Browsing by Author "Machado, R."

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    Adaptive density control in heterogeneous wireless sensor networks with and without power management
    (IEEE, 2010-04-30) Machado, R.; Ansari, N.; Wang, G.; Tekinay, Şirin; Electrical & Electronics Engineering; TEKİNAY, Şirin
    The authors study the design of heterogeneous two-tier wireless sensor networks (WSNs), where one tier of nodes is more robust and computationally intensive than the other tier. The authors find the ratios of densities of nodes in each tier to maximise coverage and network lifetime. By employing coverage processes and optimisation theory, the authors show that any topology of WSN derived from random deployments can result in maximum coverage for the given node density and power constraints by satisfying a set of conditions. The authors show that network design in heterogeneous WSNs plays a key role in determining key network performance parameters such as network lifetime. The authors discover a functional relationship between the redundancy, density of nodes in each tier for active coverage and the network lifetime. This relationship is much less pronounced in the absence of heterogeneity. The results of this work can be applied to network design of multi-tier networks and for studying the optimal duty cycles for power saving states for nodes in each tier.
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    Coverage properties of clustered wireless sensor networks
    (ACM, 2010-08) Machado, R.; Zhang, W.; Wang, G.; Tekinay, Şirin; Electrical & Electronics Engineering; TEKİNAY, Şirin
    This article studies clustered wireless sensor networks (WSNs), a realistic topology resulting from common deploymentmethods.We study coverage in naturally clustered networks of wireless sensor nodes, as opposed to WSNs where clustering is facilitated by selection. We show that along with increasing the vacancy in random placement of nodes in a WSN, it also alters the connectivity properties in the network.We analyze varying levels of redundancy to determine the probability of coverage in the network. The phenomenon of clustering in networks of wireless sensor nodes raises interesting questions for future research and development. The article provides a foundation for the design to optimize network performance with the constraint of sensing coverage.
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    Diffusion-based approach to deploying wireless sensor networks
    (Inderscience, 2010) Machado, R.; Tekinay, Şirin; Zhang, W.; Wang, G.; Electrical & Electronics Engineering; TEKİNAY, Şirin
    An important objective of Wireless Sensor Networks (WSNs) is to reliably sense data about the environment in which they are deployed. Reliability in WSNs has been widely studied in terms of providing reliable routing protocols for message dissemination and reliability of communication from sink to sensors. In this work, we define a reliability metric by the amount of data sensed by the network. In order to satisfy this reliability constraint, we propose a diffusion-based approach for a deployment pattern for the sensor nodes. We show that this deployment pattern achieves sufficient coverage and connectivity and requires lesser number of sensors than popular regular deployment patterns. We further obtain the bounds on establishing connectivity between nodes in the WSN and extend this analysis for heterogeneous WSNs.
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    Redundancy estimation and adaptive density control inwireless sensor networks
    (Old City Publishing, 2010) Machado, R.; He, H.; Wang, G.; Tekinay, Şirin; Electrical & Electronics Engineering; TEKİNAY, Şirin
    While dense random deployment satisfies coverage and sensing requirements, constructing dense networks of sensor nodes poses the problems of obtaining node location information.We provide an analytical framework for estimating the redundancy in a single-hop WSN of random deployment of nodes without the need of location information of nodes. We use an information theoretic approach to estimate the redundancy and provide the Cramer-Rao bound on the error in the estimation. We illustrate this redundancy estimation approach and calculate the bounds on the error in the estimation for a WSN with 1-redundancy. We also analytically show the inter-dependence between redundancy and network lifetime for random deployment. We further study the energy model of a WSN as interdependence between the environmental variation and its impact on the energy consumption at individual nodes. Defining network energy as the sum of residual battery energy at nodes, we provide an analytical framework for the dependence of node energy and sensitivity of network energy as a function of environmental variation and reliability parameters. Using a neural network based approach, we perform adaptive density control and show how reliability requirements and environment variation influences the rate of change of network energy.