Solving the Generalized Vertex Cover Problem by Genetic Algorithm

In this paper an evolutionary approach to solving the generalized vertex cover problem (GVCP) is presented. Binary representation and standard genetic operators are used along with the appropriate objective function. The experiments were carried out on randomly generated instances with up to 500 vertices and 100000 edges. Performance of the genetic algorithm (GA) is compared with CPLEX solver and 2-approximation algorithm based on LP relaxation. The genetic algorithm outperformed both CPLEX solver and 2-approximation heuristic

Optimizing Mixing in Pervasive Networks: A Graph-Theoretic Perspective

One major concern in pervasive wireless applications is location privacy, where malicious eavesdroppers, based on static device identifiers, can continuously track users. As a commonly adopted countermeasure to prevent such identifier-based tracking, devices regularly and simultaneously change their identifiers in special areas called mix-zones. Although mix-zones provide spatio-temporal de-correlations between old and new identifiers, pseudonym changes, depending on the position of the mix-zone, can incur a substantial cost on the network due to lost communications and additional resources such as energy. In this paper, we address this trade-off by studying the problem of determining an optimal set of mix-zones such that the degree of mixing in the network is maximized, whereas the overall network-wide mixing cost is minimized. We follow a graph-theoretic approach and model the optimal mixing problem as a novel generalization of the vertex cover problem, called the Mix Cover (MC) problem. We propose three bounded-ratio approximation algorithms for the MC problem and validate them by an empirical evaluation of their performance on real data. The combinatorics-based approach followed here enables us to study the feasibility of determining optimal mix-zones regularly and under dynamic network conditions

Optimizing Mix-zone Coverage in Pervasive Wireless Networks

Location privacy is a major concern in pervasive networks where static device identifiers enable malicious eavesdroppers to continuously track users and their movements. In order to prevent such identifier-based tracking, devices could coordinate regular identifier change operations in special areas called mix-zones. Although mix-zones provide spatio-temporal de-correlation between old and new identifiers, depending on the position of the mix-zone, identifier changes can generate a substantial inconvenience (or ``cost") to the users in terms of lost communications and increased energy consumption. In this paper, we address this trade-off between privacy and cost by studying the problem of determining an optimal set of mix-zones such that the degree of mixing in the network is maximized and the overall network-wide mixing cost is minimized. We follow a graph-theoretic approach and model the optimal mixing problem as a novel generalization of the vertex cover problem, called the \textit{Mix Cover (MC)} problem. We propose three approximation algorithms for the MC problem and derive a lower bound on the solution quality guaranteed by them. We also outline two other heuristics for solving the MC problem, which are simple but do not provide any guarantees on the solution quality. By means of extensive empirical evaluation using real data, we compare the performance and solution quality of these algorithms. The combinatorics-based approach used in this work enables us to study the feasibility of determining optimal mix-zones regularly and under dynamic network conditions