Survivable Networks via UAV Swarms Guided by Decentralized Real-Time Evolutionary Computation

TL;DR

This paper presents a decentralized, real-time evolutionary algorithm-based UAV swarm approach to maintain communication in survivable networks, effectively adapting to dynamic ground agent movements.

Contribution

It introduces a novel decentralized, nature-inspired method combining swarm intelligence and evolutionary computation for network survivability.

Findings

Maintains communication despite complex ground agent movements.

Effective in scenarios with random walks and unpredictable conditions.

Demonstrates robustness of the decentralized GA approach.

Abstract

The survivable network concept refers to contexts where the wireless communication between ground agents needs to be maintained as much as possible at all times, regardless of any adverse conditions that may arise. In this paper we propose a nature-inspired approach to survivable networks, in which we bring together swarm intelligence and evolutionary computation. We use an on-line real-time Genetic Algorithm to optimize the movements of an UAV swarm towards maintaining communication between the ground agents. The proposed approach models the ground agents and the UAVs as boids-based swarms, and optimizes the movement of the UAVs using different instances of the GA running independently on each UAV. The UAV coordination mechanism is an implicit one, embedded in the fitness function of the Genetic Algorithm instances. The behaviors of the individual UAVs emerge into an aggregated optimization of the overall network survivability. The results show that the proposed approach is able to maintain satisfactory network survivability levels regardless of the ground agents' movements, including for cases as complex as random walks.