As increasing functionality in mobile devices leads to rapid battery drain, energy management has gained increasing importance. However, differences in user’s usage contexts and patterns can be leveraged for saving energy. On the other hand, the increasing sensitivity of users’ data, coupled with the need to ensure security in an energy-aware manner, demands careful analyses of trade-offs between energy and security. The research described in this thesis addresses this challenge by 1)modeling the problem of context-adaptive energy-aware security as a combinatorial optimization problem (Context-Sec); 2) proving that the decision version of this problem is NP-Complete, via a reduction from a variant of the well-known Knapsack problem; 3) developing three different algorithms to solve a related offline version of Context-Sec; and 4) implementing tests and compares the performance of the above three algorithms with data-sets derived from real-world smart-phones on wireless networks. The first algorithm presented is a pseudo-polynomial dynamic programming (DP)algorithm that computes an allocation with optimal user benefit using recurrence of the relations; the second algorithm is a greedy heuristic for allocation of security levels based on user benefit per unit of power consumption for each level; and the third algorithm is a Fully Polynomial Time Approximation Scheme (FPTAS) which has a polynomial time execution complexity as opposed to the pseudo-polynomialDP based approach. To the best of the researcher’s knowledge, this is the first work focused on modeling, design, implementation and experimental performance.
| Identifer | oai:union.ndltd.org:unf.edu/oai:digitalcommons.unf.edu:etd-1920 |
| Date | 01 January 2019 |
| Creators | Singh, Preeti |
| Publisher | UNF Digital Commons |
| Source Sets | University of North Florida |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | UNF Graduate Theses and Dissertations |
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