OFDMA-based high resolution sensor node ToA estimation in non-homogenous medium of human body

© 2016 IEEE. This paper introduces a novel and effective time-of-arrival (ToA) based range measurement of sensor nodes deployed within Non-homogenous (NH) medium consisting of time and frequency dispersive sub-medias via orthogonal frequency division multiple access (OFDMA) subcarriers. In the proposed technique, each sensor node exploits pre-allocated orthogonal subcarriers to construct a ranging waveform which enables ToA estimation in dispersive NH media. Here, a set of measurements applying different carrier frequencies are employed to construct a system of linearly independent equations for the available NH channel. The system of equations is used to calculate the thickness of each sub-media. The results are incorporated to refine the estimated ToA and calculate the actual distance between each transmitter and receiver within NH media. Simulation results confirm that the proposed technique is feasible for ranging within NH medium such as human body considering time and frequency dispersion impacts on wide-band waveform

COMMUNITY DETECTION IN COMPLEX NETWORKS AND APPLICATION TO DENSE WIRELESS SENSOR NETWORKS LOCALIZATION

Complex network analysis is applied in numerous researches. Features and characteristics of complex networks provide information associated with a network feature called community structure. Naturally, nodes with similar attributes will be more likely to form a community. Community detection is described as the process by which complex network data are analyzed to uncover organizational properties, and structure; and ultimately to enable extraction of useful information. Analysis of Wireless Sensor Networks (WSN) is considered as one of the most important categories of network analysis due to their enormous and emerging applications. Most WSN applications are location-aware, which entails precise localization of the deployed sensor nodes. However, localization of sensor nodes in very dense network is a challenging task. Among various challenges associated with localization of dense WSNs, anchor node selection is shown as a prominent open problem. Optimum anchor selection impacts overall sensor node localization in terms of accuracy and consumed energy. In this thesis, various approaches are developed to address both overlapping and non-overlapping community detection. The proposed approaches target small-size to very large-size networks in near linear time, which is important for very large, densely-connected networks. Performance of the proposed techniques are evaluated over real-world data-sets with up to 106 nodes and syntactic networks via Newman\u27s Modularity and Normalized Mutual Information (NMI). Moreover, the proposed community detection approaches are extended to develop a novel criterion for range-free anchor selection in WSNs. Our approach uses novel objective functions based on nodes\u27 community memberships to reveal a set of anchors among all available permutations of anchors-selection sets. The performance---the mean and variance of the localization error---of the proposed approach is evaluated for a variety of node deployment scenarios and compared with random anchor selection and the full-ranging approach. In order to study the effectiveness of our algorithm, the performance is evaluated over several simulations that randomly generate network configurations. By incorporating our proposed criteria, the accuracy of the position estimate is improved significantly relative to random anchor selection localization methods. Simulation results show that the proposed technique significantly improves both the accuracy and the precision of the location estimation