A Networked Multi-Agent System for Mobile Wireless Infrastructure on Demand

TL;DR

The results demonstrate that the system effectively offers mobile wireless infrastructure on demand, extending the operational range of task agents and supporting complex mobility patterns, all while ensuring connectivity and being resilient to agent failures.

Abstract

Despite the prevalence of wireless connectivity in urban areas around the globe, there remain numerous and diverse situations where connectivity is insufficient or unavailable. To address this, we introduce mobile wireless infrastructure on demand, a system of UAVs that can be rapidly deployed to establish an ad-hoc wireless network. This network has the capability of reconfiguring itself dynamically to satisfy and maintain the required quality of communication. The system optimizes the positions of the UAVs and the routing of data flows throughout the network to achieve this quality of service (QoS). By these means, task agents using the network simply request a desired QoS, and the system adapts accordingly while allowing them to move freely. We have validated this system both in simulation and in real-world experiments. The results demonstrate that our system effectively offers mobile wireless infrastructure on demand, extending the operational range of task agents and supporting complex mobility patterns, all while ensuring connectivity and being resilient to agent failures.

Figures (15)

• Figure 1: Mobile wireless infrastructure on demand. A team of UAVs acting as network providers continuously reconfigure their positions and communication routes, provisioning task users with their required quality of service in terms of wireless connectivity.
• Figure 2: Task agents (red) demand a level of connectivity to the MID system, provisioned by the network agents (blue).
• Figure 3: Characterization of the expected rate function $R(x_i,x_j)$ and one standard deviation following model equations \ref{['eq:barr']} and \ref{['eq:tilder']}, where $d=\|x_i-x_j\|$ is the distance between the agents $x_i$ and $x_j$, and $P_{L_0}=-53~\text{dBm}$, $n=2.52$, $P_{N_0}=-70~\text{dBm}$, $a=0.2$ and $b=0.6$.
• Figure 4: Illustrative example of the robust routing solution obtained at the source by solving \ref{['eq:maxs']}. A low probability of error requires less variance on the transmission rate, which is achieved by splitting data between the direct link and the relays. On the other hand, a high margin requires all the data to be transmitted to the relays, which present the higher channel gain.
• Figure 5: The high-level control loop uses the current positions of network and task agents to estimate the channel rates. Then the network planner selects the optimal routing strategy according to these rates, and the connectivity planner provides trajectories of the network agents to ensure a cohesive communication network.