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Instant AoI Optimization through Relay Location Selection in Disaster Multi-hop Communication

Yang Gao, Zezhi Zeng

TL;DR

The paper tackles AoI optimization in a post-disaster, UAV-relayed two-hop IoT network. It develops an end-to-end instant AoI formulation and derives a closed-form relay location that minimizes AoI by balancing the two hop transmission times, showing that the optimum occurs at $t_1=t_2$ under $P_{relay}>P_{node}$ and equal noises, with a minimum AoI of $\min \Delta(T) = \frac{5}{2}t_2$. The key contributions include analytic expressions for network delay in the two regimes ($t_1<t_2$ and $t_1>t_2$), a formal AoI formulation and proof that $t_1=t_2$ minimizes AoI lower bound, and a noise-sensitive generalization yielding explicit optimal relay positions $l = \frac{d}{1+\sqrt{\beta}}$ (equal noises) or $l = \frac{d}{1+\sqrt{\beta \frac{N_1}{N_2}}}$ (unequal noises). Validation via MATLAB simulations confirms the theory: the relay located at the midpoint in the time domain minimizes both delay and AoI, with performance degrading when the relay moves away from this point and shifting of the optimal location under asymmetric noise. This work provides practical guidance for UAV relay deployment to ensure timely information delivery in disaster response scenarios, with potential extension to multi-hop networks.

Abstract

Meteorological disasters such as typhoons, forest fires, and floods can damage the communication infrastructures, which will further disable the communication capabilities of cellular networks. The multi-hop wireless communication based on IoT devices (e.g., rescue robots, UAVs, and mobile devices) becomes an available and rapidly deployable communication approach for search and rescue operations. However, Age of Information (AoI), an emerging network performance metric, has not been comprehensively investigated in this multi-hop model. In this paper, we first construct a UAV-relayed wireless network model and formulate the end-to-end instant AoI. Then we derive the optimal location of the relay UAV to achieve the minimum instant AoI by mathematical analysis. Simulations show that the derived relay location can always guarantee the optimal AoI and outperform other schemes.

Instant AoI Optimization through Relay Location Selection in Disaster Multi-hop Communication

TL;DR

The paper tackles AoI optimization in a post-disaster, UAV-relayed two-hop IoT network. It develops an end-to-end instant AoI formulation and derives a closed-form relay location that minimizes AoI by balancing the two hop transmission times, showing that the optimum occurs at under and equal noises, with a minimum AoI of . The key contributions include analytic expressions for network delay in the two regimes ( and ), a formal AoI formulation and proof that minimizes AoI lower bound, and a noise-sensitive generalization yielding explicit optimal relay positions (equal noises) or (unequal noises). Validation via MATLAB simulations confirms the theory: the relay located at the midpoint in the time domain minimizes both delay and AoI, with performance degrading when the relay moves away from this point and shifting of the optimal location under asymmetric noise. This work provides practical guidance for UAV relay deployment to ensure timely information delivery in disaster response scenarios, with potential extension to multi-hop networks.

Abstract

Meteorological disasters such as typhoons, forest fires, and floods can damage the communication infrastructures, which will further disable the communication capabilities of cellular networks. The multi-hop wireless communication based on IoT devices (e.g., rescue robots, UAVs, and mobile devices) becomes an available and rapidly deployable communication approach for search and rescue operations. However, Age of Information (AoI), an emerging network performance metric, has not been comprehensively investigated in this multi-hop model. In this paper, we first construct a UAV-relayed wireless network model and formulate the end-to-end instant AoI. Then we derive the optimal location of the relay UAV to achieve the minimum instant AoI by mathematical analysis. Simulations show that the derived relay location can always guarantee the optimal AoI and outperform other schemes.
Paper Structure (19 sections, 1 theorem, 35 equations, 10 figures, 1 table)

This paper contains 19 sections, 1 theorem, 35 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Internet of Things (IoTs)
  4. Multi-hop Communication
  5. Age of Information (AoI)
  6. System Modeling
  7. Communication Model
  8. Network Delay
  9. AoI Formulation
  10. Mathematical Analysis
  11. Network Delay
  12. Case $1$: $t_1<t_2$
  13. Case $2$: $t_1>t_2$
  14. Instant AoI
  15. Effect of Noise on the Optimal Location
  16. ...and 4 more sections

Key Result

Theorem 1

When the transmission signal power of the relay is greater than that of the user nodes (i.e, source node, destination node), the average instantaneous AoI of the receiver reaches its minimum when $t_1=t_2$, is

Figures (10)

  • Figure 1: System model for multi-hop wireless communication
  • Figure 2: An example of varying AoI at the receiving end
  • Figure 3: Typical System Model for multi-hop Communication.
  • Figure 4: The 1st packet reaches the destination node.
  • Figure 5: AoI respect to $t_1<t_2$.
  • ...and 5 more figures

Theorems & Definitions (2)

  • Theorem 1: $t_1=t_2\in\operatornamewithlimits{arg\,min}_{t_1,t_2} \Delta(T)$, concerning $P_{node}<P_{relay}$, under the same noise $N_1=N_2=N$, where $N_1$ is the noise between the source and the relay, and $N_2$ is the noise between the relay and the destination
  • proof