We focus on how to jam UAVs network efficiently. The system model is described and the problem is formulated. Based on two properties and a theorem which helps to decide good location for a jammer, we present the Triangle method to find good locations for jammers. The Triangle method is easy to understand and has overall computational complexity of ON2. We also present a genetic algorithm-(GA-) based jamming method, which has computational complex of OLMN2. New chromosome, mutation, and crossover operations are redefined for the GA method. The simulation shows that Triangle and GA methods perform better than Random method. If the ratio of jammers' number to UAVs' number is low (lower than 1/5 in this paper), GA method does better than Triangle method. Otherwise, Triangle method performs better.
Zhang, Y., & Yang, L. (2014). Triangle and GA methods for UAVs jamming. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/713430