Neighbor List Research Articles

Two-stage weighted centroid localization for large-scale wireless sensor networks in ambient intelligence environment

Wireless sensor networks (WSNs) are a necessary technology for the development of ambient intelligence (AmI) applications. One of the major concerns of designing WSNs for AmI applications is the localization of the sensor nodes. This paper proposes a new range-free localization algorithm for large-scale WSNs using received signal strength (RSS) measurements. Different from most RSS-based range-free localization in WSNs, the proposed two-stage weighted centroid localization (TS-WCL) algorithm utilizes the RSS measurements between pairs of anchors, except for the RSS measurements between the unknown node and anchor nodes. In the first stage of TS-WCL, the unknown node constructs a virtual neighboring anchor list by doing half-symmetric lens presence tests. A half-symmetric lens presence test determines a residence area of the unknown node using the locations of two neighboring anchors and the corresponding RSS measurements. The centroid of the estimated residence area is then regarded as a virtual neighboring anchor of the unknown node. In the second stage, a weighted centroid formula is used to estimate the location of the unknown node based on the virtual neighboring anchor list. Simulation results show that the proposed method has better performance compared to other three typical range-free localization methods.

Improved protocols for spreading vehicle emergency messages in mobile ad hoc networks

This paper proposes relay node selection schemes for propagating vehicle emergency messages (EM) in vehicle mobile ad hoc networks (VMANETs). In the majority of VMANET applications, an EM is propagated in the form of broadcasting. But, typical broadcasting schemes cause some problems, such as high network load, delayed multi-hop propagation, and absence of a range control function, and so on. In this paper we present two competition based relay node selection schemes; one scheme is the Stem and Branch (S&B) protocol that makes use of the sender's location information, the other scheme is the least common neighbor (LCN) protocol that utilizes the neighbor lists. According to simulation results of highway traffic scenarios, the performance of proposed S&B and LCN protocols is better than that of the previous protocols in all areas and the number of forward messages is largely decreased in these proposed protocols.

Configuration discovery of modular self-reconfigurable robots: Real-time, distributed, IR+XBee communication method

We consider the problem of discovering and representing the topology of a modular self-reconfigurable robot (MSR) in which modules do not have a priori information about the locations of and connections with other modules participating in the configuration. To represent the topology of the configuration, we propose to combine two main features; the geometric shape of the module, and the graph that represents connectivity among modules. In order to obtain the graph, it is necessary to discover the modules that are in the current configuration. This process is done by sharing IDs, creating a local configuration structure (LCS) or list of local neighbors, propagating LCSs among the modules, and building up an adjacency matrix based on the LCSs. To increase the autonomy and robustness of the system, we consider a distributed architecture in which each module is able to discover the configuration by itself. This work is based on the combination of Infra-Red and XBee communication protocols, and the limited computational resources available in a module. For testing, demonstration, and validation purposes, we have implemented the approach in a robotic platform called ModRED (Modular Robot for Exploration and Discovery).

An efficient Discrete Element Lattice Boltzmann model for simulation of particle-fluid, particle-particle interactions

In this study, an efficient Discrete Element Lattice Boltzmann Model (DE-LBM) is introduced to simulate mechanical behaviours of multiphase systems involving particle-fluid and particle-particle interactions. The LBM is based on the Multiple Relaxation Time (MRT-LBM) formalism for the fluid phase and the Discrete Element Method for particle motions. A novel algorithm is developed for detecting the particle contact base on particle overlapping areas computed directly from the grid-based LBM data. This contact algorithm achieves the same accuracy in determining the particle contact as provided by the Hertz contact model but is far more efficient computationally. The DE-LBM coupling approach is also modified to unify the different schemes developed previously. A modified Verlet List method for updating the solid occupation fraction is proposed to further speed up the simulation. The new model is validated by a series of simulations including the single particle settling and well-known ‘Drafting, Kissing and Tumbling’ (DKT) phenomenon found in suspensions. The settling of a large number (2500) of particles in a still fluid is also simulated with predicted concentration profiles matching well the analytic solution. These applications demonstrate the potential of the present DE-LBM model as a powerful numerical tool for simulating multiphase particulate systems encountered in many engineering and science disciplines.

Using AHP Method for Educational and Vocational Guidance

This paper presents a decision support tool for educational and vocational guidance, based on the supervised classification method k-nearest neighbors (KNN). This method consists in determining, for each new observation to be classified, the list of nearest neighbors of the observations already classified. The use of the KNN method requires choosing a distance and the most classical one is the Euclidean distance. In the context of this work, two functions were tested to measure resemblance as far as similarity and dissimilarity are concerned. General Terms Educational and vocational guidance, k-nearest neighbors, similarity, dissimilarity

Regular-hexagon-equilateral-triangle area grouping–based broadcast protocol for safety message in urban vehicular ad hoc networks

Road-based directional broadcast protocols are proposed in the literatures to offset efficiency of message dissemination of traditional broadcast protocol in urban vehicular ad hoc networks. However, these protocols cannot provide enough reliability and efficiency for vehicles’ misclassification at intersection or on straight road. Therefore, we present regular-hexagon-equilateral-triangle area grouping–based broadcast protocol for urban vehicular ad hoc networks. The area covered by relay node is averagely divided using regular-hexagon-equilateral-triangle, and vehicles are grouped according to the area that they reside. The algorithm for constructing regular-hexagon-equilateral-triangle is proposed. We adopt same vehicles’ grouping method at intersection and on straight road, and no neighbor list is maintained to identify road intersection. We design waiting time formula to calculate time for node forwarding message. It is ruled that node with waiting time dropping to zero first is defined as the relay node, and this relay node transmits message. So there is only one relay node which forwards message in each group. It also rules in regular-hexagon-equilateral-triangle area grouping–based broadcast protocol that each relay node forwards the same message only once, therefore, it limits redundant message retransmission. Using the vehicles’ grouping method and selection strategy of relay node as mentioned above, the proposed protocol enables message to be transmitted in different directions along different roads at the same time. Simulation indicates that our protocol has a better performance

QoI-Aware DODAG Construction in RPL-Based Event Detection Wireless Sensor Networks

LLNs are gradually attracting people’s attention for the feature of low energy consumption. RPL is specifically designed for LLNs to construct a high energy efficiency network topology. In the noisy environment, the packet loss rate of RPL-based WSN increases during data transmission. The DODAG constructed by RPL-based WSN increases in depth when communication is affected by noise. In this situation the data transmission in the network will consume more energy. In event detection WSNs, the appropriate network topology enables root to use less sensor data to fuse enough information to determine whether or not an event has occurred. In this paper, we will improve RPL in two ways to optimize topology and reduce energy consumption.(1)The neighbor list and the information of DIO sender’s parent of a node are used to construct a better DODAG in the noisy environment.(2)SPRT and the quality of information of the data are used in the event detecting process for saving energy consumption. Simulation results show that, compared with the original RPL, QoI-aware RPL can reduce the energy consumption by collecting the same quality of information with less data transmission.

MRPrimerV: a database of PCR primers for RNA virus detection.

Many infectious diseases are caused by viral infections, and in particular by RNA viruses such as MERS, Ebola and Zika. To understand viral disease, detection and identification of these viruses are essential. Although PCR is widely used for rapid virus identification due to its low cost and high sensitivity and specificity, very few online database resources have compiled PCR primers for RNA viruses. To effectively detect viruses, the MRPrimerV database (http://MRPrimerV.com) contains 152 380 247 PCR primer pairs for detection of 1818 viruses, covering 7144 coding sequences (CDSs), representing 100% of the RNA viruses in the most up-to-date NCBI RefSeq database. Due to rigorous similarity testing against all human and viral sequences, every primer in MRPrimerV is highly target-specific. Because MRPrimerV ranks CDSs by the penalty scores of their best primer, users need only use the first primer pair for a single-phase PCR or the first two primer pairs for two-phase PCR. Moreover, MRPrimerV provides the list of genome neighbors that can be detected using each primer pair, covering 22 192 variants of 532 RefSeq RNA viruses. We believe that the public availability of MRPrimerV will facilitate viral metagenomics studies aimed at evaluating the variability of viruses, as well as other scientific tasks.

Computing longest increasing subsequences over sequential data streams

In this paper, we propose a data structure, a quadruple neighbor list (QN-list, for short), to support real time queries of all <u>l</u>ongest <u>i</u>ncreasing <u>s</u>ubsequence (LIS) and LIS with constraints over sequential data streams. The QN-List built by our algorithm requires O ( w ) space, where w is the time window size. The running time for building the initial QN-List takes O ( w log w ) time. Applying the QN-List, insertion of the new item takes O (log w ) time and deletion of the first item takes O ( w ) time. To the best of our knowledge, this is the first work to support both LIS enumeration and LIS with constraints computation by using a single uniform data structure for real time sequential data streams. Our method outperforms the state-of-the-art methods in both time and space cost, not only theoretically, but also empirically.

Tube Concept for Entangled Stiff Fibers Predicts Their Dynamics in Space and Time.

We study dynamically crowded solutions of stiff fibers deep in the semidilute regime, where the motion of a single constituent becomes increasingly confined to a narrow tube. The spatiotemporal dynamics for wave numbers resolving the motion in the confining tube becomes accessible in Brownian dynamics simulations upon employing a geometry-adapted neighbor list. We demonstrate that in such crowded environments the intermediate scattering function, characterizing the motion in space and time, can be predicted quantitatively by simulating a single freely diffusing phantom needle only, yet with very unusual diffusion coefficients.

Modeling Soft Tissue Damage and Failure Using a Combined Particle/Continuum Approach.

Biological soft tissues experience damage and failure as a result of injury, disease, or simply age; examples include torn ligaments and arterial dissections. Given the complexity of tissue geometry and material behavior, computational models are often essential for studying both damage and failure. Yet, because of the need to account for discontinuous phenomena such as crazing, tearing, and rupturing, continuum methods are limited. Therefore, we model soft tissue damage and failure using a particle/continuum approach. Specifically, we combine continuum damage theory with Smoothed Particle Hydrodynamics (SPH). Because SPH is a meshless particle method, and particle connectivity is determined solely through a neighbor list, discontinuities can be readily modeled by modifying this list. We show, for the first time, that an anisotropic hyperelastic constitutive model commonly employed for modeling soft tissue can be conveniently implemented within a SPH framework and that SPH results show excellent agreement with analytical solutions for uniaxial and biaxial extension as well as finite element solutions for clamped uniaxial extension in 2D and 3D. We further develop a simple algorithm that automatically detects damaged particles and disconnects the spatial domain along rupture lines in 2D and rupture surfaces in 3D. We demonstrate the utility of this approach by simulating damage and failure under clamped uniaxial extension and in a peeling experiment of virtual soft tissue samples. In conclusion, SPH in combination with continuum damage theory may provide an accurate and efficient framework for modeling damage and failure in soft tissues.

PARAVT: Parallel Voronoi tessellation code

Abstract In this study, we present a new open source code for massive parallel computation of Voronoi tessellations (VT hereafter) in large data sets. The code is focused for astrophysical purposes where VT densities and neighbors are widely used. There are several serial Voronoi tessellation codes, however no open source and parallel implementations are available to handle the large number of particles/galaxies in current N -body simulations and sky surveys. Parallelization is implemented under MPI and VT using Qhull library. Domain decomposition takes into account consistent boundary computation between tasks, and includes periodic conditions. In addition, the code computes neighbors list, Voronoi density, Voronoi cell volume, density gradient for each particle, and densities on a regular grid. Code implementation and user guide are publicly available at https://github.com/regonzar/paravt .

A multiobjective discrete cuckoo search algorithm for community detection in dynamic networks

Evolutionary clustering is a popular method for community detection in dynamic networks by introducing the concept of temporal smoothness. Some evolutionary based clustering approaches need an input parameter to control the preference degree of snapshot and temporal cost. To break the limitation of parameter selection and increase accuracy of detecting communities, we propose a multiobjective discrete cuckoo search algorithm to discover communities in dynamic networks. Firstly, ordered neighbor list method is used to encode the location of nest for population initialization. Secondly, a discrete framework of cuckoo search algorithm is proposed with a modified nest location updating strategy and abandon operator. Finally, based on the proposed discrete framework, a multiobjective discrete cuckoo search algorithm is proposed by integrating the non-dominated sorting method and the crowding distance method. Experimental results on synthetic and real networks demonstrate that the proposed algorithm is effective and has higher accuracy than other compared algorithms.

Dimensionality Reduction based on Hubness Property using Feature Weighting Method for Clustering

Grouping of high dimensional information is an imperative exploration subject in the information mining, in light of the fact that the genuine datasets frequently have high dimensional components. The objective of the clustering is to group the features which should be similar to each other. Many text mining approaches are optimized to mine the sparse data which incurs high computation cost. In this paper, we process a novel technique named as affine subspace clustering which incorporates the Hubness property to handle the local feature relevance value and Curse of dimensionality. The Hubness property reduces the discrimination problem in the cluster formation and used as clustering method with effects relevant to cluster structures. Rather than endeavoring to keep away from the scourge of dimensionality by watching a lower dimensional component subspace, we use substantial dimensionality by exploiting downward closure property and outlier detection in the k nearest neighbor list. Additionally we combine Feature weighting method to minimize the average inside cluster scattering and augment the average between cluster scatterings along all the element spaces. The experimental results prove that proposed system yields the good performance in numerous settings, especially within the sight of huge amounts of commotion. The proposed techniques are optimized for the most part to detect the cluster center accuracy and extended properly to handle clusters of random sizes. Average inside cluster scattering is minimized and average between-cluster scattering is expanded along all the element spaces.

An Efficient Unsupervised Clustered Adaptive Antihub Technique for Outlier Detection in High Dimensional Data

Objective: The objective of this paper is to find the inconsistent objects in data which has high dimension through reduced computation time and increased accuracy. Methods: Hubness specifically Antihubs (points that rarely occur in k nearest neighbor lists) is the newly recognized concept for handling data which has high dimension. The advanced version of Antihub is Antihub2 which is for reconsidering the outlier score of a point obtained by the Antihub method. However, regarding computation time, Antihub2 runs slower. This paper institutes an approach called AdaptiveAntihub2Clust, which is a clustered Adaptive Antihub technique for unsupervised outlier detection to reduce computation time and to improve the accuracy. Findings: The results of an existing Antihub2 method is compared with the proposed method called AdaptiveAntihub2Clust. The experimental results elucidate that AdaptiveAntihub2Clust outperforms well than Antihub2 and also resolved that there is not only a substantial decrease in computation time but also progress in accuracy occurred while the newly built approach is practically used for finding outliers. Applications: The irrelevant objects may ascend due to numerous faults. Detection of such objects identifies the mistakes and fraud before they deteriorate with terrible significances and cleanses the data for further processing.

Efficient neighbor list calculation for molecular simulation of colloidal systems using graphics processing units

We present an algorithm based on linear bounding volume hierarchies (LBVHs) for computing neighbor (Verlet) lists using graphics processing units (GPUs) for colloidal systems characterized by large size disparities. We compare this to a GPU implementation of the current state-of-the-art CPU algorithm based on stenciled cell lists. We report benchmarks for both neighbor list algorithms in a Lennard-Jones binary mixture with synthetic interaction range disparity and a realistic colloid solution. LBVHs outperformed the stenciled cell lists for systems with moderate or large size disparity and dilute or semidilute fractions of large particles, conditions typical of colloidal systems.

Extension of the AMBER molecular dynamics software to Intel’s Many Integrated Core (MIC) architecture

We present an implementation of explicit solvent particle mesh Ewald (PME) classical molecular dynamics (MD) within the PMEMD molecular dynamics engine, that forms part of the AMBER v14 MD software package, that makes use of Intel Xeon Phi coprocessors by offloading portions of the PME direct summation and neighbor list build to the coprocessor. We refer to this implementation as pmemd MIC offload and in this paper present the technical details of the algorithm, including basic models for MPI and OpenMP configuration, and analyze the resultant performance. The algorithm provides the best performance improvement for large systems (>400,000 atoms), achieving a ∼35% performance improvement for satellite tobacco mosaic virus (1,067,095 atoms) when 2 Intel E5-2697 v2 processors (2 ×12 cores, 30M cache, 2.7 GHz) are coupled to an Intel Xeon Phi coprocessor (Model 7120P—1.238/1.333 GHz, 61 cores). The implementation utilizes a two-fold decomposition strategy: spatial decomposition using an MPI library and thread-based decomposition using OpenMP. We also present compiler optimization settings that improve the performance on Intel Xeon processors, while retaining simulation accuracy.

3G Wireless Network Optimization

The importance of wireless communication cannot be overlooked in this modern day of sophisticated communication world. Certainly, like any other technology, wireless communication has major challenges, most of which tremendously affect our daily conversations. This research is focused on wireless network optimization specifically on the Code Division Multiplexing Access (CDMA) technology. Emphasis will be on the importance of optimization and the various strategies adopted to optimize a wireless network. The beginning part of the research contains a comprehensive introduction of wireless optimization and specifically touched on its definition as well as the most essential aspect of its application in the wireless network. The research explicitly identified important Key performance indicators which usually monitors the performance of the wireless network. Also, in order to solve wireless network deficiencies, several approaches are used to achieve this goal. The research touched on these approaches and described how exactly they are applied to optimize a network. Wireless optimization is mainly achieved with the use of special tools such as drive test tools. As part of the research, the most important tools were identified as well as their applications. Additionally, a broad analysis was made on some common wireless issues as well as strategies adopted to resolve them. Some of the common problems identified include: handoff issues, call failure issues PN planning issues, Neighbor list planning issues as well as pilot pollution issues. Furthermore, in order to better understand the optimization in the practical sense, a case study was analyzed by comparing specific samples of results gathered before and after optimization. The results clearly demonstrated that wireless optimization improves wireless performance tremendously, which goes to attest to the fact that, the end results have a positive impact on subscribers and the composite society at large with tremendous benefits ranging from financial to great customer satisfaction.

Accelerating electrostatic interaction calculations with graphical processing units based on new developments of Ewald method using non-uniform fast Fourier transform.

We present new algorithms to improve the performance of ENUF method (F. Hedman, A. Laaksonen, Chem. Phys. Lett. 425, 2006, 142) which is essentially Ewald summation using Non-Uniform FFT (NFFT) technique. A NearDistance algorithm is developed to extensively reduce the neighbor list size in real-space computation. In reciprocal-space computation, a new algorithm is developed for NFFT for the evaluations of electrostatic interaction energies and forces. Both real-space and reciprocal-space computations are further accelerated by using graphical processing units (GPU) with CUDA technology. Especially, the use of CUNFFT (NFFT based on CUDA) very much reduces the reciprocal-space computation. In order to reach the best performance of this method, we propose a procedure for the selection of optimal parameters with controlled accuracies. With the choice of suitable parameters, we show that our method is a good alternative to the standard Ewald method with the same computational precision but a dramatically higher computational efficiency.

Design and evaluation of ANR algorithm for LTE real scenario with high interference

The operation of mobile networks has three basic objectives to fulfil: quality–coverage–capacity, at a cost of efficient operation. Quality can be expressed as the network's ability to maintain a call or data session, which must be terminated at will by users. When the network is not able to fulfil this condition, dropped calls occur. To ensure quality and user mobility, it is necessary to design a proper neighbour cell list to allow transfer between cells. In this sense, the 3GPP standard considers the ANR (automatic neighbour relation) technique for LTE as an automatic method of defining neighbour lists that optimises network performance. Presented is a method to define an optimised neighbour cell list, which is designed and implemented in a real UMTS network of Telefonica within a high interference scenario, and which is easily extrapolated to the LTE network.