Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Distributed load flow analysis using graph theory(2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Shivarudraswamy, R.In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.Item Distributed load flow analysis using graph theory(2011) Sharma, D.P.; Chaturvedi, A.; Purohit, G.; Shivarudraswamy, R.In today scenario, to meet enhanced demand imposed by domestic, commercial and industrial consumers, various operational & control activities of Radial Distribution Network (RDN) requires a focused attention. Irrespective of sub-domains research aspects of RDN like network reconfiguration, reactive power compensation and economic load scheduling etc, network performance parameters are usually estimated by an iterative process and is commonly known as load (power) flow algorithm. In this paper, a simple mechanism is presented to implement the load flow analysis (LFA) algorithm. The reported algorithm utilizes graph theory principles and is tested on a 69- bus RDN.Item A geographical location aware energy efficient routing scheme for query based Wireless Sensor Networks(2013) Kumar, P.; Chaturvedi, A.; Shrivastava, S.In this paper, a geographical location based routing scheme is proposed that works effectively for different hierarchical networks and thus supports an important aspect of network scalability. Further, herein a heuristic is proposed that minimizes the occurrence of hot spot to improve the overall life time of Wireless Sensor Networks (WSNs). This utilizes residual energy estimation based on energy of each sensor node. Its implementation mainly comprises of the geographical location of each sensor in Binary Location Index (BLI) representation form, precise updates about the link with shortest path, and coverage of the each sensor; the method which is without change of cluster head is compared with the change of cluster head based on BLI for fixed single sink.Item Location movement and multiplicity attributes of sink in query-based wireless sensor networks(Inderscience Publishers, 2015) Kumar, P.; Chaturvedi, A.The principal issue in wireless sensor networks (WSNs) is the efficient use of finite energy of each sensor node as it predominantly influences the life time of WSN. In this work, we proposed four case studies on a typical square shape geographical area. In all these cases, the network architecture is hierarchical and same network environment is considered. These cases differ from each other on sink node(s) features viz. single/multiple, optimality of locations and stationary/moveable. As sink node plays an important role in a bridging the communication between the remote entities (sensor nodes) and access points/users; the proposed algorithms are formulated by keeping sink node(s) in centre stage. Further, the paper concludes by comparing various outcome measures like number of queries supported, average residual energy status (RES) estimation that directly impact the 'hot spot' phenomenon, thus enhances network life time while ensuring avoidance of 'islanding' and still adhere to energy efficient usage of network resources. © © 2015 Inderscience Enterprises Ltd.Item Sink attributes analysis for energy efficient operations of wireless sensor networks under randomly varying temporal and spatial aspects of query generation(Elsevier GmbH, 2015) Kumar, P.; Chaturvedi, A.Rapid advances and the development, compactness and economic viability; in IC technology, network hardware components and associated software have completely change the networking paradigm. The wireless sensor networks (WSNs) have also been not isolated from this unexpected changeover. This paper addresses three principal aspects that have been of interest in the WSN researcher community. These are investigating the suitable cluster formation scheme from some prominent scheme, incorporating the Spatio-temporal aspects of random query generation and subsequently model it using appropriate and extensively used probabilistic distribution functions, and exploring the importance of sink node(s) attributes towards much better energy profile of the WSN, as the energy consumption have been a vital component in deciding the overall network service conditions. The integration of these three aspects led to various case studies, which principally involves, uses of SKM, SFCM, DKM and DFCM as clustering schemes, uniform and Poisson probability mass functions uses to mathematically model the Spatio-temporal dependence of query distribution pattern, and the network surveillance by a single stationary sink, a moveable sink and four stationary sinks. The simulation results of various case studies are analyzed and compared. © 2015 Elsevier GmbH.Item Probabilistic query generation and fuzzy c -means clustering for energy-efficient operation in wireless sensor networks(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2016) Kumar, P.; Chaturvedi, A.Depending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution-based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c-means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed. © Copyright 2016 John Wiley & Sons, Ltd.Item Spatio-temporal probabilistic query generation model and sink attributes for energy-efficient wireless sensor networks(Institution of Engineering and Technology journals@theiet.org, 2016) Kumar, P.; Chaturvedi, A.Proliferation in Micro-Electro-Mechanical-Systems (MEMS) technology along with advancement in distributed computing infrastructure has facilitated the versatile usage and deployment of wireless sensors networks (WSNs) in last one and half decades. WSNs support large number of applications from the civilian and military regimes. Irrespective of these regimes; owing to difficulty associated with battery replenishment, proper energy usage has been at centre stage in WSNs operations. The lifetime of WSNs typically depends upon sensor's energy dissipation pattern, which is non-homogeneous with respect to spatial distribution over any short epochs. The genesis behind this nonhomogeneity is random generation of queries, which owes to application specific spatio-temporal parameters. Importance of spatio-temporal parameters is ubiquitous in WSNs paradigm and uncertainties are inevitable with these parameters, although the degree of uncertainties varies in accordance to applications served. Thus, from network design perspectives, precision involved with spatio-temporal aspects must be given due priority to obtain a mathematical model that maintains a good rapport with realistic query generation process. With these motivations, the study explores: (i) uses of energy-efficient clustering schemes, (ii) incorporation of spatio-temporal parameters uncertainties into probabilistic model of query generation using fuzzy-intervals bound, and (iii) sink attributes to enhance network lifetime. For various network surveillance scenarios; the performance measures average residual energy status and service-time-duration are estimated and analysed. © The Institution of Engineering and Technology 2016.Item Spatial–Temporal Aspects Integrated Probabilistic Intervals Models of Query Generation and Sink Attributes for Energy Efficient WSN(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Kumar, P.; Chaturvedi, A.With advancement in device miniaturization and efficacy of network protocols, in a variety of civilian and military applications, wireless sensor networks (WSNs) architectures find room as viable network paradigm. Invariably, in all these WSN architectures, devising suitable algorithms for the efficient network resources utilization has been a challenging task. In certain events driven scenarios, random arrival pattern of queries generation; their geographical distribution (spatial aspect) and generation rate (temporal aspect) are hard to predict precisely. However, these phenomenons could be appropriately modelled using probabilistic framework while yielding adequate accuracy. Usually, in adopted probabilistic models, the associated control parameters are treated as crisp numbers, which fail to encompass uncertainties that are inevitably associated with the modeled parameters. To include impact of such uncertainties, we propose a modified Poisson PMF expressions in that dependency on spatial and temporal aspects is incorporated based on interval concepts. The paper also validates the dynamic fuzzy c-means algorithm as the most efficient clusters formation scheme. Sink node is an important entity/interface between end users and remotely located sensor nodes. To exploit implications of sink nodes attributes, three different case studies are presented. Wherein, we explore the network surveillance by a single stationary/portable sink and four stationary sinks. Obtained simulation results are analyzed for different scenarios which in principle governed by usage of four distinct clustering schemes and sink(s) attribute driven network surveillance. © 2017, Springer Science+Business Media New York.Item Fuzzy-interval based probabilistic query generation models and fusion strategy for energy efficient wireless sensor networks(Elsevier B.V., 2018) Kumar, P.; Chaturvedi, A.Maintaining the desired service norm in wireless sensor networks (WSNs) over a stipulated lifetime is an important issue as it influences the application or utility of such networks. Inevitably, the impact of uncertainties in query generation process is of significant importance and it rely upon the associated spatio-temporal parameters. Usage of a probabilistic model is investigated to treat the inherent uncertainties. Queries inter-arrival-time-rate (?t) and spatial distribution or density (?a) are incorporated to regulate the parametric Poisson PMF model. Instead of considering crisp values of ?a and ?t that devoid parametric uncertainty, the values are inferred using plane-intervals and fuzzy-intervals. A mathematical framework is presented considering Poisson PMF model with parametric intervals, sink attributes in particular its multiplicity and motion aspects, and the quadrants fusion concept by deliberately modeling the problem in high-dimension space. To validate the proposed approach, uses of four different clustering schemes namely SKM, SFCM, DKM and DFCM are investigated. Combinations of sink attributes and quadrants fusion are carried out as different network scenarios. Obtained simulation results demonstrate the benefit of involving specific sink attributes and enabling quadrants fusion strategy. Based on energy metrics assessment, inference about early estimate of initial energy reserve (IER) or its sufficiency is established. © 2018 Elsevier B.V.Item Sparsity inspired pan-sharpening technique using multi-scale learned dictionary(Elsevier B.V., 2018) Gogineni, R.; Chaturvedi, A.The significant issues in remote sensing image fusion are enhancing the spatial details and preserving the essential spectral information. The classical pan-sharpening methods often incur spectral distortion and still striving to produce the fused images with prominent spatial and spectral attributes. Motivated by the desirable results of sparse representation (SR) theory, a novel pan-sharpening method is developed based on SR of high frequency (HF) components over a multi-scale learned dictionary (MSLD). MSLD technique acquires the capability of extracting the intrinsic characteristics of images, wherein, it possess the features of both multi-scale representation and learned dictionaries. In this paper, the dictionaries are adaptively learned from HF sub-images derived from the two versions of panchromatic image, realized at different spatial resolutions. A fast and computationally efficient algorithm is used for dictionary learning. The notion of SR together with patch recurrence over different scales is incorporated to estimate the high frequency details. The fused image is reconstructed by injecting the band specific spatial details into the up-sampled multi-spectral images. The performance of the proposed method is appraised with the datasets from different satellite sensors namely, QuickBird, IKONOS, WorldView-2 and Pléiades. The observations inferred from visual perception and quality indices analysis manifest the efficiency of proposed method over several well-known methods for the datasets considered at reduced-scale and full-scale resolutions. Further, the quantitative analysis of obtained performance measures confirms the efficacy of the proposed method for the reduced-scale and full-scale data sets. Especially, at a reduced-scale, proposed method yields an optimal value of Correlation coefficient, Structural similarity and Q4. In a comparative sense, usage of the proposed method at full-scale results in 4% and 2.56% improvement in the Spatial distortion index for QuickBird and WorldView-2 data respectively contrary to the best reported outcome obtained from Sparse Representation of injected details (SR-D) scheme. Invariably, for full-scale data, the QNR attains its optimal value. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
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