LB-OPAR: Load Balanced Optimized Predictive and Adaptive Routing for Cooperative UAV Networks

TL;DR

This paper introduces LB-OPAR, a load-balanced, adaptive routing algorithm for SDN-based cooperative UAV networks, significantly improving throughput, success rate, and flow completion time in highly dynamic environments.

Contribution

It extends the existing OPAR algorithm to include load balancing and adaptability, providing an efficient, analytical solution for routing in dynamic UAV networks.

Findings

LB-OPAR improves flow completion time by 20%.

LB-OPAR increases flow success rate by 30%.

LB-OPAR achieves up to 400% higher throughput.

Abstract

Cooperative ad-hoc UAV networks have been turning into the primary solution set for situations where establishing a communication infrastructure is not feasible. Search-and-rescue after a disaster and intelligence, surveillance, and reconnaissance (ISR) are two examples where the UAV nodes need to send their collected data cooperatively into a central decision maker unit. Recently proposed SDN-based solutions show incredible performance in managing different aspects of such networks. Alas, the routing problem for the highly dynamic UAV networks has not been addressed adequately. An optimal, reliable, and adaptive routing algorithm compatible with the SDN design and highly dynamic nature of such networks is required to improve the network performance. This paper proposes a load-balanced optimized predictive and adaptive routing (LB-OPAR), an SDN-based routing solution for cooperative UAV networks. LB-OPAR is the extension of our recently published routing algorithm (OPAR) that balances the network load and optimizes the network performance in terms of throughput, success rate, and flow completion time (FCT). We analytically model the routing problem in highly dynamic UAV network and propose a lightweight algorithmic solution to find the optimal solution with $O(|E|^2)$ time complexity where $|E|$ is the total number of network links. We exhaustively evaluate the proposed algorithm's performance using ns-3 network simulator. Results show that LB-OPAR outperforms the benchmark algorithms by $20\%$ in FCT, by $30\%$ in flow success rate on average, and up to $400\%$ in throughput.