Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Toward a unified theory of task -oriented and relationship -oriented leader behavior: A multi -country generalizability study

The theoretical foundation of this study comes from the significant recurrence throughout the leadership literature of two distinct behaviors, task orientation and relationship orientation. Task orientation and relationship orientation are assumed to be generic behaviors, which are universally observed and applied in organizations, even though they may be uniquely enacted in organizations across cultures. The lack of empirical evidence supporting these assumptions provided the impetus to hypothetically develop and empirically confirm the universal application of task orientation and relationship orientation and the generalizability of their measurement in a cross-cultural setting. Task orientation and relationship orientation are operationalized through consideration and initiation of structure, two well-established theoretical leadership constructs. Multiple-group mean and covariance structures (MACS) analyses are used to simultaneously validate the generalizability of the two hypothesized constructs across the 12 cultural groups and to assess whether the similarities and differences discovered are measurement and scaling artifacts or reflect true cross-cultural differences. The data were collected by the author and others as part of a larger international research project. The data are comprised of 2341 managers from 12 countries/regions. The results provide compelling evidence that task orientation and relationship orientation, reliably and validly operationalized through consideration and initiation of structure, are generalizable across the countries/regions sampled. But the results also reveal significant differences in the perception of these behaviors, suggesting that some aspects of task orientation and relationship orientation are strongly affected by cultural influences. These (similarities and) differences reflect directly interpretable, error-free effects among the constructs at the behavioral level. Thus, task orientation and relationship orientation can demonstrate different relations among cultures, yet still be defined equivalently across the 11 cultures studied. The differences found in this study are true differences and may contain information about cultural influences characterizing each cultural context (i.e. group). The nature of such influences should be examined before the results can be meaningfully interpreted. To examine the effects of cultural characteristics on the constructs, additional hypotheses on the constructs' latent parameters can be tested across groups. Construct-level tests are illustrated in hypothetical examples in light of the study's results. The study contributes significantly to the theoretical understanding of the nature and generalizability of psychological constructs. The theoretical and practical implications of embedding context into a unified theory of task orientated and relationship oriented leader behavior are proposed. Limitations and contributions are also discussed. ^