Self-organizing Algorithm Research Articles

A local dynamic cluster self-organization algorithm in wireless sensor networks for rainfall monitoring

Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring, which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collaborative self-organization algorithm in WSNs. In this letter, a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head, which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster, which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that, LDCS can not only relieve the problem of “too frequent leader switches” in IDSQ, also make full use of the history monitoring information of target and continuous monitoring of sensor nodes that failed in DCS.

Scale-free, self-organizing very large sensor networks

In this paper we introduce SFSN, an algorithm for self-organization of Very Large Sensor Networks (VLSN). The 10 6 or more tiny and inexpensive sensors of a VLSN are indistinguishable from one another; they do not have either a physical or a logical address, as required by the traditional communication protocols. The self-organization scheme limits the number of partners each sensor collaborates with, thus, it limits the amount of communication and the complexity of coordination. The system is scalable, the amount of state information each node has to maintain is strictly limited regardless of the total number of sensors in the network. The systems we consider mimic biological systems where individual cells of the same type are indistinguishable.

Modeling the Emergence of Whisker Direction Maps in Rat Barrel Cortex

Based on measuring responses to rat whiskers as they are mechanically stimulated, one recent study suggests that barrel-related areas in layer 2/3 rat primary somatosensory cortex (S1) contain a pinwheel map of whisker motion directions. Because this map is reminiscent of topographic organization for visual direction in primary visual cortex (V1) of higher mammals, we asked whether the S1 pinwheels could be explained by an input-driven developmental process as is often suggested for V1. We developed a computational model to capture how whisker stimuli are conveyed to supragranular S1, and simulate lateral cortical interactions using an established self-organizing algorithm. Inputs to the model each represent the deflection of a subset of 25 whiskers as they are contacted by a moving stimulus object. The subset of deflected whiskers corresponds with the shape of the stimulus, and the deflection direction corresponds with the movement direction of the stimulus. If these two features of the inputs are correlated during the training of the model, a somatotopically aligned map of direction emerges for each whisker in S1. Predictions of the model that are immediately testable include (1) that somatotopic pinwheel maps of whisker direction exist in adult layer 2/3 barrel cortex for every large whisker on the rat's face, even peripheral whiskers; and (2) in the adult, neurons with similar directional tuning are interconnected by a network of horizontal connections, spanning distances of many whisker representations. We also propose specific experiments for testing the predictions of the model by manipulating patterns of whisker inputs experienced during early development. The results suggest that similar intracortical mechanisms guide the development of primate V1 and rat S1.

Study on probability distributions for evolution in modified extremal optimization

It is widely believed that the power-law is a proper probability distribution being effectively applied for evolution in τ -EO (extremal optimization), a general-purpose stochastic local-search approach inspired by self-organized criticality, and its applications in some NP-hard problems, e.g., graph partitioning, graph coloring, spin glass, etc. In this study, we discover that the exponential distributions or hybrid ones (e.g., power-laws with exponential cutoff) being popularly used in the research of network sciences may replace the original power-laws in a modified τ -EO method called self-organized algorithm (SOA), and provide better performances than other statistical physics oriented methods, such as simulated annealing, τ -EO and SOA etc., from the experimental results on random Euclidean traveling salesman problems (TSP) and non-uniform instances. From the perspective of optimization, our results appear to demonstrate that the power-law is not the only proper probability distribution for evolution in EO-similar methods at least for TSP, the exponential and hybrid distributions may be other choices.

Classification of Genomic Sequences via Wavelet Variance and a Self-Organizing Map with an Application to Mitochondrial DNA

We present a new methodology for discriminating genomic symbolic sequences, which combines wavelet analysis and a self-organizing map algorithm. Wavelets are used to extract variation across various scales in the oligonucleotide patterns of a sequence. The variation is quantified by the estimated wavelet variance, which yields a feature vector. Feature vectors obtained from many genomic sequences, possibly of different lengths, are then classified with a nonparametric self-organizing map scheme. When applied to nearly 200 entire mitochondrial DNA sequences, or their fragments, the method predicts species taxonomic group membership very well, and allows the results to be visualized. When only thousands of nucleotides are available, wavelet-based feature vectors of short oligonucleotide patterns are more efficient in discrimination than frequency-based feature vectors of long patterns. This new data analysis strategy could be extended to numeric genomic data. The routines needed to perform the computations are readily available in two packages of software R.

A New Algorithm of Self Organization in Wireless Sensor Network

Self organization is one of the most important characteristics in an Ad-hoc Sensor Network. Thousands of Sensors are deployed in a geographical area randomly without considering the location factor. After deployment, sensors are to self organize themselves to form a network of their own. How well the network is formed determines the life of the whole network as well as the quality of data transmission. Self organization based on clustering has proven to be very useful in this regard. Since hierarchical clustering reduces energy consumption by routing data from one node to another. In this paper, we discuss a new algorithm for self organization of sensors deployed in a geographical area. The algorithm forms clusters of sensors by ordering them using a unique triangulation method. This algorithm not only considers all sensors but also groups them so that their inherent clustering property is preserved.

An efficient cluster-based self-organisation algorithm for wireless sensor networks

The paper proposes an Efficient Cluster-based Self-organisation Algorithm (ECSA) for partitioning Wireless Sensor Networks (WSNs) into clusters, thus giving at the network a hierarchical organisation. Each sensor uses its weight based on its k-density and its residual energy to elect a cluster-head in its 2-hop neighbourhood. ECSA operates without a centralised controller, and does not require that the location of sensors be known. Firstly, we estimate the number of clusters formed with ECSA according to transmission range, then we evaluate the amount of packets sent to the sink per energy dissipation by using the same model presented by Heinzelman et al. (2002), and the number of nodes alive per number of packets received at the sink. Simulation results illustrate that ECSA can evenly distribute energy consumption of sensors and consequently maximise network lifetime when compared to LEACH and LEACH-C.

Minimum hop routing algorithm for WSN based on topology optimization

For the purpose of energy conservation,a new routing algorithm named MH-TO was proposed based on the idea of the minimum hop routing. The new algorithm optimized the network topology through a power control method of half-match mechanism. Then a "tower model" was introduced to make that all nodes obtain minimum hop information. The packet was sent by the minimum hop path to the sink node when routing. According to the analysis and the simulation results,compared with the self-organized algorithm based on minimum hop,the new algorithm can save energy and balance energy consumption,which extends the life cycle of WSN.

Intelligent control systems. I. Quantum computing and self-organization algorithm

The role and methodology of application of quantum computing in problems of design of robust intelligent control systems under the conditions of contingency control situations are discussed. The quantum self-organization control algorithm, which contains the self-organization control algorithm for robust knowledge bases as the particular case, is developed. The satisfaction of the introduced thermodynamic criterion (in the form of the minimum of the generalized entropy in the established relation between such qualitative control characteristics as stability, controllability, and robustness) is the goal of application of the quantum self-organization control algorithm for knowledge bases used in the control loop. The essentially nonlinear and globally unstable control object is used as the example for application of the quantum self-organization control algorithm to the solution of the vector optimization problem based on the decomposition of knowledge bases.

Self-organizing algorithms for cache cooperation in content distribution networks

Self-organizing algorithms for cache cooperation in content distribution networks

Self-organizing algorithms for cache cooperation in content distribution networks

Introduction: The on-demand delivery of video content is anticipated to show tremendous growth over the next few years, driven by the huge popularity of user-generated video clips and the expansion of VoD libraries. Even stronger growth is likely to be fueled by the proliferation of IPTV services with personalized features such as CatchUp/PauseLive TV and NPVR capabilities. The common characteristic of these ‘time-shifted’ TV services, as well as VoD libraries and sites like YouTube, is that users can select from a vast collection of content material at any time they want. This runs counter to the broadcast paradigm embraced in conventional TV networks, and raises a need for unicast sessions, increasing the overall bandwidth demands by orders of magnitude. Caching strategies provide an effective mechanism for mitigating the massive bandwidth requirements associated with large-scale distribution of personalized high-definition video content. In essence, caching strategies exploit storage capacity to absorb traffic by replicating the most popular content closer to the network edge rather than storing it in a central location. The reduction in the traffic load translates into a smaller required transport capacity and capital expense as well as fewer performance bottlenecks, thus enabling better service quality at a lower price. In the present paper we develop light-weight distributed cooperative content placement algorithms so as to maximize the traffic volume served from cache, and thus minimize the bandwidth cost. As a canonical scenario, we focus on a cluster of distributed caches, connected either directly, or via a parent node, and formulate the content placement problem as an integer linear program in order to obtain a benchmark of the globally optimal performance. Under certain symmetry assumptions, the optimal solution of the relaxed linear program is found to have a rather simple structure. Besides interesting in its own right, the knowledge of the optimal structure offers useful insight for the design of low-complexity cooperative content placement and eviction algorithms. The performance of the proposed distributed algorithms is proved to be within a small constant factor from the performance of an optimal centralized scheme, while requiring only limited knowledge of global popularities, even in asymmetric scenarios. In contrast to [1, 2], we will focus on specific topologies, motivated by real system deployments, and use the insight from the linear program and the simple structure of its optimal solution to develop efficient distributed content placement algorithms with far more favorable performance ratios than the ones in [1, 2].

Conformational Analysis of Macrocycles: Finding What Common Search Methods Miss

As computational drug design becomes increasingly reliant on virtual screening and on high-throughput 3D modeling, the need for fast, robust, and reliable methods for sampling molecular conformations has become greater than ever. Furthermore, chemical novelty is at a premium, forcing medicinal chemists to explore more complex structural motifs and unusual topologies. This necessitates the use of conformational sampling techniques that work well in all cases. Here, we compare the performance of several popular conformational search algorithms on three broad classes of macrocyclic molecules. These methods include Catalyst, CAESAR, MacroModel, MOE, Omega, Rubicon and two newer self-organizing algorithms known as stochastic proximity embedding (SPE) and self-organizing superimposition (SOS) that have been developed at Johnson & Johnson. Our results show a compelling advantage for the three distance geometry methods (SOS, SPE, and Rubicon) followed to a lesser extent by MacroModel. The remaining techniques, particularly those based on systematic search, often failed to identify any of the lowest energy conformations and are unsuitable for this class of structures. Taken together with our previous study on drug-like molecules (Agrafiotis, D. K.; Gibbs, A.; Zhu, F.; Izrailev, S.; Martin, E. Conformational Sampling of Bioactive Molecules: A Comparative Study. J. Chem. Inf. Model., 2007, 47, 1067-1086), these results suggest that SPE and SOS are two of the most robust and universally applicable conformational search methods, with the latter being preferred because of its superior speed.

A Self-Organizing Algorithm for Modeling Protein Loops

Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies.

Supervised Self-Organization of Homogeneous Swarms Using Ergodic Projections of Markov Chains

This paper formulates a self-organization algorithm to address the problem of global behavior supervision in engineered swarms of arbitrarily large population sizes. The swarms considered in this paper are assumed to be homogeneous collections of independent identical finite-state agents, each of which is modeled by an irreducible finite Markov chain. The proposed algorithm computes the necessary perturbations in the local agents' behavior, which guarantees convergence to the desired observed state of the swarm. The ergodicity property of the swarm, which is induced as a result of the irreducibility of the agent models, implies that while the local behavior of the agents converges to the desired behavior only in the time average, the overall swarm behavior converges to the specification and stays there at all times. A simulation example illustrates the underlying concept.

Link of Data Synchronization to Self-Organizing Map Algorithm

We have recently developed a method for feature extraction from multivariate data using an analogue of Kuramoto's dynamics for modeling collective synchronization in a network of coupled phase oscillators. In our method, which we call data synchronization, phase oscillators carrying multivariate data in their natural and updated rhythms achieve partial synchronizations. Their common rhythms are interpreted as the template vectors representing the general features of the data set. In this study, we discuss the link of data synchronization to the self-organizing map algorithm as a popular method for data mining and show through numerical experiments how our method can overcome the disadvantages of the self-organizing map algorithm in that unintentional selections of inappropriate reference vectors lead to false feature patterns.

A semantic self-organising webpage-ranking algorithm using computational geometry across different knowledge domains

In this paper we introduce a method for Web page-ranking, based on computational geometry to evaluate and test by examples, order relationships among web pages belonging to different knowledge domains. The goal is, through an organising procedure, to learn from these examples a real-valued ranking function that induces ranking via a convexity feature. We consider the problem of self-organising learning from numerical data to be represented by a well-fitted convex polygon procedure, in which the vertices correspond to descriptors representing domains of web pages. Results and statistical evaluation of procedure show that the proposed method may be characterised as accurate.

Global optimization of thermal conductivity using stochastic algorithms

In this article, the performance of a self-organizing migration algorithm (SOMA), a new stochastic optimization algorithm, has been compared with a genetic algorithm with floating-point representation (GAF) and differential evolution (DE) for an engineering application. This application is the estimation of the apparent thermal conductivity of foods at freezing temperature using an inverse method. Assuming two piecewise functions for apparent thermal conductivity in function of the temperature data, the heat diffusion equation was solved to estimate the unknown variables of inverse problem. The thermal conductivity is continuously adjusted by three approaches of stochastic optimization algorithms, used to minimize a performance criterion based on error information for the inverse problem. The variables that provide the best fitness between the experimental and predicted time-temperature curves at centre of the food under freezing conditions were obtained. Moreover, a statistical analysis showed the agreement between reported and estimated curves. In this application domain, the SOMA and DE approaches outperform the GAF.

自己組織化マップによる骨格性下顎前突患者の軟組織側貌パターンの抽出

The objective of this study was to visually classify soft tissue profile patterns of patients with skeletal mandibular prognathism using self-organizing maps (SOM). The subjects of this retrospective study were 90 Japanese adult females (mean age: 22.6±4.2 years) who underwent surgical orthodontic treatment for skeletal mandibular prognathism. The control subjects were 27 Japanese adult females (mean age: 23.8±4.4 years) who underwent non-surgical orthodontic treatment. Pre-treatment cephalograms for the 90 mandibular prognathism and post-treatment cephalograms for the control subjects were traced, and 48 points of measurement were digitalized. Among the 48 points, 27 soft tissue points were used as input data for the SOM with multiple calculation units. The unit groups performed automatic learning of the input data by means of a self-organizing algorithm, and virtual profiles were obtained for each unit. As a result, four soft tissue profile patterns were visualized, and close relationships to the skeletal and dental characteristics were found in each pattern. The most typical soft tissue pattern in Japanese mandibular prognathism seems to be a pattern showing a moderately retrusive maxilla and a moderately protrusive mandible with the lower lip protrusion and the small nasolabial angle.

Empire of colonies: Self-stabilizing and self-organizing distributed algorithm

Self-stabilization ensures automatic recovery from an arbitrary state; we define self-organization as a property of algorithms which display local attributes. More precisely, we say that an algorithm is self-organizing if (1) it converges in sublinear time and (2) reacts “fast” to topology changes. If s ( n ) is an upper bound on the convergence time and d ( n ) is an upper bound on the convergence time following a topology change, then s ( n ) ∈ o ( n ) and d ( n ) ∈ o ( s ( n ) ) . The self-organization property can then be used for gaining, in sub-linear time, global properties and reaction to changes. We present self-stabilizing and self-organizing algorithms for many distributed algorithms, including distributed snapshot and leader election. We present a new randomized self-stabilizing distributed algorithm for cluster definition in communication graphs of bounded degree processors. These graphs reflect sensor networks deployment. The algorithm converges in O ( log n ) expected number of rounds, handles dynamic changes locally and is, therefore, self-organizing. Applying the clustering algorithm to specific classes of communication graphs, in O ( log n ) levels, using an overlay network abstraction, results in a self-stabilizing and self-organizing distributed algorithm for hierarchy definition. Given the obtained hierarchy definition, we present an algorithm for hierarchical distributed snapshots. The algorithms are based on a new basic snap-stabilizing snapshot algorithm, designed for message passing systems in which a distributed spanning tree is defined and in which processors communicate using bounded links capacity. The algorithm is on-demand self-stabilizing when no such distributed spanning tree is defined. Namely, it stabilizes regardless of the number of snapshot invocations. The combination of the self-stabilizing and self-organizing distributed hierarchy construction and the snapshot algorithm forms an efficient self-stabilizer transformer. Given a distributed algorithm for a specific task, we are able to convert the algorithm into a self-stabilizing algorithm for the same task with an expected convergence time of O ( log 2 n ) rounds.

Core image segmentation method by combining watershed and ISODATA algorithm

Watershed based image segmentation can make the edge close and precise, but has the drawback of over-segmentation. ISODATA can conglomerate gravel, but has the edge bias error. Combining watersheds and ISODATA, this paper proposed an image segmentation method to solve these problems. Firstly, watershed segmentation algorithm was applied to get the initial over-segmentation image, and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) was applied to get the conglomeration image. Then, labeled images and inverse wavelet transform were used together to get the full-resolution image. The results based on ISODATA algorithm were applied to solve over-segmentation problem caused by watershed. Experimental results show that it is a practicable method for the image segmentation.