With the significant growth of purpose-oriented networks in the realm of public affairs, the challenges for public and nonprofit organizations in deciding which networks to engage with have also increased. This work explores whether organizations with different collaborative motivations prefer networks with different sets of characteristics. By bridging organizational theory and social network literatures, this study proposes a conceptual framework regarding the connections between organizational collaborative motivations and network characteristics. The conceptual framework is then further investigated using a two-phase exploratory sequential mixed-methods design and data from both local and statewide public and nonprofit organizations with experience working in networks. The first phase is a qualitative exploration of the conceptual framework using interview data, and the second phase draws on survey data and crisp-set qualitative comparative analysis (cs-QCA) to refine the theoretical framework. The two-phase study indicates that organizations with different collaborative motivations indeed prefer different kinds of networks. The results also identify multiple pathways for each motivation and which network characteristics need to be combined for each motivation, as well as offer some universal combinations that work for different motivations. The findings are valuable for both network managers' efforts in recruiting members and public and nonprofit organization managers' attempts at seeking beneficial collaborations. / Doctor of Philosophy / Public and nonprofit organizations are often expected to work with others through different collaborative entities, such as organizational networks, to solve complex problems. Given the significant growth of networks in the field of public affairs and the limited time and resources that organizations have available, however, it can be challenging for organizations to decide which networks to participate in. To help make this decision easier, this study sets out to find combinations of network features that match with different collaborative motivations. A framework linking organizational and social network theories is first created, and then examined through a two-phase mixed-methods approach. The first phase involves interviewing a selection of local public and nonprofit organizations and using their feedback to improve the framework. The second phase is an online survey targeting public and nonprofit organizations across Virginia, which further investigates the refined framework. The framework developed from this study not only finds connections between organizational collaborative motivations and network characteristics, but also offers a tool for organizations to identify networks that better match their goals for collaboration and a roadmap for network managers to design networks that better meet the needs of their participants.

The main purpose of this thesis work is to find and compare different network characteristics of MPS (Mobile Positioning System) in the different wireless technology domains. Since decades ago MNO’s (Mobile Network Operators) added many new services based on the geographical areas of subscribers and their needs. Here we define wireless networks and go through different types of technologies and do the comparison when they collect different types of data for their location-based services and see if we could have the same accuracy with 2G (second generation) of mobile network as like as 3G (third generation) and higher. Finally, we will come up with a proposal for new age technology.

Situation (al) awareness (SA) is critical to analyze, predict and perform tasks effectively in a dynamic environment. Many studies on SA have ignored network dynamism and its effect on SA, focusing on simple environments. Many studies involving the network and SA have refrained from attempting to model information space dynamism (i.e. dynamic scenarios which may have more than one probable outcome). Few studies have identified the need for a flexible, robust and overarching framework which could model both the network and information space dynamisms and provide for analysis of different types of networks (heterogeneous/homogeneous) at multiple scales. We utilize the NCOPP (Network Centric Operations Performance & Prediction), a uniform framework with "plug-&-play" capabilities to provide analysis and performance prediction of networked information systems. In this work, we demonstrate the flexibility of the NCOPP framework and its ability to model a hierarchical sensor system satisfactorily. We model the network & information space dynamisms using probability and statistics theory (e.g. Bayesian prediction, probability distribution curves). We model the behavior of entities/nodes involved in the process of sharing information to achieve greatly improved situation awareness about a dynamic environment within hierarchical information network systems. Our behavior model mathematically represents how successful/unsuccessful predictions critically impact the achievement of effective situation awareness. In the behavior model, we tie together the cost of considering predictions which accounts for limited resources and the indirect effect of unsuccessful predictions. We research and show how the NCOPP framework can model real world networked information systems at different levels of granularity. We leverage the framework's capabilities to perform experiments that not only assist in an objective comparison of distributed information filtering and central data processing paradigms but also provide important insights into the effect of network dynamism on the quality and completeness of information in the system. We demonstrate the ability of incorporating key network information, in the process of achieving SA to improve the performance of the system. We exhibit the improvement in performance achieved with inclusion of the network characteristics during dynamic allocation of resources. We were able to show that simple hierarchical filtering (via distributed processing) results in significant reduction in the information in regards to "false alarms" when compared to systems employing central information processing. Experimental results show a direct positive impact in the completeness of SA when information sharing in hierarchical systems is supplemented by network delay information. Overall, we demonstrated the ability of the NCOPP framework to provide meaningful insights into the interactions of key factors involved in operation of networked information systems, with a particular emphasis on SA. / Master of Science

The focus of this thesis is the fast network anomaly detection based on automata learning. It describes and compares several chosen automata learning algorithms including their adaptation for the learning of network characteristics. In this work, various network anomaly detection methods based on learned automata are proposed which can detect sequential as well as statistical anomalies in target communication. For this purpose, they utilize automata's mechanisms, their transformations, and statistical analysis. Proposed detection methods were implemented and evaluated using network traffic of the protocol IEC 60870-5-104 which is commonly used in industrial control systems.