Faculty Publications
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Item Life time enhancement of wireless Sensor Network using fuzzy c-means clustering algorithm(Institute of Electrical and Electronics Engineers Inc., 2014) Kumar, P.; Chaturvedi, A.The major issues in wireless Sensor Networks (WSNs) are efficient uses of limited resources and appropriate routing of network paths under severely constrained energy scenarios. To overcome these issues; k-means and fuzzy c-means algorithms are investigated to form clusters and for subsequent selection of cluster heads. For all these clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) and estimation of Euclidean distances. Depending upon the Euclidean distance measure between the sink node and center of gravity of clusters; these clusters are classified into five types. Further, RES estimations are presented for cluster heads as well simple sensor network nodes. © 2014 IEEE.Item Performance measures of fuzzy C-means algorithm in wireless sensor networks(Inderscience Publishers, 2017) Kumar, P.; Chaturvedi, A.The major issues that govern performance of wireless sensor networks (WSNs) are efficient uses of limited resources and appropriate routing decisions of network paths under the severely constrained energy scenario. In this work, to address these issues uses of k-means and fuzzy C-means algorithms are investigated for clusters formation and subsequent selection of cluster heads (CHs). For all these newly formed clusters; selection of cluster head is done based on member sensor nodes residual energy status (RES) followed by estimation of Euclidean distances. Depending upon the Euclidean distance measures between the sink node and the estimated energy-centroid (EC) of clusters these clusters are classified into five types. The RES estimation is exercised for all the CHs and sensor nodes (SNs) of the network. Outcomes of simulation results indicate superior performance of fuzzy-c means algorithm compared to k-means algorithm. Further, a case study is presented, wherein the sink is allowed to have some movements in the service area. Here, different quadrant of service area exhibits different pattern of query spatial distribution. The optimal location of sink is sought to support energy efficient operational aspects of the WSNs. © © 2017 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 Correlation analysis and statistical characterization of heterogeneous sensor data in environmental sensor networks(Elsevier B.V., 2019) Rajesh, G.; Chaturvedi, A.In wireless sensor networks, missing data is an inevitable phenomenon due to the inherent limitations of the sensor nodes, such as battery power constraints of nodes, missing communication links, bandwidth limitation, etc. Missing data adversely affects the quality of data received by the sink node. Since the data acquired by the sensor nodes in a multimodal environmental sensor network are spatially and temporally correlated, these correlations play a pivotal role in missing data recovery and data prediction. This paper proposes an analytical framework to characterize the correlation between two different pairs of modalities in an environmental sensor network using a set of classical and robust measures of correlation coefficient estimates. Monte Carlo simulation is performed to approximately model sensed environmental data characteristics. Three classical estimates (Pearson's correlation coefficient, Spearman's rank correlation coefficient, and Kendall's-tau rank correlation coefficient), and four robust estimates of correlation coefficients are used to establish the correlation between different pairs of sensed modalities in the data characteristics. The efficacy of these estimates is obtained using the two performance metrics, mean-squared error (MSE) and relative estimation efficiency (RE). Stationarity analysis among the acquired environmental variables shed light upon the best estimates of the correlation coefficient, which could be used for prediction of temperature modality in a known region of slope/stationarity in the data characteristics. The robustness of the correlation coefficient estimates in the presence of outliers present in the data due to noise, errors, low residual battery power of sensor nodes, etc. is also investigated. © 2019 Elsevier B.V.