School of Computer and Information Sciences

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    A balanced 1-hop clustering scheme for Mobile Ad hoc Network
    ( 2008-12-01) Udgata, Siba K. ; Mallikarjun, T.
    Mobile Ad hoc Networks (MANETs), without any fixed infrastructures, allow mobile terminals to setup a temporary network for instant communication. Hence, MANETs promise to have many applications including disaster management, emergency relief, mobile conferencing, battle field communication, and so on. Clustering is an important approach in ad hoc networks for achieving scalability, ease of routing, basic performance guarantee such as throughput and delay, in the presence of large number of mobile nodes and high mobility. Connectivity among cluster heads is required for most applications such as message broadcasting, data communication, routing synchronization in distributed computing environment among many others. A number of 1-hop clustering protocols have been proposed, essentially based on the notion of Connected Dominating Set (CDS) for better connectivity. Conventional 1-hop clustering algorithms assume an existing MANET having some nodes and implement their algorithms on that network. Unlike those algorithms, we propose a new 1-hop clustering algorithm which forms the clusters at boot time of each node without any assumption of a MANET existing or already working. Our algorithm focuses on load balanced clusters and minimizing message exchanges in cluster formation. We tested our proposed scheme using ns 2.29 simulator. © 2008 IEEE.
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    A Blockchain-based Cyber Attack Detection Scheme for Decentralized Internet of Things using Software-Defined Network
    ( 2021-07-01) Guha Roy, Deepsubhra ; Srirama, Satish Narayana
    Due to using less secured and movable devices in Internet of Things (IoT) platform, cyber-attacks have been a major issue nowadays. Different researches have been conducted to detect the probable security attacks, but faced constraints like storage, computation cost, system failure and high latency. Existing systems require continuous monitoring, controlling, and collecting the data in entire network for delivering services with the maximum security and defense mechanism against cyber-attacks. In this context, a decentralized mechanism of security has been presented in this article using a software-defined network (SDN) integrated with blockchain for IoT in mobile edge and fog computing. The SDN continuously monitors and analyzes the system traffic for providing an attack identification model. The blockchain has been used to overcome the failure issues addressed in the existing models by delivering decentralized attack identification scheme which detects attacks in fog and reduces it in the edge node.
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    A cluster-oriented mutual exclusion algorithm for mobile ad hoc network
    ( 2003-12-01) Udgata, Siba K. ; Bagga, Jay
    Mobile Ad hoc Networks (MANET) are gaining importance in recent years due to its easy deployability, cost effectiveness in terms of time and infrastructure requirement and special-purpose applications. These Networks are mainly meant for collaborative and distributed applications that require resource sharing among the network nodes. Thus distributed mutual exclusion is also important in MANET environment. In this paper an attempt has been made to propose a new cluster oriented mutual exclusion algorithm based not on tokens but on permissions. The proposed algorithm always views the ad hoc network as a dynamic graph comprising of a set of star graphs known as clusters. The respective cluster leaders are determined so that all the cluster leaders form a connected tree structure. The well-studied spanning tree and depth first search algorithms of graph theory are used to find the subgraphs/clusters and leaders.
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    A clustering-based indexing approach for biometric databases using decision-level fusion
    ( 2017-01-01) Kavati, Ilaiah ; Prasad, Munaga V.N.K. ; Bhagvati, Chakravarthy
    In this paper, we propose a clustering-based indexing mechanism for biometric databases. The proposed technique relies mainly on a small set of preselected images called representative images. First, the database is partitioned into set of clusters and one image from each cluster is selected for the representative image set. Then, for each image in the database, an index code is computed by comparing it against the representative images. Further, an efficient storage structure (i.e., index space) is developed and the biometric images are arranged in it like traditional database records so that a quick search is possible. During identification, list of candidates which are very similar to the query are retrieved from the index space. Further, to make full use of the clustering, we also retrieve the candidate identities from the selected clusters which are similar to query. Finally, the candidate identities from the index space and cluster space are fused using decision-level fusion. Experimental results on different databases show a significant performance improvement in terms of response time and identification accuracy compared to the existing indexing methods.
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    A Combined System Metrics Approach to Cloud Service Reliability Using Artificial Intelligence
    ( 2022-03-01) Chhetri, Tek Raj ; Dehury, Chinmaya Kumar ; Lind, Artjom ; Srirama, Satish Narayana ; Fensel, Anna
    Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (self-monitoring, analysis, and reporting technology) hard drive metrics with other system metrics, such as central processing unit (CPU) utilisation. Therefore, we propose a combined system metrics approach for failure prediction based on artificial intelligence to improve reliability. We tested over 100 cloud servers’ data and four artificial intelligence algorithms: random forest, gradient boosting, long short-term memory, and gated recurrent unit, and also performed correlation analysis. Our correlation analysis sheds light on the relationships that exist between system metrics and failure, and the experimental results demonstrate the advantages of combining system metrics, outperforming the state-of-the-art.
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    A comparative study of nomadic mobile service provisioning approaches
    ( 2007-12-01) Pawar, Pravin ; Srirama, Satish ; Van Beijnum, Bert Jan ; Van Halteren, Aart
    In today's world of pervasive computing, the mobile devices are enriched with a variety of features and being used as a personal information delivery platform. The increased processing, storage and communication capabilities of these devices combined with the latest developments in the area of Service Oriented Architecture enables a new class of services, called Nomadic Mobile Services (NMS). Recent research has resulted in different NMS provisioning approaches; each one employs/defines a different architecture and addresses a different mix of issues. This paper provides a comparative study of three NMS provisioning approaches based on their architectural design, development choices and prototyped applications. Each approach has its own merits considering the applications they aim at. However, in the future, a solution which uses web services for better interoperability and employes proxy approach for better QoS could be a possible technical design. © 2007 IEEE.
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    A Comparison Analysis of Collaborative Filtering Techniques for Recommeder Systems
    ( 2021-01-01) Aramanda, Amarajyothi ; Md. Abdul, Saifullah ; Vedala, Radha
    In E-commerce environment, a recommender system recommend products of interest to its users. Several techniques have been proposed in the recommender systems. One of the popular techniques is collaborative filtering. Generally, the collaborative filtering technique is employed to give personalized recommendations for any given user by analyzing the past activities of the user and users similar to him/her. The memory-based and model-based collaborative filtering techniques are two different models which address the challenges such as quality, scalability, sparsity, and cold start, etc. In this paper, we conduct a review of traditional and state-of-art techniques on how they address the different challenges. We also provide the comparison results of some of the techniques.
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    A Comparison of Trust in MANETs and WSNs
    ( 2016-08-16) Reddy, Vijender Busi ; Negi, Atul ; Venkataraman, S.
    Trust is a soft security solution for Ad-hoc Wireless Networks such as Mobile Adhoc Networks (MANETs), Wireless Sensor networks (WSNs). Due to openness of the networks a malicious node can easily join and disrupt the network. Trust models are used to discriminate between legitimate and malicious nodes in the network. The characteristics of MANETs and WSNs are different so, there are some significant differences in the Trust mechanisms also. In this paper we briefly explain some of the trust models in MANETs and WSNs. We highlight some of the important issues to be considered in designing of a new trust model. We also discuss the major differences of trust models for MANETs and WSNs.
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    A comprehensive survey on nature-inspired algorithms and their applications in edge computing: Challenges and future directions
    ( 2022-04-01) Adhikari, Mainak ; Srirama, Satish Narayana ; Amgoth, Tarachand
    Driven by the vision of real-time applications and smart communication, recent years have witnessed a paradigm shift from centralized cloud computing toward distributed edge computing. The main features of edge computing are to drag the cloud services toward the network edge with dramatic reductions of latency while increasing the resource utilization of the network and computing devices. Being the natural extension of cloud computing, edge computing inherits a variety of research challenges and brings forth different new issues to solve. These challenges are dealing with solving complex optimization problems including scheduling and processing real-time applications. Nature-inspired meta-heuristic (NIMH) algorithm is an overarching term in the field of an optimization problem that provides robust solutions to the NP-complete problems, from computationally tractable approximate solutions to real-time optimization strategies. Nowadays, different NIMH algorithms have been applied in the field of edge computing for solving various research challenges including resource placement and scheduling, communication, mobility, and edge controlling with higher efficiency. In this survey, we classify the existing NIMH into three categories based on their nature of works and included fuzzy logic and systems in the field of edge networks along with different research challenges. Further, we introduce different challenges and future directions to identify promising research works in edge computing.
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    A Computationally Efficient Data-Dependent Projection for Dimensionality Reduction
    ( 2020-01-01) Pasunuri, Raghunadh ; Venkaiah, Vadlamudi China
    Principal component analysis (PCA) is a commonly used statistical technique for unsupervised dimensionality reduction, with a drawback of high-computational cost. Random projection (RP) is a matrix-based dimensionality reduction (DR) technique, which projects data by using a projection matrix i.e., constructed with random vectors. Random projection projects the high-dimensional data into low-dimensional feature space with the help of a projection matrix, which is constructed independent of input data. RP uses randomly generated matrices for projection purpose, even though it is computationally more advantageous than PCA, it has been giving unstable results, due to its randomness and data-independence property. Here in this work, we propose a via-medium solution which captures the structure-preserving feature of PCA and the pair-wise distance preserving feature from RP, and also takes less computational cost compared to PCA. Extensive experiments on low and high-dimensional data sets illustrate the efficiency and effectiveness of our proposed method.
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    A Context Sensitive Offloading Scheme for Mobile Cloud Computing Service
    ( 2015-08-19) Zhou, Bowen ; Dastjerdi, Amir Vahid ; Calheiros, Rodrigo N. ; Srirama, Satish Narayana ; Buyya, Rajkumar
    Mobile cloud computing (MCC) has drawn significant research attention as the popularity and capability of mobile devices have been improved in recent years. In this paper, we propose a prototype MCC offloading system that considers multiple cloud resources such as mobile ad-hoc network, cloudlet and public clouds to provide an adaptive MCC service. We propose a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and which potential cloud resources as the offloading location based on the device context. We also conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm can select suitable wireless medium and cloud resources based on different context of the mobile devices, and achieve significant performance improvement.
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    A correspondence based approach to segmentation of cursive words
    ( 1995-01-01) Negi, A. ; Swaroop, K. S. ; Agarwal, A.
    A novel contour-based approach to off-line segmentation of cursive words is proposed. The approach avoids expensive processing such as slant correction, thinning, filtering, and regional pixel labeling. Correspondences between the maximum and minimum excursions of the handwriting image are established and categorised. Valid established correspondences additionally provide three types of useful singular features about the segmented letter shapes. These singular features of the handwriting are first defined and isolated as: Loops, ascenders and descenders. A consistent analysis of established valid correspondences gives the segmentation. Correspondences also provide stroke width estimates, character width, and slant estimates that are useful to a recognition system. Results of the proposed approach on images, with variations in slant and types of ligatures are also presented.
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    A correspondence based approach to segmentation of cursive words
    ( 1995-01-01) Negi, A. ; Swaroop, K. S. ; Agarwal, A.
    A novel contour-based approach to off-line segmentation of cursive words is proposed. The approach avoids expensive processing such as slant correction, thinning, filtering, and regional pixel labeling. Correspondences between the maximum and minimum excursions of the handwriting image are established and categorised. Valid established correspondences additionally provide three types of useful singular features about the segmented letter shapes. These singular features of the handwriting are first defined and isolated as: Loops, ascenders and descenders. A consistent analysis of established valid correspondences gives the segmentation. Correspondences also provide stroke width estimates, character width, and slant estimates that are useful to a recognition system. Results of the proposed approach on images, with variations in slant and types of ligatures are also presented.
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    A cross - Domain role mapping and authorization framework for RBAC in grid systems
    ( 2009-01-01) Geethakumari, G. ; Negi, Atul ; Sastry, V. N.
    Highly computational resource sharing environments like grids pose major security issues. Secure interoperability has been a growing concern for such multi domain computing systems. Collaboration in such a diverse environment requires integration of all local policies to compose a global access control policy for controlling information and resource. Access control in such an environment is still an open problem. The much standardized Role Based Access Control (RBAC) is yet to be fully utilized in a multi domain grid environment like the Grids. Here, we present an architectural framework for adaptation and implementation of RBAC for grid access control. Our approach includes solutions for delegation and revocation in a single domain grid enterprise. The classical Role Based Access Control, though an effective access control standard, does not address the issue of resolving a local role into a global role. So, we also propose an architecture based on RBAC, which can establish role equivalence among the domains by mapping a local domain role to its equivalent global role. We use the approach of weighted ranking for the same. The final authorization decision is made based on the mapped global role ranking and also the resource access policies. © 2009 Technomathematics Research Foundation.
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    A cross-category model of households' incidence and quantity decisions
    ( 2008-03-01) Niraj, Rakesh ; Padmanabhan, V. ; Seetharaman, P. B.
    This paper advances the literature on multicategory demand models by simultaneously handling more than one purchase decision of the household. We propose a two-stage bivariate logit model of incidence and quantity outcomes in multiple categories. Our results show that cross-category promotional spillovers are asymmetric between the two product categories of bacon and eggs. The total retail profit responds more to bacon price than to egg price. Promoting bacon is found to have a bigger impact on egg profit than the impact of egg promotion on bacon profit. We decompose (1) the total retail profits, as well as (2) the cross-category profit impact of a price promotion, into its two components, and find that (1) 23% (67%) of the total retail profit impact of a promotion on bacon (eggs) arises on account of quantity effects, and (2) 40% (33%) of the increase in egg (bacon) profit from promoting bacon (eggs) is on account of quantity effects. © 2008 INFORMS.
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    A curriculum-based approach for feature selection
    ( 2017-01-01) Kalavala, Deepthi ; Bhagvati, Chakravarthy
    Curriculum learning is a learning technique in which a classifier learns from easy samples first and then from increasingly difficult samples. On similar lines, a curriculum based feature selection framework is proposed for identifying most useful features in a dataset. Given a dataset, first, easy and difficult samples are identified. In general, the number of easy samples is assumed larger than difficult samples. Then, feature selection is done in two stages. In the first stage a fast feature selection method which gives feature scores is used. Feature scores are then updated incrementally with the set of difficult samples. The existing feature selection methods are not incremental in nature; entire data needs to be used in feature selection. The use of curriculum learning is expected to decrease the time needed for feature selection with classification accuracy comparable to the existing methods. Curriculum learning also allows incremental refinements in feature selection as new training samples become available. Our experiments on a number of standard datasets demonstrate that feature selection is indeed faster without sacrificing classification accuracy.
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    A data locality based scheduler to enhance MapReduce performance in heterogeneous environments
    ( 2019-01-01) Naik, Nenavath Srinivas ; Negi, Atul ; Tapas, Tapas Bapu ; Anitha, R.
    MapReduce is an essential framework for distributed storage and parallel processing for large-scale data-intensive jobs proposed in recent times. Hadoop default scheduler assumes homogeneous environment. This assumption of homogeneity does not work at all times in practice and limits the performance of MapReduce. Data locality is essentially moving computation closer (faster access) to the input data. Fundamentally, MapReduce does not always look into the heterogeneity from a data locality perspective. Improving data locality for MapReduce framework is an important issue to improve the performance of large-scale Hadoop clusters. This paper proposes a novel data locality based scheduler which allocates input data blocks to the nodes based on their processing capacity. Also schedules map andreduce tasks to the nodes based on their computing ability in the heterogeneous Hadoop cluster. We evaluate proposed scheduler using different workloads from Hi-Bench benchmark suite. The experimental results prove that our proposed scheduler enhances the MapReduce performance in heterogeneous environments. Minimizes job execution time, and also improves data locality for different parameters as compared to the Hadoop default scheduler, Matchmaking scheduler and Delay scheduler respectively.
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    A Decentralized Group Signature Scheme for Privacy Protection in a Blockchain
    ( 2021-06-01) Devidas, S. ; Rao, Y. V.Subba ; Rekha, N. Rukma
    Group signature schemes play a vital role in protecting identity privacy of a member of a group who signs a message using the group signature. However, in the existing group signature schemes the centralized group manager has control over all the participants, and these managers can be malicious. They may take a biased decision when there is a dispute among the group members or while revealing the identity of a group member. To overcome the trust issues related to centralized group managers and to improve user privacy, a decentralized group signature scheme (DGSS) is proposed by decentralizing the role of the group manager. The proposed scheme will be more suitable for decentralized environments like a blockchain. Security analysis along with the proof of correctness is also provided for the proposed scheme. A framework for a blockchain-based e-auction protocol using the DGSS is also proposed in this paper.
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    A defeasible logic for modelling policy-based intentions and motivational attitudes
    ( 2009-01-01) Governatori, Guido ; Padmanabhan, Vineet ; Rotolo, Antonino ; Sattar, Abdul
    In this paper we show how defeasible logic could formally account for the non-monotonic properties involved in motivational attitudes like intention and obligation. Usually, normal modal operators are used to represent such attitudes wherein classical logical consequence and the rule of necessitation comes into play, i.e., ⊢ A/⊢□A that is from ⊢A derive ⊢□A. This means that such formalisms are affected by the Logical Omniscience problem. We show that policy-based intentions exhibit non-monotonic behaviour which could be captured through a non-monotonic system like defeasible logic. To this end we outline a defeasible logic of intention that specifies how modalities can be introduced and manipulated in a non-monotonic setting without giving rise to the problem of logical omniscience. In a similar way we show how to add deontic modalities defeasibly and how to integrate them with other motivational attitudes like beliefs and goals. Finally we show that the basic aspect of the BOID architecture is captured by this extended framework. © The Author 2009. Published by Oxford University Press. All rights reserved.
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    A defeasible logic of policy-based intention
    ( 2003-01-01) Governatori, Guido ; Padmanabhan, Vineet
    Most of the theories on formalising intention interpret it as a unary modal operator in Kripkean semantics, which gives it a monotonic look. We argue that policy-based intentions [8] exhibit non-monotonic behaviour which could be captured through a non-monotonic system like defeasible logic. To this end we outline a defeasible logic of intention. The proposed technique alleviates most of the problems related to logical omniscience. The proof theory given shows how our approach helps in the maintenance of intention-consistency in agent systems like BDI.