Abstract-This paper describes a method of implementing two factor authentication using mobile phones. The proposed method guarantees that authenticating to services, such as online banking or ATM machines, is done in a very secure manner. The proposed system involves using a mobile phone as a software token for One Time Password generation. The generated One Time Password is valid for only a short userdefined period of time and is generated by factors that are unique to both, the user and the mobile device itself. Additionally, an SMS-based mechanism is implemented as both a backup mechanism for retrieving the password and as a possible mean of synchronization. The proposed method has been implemented and tested. Initial results show the success of the proposed method.
The Employee Timetabling Problem (ETP) is concerned with assigning a number of employees into a given set of shifts over a fixed period of time while meeting the employee's preferences and organizational work regulations. The problem also attempts to optimize the performance criteria and distribute the shifts equally among the employees. The problem is known to be a complex optimization problem. It has received intensive research during the past few years given its common use in industries and organizations. Several formulations and algorithms based on incomplete search approaches have been proposed to solve employee timetabling problems. In this paper, we propose a complete search approach using Boolean satisfiability (SAT) and integer linear programming (ILP) to solve these problems. The 0-1 ILP model of interest is developed and solved using advanced SAT and ILP solvers. A tool has also been developed to automate the process of producing and solving the ILP model. Experimental results indicate that the proposed approach can effectively handle employee timetabling problems.
Improvements over recent years in the performance of Integer Linear Programming (ILP) and Boolean Satisfiability (SAT) solvers have encouraged the modeling of complex engineering problems as ILP. An example is the Clustering Problem in Mobile Ad-Hoc Networks (MANETs). The Clustering Problem in MANETs consists of selecting the most suitable nodes of a given MANET topology as clusterheads, and ensuring that regular nodes are connected to clusterheads such that the lifetime of the network is maximized. This paper proposes enhanced ILP formulations for the Clustering Problem, through the enablement of multi-hop connections and intra-cluster communication, and assesses the performance of state-of-the art generic ILP and SAT solvers in solving the enhanced formulations.Index Terms -Integer Linear Programming, Boolean Satisfiability, Mobile Ad-Hoc Networks, Clustering Problem, Optimization. INTRODUCTIONThe recent introduction of intelligent algorithms in Integer Linear Programming (ILP) and Boolean Satisfiability (SAT) solvers significantly improved the performance of the solvers and allowed for a wide range of challenging engineering problems to be tackled by ILP and SAT. Generic-based ILP solvers have been successfully applied to solve several networking optimization problems; however, fewer attempts have been made using SAT solvers. One such problem is the clustering problem in Mobile Ad-Hoc Networks (MANETs). MANETs are used in a wide-range of applications such as battlefield communication, law enforcement operations, and disaster recovery [1]. The proposed solution to the scalability issue in flat MANET networks is the concept of clustering. Clustering involves the creation of a hierarchical network where the network is divided into clusters, with certain nodes in each cluster being chosen to be clusterheads. The process of establishing and interconnecting clusters, through the selection of clusterheads and connection of regular nodes to clusterheads is known as the clustering problem. The clustering problem can be modeled as an ILP optimization problem. The primary objective of this paper is to present enhancements to the ILP formulation of the clustering problem in MANETs presented in [2]. These enhancements include ILP formulations enabling multihop connections and intra-cluster communication, allowing for more complex network topologies to be generated through ILP. Additionally, this paper presents an evaluation of the performance of the state-of-the-art generic-based and 0-1 SAT-based ILP solvers in handling the proposed enhancements in the ILP formulation of the clustering problem.
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