Optimizing Age-of-Information in Piggyback Networks with Recurrent Data Generation

TL;DR

This work tackles minimizing AoI in Store-Carry-Forward Piggyback networks by routing patrolling drones to repeatedly collect data from dispersed IoT nodes. It proves the decision version of the MAI-constrained routing problem is NP-Complete and presents two approximation strategies, Shortest Round Trip Time (SRTT) and Edge Enforcement (Enforced), each with a 1.5-approximation bound, plus a hybrid method that leverages a state-of-the-art TSP solver. The methods are evaluated on synthetic 8-node and 20-node scenarios, showing that Enforced and LKH frequently approach optimal MAI (near 1.15–1.2x) while DP remains impractical for larger instances; the hybrid approach often achieves optimal MAI in many cases within reasonable computation times. The results highlight a practical path to maintaining fresh data in IoT collections via UAV-enabled patrolling routes, with implications for real-time planning and scalable drone-enabled data gathering.

Abstract

Age-of-information (AoI) is a critical metric that quantifies the freshness of data in communication systems. In the era of the Internet of Things (IoT), data collected by resource-constrained devices often need to be transmitted to a central server to extract valuable insights in a timely manner. However, maintaining a stable and direct connection between a vast number of IoT devices and servers is often impractical. The Store-Carry-Forward (SCF) communication paradigm, such as Piggyback networks, offers a viable solution to address the data collection and transmission challenges in distributed IoT systems by leveraging the mobility of mobile nodes. In this work, we investigate AoI within the context of patrolling data collection drones, where data packets are generated recurrently at devices and collected by a patrolling drone to be delivered to a server. Our objective is to design a patrolling route that minimizes the Maximum Age-of-Information (MAI) across the system. We demonstrate that determining whether a route with an MAI below a certain threshold can be constructed is NP-Complete. To address this challenge, we propose two approaches with approximation guarantees. Our evaluation results show that the proposed approaches can achieve near-optimal routes in reasonable time across various scenarios

Figures (6)

• Figure 1: An example of a patrolling data collection drone system with a server $v_0$ and three IoT devices $v_1$, $v_2$, and $v_3$. The IoT devices recurrently generate data, which the drone collects upon visiting each device. The drone then transports the collected data back to the server for processing.
• Figure 2: A subfigure of the graph $G$ used for the tightness analysis of the proposed approaches. The minimum spanning tree of $G$ is shown in solid line. In this example, $t_{0,1} = t_{0,2} = t_{1,2} = 1$ and $t_{1,3} = t_{2,3} = 100$
• Figure 3: An example where the shortest route has a larger MAI compared to a route with a longer round trip time.
• Figure 4: MAIs of the routes generated by different approaches in the 8-node scenarios, normalized to the optimal solution
• Figure 5: MAIs of the routes generated by different approaches in the 20-node scenarios, normalized to the optimal solution.

Theorems & Definitions (12)

• Lemma 1
• proof
• Theorem 2
• proof
• Lemma 3
• proof
• Theorem 4
• proof
• Theorem 5
• proof