WiSwarm: Age-of-Information-based Wireless Networking for Collaborative Teams of UAVs (2212.03298v1)

Abstract: The Age-of-Information (AoI) metric has been widely studied in the theoretical communication networks and queuing systems literature. However, experimental evaluation of its applicability to complex real-world time-sensitive systems is largely lacking. In this work, we develop, implement, and evaluate an AoI-based application layer middleware that enables the customization of WiFi networks to the needs of time-sensitive applications. By controlling the storage and flow of information in the underlying WiFi network, our middleware can: (i) prevent packet collisions; (ii) discard stale packets that are no longer useful; and (iii) dynamically prioritize the transmission of the most relevant information. To demonstrate the benefits of our middleware, we implement a mobility tracking application using a swarm of UAVs communicating with a central controller via WiFi. Our experimental results show that, when compared to WiFi-UDP/WiFi-TCP, the middleware can improve information freshness by a factor of 109x/48x and tracking accuracy by a factor of 4x/6x, respectively. Most importantly, our results also show that the performance gains of our approach increase as the system scales and/or the traffic load increases.

• The paper presents an AoI-based middleware that customizes WiFi transmissions to prevent collisions and discard outdated UAV data.
• Experimental results demonstrated a 109x and 48x improvement in data freshness over UDP and TCP, with tracking error reduced by 4x.
• The approach scales multi-UAV systems effectively, bridging AoI theory with practical wireless solutions without altering lower network protocols.

Age-of-Information-Based Middleware for Wireless UAV Swarms

The paper "WiSwarm: Age-of-Information-based Wireless Networking for Collaborative Teams of UAVs," addresses a critical challenge in multi-agent systems: maintaining information freshness over wireless communication channels, specifically WiFi. The authors present a middleware solution that leverages the Age-of-Information (AoI) metric to enhance the performance of time-sensitive applications such as unmanned aerial vehicle (UAV) swarms used in search and rescue or mobility tracking.

Problem Context

In collaborative multi-agent systems, ensuring timely and up-to-date information is crucial for effective operation. The authors discuss the inadequacies of traditional WiFi networks in supporting such systems, especially at scale where latency and packet collisions become significant issues. WiFi suffers from performance degradation due to its distributed random access method, which leads to packet collisions and eventually results in stale data delivery as the number of transmitting sources increases.

Contribution and Approach

The principal contribution of the paper is the development of an AoI-based application layer middleware that customizes WiFi networks without altering the lower networking protocol layers. This middleware achieves:

1. Prevention of packet collisions.
2. Discarding of irrelevant and outdated packets.
3. Prioritization of essential data transmission.

The middleware operates by controlling the information flow within the network using techniques that mimic centralized resource allocation. It benefits from an innovative design that relies on the AoI metric, allowing applications to prioritize freshness over traditional packet delay metrics.

Experimental Analysis

The authors evaluate their middleware, termed WiSwarm, via both flight and stationary experiments. The setup includes up to five UAVs and fourteen Raspberry Pi devices emulating UAVs, demonstrating the middleware's robust performance under various conditions. Notable findings include:

• WiSwarm improved information freshness by factors of 109 and 48 when compared to UDP and TCP over WiFi, respectively.
• Tracking accuracy improved significantly, with error reduction by a factor of four.
• Scalability tests reveal that while baseline WiFi setups struggle with increasing numbers of UAVs (maximum support for two), WiSwarm comfortably manages up to five UAVs.

Theoretical and Practical Implications

Theoretically, this work bridges the gap between AoI theory and practical networking implementations for real-time applications. It validates the efficacy of AoI-centric middleware in real-world settings, showing that such designs can be effectively deployed to enhance system performance without requiring changes to the existing hardware or lower layers of the networking stack.

Practically, WiSwarm represents a substantial advancement for applications involving UAVs or similar multi-agent systems. Its ability to maintain data freshness enhances operational reliability and efficiency, making it suitable for time-critical applications like autonomous navigation and environmental monitoring.

Future Directions

Future research directions could explore extending this middleware to other communication technologies and further optimizing the polling and prioritization mechanisms to improve performance under varying network conditions. Additionally, integrating machine learning techniques for adaptive scheduling based on dynamic network conditions could further enhance the middleware's capabilities.

In conclusion, the authors effectively demonstrate how the AoI metric can be practically applied to enhance wireless communication within collaborative UAV systems. Their middleware reflects a significant step towards more responsive and reliable multi-agent systems, with promising potential for expanded application across various domains of autonomous systems and critical communication networks.