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Controlling the handover mechanism in wireless mobile nodes using game theory
This paper proposes a novel network selection mechanism as an extension
to the FMIPv6 [2] protocol, which improves handover latency in the MIPv6 [1] in
the case where the Mobile Nodes (MN) have a single wireless interface with multiple
Care-of-Addresses (CoAâs). Moreover, this paper proposes a novel interface/network
selection mechanism, which is an extension to the MFMIPv6 [5], which work when
the mobile node has more than one wireless interface. Generally, the previous access
router (PAR) in the FMIPv6 protocol forwards all the arrived packets to the new
access router (NAR) by setting up a tunnel to it in order to prevent packet losses
incurred by latency during handover procedure. However, there is no protocol which
offers the user and/or the application to dynamically choose the right NAR (i.e. the
one offers a better service). Whatâs more, one of the main objectives of the next
generation networks will be heterogeneity in the wireless access environment in
which a mobile terminal will be able to connect to multiple radio networks
simultaneously. For these reasons, network selection and efficient load balancing
mechanisms among different networks will be required to achieve high-speed
connectivity with seamless mobility. To this end; Game Theory [3], naturally
becomes a useful and powerful tool to research this kind of problems. Game theory
is a mathematical tool developed to understand competitive situations in which
rational decision makers interact to achieve their objectives. The mechanism
improves the handover latency, the user ability to choose the right interface/network
and controls when to force the MN to make the handover
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
A Framework for the Flexible Integration of a Class of Decision Procedures into Theorem Provers
The role of decision procedures is often essential in theorem proving. Decision procedures can reduce the search space of heuristic components of a prover and increase its abilities. However, in some applications only a small number of conjectures fall within the scope of the available decision procedures. Some of these conjectures could in an informal sense fall âjust outsideâ that scope. In these situations a problem arises because lemmas have to be invoked or the decision procedure has to communicate with the heuristic component of a theorem prover. This problem is also related to the general problem of how to exibly integrate decision procedures into heuristic theorem provers. In this paper we address such problems and describe a framework for the exible integration of decision procedures into other proof methods. The proposed framework can be used in different theorem provers, for different theories and for different decision procedures. New decision procedures can be simply âplugged-inâ to the system. As an illustration, we describe an instantiation of this framework within the Clam proof-planning system, to which it is well suited. We report on some results using this implementation
A Duality Procedure to Elicit Nonlinear Multiattribute Utility Functions.
The practical implementation of the Multiattribute Utility Theory is limited, partly for the lack of operative methods to elicit the parameters of the Multiattribute Utility Function, particularly when this function is not linear. As a consequence, most studies are restricted to linear specifications, which are easier to estimate and to interpret. We propose an indirect method to elicit the parameters of a nonlinear utility function to be compatible with the actual behaviour of decision makers, rather than with their answers to direct surveys. The idea rests on approaching the parameter estimation problem as a dual of the decision problem and making the observed decisions to be compatible with a rational decision making process.Multiple-Criteria Analysis, Multi-Attribute Utility Function, Simulation, Agriculture.
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
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