Stochastic Second-Order Cone Programming in Mobile Ad-Hoc Networks: Sensitivity to Input Parameters

AbstractIn this paper sensitivity analysis is adopted in order to reveal the role of randomness of a stochastic second-order cone program (Maggioni et al., 2009) for mobile ad-hoc networks starting from the semidefinite stochastic locationaided routing (SLAR) model, described in Ariyawansa and Zhu (2006) and Zhu et al. (2011). The algorithm looks for a destination node and sets up a route by means of the expected zone, the region where the sender node expects to find the destination node and the requested zone defined by the sender node for spreading the route request to the destination node. The movements of the destination node are represented by ellipses scenarios, randomly generated by uniform and normal distributions in a neighborhood of the initial position of the destination node. Sensitivity analysis is performed by considering an increasing number of scenarios, different costs of flooding and latency penalty. Evaluation of Expected Value of Perfect Information EVPI and Value of Stochastic Solution VSS (Maggioni and Wallace, 2010; Birge, 1970) allows us to find the range of values in which it is convenient the deterministic versus the stochastic approach.

[1]  Monique Laurent,et al.  Semidefinite optimization , 2019, Graphs and Geometry.

[2]  Stein W. Wallace,et al.  Analyzing the quality of the expected value solution in stochastic programming , 2012, Ann. Oper. Res..

[3]  R. Wets,et al.  Stochastic programming , 1989 .

[4]  K. A. Ariyawansa,et al.  Stochastic semidefinite programming: a new paradigm for stochastic optimization , 2006, 4OR.

[5]  John M. Wilson,et al.  Introduction to Stochastic Programming , 1998, J. Oper. Res. Soc..

[6]  K. A. Ariyawansa,et al.  A preliminary set of applications leading to stochastic semidefinite programs and chance-constrained semidefinite programs , 2011 .

[7]  Marida Bertocchi,et al.  Sensitivity Analysis in Stochastic Second Order Cone Programming for Mobile Ad Hoc Networks , 2010 .

[8]  Nitin H. Vaidya,et al.  Location-aided routing (LAR) in mobile ad hoc networks , 1998, MobiCom '98.

[9]  Stein W. Wallace,et al.  Decision Making Under Uncertainty: Is Sensitivity Analysis of Any Use? , 2000, Oper. Res..

[10]  Etienne de Klerk,et al.  Aspects of Semidefinite Programming , 2002 .

[11]  Henry Wolkowicz,et al.  Handbook of Semidefinite Programming , 2000 .

[12]  George B. Dantzig,et al.  Linear Programming Under Uncertainty , 2004, Manag. Sci..

[13]  Donald Goldfarb,et al.  Second-order cone programming , 2003, Math. Program..

[14]  Alexander Shapiro,et al.  Stochastic programming approach to optimization under uncertainty , 2007, Math. Program..

[15]  Xiong Zhang,et al.  Solving Large-Scale Sparse Semidefinite Programs for Combinatorial Optimization , 1999, SIAM J. Optim..

[16]  Junshan Zhang,et al.  Location-aided routing with uncertainty in mobile ad hoc networks: A stochastic semidefinite programming approach , 2011, Math. Comput. Model..

[17]  Elisabetta Allevi,et al.  Stochastic Second-Order Cone Programming in Mobile Ad Hoc Networks , 2009 .

[18]  E. Beale ON MINIMIZING A CONVEX FUNCTION SUBJECT TO LINEAR INEQUALITIES , 1955 .

[19]  Michal Kaut,et al.  Evaluation of scenario-generation methods for stochastic programming , 2007 .