Privacy policy is a legal document in which the users are informed about the data practices used by the organizations. Past research indicates that the privacy policies are long and hard to understand. They are also known to have incomplete content. Users are not inclined to read the policy as they have to read long policies to find information about data practices of an organization. The solution that we are proposing in this research is to assist users with finding relevant content to their queries using semantic approach. This thesis presents the development of domain ontology for privacy policies. Natural Language Processing was used to understand the content of the policies and capture vocabulary for the ontology. This vocabulary was further used to build the ontology so that the ontology highlights relevant sentences related to a privacy concern. We validated and evaluated the ontology using different methods: competency questions, data driven, metric based and user evaluation. Results from the evaluation of ontology show that the amount of text to read is significantly reduced as the users have to only read selected text that ranged from 1% to 30% of a privacy policy. The amount of text depended on the query and its associated keywords. This signifies that the time required to read a policy is significantly reduced as the ontology directs user to the right content for a query. This finding was also confirmed by the results of the user study session. The results from the user study session indicated that the users found ontology helpful in finding relevant selected sentences to read as compared to reading the entire policy.

Two-tiered sensor networks, where higher-powered relay nodes are used as cluster heads, have been proposed recently for designing sensor networks. Assigning sensor nodes to clusters, in an energy efficient way, is known to improve the lifetime of such networks. In this paper we have proposed an efficient distributed algorithm for assigning sensor nodes to clusters in two-tiered networks, using both single-hop and multi- hop routing schemes. Our distributed clustering strategy allocates sensor nodes to clusters, based on limited local information only. However, the solutions generated are shown to be comparable to optimal solutions obtained using an ILP formulation. We have also compared our approach to a number of existing heuristics recently proposed in the literature and have shown, through simulations, that our approach consistently outperforms current heuristics. In summary, the quality of the solutions obtained using our approach is comparable to those obtained using an ILP formulation, but the solutions can be generated very quickly, making it suitable for practical-sized networks with hundreds of sensor nodes.