme is based on a comprehensive evaluation of data transmission rate, transmission delay, and other factors related to the operation status of the self-organized network and improves the efficiency of the overall work of the self-organized network by reducing the performance consumption of individual nodes of the self-organized network and balancing the network load.

Distributed Route Guidance Systems with Self-Organized Multi-Layered Vector Fields

2005 ◽  
Vol 9 (2) ◽  
pp. 106-113 ◽  
Author(s):  
Kosuke Sekiyama ◽  
◽  
Yasuhiro Ohashi
Keyword(s):  
Vector Fields ◽  
Traffic Signals ◽  
The Self ◽  
Route Guidance ◽  
Traffic Conditions ◽  
Distributed Approach ◽  
Self Organized ◽  
Efficient Route ◽  
Guidance Systems ◽  
Self Organizing

This paper deals with novel distributed route guidance that cooperates with self-organizing control of traffic signal networks. Self-organizing control of traffic signals provides a fully distributed approach to coordinate a number of signals distributed in a wide area based on local information of traffic flows so that split and offset control parameters between traffic signals are adjusted for efficient traffic flow. The self-organizing route guidance systems (SRGS) concept is introduced for efficient route guidance to facilitate offset adjustment of the self-organizing control of signal networks by self-organizing multilayered vector fields. Simulation demonstrates the effectiveness of the proposal under nonstationary traffic conditions.

scholarly journals Score Prediction of Sports Events Based on Parallel Self-Organizing Nonlinear Neural Network

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Junyao Ling
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Phase Space ◽  
Development Trend ◽  
The Self ◽  
Minimum Error ◽  
Main Research ◽  
True Value ◽  
Self Organizing Networks ◽  
Self Organizing

This paper introduces the basic concepts and main characteristics of parallel self-organizing networks and analyzes and predicts parallel self-organizing networks through neural networks and their hybrid models. First, we train and describe the law and development trend of the parallel self-organizing network through historical data of the parallel self-organizing network and then use the discovered law to predict the performance of the new data and compare it with its true value. Second, this paper takes the prediction and application of chaotic parallel self-organizing networks as the main research line and neural networks as the main research method. Based on the summary and analysis of traditional neural networks, it jumps out of inertial thinking and first proposes phase space. Reconstruction parameters and neural network structure parameters are unified and optimized, and then, the idea of dividing the phase space into multiple subspaces is proposed. The multi-neural network method is adopted to track and predict the local trajectory of the chaotic attractor in the subspace with high precision to improve overall forecasting performance. During the experiment, short-term and longer-term prediction experiments were performed on the chaotic parallel self-organizing network. The results show that not only the accuracy of the simulation results is greatly improved but also the prediction performance of the real data observed in reality is also greatly improved. When predicting the parallel self-organizing network, the minimum error of the self-organizing difference model is 0.3691, and the minimum error of the self-organizing autoregressive neural network is 0.008, and neural network minimum error is 0.0081. In the parallel self-organizing network prediction of sports event scores, the errors of the above models are 0.0174, 0.0081, 0.0135, and 0.0381, respectively.

Context Machines: A Series of Situated and Self-Organizing Artworks

Leonardo ◽  
2013 ◽  
Vol 46 (2) ◽  
pp. 114-122 ◽  
Author(s):  
Benjamin David Robert Bogart ◽  
Philippe Pasquier
Keyword(s):  
High Resolution ◽  
The Self ◽  
Self Organizing Map ◽  
Site Specific ◽  
Photographic Images ◽  
Associative Process ◽  
Self Organized ◽  
Over Time ◽  
Self Organizing

The authors discuss the development of self-organizing artworks. Context Machines are a family of site-specific, conceptual and generative artworks that capture photographic images from their environment in the construction of creative compositions. Resurfacing produces interactive temporal landscapes from images captured over time. Memory Association Machine's free-associative process, modeled after Gabora's theory of creativity, traverses a self-organized map of images collected from the environment. In the Dreaming Machine installations, these free associations are framed as dreams. The self-organizing map is applied to thousands of images in Self-Organized Landscapes—high-resolution collages intended for print reproduction. Context Machines invite us to reconsider what is essentially human and to look at ourselves, and our world, anew.

scholarly journals Development of an algorithm for routing self­organizing radio networks

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
V. O. Breslavsʹkyy ◽  
◽  
O. A. Laptyev ◽  
A. M. Pravdyvyy ◽  
S. A. Zozulya
Keyword(s):  
Information Exchange ◽  
Routing Algorithm ◽  
Base Stations ◽  
Dipole Antennas ◽  
Radio Channels ◽  
Self Organized ◽  
Transmission Of Information ◽  
The University ◽  
To Receive ◽  
Self Organizing

The routing algorithm in self-organizing radio channels is proponent. The following peculiarities: reduction of the overworld of the flow of critical signals in the grid for the transmission of the function of vibration (retrofitting) to the routes of all the nodes; Shows the adaptation and self-updating of the net for an alternative route when going out of the way of active retransmission universities. It has been deliver that the algorithm is protonated for the supremacy of the parameters for the reduction of the characteristics in proportion to the algorithms used. Self-organizing routing algorithm (SRA) of signs for mobile childless self-organized self-organizing links, in which universities may have the same status. The functions of the base stations distributed among the participants of the informational relationship. The SRA algorithm is intelligent, in which the possibility is laid by the university to independently decide on the fate of the route that/or is renewed. The measure, prompted in accordance with the SRA protocol, in the event that it is necessary to update the packages in the decimal universities, the form of the new session, so that it is possible to distribute information from the new universities. In case of transmission of information from a group of universities of the same university, the identifier allows the university to receive information and distribute information. In the designated algorithm, to encourage the route, the universities will only rotate the necessary fields and do not rotate the required fields, allowing the speed of an hour of processing and taking decisions about participating in the route. To process the package, it is not necessary to insert the package in one piece, to finish it without looking over the fields and adjusting the values. In the SRA algorithm for verifying the delivery of packets, the power of dipole antennas is to broadcast on all sides, so that it is possible to see any packets in the confirmation or to change a number of such packets. Fragmentation of the routing algorithm of self-organizing radio channels based on the parameter of interlocking the network traffic, changing the basic protocols of information exchange by 15%. This is general result to bring the overvoltage of developed this algorithm.

scholarly journals Tuning the Spatially Controlled Growth, Structural Self-Organizing and Cluster-Assembling of the Carbyne-Enriched Nanostructured Metamaterials during Ion-Assisted Pulse-Plasma Deposition

2021 ◽  
Author(s):  
Alexander Lukin ◽  
Oğuz Gülseren
Keyword(s):  
Pattern Formation ◽  
Electric Field ◽  
Spatial Structure ◽  
Plasma Deposition ◽  
The Self ◽  
Self Organization ◽  
Structure And Properties ◽  
Self Organized ◽  
Wave Patterns ◽  
Self Organizing

Structural self-organizing and pattern formation are universal and key phenomena observed during growth and cluster-assembling of the carbyne-enriched nanostructured metamaterials at the ion-assisted pulse-plasma deposition. Fine tuning these universal phenomena opens access to designing the properties of the growing carbyne-enriched nano-matrix. The structure of bonds in the grown carbyne-enriched nano-matrices can be programmed by the processes of self-organization and auto-synchronization of nanostructures. We propose the innovative concept, connected with application of the universal Cymatics phenomena during the predictive growth of the carbyne-enriched nanostructured metamaterials. We also propose the self-organization approach for increase stability of the long linear carbon chains. The main idea of suggested concept is manipulating by the self-organized wave patterns excitation phenomenon and their distribution by the spatial structure and properties of the nanostructured metamaterial grows region through the new synergistic effect. Mentioned effect will be provided through the vibration-assisted self-organized wave patterns excitation along with simultaneous manipulating by their properties through the electric field. We propose to use acoustic activation of the plasma zone of nano-matrix growing. Interaction between the inhomogeneous electric field distribution generated on the vibrating layer and the plasma ions will serve as the additional energizing factor controlling the local pattern formation and self-organizing of the nano-structures. Suggested concept makes it possible to provide precise predictive designing the spatial structure and properties of the advanced carbyne-enriched nanostructured metamaterials.

scholarly journals An EEG Feature Extraction Method Based on Sparse Dictionary Self-Organizing Map for Event-Related Potential Recognition

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 259
Author(s):  
Shang Feng ◽  
Haifeng Li ◽  
Lin Ma ◽  
Zhongliang Xu
Keyword(s):  
Feature Extraction ◽  
Event Related Potential ◽  
The Self ◽  
Computer Interface ◽  
Self Organizing Map ◽  
Eeg Signal ◽  
Sparse Decomposition ◽  
Feature Extraction Method ◽  
Self Organized ◽  
Self Organizing

In the application of the brain-computer interface, feature extraction is an important part of Electroencephalography (EEG) signal classification. Using sparse modeling to extract EEG signal features is a common approach. However, the features extracted by common sparse decomposition methods are only of analytical meaning, and cannot relate to actual EEG waveforms, especially event-related potential waveforms. In this article, we propose a feature extraction method based on a self-organizing map of sparse dictionary atoms, which can aggregate event-related potential waveforms scattered inside an over-complete sparse dictionary into the code book of neurons in the self-organizing map network. Then, the cosine similarity between the EEG signal sample and the code vector is used as the classification feature. Compared with traditional feature extraction methods based on sparse decomposition, the classification features obtained by this method have more intuitive electrophysiological meaning. The experiment conducted on a public auditory event-related potential (ERP) brain-computer interface dataset showed that, after the self-organized mapping of dictionary atoms, the neurons’ code vectors in the self-organized mapping network were remarkably similar to the ERP waveform obtained after superposition and averaging. The feature extracted by the proposed method used a smaller amount of data to obtain classification accuracy comparable to the traditional method.

Problem behavior in autism spectrum disorders: A paradigmatic self-organized perspective of network structures

2019 ◽  
Vol 42 ◽  
Author(s):  
Lucio Tonello ◽  
Luca Giacobbi ◽  
Alberto Pettenon ◽  
Alessandro Scuotto ◽  
Massimo Cocchi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Problem Behaviors ◽  
Autism Spectrum ◽  
The Self ◽  
Network Structures ◽  
Self Organized ◽  
Critical Equilibrium ◽  
Self Organized Criticality ◽  
Acute Agitation ◽  
Spectrum Disorders

AbstractAutism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.

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