Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems

TL;DR

The paper addresses efficient, high-rate distributed control over wireless multi-hop networks by introducing control-guided communication, a co-design that lets the control system predict future network demands and the wireless layer adaptively allocate or shut down resources. By embedding self-triggered control decisions into network scheduling, the approach achieves energy savings and resource reallocation while maintaining fast update intervals, demonstrated on a real testbed with five cart-pole systems. The key contributions include a concrete co-design framework, a time-triggered low-power wireless protocol with an online scheduler, and a distributed self-triggered control strategy that enables reallocation of freed bandwidth to additional traffic. The results show up to 87% energy savings and update rates in the tens of milliseconds, highlighting practical impact for scalable cyber-physical systems and multi-robot collaborations.

Abstract

In future autonomous systems, wireless multi-hop communication is key to enable collaboration among distributed agents at low cost and high flexibility. When many agents need to transmit information over the same wireless network, communication becomes a shared and contested resource. Event-triggered and self-triggered control account for this by transmitting data only when needed, enabling significant energy savings. However, a solution that brings those benefits to multi-hop networks and can reallocate freed up bandwidth to additional agents or data sources is still missing. To fill this gap, we propose control-guided communication, a novel co-design approach for distributed self-triggered control over wireless multi-hop networks. The control system informs the communication system of its transmission demands ahead of time, and the communication system allocates resources accordingly. Experiments on a cyber-physical testbed show that multiple cart-poles can be synchronized over wireless, while serving other traffic when resources are available, or saving energy. These experiments are the first to demonstrate and evaluate distributed self-triggered control over low-power multi-hop wireless networks at update rates of tens of milliseconds.

Figures (5)

• Figure 1: We consider multiple physical systems connected over a wireless multi-hop network. Each system is associated with a self trigger that computes at the current communication instant when it needs to communicate next. This information is piggybacked onto the message it sends. The network manager uses this information to compute a communication schedule respecting these demands and, if possible, reallocating bandwidth to additional data sources.
• Figure 2: Time-triggered operation of the multi-hop low-power wireless protocol. Communication occurs in rounds with a constant period $T$. Each round consists of a schedule slot and up to $K$ data slots. The schedule slot serves to inform all nodes of the number of subsequent data slots in the round and the allocation of control or other messages to the scheduled data slots.
• Figure 3: Cyber-physical testbed with 15 wireless DPP nodes and five cart-pole systems (A and B are real systems; C, D, and E are simulated systems). The network has a diameter of three hops. Node 10 is the network manager.
• Figure 4: Control performance and bandwidth utilization over time, recorded during one of our experiments. The scheduling policy described in Sec. \ref{['sec:protocol']} is used but applications can also specify any other policy. Each vertical line in the lower figure represents a communication round. The control traffic demands vary over time between 0 and 4 slots. One slot is always used for other traffic and the remaining free slots are shut down to save communication energy.
• Figure 5: Trade-off between control performance, communication energy efficiency, and flexibility in serving other traffic for different fractions of control traffic, reported in terms of the median and 25th/75th percentiles. Control performance decreases when less bandwidth is used for control traffic. Conversely, freed resources that are not needed for control traffic result in considerable communication energy savings or allow to serve other traffic (e.g., status, sensors).