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33 Editors Computational Intelligence in Data Mining - Volume Proceedings of the International Conference on CIDM, 20-21 December 2014 SMART INNOVATION, SYSTEMS AND TECHNOLOGIES Lakhmi C Jain Himansu Sekhar Behera Jyotsna Kumar Mandal Durga Prasad Mohapatra Smart Innovation, Systems and Technologies Volume 33 Series editors Robert J Howlett, KES International, Shoreham-by-Sea, UK e-mail: Lakhmi C Jain, University of Canberra, Canberra, Australia, and University of South Australia, Adelaide, Australia e-mail: About this Series The Smart Innovation, Systems and Technologies book series encompasses the topics of knowledge, intelligence, innovation and sustainability The aim of the series is to make available a platform for the publication of books on all aspects of single and multi-disciplinary research on these themes in order to make the latest results available in a readily-accessible form Volumes on interdisciplinary research combining two or more of these areas is particularly sought The series covers systems and paradigms that employ knowledge and intelligence in a broad sense Its scope is systems having embedded knowledge and intelligence, which may be applied to the solution of world problems in industry, the environment and the community It also focusses on the knowledgetransfer methodologies and innovation strategies employed to make this happen effectively The combination of intelligent systems tools and a broad range of applications introduces a need for a synergy of disciplines from science, technology, business and the humanities The series will include conference proceedings, edited collections, monographs, handbooks, reference books, and other relevant types of book in areas of science and technology where smart systems and technologies can offer innovative solutions High quality content is an essential feature for all book proposals accepted for the series It is expected that editors of all accepted volumes will ensure that contributions are subjected to an appropriate level of reviewing process and adhere to KES quality principles More information about this series at Lakhmi C Jain Himansu Sekhar Behera Jyotsna Kumar Mandal Durga Prasad Mohapatra • • Editors Computational Intelligence in Data Mining - Volume Proceedings of the International Conference on CIDM, 20-21 December 2014 123 Editors Lakhmi C Jain University of Canberra Canberra Australia and University of South Australia Adelaide, SA Australia Himansu Sekhar Behera Department of Computer Science and Engineering Veer Surendra Sai University of Technology Sambalpur, Odisha India Jyotsna Kumar Mandal Department of Computer Science and Engineering Kalyani University Nadia, West Bengal India Durga Prasad Mohapatra Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela India ISSN 2190-3018 ISSN 2190-3026 (electronic) Smart Innovation, Systems and Technologies ISBN 978-81-322-2201-9 ISBN 978-81-322-2202-6 (eBook) DOI 10.1007/978-81-322-2202-6 Library of Congress Control Number: 2014956493 Springer New Delhi Heidelberg New York Dordrecht London © Springer India 2015 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made Printed on acid-free paper Springer (India) Pvt Ltd is part of Springer Science+Business Media ( Preface The First International Conference on “Computational Intelligence in Data Mining (ICCIDM-2014)” was hosted and organized jointly by the Department of Computer Science and Engineering, Information Technology and MCA, Veer Surendra Sai University of Technology, Burla, Sambalpur, Odisha, India between 20 and 21 December 2014 ICCIDM is an international interdisciplinary conference covering research and developments in the fields of Data Mining, Computational Intelligence, Soft Computing, Machine Learning, Fuzzy Logic, and a lot more More than 550 prospective authors had submitted their research papers to the conference ICCIDM selected 192 papers after a double blind peer review process by experienced subject expertise reviewers chosen from the country and abroad The proceedings of ICCIDM is a nice collection of interdisciplinary papers concerned in various prolific research areas of Data Mining and Computational Intelligence It has been an honor for us to have the chance to edit the proceedings We have enjoyed considerably working in cooperation with the International Advisory, Program, and Technical Committees to call for papers, review papers, and finalize papers to be included in the proceedings This International Conference ICCIDM aims at encompassing a new breed of engineers, technologists making it a crest of global success It will also educate the youth to move ahead for inventing something that will lead to great success This year’s program includes an exciting collection of contributions resulting from a successful call for papers The selected papers have been divided into thematic areas including both review and research papers which highlight the current focus of Computational Intelligence Techniques in Data Mining The conference aims at creating a forum for further discussion for an integrated information field incorporating a series of technical issues in the frontier analysis and design aspects of different alliances in the related field of Intelligent computing and others Therefore the call for paper was on three major themes like Methods, Algorithms, and Models in Data mining and Machine learning, Advance Computing and Applications Further, papers discussing the issues and applications related to the theme of the conference were also welcomed at ICCIDM v vi Preface The proceedings of ICCIDM have been released to mark this great day in ICCIDM which is a collection of ideas and perspectives on different issues and some new thoughts on various fields of Intelligent Computing We hope the author’s own research and opinions add value to it First and foremost are the authors of papers, columns, and editorials whose works have made the conference a great success We had a great time putting together this proceedings The ICCIDM conference and proceedings are a credit to a large group of people and everyone should be there for the outcome We extend our deep sense of gratitude to all for their warm encouragement, inspiration, and continuous support for making it possible Hope all of us will appreciate the good contributions made and justify our efforts Acknowledgments The theme and relevance of ICCIDM has attracted more than 550 researchers/ academicians around the globe, which enabled us to select good quality papers and serve to demonstrate the popularity of the ICCIDM conference for sharing ideas and research findings with truly national and international communities Thanks to all who have contributed in producing such a comprehensive conference proceedings of ICCIDM The organizing committee believes and trusts that we have been true to the spirit of collegiality that members of ICCIDM value, even as maintaining an elevated standard as we have reviewed papers, provided feedback, and present a strong body of published work in this collection of proceedings Thanks to all the members of the Organizing committee for their heartfelt support and cooperation It has been an honor for us to edit the proceedings We have enjoyed considerably working in cooperation with the International Advisory, Program, and Technical Committees to call for papers, review papers, and finalize papers to be included in the proceedings We express our sincere thanks and obligations to the benign reviewers for sparing their valuable time and effort in reviewing the papers along with suggestions and appreciation in improvising the presentation, quality, and content of this proceedings Without this commitment it would not be possible to have the important reviewer status assigned to papers in the proceedings The eminence of these papers is an accolade to the authors and also to the reviewers who have guided for indispensable perfection We would like to gratefully acknowledge the enthusiastic guidance and continuous support of Prof (Dr.) Lakhmi Jain, as and when it was needed as well as adjudicating on those difficult decisions in the preparation of the proceedings and impetus to our efforts to publish this proceeding Last but not the least, the editorial members of Springer Publishing deserve a special mention and our sincere thanks to them not only for making our dream come true in the shape of this proceedings, but also for its brilliant get-up and in-time publication in Smart, Innovation, System and Technologies, Springer vii viii Acknowledgments I feel honored to express my deep sense of gratitude to all members of International Advisory Committee, Technical Committee, Program Committee, Organizing Committee, and Editorial Committee members of ICCIDM for their unconditional support and cooperation The ICCIDM conference and proceedings are a credit to a large group of people and everyone should be proud of the outcome Himansu Sekhar Behera About the Conference The International Conference on “Computational Intelligence in Data Mining” (ICCIDM-2014) has been established itself as one of the leading and prestigious conference which will facilitate cross-cooperation across the diverse regional research communities within India as well as with other International regional research programs and partners Such an active dialogue and discussion among International and National research communities is required to address many new trends and challenges and applications of Computational Intelligence in the field of Science, Engineering and Technology ICCIDM 2014 is endowed with an opportune forum and a vibrant platform for researchers, academicians, scientists, and practitioners to share their original research findings and practical development experiences on the new challenges and budding confronting issues The conference aims to: • Provide an insight into current strength and weaknesses of current applications as well as research findings of both Computational Intelligence and Data Mining • Improve the exchange of ideas and coherence between the various Computational Intelligence Methods • Enhance the relevance and exploitation of data mining application areas for enduser as well as novice user application • Bridge research with practice that will lead to a fruitful platform for the development of Computational Intelligence in Data mining for researchers and practitioners • Promote novel high quality research findings and innovative solutions to the challenging problems in Intelligent Computing • Make a tangible contribution to some innovative findings in the field of data mining • Provide research recommendations for future assessment reports ix A Novel Approach for Intellectual Image Retrieval … 695 retrieval because it is one of the most straightforward features utilized by humans for visual recognition However, image retrieval using color features often gives disappointing results because, in many cases, images with similar colors not have similar content 1.2 Image Feature Extraction Image Feature Extraction is an important process involved in image retrieval Image features are extracted in spatial-domain and frequency domain For extracting features in spatial domain we have used the Principal Component Analysis (PCA) algorithm The advantage of PCA algorithm is that it reduces the dimensions of the feature space The output of PCA algorithm is Eigen values and Eigen vectors which are representing spatial-domain features of the image Frequency domain feature extraction part involves computation of DCT and DWT of the image The DCT and DWT coefficients serve as frequency-domain features The feature extraction process is depicted in Fig 1.3 Artificial Neural Network Artificial neural networks are computational models based on concept of human central nervous systems, designed interconnected neurons which computed values from input, used in machine learning and pattern recognition Pattern recognition, prediction, optimization, control etc are application area of artificial neural Neural networks are emerging successful classifier which search for a functional relationship between the group membership and the attributes of the Fig Feature extraction process 696 A Khodaskar and S Ladhake object Neural networks have been successfully applied to a variety of real world classification applications include bankruptcy prediction, handwriting recognition, speech recognition, product inspection, fault detection, medical diagnosis Neural networks is used which improve classifier performance Neural networks work as competitive alternative to traditional classifiers for many practical classification problems Neural classification includes network training, model design and selection sample size issues Related Work Sadek et al presented Image Retrieval using Cubic Splines Neural Networks [7] They proposed a new architecture for a CBIR system; the Splines Neural Networkbased Image Retrieval (SNNIR) system SNNIR utilized a rapid and precise network model that employs a cubic-splines activation function By using the cubicsplines network, the proposed system could determine nonlinear relationship between images features, which gives more accurate similarity comparison between images Olkiewicz and Markowska-Kaczmar proposed Emotion-based Image Retrieval using artificial Neural Network Approach [8] They have presented the approach for content based image retrieval systems based on its emotional content They examine possibilities of use of an artificial neural network for labelling images with emotional keywords based on visual features only and examine an influence of used emotion filter on process of similar images retrieval Venkatraman and Kulkarni designed MapReduce neural network framework for efficient content based image retrieval from large datasets in the cloud computing background, which make CBIR system effective and scalable for real-time processing of very large image collections [9] Neural network in combination with other techniques like fuzzy logic are used for images retrieval to improve performance of image retrieval based on their content Verma and Kulkarni proposed methodology by combining neural networks and fuzzy logic for interpretation of queries, feature extraction and classification of features in CBIR [10], which improve the overall performance of the CBIR systems Chowdhry proposed artificial neural network in combination with support vector machine and single value decomposition for image retrieval which improve system performance as well as bridge semantic gap [11] They use back propagation algorithm for training and testing data 2.1 Advanced Development in ANN and Image Retrieval Images retrieval system worked based on two phases, enrolment phase and retrieval phase for retrieval performance improvement, enrolment phase consist of feature extraction and second, retrieving phase use the artificial neural network and A Novel Approach for Intellectual Image Retrieval … 697 similarity measurement [2] The features of multilevel Discrete Wavelet Transform and Feed Forward Artificial Neural Network are combined to denoising image [12] Low-level attributes presented in form of high-level semantic concepts by using a high- level characteristics vector, which is formed by using the artificial neural network Intelligence [8] Human emotions is one of the important factor of searching images in an image database through content based image retrieval systems in which use an artificial neural network for labeling images with emotional keywords based on visual features [7] The artificial intelligence explosive ordnance disposal system is a neural network AI-based multiple-incident identification, recording, and tracking system, featuring state-of-the-art search, retrieval, and image and text management [12] Artificial neural network based model is designed to retrieval of the direct normal, diffuse horizontal and global horizontal irradiances using SEVIRI images [13] A new matching strategy for content based image retrieval system is based on artificial neural network which selected features of query image are the input and its output is one of the multi classes that have the largest similarity to the query image [14] Feature space of a content-based retrieval system is nonlinearly transformed into a new space, where the distance between the feature vectors is adjusted by learning and, transformed by an artificial neural network architecture [15] In the proposed system, artificial neural network is used for interpretation of semantic concepts extraction Multi-layer feed forward back propagation is trained for 500 sample images The colour, texture and shape features are used for training Proposed System Let f(x, y) be an input image with dimension [m × n] Let S ¼ fsi ; i ¼ 1; ; Ng denote the set of regions produced for an image by segmentation, O ¼ foj ; j ¼ 1; ; Mg denote the set of objects extracted from segments È É C ¼ ck ; k ¼ 1; ; M0 denote the semantic concepts È É Fs ¼ f ; ; M ỵ M0 is the set of semantic features Let k : f ! Fs is a transformation function which maps from input image to set of semantic features R ¼ frk ; k ¼ 1; ; Kg denote the set of supported spatial relations Then, the degree to which si satisfies relation rk with respect to sj can be denoted as Irk (si, sj), where the values of function Irk More specifically, the mean values, Irkmean, of Irk are estimated, for every k over all region pairs of segments assigned to objects (op, oq), p ≠ q Additionally, the variance values r2rk are obtained for each of the relations 698 A Khodaskar and S Ladhake PN r2rk ẳ iẳ1 Irk i Irk mean ị2 N ð1Þ where, N denotes the plurality of object pairs (op, oq) for which rk is satisfied Feed forward back propagation algorithm of ANN is used to classify images Ability of neural network is to provide machine intelligence to system and actively support pattern recognition Backpropagation algorithm is used to handle errors in multilayer Multi-layer means multiple layers of weights The back propagation algorithm uses the Delta Rule A multi-layer, feedforward, backpropagation neural network consist of three layers nodes, input layer, one or more intermediate layer and output layer Depending on the problem, the output layer can consist of one or more nodes In classification applications, number of output nodes depends on classes 3.1 Proposed Framework A framework for Interactive Image Retrieval (IIR) based on image content analysis using Artificial Neural Network is shown in Fig The Multilayer Back Propagation Feed Forward algorithm is proposed for interactive image retrieval, which takes query by image as an input and retrieves the most relevant images from the image dataset Image corpus Feature Extraction Query Image Feature Extraction Training of ANN (Multilayer Feed Forward Back propagation algorithm) ANN Testing (Multilayer Feed Forward Back propagation algorithm) Relevant Images Fig Proposed framework A Novel Approach for Intellectual Image Retrieval … 3.2 Algorithm 699 700 A Khodaskar and S Ladhake In the first part, Semantic features are extracted from the image database This extracted feature space is provided to ANN’s Multilayer back-propagation network as training matrix In the second part, the query image is given for feature extraction and applied to Multilayer back-propagation network for testing The result of ANN will be used for accomplishing the image content analysis Result Multi-Layer Feed Forward Back-Propagation Network gives human intelligence classification and learning ability in relevance feedback We analyzed 500 images data set in training phase by changing threshold value as per requirement Experimental result shows improves classification performance Network weights are randomly varies in training phase for every execution by keeping other factors like training data, learning rate, are kept constant The algorithm gives deviation in results when work with small data set When we increase data set on the basis of ground truth, result is variations are minimized and in order optimise result combine the results of multiple neural network classifications 4.1 Image Database In this research work, we have used the standard image database comprising of 500 images It contains images from different categories such as buses, flowers, nature, river etc The subset of this database is shown in Fig A given query image is feature extracted and searched for similar images For each query image, relevant images (Fig 5) are considered to be those and only those which belong to the same category as the query image as shown in Fig The performance of ANN training and testing is shown in Fig It shows number epochs required for training, Mean Square Error (MSE) for Train, Validation, Test processes In this section, we present experimental results from testing the proposed approach in the domain of beach vacation images A set of 50 randomly selected images belonging to the beach vacation domain were used to assemble a training set for the low-level implicit knowledge acquisition In Table 1, quantitative performance measures are given in terms of precision and recall A Novel Approach for Intellectual Image Retrieval … Fig Sub-set of images used for training Fig Query image Fig Relevant images retrieved 701 702 A Khodaskar and S Ladhake Fig ANN performance Table Numerical evaluation for the beach vacation domain Object Precision Recall Sky Sea Sand Person Accuracy 57.31 93.68 87.15 80.00 79.53 95.11 72.48 90.24 76.28 83.52 Conclusion In this paper, a novel approach for intellectual Image Retrieval (IIR) based on image content analysis using Artificial Neural Network is presented The Multilayer Back Propagation Feed Forward algorithm is proposed for interactive image retrieval The feature vector used for ANN training comprises of spatial-domain as well as frequency domain features which helps ANN to classify the images effectively The standard image database is used for ANN training with a size of 500 images The performance of the system is evaluated on the basic parameters, precision and recall After the rigorous experimentation it is revealed that, the proposed system shows the improved performance of image retrieval References Sciascio, E Di., Mingolla G., Mangiallo, M.: Content-based image retrieval over the web using query by sketch and relevance feedback Visual Inf Inf Syst Lect Notes Comput Sci 1614, 123–130 (1999) Bhagat, A.P., Atique, M.: Web based image retrieval system using color, texture and shape analysis: comparative analysis Int J Adv Comput Res (2013) A Novel Approach for Intellectual Image Retrieval … 703 Philippe, H.G., Matthieu, C.: Active learning methods for interactive image retrieval IEEE Trans Image Process (2008) Jian, M., Dong, J., Tang, R.: Combining color, texture and region with objects of user’s interest for CBIR IEEE (2007) Marco, A., Silvana, G.D., Marcello, G.: Design and implementation of web-based systems for image segmentation and CBIR IEEE Trans Instrum Meas 55 (2006) Bispo dos Santos, J., de Almeida, J.R., Silva, L.A.: Pattern recognition in mammographic images used by the residents in mammography In: IEEE Conference on Computer Medical Applications, pp 1–6 (2013) Sadek, S., Al-Hamadi, A., Michaelis, B., Sayed, U.: Image retrieval using cubic splies neural networks IJVIPNS-IJENS 9, 5–10 (2009) Olkiewicz, K.A., Markowska-Kaczmar, U.: Emotion-based image retrieval—an artificial neural network approach In: IEEE Proceedings, pp 89–96 (2010) Venkatraman, S., Kulkarni, S.: Hybrid intelligent systems In: IEEE International Conference pp 63–68 (2012) 10 Verma, V., Kulkarni, S.: Neural network for content based image retrieval IGI pub (2007) 11 Chowdhry, B.S.: Image Retrieval Based on Color and Texture Feature using Artificial Neural Network Springer IMTIC, pp 501–512 (2012) 12 Saikia, T., Sarma, K.K.: Multilevel-DWT based image de-noising using feed forward artificial neural network In: IEEE Conference on Signal Processing and Integrated Networks, pp 791–794 (2014) 13 Holmström, L., Koistinen, P.: Using additive noise in back-propagation training IEEE Trans Neural Netw 24–38 (1992) 14 Leverington, D.W.: Discriminating lithology in arctic environments from earth orbit: an evaluation of satellite imagery and classification algorithms Ph.D Thesis (2001) 15 Awad, Mohamad: Sea water chlorophyll-a estimation using hyperspectral images and supervised artificial neural network Elsevier Ecol Inf 24, 60–68 (2014) 16 George, L.E., Mohammed, E.Z.: Tissues image retrieval system based on co-occuerrence, run length and roughness features In: IEEE Conference on Computer Medical Applications, pp 1–6 (2013) 17 Houmao, W., Jiakui T.: Aerosol retrieval from remote sensing image using artificial neural network In: IEEE International Conference on Computer Application and System Modeling, vol 5, pp V5-551–V5-555 (2010) 18 ORibeiro, E.F., Barcelos, C.A.Z., Batista, M.A.: High-level semantic based image characterization using artificial neural networks In: IEEE 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using SEVIRI images Elsevier Solar Energy 89 1–16 (2013) 25 Elalami, M.E.: A new matching strategy for content based image retrieval system Elsevier Appl Soft Comput 14, 407–418 (2014) 704 A Khodaskar and S Ladhake 26 Pulvirenti, L.E.: Empirical algorithms to retrieve surface rain-rate from special sensor Microwave Imager over a mid-latitude basin IEEE Geosci Remote Sens Sympos 3, 1872–1874 (2002) 27 Thilagavathy, A., Aarthi, K., Chilambuchelvan, A.: A hybrid approach to extract scene text from videos IEEE Comput 1017–1022 (2012) 28 Carkacioglu, A., Vural, F.-Y.: Learning similarity space IEEE Conf Image Process 1, I405–I-408 (2002) 29 Wuryandari, A.I., Wijaya, R.: Gathering information realtime and anywhere (GIRA) using an ANN algorithm In: IEEE Conference on System Engineering and Technology, pp 1–6 (2012) 30 Nilpanich, S., Hua, K.A., Petkova, A., Ho, Y.H.: A Lazy Processing approach to user relevance feedback for content-based image retrieval In: IEEE Symposium on Multimedia, pp 342–346 (2010) Hierarchical Agents Based Fault-Tolerant and Congestion-Aware Routing for NoC Chinmaya Kumar Nayak, Satyabrata Das and Himnsu Sekhar Behera Abstract In communication medium a single fault will affect the complete system So in the designing of Network-on-Chip (NoC) based systems the reliability is an important aspect Also we have to concentrate on the performance improvement in the fault tolerant NoC architectures In this paper, we are going to achieve high level performance by using hierarchical agents by proposing Fault-tolerant NoC architecture The fault information will be collected and will be distributed after processing from these agents which are placed everywhere in the network Along with that the permanent Faults information that occur in the interfaces of network, links and in different parts of the routers will be exploited from the enhanced fault tolerant and congestion aware routing method Keywords NoC Á Congestion aware routing Á DTM Introduction The faults will incurs in more numbers in the NoC based systems on chip as the technologies scale down, mainly while running at high clock frequencies In previous work such as paper [1, 2], it is stated that Noc is the favorable interconnection infrastructure for inter-core communication due to its scalability, higher throughput and reusability The overall performance, reliability and power consumption are some of the important issues in NoC-based many-core systems, which will be more C.K Nayak (&) Department of CSE, GITA, BBSR, Burla, Odisha, India e-mail: S Das Á H.S Behera Department of CSE & IT, VSSUT, Burla, Odisha, India e-mail: H.S Behera e-mail: © Springer India 2015 L.C Jain et al (eds.), Computational Intelligence in Data Mining - Volume 3, Smart Innovation, Systems and Technologies 33, DOI 10.1007/978-81-322-2202-6_64 705 706 C.K Nayak et al important when there is increase in number of cores and the number of network nodes In NoC-based many-core systems, fault tolerance against permanent faults is necessary since the current and future process technologies have a considerable amount of device failures that may occur in both manufacturing and operational phases One of the reasons for the performance degradation in NoCs tolerating permanent faults is the lack of non-local fault awareness In this paper, we propose a very low cost architecture to acquire regional and global fault information through hierarchical and distributed agents [1, 3] This architecture includes an agent-based management structure and a new routing method capable to exploit nonlocal fault information to tolerate permanent faults in the links, network interfaces and different parts of the routers So far, permanent fault tolerance has been considered in many fault tolerant routing algorithms However, due to the size of complex NoCbased systems which may include tens or hundreds of cores, as the methods introduced in paper [3] through paper [4], we only consider distributed and scalable routing algorithms that are capable to utilize the proposed architecture The previous works related to the hierarchical agents can be found in paper [5] through paper [6] A NoC monitoring scheme based on hierarchical agents is addressed mainly to minimize network power consumption is given in paper [5] A Dynamic Thermal Management (DTM) solution is proposed that uses a systemlevel approach featuring agent-based power distribution to balance the power consumption of multi- and many-core architectures is given in paper [7] It uses software-based agents and they should be run on the processing elements of the system System level design principles and the basic concepts of the general approach for the hierarchical agent monitoring is proposed for parallel and distributed systems in the form of high level abstraction given in paper [8] In addition, it includes an approach for dynamic voltage and frequency scaling used in power monitoring However, it does not present any detailed design or physical platform especially for fault-tolerance Had proposed a preliminary agent based management structure, the basic agent tasks and a simple fault information classification is given in paper [6] In this paper, we address the design and usage of hardware agents inside the network nodes in a distributed and hierarchical management structure [4, 9] The proposed architecture optimally utilizes local and non-local fault information in addition to the congestion information For this purpose, a detailed fault information classification is provided for the routing process The proposed routing method decreases the overall packet latencies and handles the fault information and congestion information, simultaneously Using the hierarchical agents, the appropriate portions of the local fault information in each node will be sent to the direct and indirect neighbour nodes to be used as regional or global fault information The routing algorithms should be able to utilize the new fault information provided during the life time of the system, in their structures This architecture can be applied to any router incorporating a distributed and scalable fault-tolerant routing algorithm Hierarchical Agents Based Fault-Tolerant … 707 Further, In Sect the fault information classification needed for the routing process is presented, in Sect the proposed agent-based management architecture is introduced, and in Sect the proposed fault-tolerant routing method is explained The experimental results are presented in Sect and finally, conclusion is given in Sect Fault Information Classifications In this section, we use the basic information of routing process to obtain a comprehensive classification of the fault information beneficial for distributed and faultaware routing algorithms A typical NoC router architecture is depicted in Fig This router consists of a controller including routing unit, switch allocator and virtual channel (VC) allocator In addition, it comprises a crossbar switch, input ports and output ports The crossbar switch includes a multiplexer for each output port Each input port includes the buffers for virtual channels, a multiplexer and a demultiplexer, and each output port is directly connected to an outgoing link Based on paper [10] some test and fault detection circuits can be incorporated in the NoC routers and links to detect the permanent faults in each sub-block with acceptable overheads Therefore, it is assumed that we are aware about the faultiness of five input ports, four unidirectional outgoing links, routing unit, switch allocator, VC allocator, and at most five multiplexers in the crossbar switch, in a five-port router in addition to the faultiness of the Network Interface (NI) and the local core or Processing Element (PE) If we assume X can be N, S, E or W which mean north, south, east or west directions, respectively, then we can say the X output direction of a router is unusable for the routing process if the X outgoing link or the crossbar multiplexer Fig A typical NoC router architecture with two virtual channels 708 C.K Nayak et al in the X output direction or the input port in the X neighbour router is faulty Logical OR can be used to state this condition as (1) by using the appropriate signals from the fault detection circuits: DirXout ¼ LinkXout or MUXX or X router In Port1ÀX ð1Þ In (1) all terms are one-bit status data showing that if any term equals ‘1’ its corresponding component is faulty, otherwise it is healthy In this equation, LinkX out and MUXX mean the status of unidirectional outgoing link and the crossbar multiplexer, respectively, in the X output direction of the current router, and In_Port(1-X) X_router stands for the status of the input port in the X neighbor router In addition, (1-X) stands for the opposite direction of X, which means S, N, W and E for N, S, E and W directions, respectively This equation is based on the fact that we can model a faulty crossbar multiplexer by assuming its corresponding unidirectional outgoing link to be faulty, and a faulty input port by assuming its unidirectional incoming link to be faulty The whole node should be considered as faulty due to the impact of some faulty components inside a router because of that the router is unable to perform its main task This case occurs when the controller is faulty based on the expression (Exp 1) Ctrl ¼ routing unit or switch allocator or VC allocator ðExp:1Þ As per exp (Exp 1) we should consider the control part, as a result the whole node is faulty if the routing unit, switch allocator or VC allocator is faulty For this case, we dedicate a bit called Node as a part of fault information regarding to this situation In NoC-based many-core systems, the local cores or the processing elements are connected to the routers via the network interfaces [7, 8] If a processing element is unusable, the high level system manager should either migrate its task to other processing elements or perform a remapping process In a NoC router we assume the processing element is unusable if it is faulty, its network interface is faulty, its related input port is faulty or output crossbar multiplexer is faulty based on (2): PE ¼ PElocal or NI or In Portlocal or MUXlocal ðExp:2Þ In the above expression, if PE (Processing elements) equals ‘1’, the local core or processing element is unusable, otherwise it is usable Proposed Agent-Based Management Architecture It is beneficial to use a scalable and distributed management method to decrease the overall packet latency in a fault-tolerant NoC comprising a large number of nodes Here, agent-based management on packet latencies architecture is proposed which is also hierarchical to be more profitable for scalable NoCs Hierarchical Agents Based Fault-Tolerant … 709 3.1 Background There are two types of agents in the proposed management structure: • Node agent (NA): Each node includes an agent called the node agent which collects, combines and distributes the fault information related to the components of its own node in addition to the local congestion information Besides, it updates the LFR, RFR1 and RFR2 • Cluster agent (CA): Each cluster that includes a number of nodes is controlled by a cluster agent A CA configures the NAs inside the cluster by sending the new fault information obtained from the other NAs inside the cluster or from the other CAs We need a cluster-based NoC if we want to obtain an efficient management approach when running different tasks The operating system should map different tasks onto the network clusters The incorporated agent hierarchy is shown in Fig This agent hierarchy differs from that of proposed in the previous works paper [5], paper [8] This is due to the fact that in the proposed structure, for faster reconfiguration the NAs communicate with their neighbour NAs even if they are located inside different clusters This is advantageous because in general, a task running in a system with many processing elements may require more than a cluster On the other hand, clusters running the same task are not necessarily neighbour clusters However, the routers should be aware about their neighbours to select the best path for sending the packets to their destinations, and to expedite this awareness their NAs should exchange the required fault and congestion information Fig Hierarchical agents in two neighbour clusters ... Lakhmi C Jain Himansu Sekhar Behera Jyotsna Kumar Mandal Durga Prasad Mohapatra • • Editors Computational Intelligence in Data Mining - Volume Proceedings of the International... e-mail: akshaya .it2 M Sithika e-mail: © Springer India 2015 L.C Jain et al (eds.), Computational Intelligence in Data Mining - Volume 3, Smart Innovation, Systems... Intelligence in Data mining for researchers and practitioners • Promote novel high quality research findings and innovative solutions to the challenging problems in Intelligent Computing • Make
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