• Title/Summary/Keyword: network clustering algorithm

Search Result 536, Processing Time 0.033 seconds

A Cluster-Based Top-k Query Processing Algorithm in Wireless Sensor Networks (무선 센서 네트워크에서 클러스터 기반의 Top-k 질의 처리)

  • Yeo, Myung-Ho;Seong, Dong-Ook;Yoo, Jae-Soo
    • Journal of KIISE:Databases
    • /
    • v.36 no.4
    • /
    • pp.306-313
    • /
    • 2009
  • Top-k queries are issued to find out the highest (or lowest) readings in many sensor applications. Many top-k query processing algorithms are proposed to reduce energy consumption; FILA installs a filter at each sensor node and suppress unnecessary sensor updates; PRIM allots priorities to sensor nodes and collects the minimal number of sensor reading according to the priorities. However, if many sensor reading converge into the same range of sensor values, it leads to a problem that many false positives are occurred. In this paper, we propose a cluster-based approach to reduce them effectively. Our proposed algorithm operates in two phases: top-k query processing in the cluster level and top-k query processing in the tree level. False positives are effectively filtered out in each level. Performance evaluations show that our proposed algorithm reduces about 70% false positives and achieves about 105% better performance than the existing top-k algorithms in terms of the network lifetime.

A Bio-inspired Hybrid Cross-Layer Routing Protocol for Energy Preservation in WSN-Assisted IoT

  • Tandon, Aditya;Kumar, Pramod;Rishiwal, Vinay;Yadav, Mano;Yadav, Preeti
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.15 no.4
    • /
    • pp.1317-1341
    • /
    • 2021
  • Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.

MLPPI Wizard: An Automated Multi-level Partitioning Tool on Analytical Workloads

  • Suh, Young-Kyoon;Crolotte, Alain;Kostamaa, Pekka
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.12 no.4
    • /
    • pp.1693-1713
    • /
    • 2018
  • An important technique used by database administrators (DBAs) is to improve performance in decision-support workloads associated with a Star schema is multi-level partitioning. Queries will then benefit from performance improvements via partition elimination, due to constraints on queries expressed on the dimension tables. As the task of multi-level partitioning can be overwhelming for a DBA we are proposing a wizard that facilitates the task by calculating a partitioning scheme for a particular workload. The system resides completely on a client and interacts with the costing estimation subsystem of the query optimizer via an API over the network, thereby eliminating any need to make changes to the optimizer. In addition, since only cost estimates are needed the wizard overhead is very low. By using a greedy algorithm for search space enumeration over the query predicates in the workload the wizard is efficient with worst-case polynomial complexity. The technology proposed can be applied to any clustering or partitioning scheme in any database management system that provides an interface to the query optimizer. Applied to the Teradata database the technology provides recommendations that outperform a human expert's solution as measured by the total execution time of the workload. We also demonstrate the scalability of our approach when the fact table (and workload) size increases.

Optimized Design of Intelligent White LED Dimming System Based on Illumination-Adaptive Algorithm (조도 적응 알고리즘 기반 지능형 White LED Dimming System의 최적화 설계)

  • Lim, Sung-Joon;Jung, Dae-Hyung;Kim, Hyun-Ki;Oh, Sung-Kwun
    • Proceedings of the KIEE Conference
    • /
    • 2011.07a
    • /
    • pp.1956-1957
    • /
    • 2011
  • 본 연구는 White LED를 이용하여 주변 밝기 변화에 빠르게 적응하는 퍼지 뉴로 Dimming Control System을 설계한다. 본 논문에서는 방사형기저함수 신경회로망(Radial Basis Function Neural Network: RBFNN)을 설계하여 실제 White LED Dimming Control System에 적용시켜 모델의 근사화 및 일반화 성능을 평가한다. 제안한 모델에서의 은닉층은 방사형기저함수를 사용하여 적합도를 구현하였고, 후반부의 연결가중치는 경사하강법을 사용한다. 이때 멤버쉽 함수의 중심점은 HCM 클러스터링 (Hard C-Means Clustering)을 적용하여 결정한다. 연결가중치는 4가지 형태의 다항식을 대입하여 출력을 평가하였다. 최종 출력의 최적화를 위하여 PSO(Particle Swarm Optimization)을 이용하여 은닉층 노드수 및 다항식 형태를 결정한다. 본 논문에서 제안한 LED Dimming Control System은 Atmega8535를 사용하여 PWM 제어 방식을 사용하고, 조도계(Cds)를 이용하여 LED의 밝기에 따른 주변의 밝기를 감지하여 조명에 적응시키는 방법을 적용하였다.

  • PDF

DeepCleanNet: Training Deep Convolutional Neural Network with Extremely Noisy Labels

  • Olimov, Bekhzod;Kim, Jeonghong
    • Journal of Korea Multimedia Society
    • /
    • v.23 no.11
    • /
    • pp.1349-1360
    • /
    • 2020
  • In recent years, Convolutional Neural Networks (CNNs) have been successfully implemented in different tasks of computer vision. Since CNN models are the representatives of supervised learning algorithms, they demand large amount of data in order to train the classifiers. Thus, obtaining data with correct labels is imperative to attain the state-of-the-art performance of the CNN models. However, labelling datasets is quite tedious and expensive process, therefore real-life datasets often exhibit incorrect labels. Although the issue of poorly labelled datasets has been studied before, we have noticed that the methods are very complex and hard to reproduce. Therefore, in this research work, we propose Deep CleanNet - a considerably simple system that achieves competitive results when compared to the existing methods. We use K-means clustering algorithm for selecting data with correct labels and train the new dataset using a deep CNN model. The technique achieves competitive results in both training and validation stages. We conducted experiments using MNIST database of handwritten digits with 50% corrupted labels and achieved up to 10 and 20% increase in training and validation sets accuracy scores, respectively.

A Research on the Analysis Method of School Exterior Space Lacking Natural Surveillance (학교 외부공간의 자연적 감시 취약지역 분석기법에 관한 연구)

  • Kweon, Ji-Hoon
    • The Journal of Sustainable Design and Educational Environment Research
    • /
    • v.11 no.1
    • /
    • pp.23-31
    • /
    • 2012
  • The number of school crime has grown continuously for last ten years and its intensity also has reached to serious condition. The concept of CPTED(Crime Prevention through Environmental Design) needs to be focused for improving school environment regarding this context. The exterior space of school environment is variously exposed to school crimes committed by colleague students and also intruders. From the perspective of school CPTED, Natural surveillance as one of the practical strategies requires the micro-scale analysis which clarifies local visibility at each different school exterior space. Thus, the purpose of this research is to develop the analysis method clarifying visibility condition at exterior space of school environment, which supports finding the condition of natural surveillance. The programmed analysis algorithm generated quantitative results clarifying Degree for static visibility and Clustering Coefficient for user tracking visibility. The result of this study produced the analysis method feasible to clarify weak natural surveillance conditions at school exterior spaces. Also, it is expected that the developed analysis method will be used to improve the layout of school exterior space from the perspective of CPTED.

The Object Recognition Using Multi-Sonar Sensor and Neural Networks (복수 초음파센서와 신경망을 이용한 형상인식)

  • Kim, Dong-Gi;O, Tae-Gyun;Gang, Lee-Seok
    • Transactions of the Korean Society of Mechanical Engineers A
    • /
    • v.24 no.11
    • /
    • pp.2875-2882
    • /
    • 2000
  • Typically, the ultrasonic sensors can be used in navigation systems for modeling of the enviornment, obstacle avoidance, and map building. In this paper, we tried to approach an object classification method using the range data of the ultrasonic sensors. A characterization of the sonar scan is described that allows the differentiation of planes, corners, edges, cylindrical and rectangular pillars by processing the scanned data from three sonars. To use the data from the ultrasonic sensors as input to the neural networks, we have introduced a clustering, threshold, and bit operation algorithm for the obtained raw data, After repeated training of the neural network, the performance of the proposed method was obtained through experiments. Also, the recognition ranges of the proposed method were investigated. As a result of experiments, we found that the proposed method successfully recognized the objects within the accuracy of 78%.

An Object Recognition Method Based on Depth Information for an Indoor Mobile Robot (실내 이동로봇을 위한 거리 정보 기반 물체 인식 방법)

  • Park, Jungkil;Park, Jaebyung
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.21 no.10
    • /
    • pp.958-964
    • /
    • 2015
  • In this paper, an object recognition method based on the depth information from the RGB-D camera, Xtion, is proposed for an indoor mobile robot. First, the RANdom SAmple Consensus (RANSAC) algorithm is applied to the point cloud obtained from the RGB-D camera to detect and remove the floor points. Next, the removed point cloud is classified by the k-means clustering method as each object's point cloud, and the normal vector of each point is obtained by using the k-d tree search. The obtained normal vectors are classified by the trained multi-layer perceptron as 18 classes and used as features for object recognition. To distinguish an object from another object, the similarity between them is measured by using Levenshtein distance. To verify the effectiveness and feasibility of the proposed object recognition method, the experiments are carried out with several similar boxes.

Managing Flow Transfers in Enterprise Datacenter Networks with Flow Chasing

  • Ren, Cheng;Wang, Sheng
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.10 no.4
    • /
    • pp.1519-1534
    • /
    • 2016
  • In this paper, we study how to optimize the data shuffle phase by leveraging the flow relationship in datacenter networks (DCNs). In most of the clustering computer frameworks, the completion of a transfer (a group of flows that can enable a computation stage to start or complete) is determined by the flow completing last, so that limiting the rate of other flows (not the last one) appropriately can save bandwidth without impacting the performance of any transfer. Furthermore, for the flows enter network late, more bandwidth can be assigned to them to accelerate the completion of the entire transfer. Based on these characteristics, we propose the flow chasing algorithm (FCA) to optimize the completion of the entire transfer. We implement FCA on a real testbed. By evaluation, we find that FCA can not only reduce the completion time of data transfer by 6.24% on average, but also accelerate the completion of data shuffle phase and entire job.

A study on motion prediction and subband coding of moving pictuers using GRNN (GRNN을 이용한 동영상 움직임 예측 및 대역분할 부호화에 관한 연구)

  • Han, Young-Oh
    • The Journal of the Korea institute of electronic communication sciences
    • /
    • v.5 no.3
    • /
    • pp.256-261
    • /
    • 2010
  • In this paper, a new nonlinear predictor using general regression neural network(GRNN) is proposed for the subband coding of moving pictures. The performance of a proposed nonlinear predictor is compared with BMA(Block Match Algorithm), the most conventional motion estimation technique. As a result, the nonlinear predictor using GRNN can predict well more 2-3dB than BMA. Specially, because of having a clustering process and smoothing noise signals, this predictor well preserves edges in frames after predicting the subband signal. This result is important with respect of human visual system and is excellent performance for the subband coding of moving pictures.