Minimizing the Cost of Mine Selection Via Sensor Networks

— In this paper, we study sensor enabled landmine networks by formulating a minimum-cost mine selection problem. The problem arises in a target defence scenario, where the objective is to destroy the intruding targets using the minimumcost pre-deployed mines. Due to the problem complexity, we first transform it using a novel bucket-tub model, and then propose several approximation algorithms. Among them, it is shown that the layering algorithm can achieve an approximation ratio of α · f, where α ≥ 1 is the tunable relaxation factor and f is the maximum number of mines that a target is associated with, and that the greedy algorithm has an approximation ratio of P j Rj, where Rj is the coefficient in the related integer program. We also present a localized greedy algorithm which is shown to produce the same solution set as the global greedy algorithm. Theoretical analysis and extensive simulations demonstrate the effectiveness of the proposed algorithms.

• Type: Conference
• Year: 2009
• Where: INFOCOM
• Authors: Changlei Liu, Guohong Cao
• Keywords: Approximation Ratio, Communications, Greedy Algorithm, INFOCOM 2009, Localized Greedy Algorithm