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Energy-Efficient UAV Replacement in Software-Defined UAV Networks

Mohammad Javad-Kalbasi, Shahrokh Valaee

TL;DR

This paper tackles energy-efficient UAV replacement in software-defined UAV networks by formulating a handover optimization problem based on a strict total ordering of UAVs and data flows. The core approach combines an integer linear program solved by the Gurobi optimizer to obtain optimal handover schedules with a novel dependency-based ordering, and a lightweight heuristic that preserves near-optimal performance with substantially reduced computation time. Through extensive simulations and Monte Carlo experiments, the authors demonstrate significant hovering-energy savings over random handovers, with the heuristic achieving energy close to the optimal while running in milliseconds versus the solver’s seconds. A computable lower bound on the best achievable latency is derived, underscoring the problem’s complexity and informing performance benchmarks. Overall, the work offers a scalable, practically applicable framework for maintaining continuous service in energy-constrained, SDN-controlled UAV networks.

Abstract

Unmanned Aerial Vehicles (UAVs) in networked environments face significant challenges due to energy constraints and limited battery life, which necessitate periodic replacements to maintain continuous operation. Efficiently managing the handover of data flows during these replacements is crucial to avoid disruptions in communication and to optimize energy consumption. This paper addresses the complex issue of energy-efficient UAV replacement in software-defined UAV network. We introduce a novel approach based on establishing a strict total ordering relation for UAVs and data flows, allowing us to formulate the problem as an integer linear program. By utilizing the Gurobi solver, we obtain optimal handover schedules for the tested problem instances. Additionally, we propose a heuristic algorithm that significantly reduces computational complexity while maintaining near-optimal performance. Through comprehensive simulations, we demonstrate that our heuristic offers practical and scalable solution, ensuring energy-efficient UAV replacement while minimizing network disruptions. Our results suggest that the proposed approach can enhance UAV battery life and improve overall network reliability in real-world applications.

Energy-Efficient UAV Replacement in Software-Defined UAV Networks

TL;DR

This paper tackles energy-efficient UAV replacement in software-defined UAV networks by formulating a handover optimization problem based on a strict total ordering of UAVs and data flows. The core approach combines an integer linear program solved by the Gurobi optimizer to obtain optimal handover schedules with a novel dependency-based ordering, and a lightweight heuristic that preserves near-optimal performance with substantially reduced computation time. Through extensive simulations and Monte Carlo experiments, the authors demonstrate significant hovering-energy savings over random handovers, with the heuristic achieving energy close to the optimal while running in milliseconds versus the solver’s seconds. A computable lower bound on the best achievable latency is derived, underscoring the problem’s complexity and informing performance benchmarks. Overall, the work offers a scalable, practically applicable framework for maintaining continuous service in energy-constrained, SDN-controlled UAV networks.

Abstract

Unmanned Aerial Vehicles (UAVs) in networked environments face significant challenges due to energy constraints and limited battery life, which necessitate periodic replacements to maintain continuous operation. Efficiently managing the handover of data flows during these replacements is crucial to avoid disruptions in communication and to optimize energy consumption. This paper addresses the complex issue of energy-efficient UAV replacement in software-defined UAV network. We introduce a novel approach based on establishing a strict total ordering relation for UAVs and data flows, allowing us to formulate the problem as an integer linear program. By utilizing the Gurobi solver, we obtain optimal handover schedules for the tested problem instances. Additionally, we propose a heuristic algorithm that significantly reduces computational complexity while maintaining near-optimal performance. Through comprehensive simulations, we demonstrate that our heuristic offers practical and scalable solution, ensuring energy-efficient UAV replacement while minimizing network disruptions. Our results suggest that the proposed approach can enhance UAV battery life and improve overall network reliability in real-world applications.

Paper Structure

This paper contains 11 sections, 1 theorem, 3 equations, 9 figures, 1 algorithm.

Table of Contents

  1. introduction
  2. Related work
  3. Motivation
  4. Contribution
  5. Problem Statement
  6. Problem Formulation
  7. Proposed Heuristic Algorithm
  8. Numerical Results
  9. Simulation Setup
  10. Performance Evaluation
  11. Conclusion

Key Result

Proposition 1

Algorithm 1 has a time complexity of $\mathcal{O}\left(n\log(n)+\sum\limits_{i=1}^{n}|\Delta_{i}|+\sum\limits_{j=1}^{m}|\Lambda_{j}|\right)$.

Figures (9)

  • Figure 1: Illustration of a data flow handover in the UAV network.
  • Figure 2: A software-defined UAV network.
  • Figure 4: Dependency graph of network given in Fig. \ref{['statement']}.
  • Figure 5: Optimal strict total ordering relation for the sample problem given in Fig. \ref{['statement']} and induced hierarchy $F_{4}\rightarrow~U_{5} \rightarrow~F_{1} \rightarrow U_{2} \rightarrow F_{3} \rightarrow U_{3} \rightarrow F_{2} \rightarrow U_{4} \rightarrow U_{1}$ shown in red dashed arrows.
  • Figure 6: Hovering energy consumption versus $m$.
  • ...and 4 more figures

Theorems & Definitions (5)

  • Definition 1
  • Definition 2
  • Definition 3
  • Definition 4
  • Proposition 1