Roteamento para Estender o Tempo de Vida de Redes de Sensores através de SDN e Caminhos Disjuntos

ABSTRACTThe recent advances in the application of Software Defined Networks(SDN) concepts in Wireless Sensor Networks (WSNs) leads theimplementation of centralized algorithms. In this work, the SDNconcept is considered, together with two routing procedures, withthe purpose of disjoint paths generation, aiming to extend WSN’slifetime. Both approaches are based on Dijkstra algorithm for theselection of minimum paths. The first approach allocates distinctpaths for various flows towards the same destination. The secondone, penalizes computed paths using an adaptive cost function.Simulation results are performed using the Cooja sensor networksimulator

A solution method for a car fleet management problem with maintenance constraints

The problem retained for the ROADEF'99 international challenge was an inventory management problem for a car rental company. It consists in managing a given fleet of cars in order to satisfy requests from customers asking for some type of cars for a given time period. When requests exceed the stock of available cars, the company can either offer better cars than those requested, subcontract some requests to other providers, or buy new cars to enlarge the available stock. Moreover, the cars have to go through a maintenance process at a regular basis, and there is a limited number of workers that are available to perform these maintenances. The problem of satisfying all customer requests at minimum cost is known to be NP-hard. We propose a solution technique that combines two tabu search procedures with algorithms for the shortest path, the graph coloring and the maximum weighted independent set problems. Tests on benchmark instances used for the ROADEF'99 challenge give evidence that the proposed algorithm outperforms all other existing methods (thirteen competitors took part to this contest

An Application for Improved Bee Colony Algorithm on A Multi-Objective Real-World Optimization Problem

This paper presents an application of improved beecolony algorithm for multi-objective optimization (IBMO) on areal-world optimization problem known as welded beam design.IBMO is founded on the principle of single objective artificial beecolony algorithm (ABC). It also combines the nondominatedsorting strategy of NSGAII and classical multi-objectiveoptimization procedures such as Pareto-Dominance, crowdingdistance, external archive, and etc. Furthermore, IBMO has animprovement method to accelerate the convergence byconsidering the number of function evaluations. By using severalbenchmark problems, the running consistency and robustness ofIBMO has been reported in a previous study of authors. In thisstudy, IBMO determines the parameters of welded beamengineering problem which has several constraints and twoobjectives: (1) minimum cost and (2) minimum end deflection.The experimental results are compared with two algorithms. Theresults clearly show that IBMO reaches the better results easilyand it is a capable tool to solve multi-objective real worldoptimization problem

Pushing the envelope of Optimization Modulo Theories with Linear-Arithmetic Cost Functions

In the last decade we have witnessed an impressive progress in the expressiveness and efficiency of Satisfiability Modulo Theories (SMT) solving techniques. This has brought previously-intractable problems at the reach of state-of-the-art SMT solvers, in particular in the domain of SW and HW verification. Many SMT-encodable problems of interest, however, require also the capability of finding models that are optimal wrt. some cost functions. In previous work, namely "Optimization Modulo Theory with Linear Rational Cost Functions -- OMT(LAR U T )", we have leveraged SMT solving to handle the minimization of cost functions on linear arithmetic over the rationals, by means of a combination of SMT and LP minimization techniques. In this paper we push the envelope of our OMT approach along three directions: first, we extend it to work also with linear arithmetic on the mixed integer/rational domain, by means of a combination of SMT, LP and ILP minimization techniques; second, we develop a multi-objective version of OMT, so that to handle many cost functions simultaneously; third, we develop an incremental version of OMT, so that to exploit the incrementality of some OMT-encodable problems. An empirical evaluation performed on OMT-encoded verification problems demonstrates the usefulness and efficiency of these extensions.Comment: A slightly-shorter version of this paper is published at TACAS 2015 conferenc

A subsampling method for the computation of multivariate estimators with high breakdown point

All known robust location and scale estimators with high breakdown point for multivariate sample's are very expensive to compute. In practice, this computation has to be carried out using an approximate subsampling procedure. In this work we describe an alternative subsampling scheme, applicable to both the Stahel-Donoho estimator and the estimator based on the Minimum Volume Ellipsoid, with the property that the number of subsamples required is substantially reduced with respect to the standard subsampling procedures used in both cases. We also discuss some bias and variability properties of the estimator obtained from the proposed subsampling process

Optimization Modulo Theories with Linear Rational Costs

In the contexts of automated reasoning (AR) and formal verification (FV), important decision problems are effectively encoded into Satisfiability Modulo Theories (SMT). In the last decade efficient SMT solvers have been developed for several theories of practical interest (e.g., linear arithmetic, arrays, bit-vectors). Surprisingly, little work has been done to extend SMT to deal with optimization problems; in particular, we are not aware of any previous work on SMT solvers able to produce solutions which minimize cost functions over arithmetical variables. This is unfortunate, since some problems of interest require this functionality. In the work described in this paper we start filling this gap. We present and discuss two general procedures for leveraging SMT to handle the minimization of linear rational cost functions, combining SMT with standard minimization techniques. We have implemented the procedures within the MathSAT SMT solver. Due to the absence of competitors in the AR, FV and SMT domains, we have experimentally evaluated our implementation against state-of-the-art tools for the domain of linear generalized disjunctive programming (LGDP), which is closest in spirit to our domain, on sets of problems which have been previously proposed as benchmarks for the latter tools. The results show that our tool is very competitive with, and often outperforms, these tools on these problems, clearly demonstrating the potential of the approach.Comment: Submitted on january 2014 to ACM Transactions on Computational Logic, currently under revision. arXiv admin note: text overlap with arXiv:1202.140