self-organizing communication protocols

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Reinforcement Learning in Self Organizing Cellular Networks

Reinforcement Learning in Self Organizing Cellular Networks

The dynamic and autonomous connections of a mmWave backhaul network is similar as ad hoc network. In ad hoc networks, the topology of a network is the set of communication links between the nodes that is used by the routing algorithm. According to ad hoc related literature, weeding out redundant and unnecessary topology information is called topology management [115,116]. Topology management plays a key role in performance of routing, scheduling, broadcasting. The wrong topology information can reduce the capacity, increase the end-to-end delay, and decrease the robustness to node failure. As the above factors are important in ad hoc networks, they have the same importance in mmWave backhaul networks as well. This is part of the reason, in recent literature, researchers model mmWave backhaul networks with graph theory and use the same protocols and concepts in ad hoc networks to manage the topology of the network [117]. However, specific propagation characteristics of mmWave communications and 3GPP requirements for mmWave backhaul in 5G NR [118] brings certain features that need to be considered in topology management of mmWave networks. In this chapter, we aim to review and analyze the requirements and possible topologies of mmWave networks.
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Raspberry Pi Based Self Organizing of Heavy Traffic using Zigbee Communication
T Srilatha, B Ravi Chandra, Dr M Narendra kumar & Dr S Sreenatha Reddy

Raspberry Pi Based Self Organizing of Heavy Traffic using Zigbee Communication T Srilatha, B Ravi Chandra, Dr M Narendra kumar & Dr S Sreenatha Reddy

ZigBee module. The €1 coin, shown for size reference, is about 23 mm (0.9 inch) in diameter. ZigBee is a specification for a suite of high level communication protocols using small, low-power digital radios based on the IEEE 802.15.4-2003 standard for wireless

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Self-Organizing and Clustering Methods in VANETs

Self-Organizing and Clustering Methods in VANETs

Binary-Partition-Assisted Broadcast Protocol : Binary partition assisted broadcast protocol (BPAB) aims to reduce and stabilize the broadcast delay. BPAB achieves a good message progress speed by selecting the farthest forwarder. This protocol deploys a combination of a binary partitioning and a novel contention mechanism. The binary partitioning scheme constantly divides the communication area into multiple partitions. The binary partition of this scheme stems from a similar concept compared with that of OB-VAN, but it introduces fewer time slots than OB-VAN during the selection of the next-hop forwarder. Only vehicles in the farthest partition contend with each other during the forwarding phase in this scheme. Thus, the collision rate is reduced, and the contention duration is stabilized. It is also shown that BPAB demonstrates a good performance in terms of the average dissemination speed compared with the other protocols, such as UMB and SB [7].
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SCADA PROTOCOLS AND COMMUNICATION TRENDS

SCADA PROTOCOLS AND COMMUNICATION TRENDS

A typical RTU in the field contains a central processor, set of Input /Output modules and communication devices to connect to field devices. The RTUs are similar to Programmable Logic Controllers (PLCs). PLCs are used with in a local area such as factory floor and are connected together usually by a local area network; where as RTUs are used in remote locations and connected by a Wide Area Network; Other wise both have CPU, I/O units and communication ports. Hence most of the discussion in this paper also applicable to PLC systems

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Smart technology for self-organizing processes

Smart technology for self-organizing processes

In education, facilitating human-machine collaboration means designing technology and ways of using it that integrate it into a complex system in which the principal interacting elements are students, teachers, intelligent machines, and ideas. As we have proposed here, the principal medium for such interaction is dialogue. Smart phones are already demonstrating that dialogue between person and machine is possible within fairly generous limits, and those limits are likely to be extended greatly. In work reported by Miyake et al. (2011), children carry on discussion with each other and with a robot (voiced and controlled by an assistant in another room, preliminary to having a robot able to carry on scripted conversation). Latent semantic analysis (Landauer & Dumais 1997) and explanatory coherence analysis (Thagard 2000) demonstrate that ideas can interact computationally with ideas in ways that produce meaningful emer- gent structures. Learning analytics, which enable the computer to discover patterns in unstructured data that are beyond human capacity, are developing in a variety of ways. Some applications raise the spectre of increasing centralized control (Yuan n.d.), but others such as diagnostic analyses and “ recommender systems ” that suggest resources students might find useful or interesting (Manouselis et al. 2012) could play positive roles in a self-organizing educational process.
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Ensuring consistency on the joint beliefs of interacting agents

Ensuring consistency on the joint beliefs of interacting agents

Interaction Protocols Speech−act like messages Synchronisation Protocols Knowledge−based protocols for message exchange and belief revision.. Communication Protocols Session, TCP/IP laye[r]

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Self-organizing communication in language games

Self-organizing communication in language games

Recently the study of the self organization and evolution of language and meaning has led to the idea that language can be seen as a complex dy- namical system [1]. In this perspective, the theoretical tools developed in complex systems science acquire a central role for the study of the self gen- erating structures of language systems.

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Protocols for Efficient Inference Communication

Protocols for Efficient Inference Communication

Interactive compression rates are directly related to Quality of Information (QoI) characteristics of semantic communica- tions. Although achieving higher compression rates is desir- able for delivering the same level of semantics, they come at the cost of higher runtime, and hence less QoI responsiveness. We attempted to establish a controlled relationship between the runtime devoted to the task and the resulting compression. This is difficult to achieve, because these protocols operate under stringent requirements. Aggressive partitioning in a small number of rounds can achieve significant compression, but also cause exponential runtime blowup. On the other hand, more relaxed partitioning (in which the maximum overlap o is high) can achieve little to no compression. Because each round of partitioning plus code exchange typically involves an exchange of 2-4 bits, multi-round schemes (like Protocol 3) have only a few rounds to prune the graph. Finally, often the transitions between over-relaxed and over-aggressive are abrupt.
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A Self-Organizing Model for Logic Regression

A Self-Organizing Model for Logic Regression

In 1966, Ukrainian cyberneticist, A.G. Ivakhnenko, discouraged by the fact that many mathematical models require the modeler to know things about the real world that are difficult or impossible to know, produced a heuristic self-organizing model, called the Group Method of Data Handling algorithm. For more information see Farlow, [1] or view one of the many websites which discuss applications of the technique. Ivakhnenko’s website at

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Movement of Self Organizing Solitons in Ferromagnet

Movement of Self Organizing Solitons in Ferromagnet

Precessing ball solitons (PBS) in a ferromagnet during the first order phase transition induced by a magnetic field directed along the axis of anisotropy, while the additional action of high-frequency field perpendicular to the main magnetic field, are analyzed. It is shown that the spatial motion of solitons, associated with thermal fluctuations in the crystal, does not destroy the equilibrium of self-organized PBS.

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Social Interaction and Self-Organizing Map

Social Interaction and Self-Organizing Map

Abstract– Self Organizing Maps or SOMs are mostly used to represent a multidimensional data in much lower dimension e.g. usually in one or two dimensions, Clustering, Exploratory data analysis and visualization, Supervised data classification, Sampling, Feature extraction, Data interpolation etc. In this paper we consider neuron societies where there are many different types of interactions. In one society, a neuron is connected with others only by the distance between two neurons. In another one, a neuron is connected with others by similarity between neurons, and so on. We here choose a special case where the interaction between neurons is weighted by the distance between them. This simplification aims to apply the new method to the creation of self-organizing maps. With this research, we expect new types of self-organizing maps to appear, ones which take into account the interactions between neurons. Here I have taken data of an automobile industry in Japan over a span of years and analyzed those data.
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Self-organizing adaptation for facial emotion mapping

Self-organizing adaptation for facial emotion mapping

The capability of the system is being investigated with the test images of unknown subject. The emotion recognition rate of unknown subject for joy is shown in Table 1. The tabulated results are obtained using 1-NN recognition method (K=1 for KNN). Figure 4 plots the overall recognition rates of single-layer Self-Organizing Maps versus the number of training subjects. We can observe from the chart that whichever methods of recognition we used (KNN, SKNN…), the higher the number of subjects, the better the recognition rates. However, for 8 training subjects, the recognition rate drops. This may be due to the fact that the number of neurons may not be enough to generalize 8 subjects. The highest recognition rate was 74.02%, using 6 training subjects and Biased-SWKNN as recognition method.
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Self Organizing Genetic Algorithm: A Survey

Self Organizing Genetic Algorithm: A Survey

step of the algorithm in order to increase the probability of having better solutions. In optimization, the solutions to a particular problem will be selected accordingly how well they solve the problem, is denoted by its fitness value. GA explores the multi-parameter space of solution alternatives for a particular problem. In each iterative step, chromosomes are altered, leading the population to even more promising regions of the search space [5]. To blend self-organization and GA, it is essential to understand its various aspects clearly. For Genetic Algorithm (GA), several operators and encoding methods are proposed in the literature [6-13]. Different operators are selected depending on the problem and also the method of encoding the chromosomes. Complete knowledge about the problem is required to select the appropriate encoding method, operators and parameter values. For non- trained users, the selection of appropriate parameters is difficult. These difficulties can be solved by self-organizing the GA for a particular problem by which the processing system is converted into a self-organizing system, which solves the problem without getting any input from the users. The remainder of the paper is organized as follows. The next section describes about the self-organizing systems. Section 3 explains the components of GA and different ways to self- organize GA. Section 4 explains mapping of self- organization and GA. Section 5 presents a brief comparison between the existing Multiple Sequence Alignment (MSA) methods and GA. Section 6 explains Self-organizing Genetic Algorithm (SOGA) for MSA. Section 7 briefly discusses SOGA variants available and their parameter settings. Shortcomings of SOGA are stated in section 8.
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The Effectiveness of Self-Organizing Network Toolbox (1)

The Effectiveness of Self-Organizing Network Toolbox (1)

This paper focuses on the use of unsupervised learning in feature-based signal classification. Unsupervised learning is a very powerful tool in building cognitive radios that require minimal preconfiguration. These radios can learn from the ground up the properties of other devices in their environment. In the context of neural networks, self-organizing maps are of- ten used for unsupervised learning [12]. In this paper we delve

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A Hybridized Self-Organizing Response Surface Methodology

A Hybridized Self-Organizing Response Surface Methodology

From the self-organizing multi-layer models it is clear that accuracy of the model increases as the order of the model increases, but only to a point. Theoretically the order of the basis should not have an effect on the accuracy of the model. The only difference the order of the basis polynomial should have is how many layers are needed to describe the order of the interactions between the variables. The authors feel this dependency is created by the regression process at each node and numerical errors of the linear equation solver used for the regressions. A numerically perfect linear equation solver should produce equal results for any order polynomial basis used. Since this kind of solver only exists for very well behaved systems, this dependency cannot be ignored for "real world" work.
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Online Full Text

Online Full Text

Abstract — This paper presents a novel architecture of SOM which organizes itself over time. The proposed method called MIGSOM (Multilevel Interior Growing Self-Organizing Maps) which is generated by a growth process. However, the network is a rectangular structure which adds nodes from the boundary as well as the interior of the network. The interior nodes will be added in a superior level of the map. Consequently, MIGSOM can have three-Dimensional structure with multi-levels oriented maps. A performance comparison of three Self-Organizing networks, the Kohonen feature Map (SOM), the Growing Grid (GG) and the proposed MIGSOM is made. For this purpose, the proposed method is tested with synthetic and real datasets. Indeed, we show that our method (MIGSOM) improves better performance for data quantification and topology preservation with similar map size of GG and SOM.
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Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps

Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps

Abstract— The Self Organizing Maps (SOM) is regarded as an excellent computational tool that can be used in data mining and data exploration processes. The SOM usually create a set of prototype vectors representing the data set and carries out a topology preserving projection from high-dimensional input space onto a low-dimensional grid such as two-dimensional (2D) regular grid or 2D map. The 2D-SOM technique can be effectively utilized to visualize and explore the properties of the data. This technique has been applied in numerous application areas such as in pattern recognition, robotics, bioinformatics and also life sciences including clustering complex gene expression patterns. In this paper, the structure of traditionally 2D-SOM map has been enhanced to a three-dimensional Self Organizing Maps (3D-SOM) maps. It has the purpose to directly cluster data into 3D-SOM space instead of 2D-SOM data clusters. The primary works mostly involved the extensions of SOM algorithm in particular the number, relation and structure arrangement of its output neurons, neighbourhood weight update processes and distances calculation in 3D xyz-axis. The proposed method has been demonstrated by computing 3D-SOM visualization on iris flowers dataset using high level computer language. The performance of 2D-SOM and 3D-SOM in terms of their quantization errors, topographic errors and computational time has been investigated and discussed. The experimental results have shown that the 3D-SOM has been able to form a 3D data representation, has slightly higher quantization error and computational time but performed better topology preservation than in 2D-SOM.
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Performance Analysis of Self Organizing Networks

Performance Analysis of Self Organizing Networks

Self-optimization process uses Key Performance Indicators as inputs. There are diverse sources for gathering of information about the condition of network, depending on RAT (Radio Access Technology), such as NodeB or eNodeB, and User Equipment, the Radio Network Controller in the case of UMTS or Mobility Management Entity in the case of LTE. While some KPI's can be vendor specific, there are standardized measurements that are also available[1]. A detailed list of LTE measurements that can be used as a support for SON is given in [2].
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Analytical SIR for Self Organizing Wireless Networks

Analytical SIR for Self Organizing Wireless Networks

are randomly positioned resulting in potentially overlap- ping cells. Randomly positioned cells model an important network scenario, which lacks any frequency planning as a result of self-configuring and self-organising networks, cognitive radio and multihop ad hoc communication. A receiver experiences interference from transmitters within its accessibility radius, R ac . Due to propagation path loss,

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A realistic simulation for self-organizing traffic lights

A realistic simulation for self-organizing traffic lights

For simulating the self-organizing traffic light controllers, a realistic traffic simu- lator is needed in which those traffic light controllers can be tested. My first goal is to get such a traffic simulator. Other programmers have already worked on such projects and the Green Light District / iAtracos project is a good starting point for implementing the simulator. Those projects already have a lot of features imple- mented and I added extra features to get a more realistic traffic simulator. Cars can accelerate and decelerate and they keep a stop distance to the previous one. They respect the maximum speed of the road they are moving on, etc.
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