As Machine Learning (ML) becomes integral to Cyber-Physical Systems (CPS), there is growing interest in shifting training from traditional cloud-based to on-device processing (TinyML), for example, due to privacy and latency concerns. However, CPS often comprise ultra-low-power microcontrollers, whose limited compute resources make training challenging. This article presents RockNet , a new TinyML method tailored for ultra-low-power hardware that achieves state-of-the-art accuracy in timeseries classification, such as fault or malware detection, without requiring offline pretraining. By leveraging that CPS consist of multiple devices, we design a distributed learning method that integrates ML and wireless communication. RockNet leverages all devices for distributed training of specialized compute efficient classifiers that need minimal communication overhead for parallelization. Combined with tailored and efficient wireless multi-hop communication protocols, our approach overcomes the communication bottleneck that often occurs in distributed learning. Hardware experiments on a testbed with 20 ultra-low-power devices demonstrate RockNet ’s effectiveness. It successfully learns timeseries classification tasks from scratch, surpassing the accuracy of the latest approach for neural network microcontroller training by up to 2x. RockNet ’s distributed ML architecture reduces memory, latency and energy consumption per device by up to 90% when scaling from one central device to 20 devices. Our results show that a tight integration of distributed ML, distributed computing, and communication enables, for the first time, training on ultra-low-power hardware with state-of-the-art accuracy.

Low-power wireless networks have the potential to enable applications that are of great importance to industry and society. However, existing network protocols do not meet the dependability requirements of many scenarios as the failure of a single node or link can completely disrupt communication and take significant time and energy to recover. This paper presents Hydra, a low-power wireless protocol that guarantees robust communication despite arbitrary node and link failures. Unlike most existing deterministic protocols, Hydra steers clear of centralized coordination to avoid a single point of failure. Instead, all nodes are equivalent in terms of protocol logic and configuration, performing coordination tasks such as synchronization and scheduling concurrently. This concept of concurrent coordination relies on a novel distributed consensus algorithm that yields provably unique decisions with low delay and energy overhead. In addition to a theoretical analysis, we evaluate Hydra in a multi-hop network of 23 nodes. Our experiments demonstrate that Hydra withstands random node failures without increasing coordination overhead and that it re-establishes efficient and reliable data exchange within seconds after a major disruption.

An important class of cyber-physical systems relies on multiple agents that jointly perform a task by coordinating their actions over a wireless network. Examples include self-driving cars in intelligent transportation and production robots in smart manufacturing. However, the scalability of existing control-over-wireless solutions is limited as they cannot resolve overload situations in which the communication demand exceeds the available bandwidth. This article presents a novel co-design of distributed control and wireless communication that overcomes this limitation by dynamically allocating the available bandwidth to agents with the greatest need to communicate. Experiments on a real cyber-physical testbed with 20 agents, each consisting of a low-power wireless embedded device and a cart-pole system, demonstrate that our solution achieves significantly better control performance under overload than the state of the art. We further prove that our co-design guarantees closed-loop stability for physical systems with stochastic linear time-invariant dynamics.

Smart manufacturing aims to overcome the limitations of today's rigid assembly lines by making the material flow and manufacturing process more flexible, versatile, and scalable. The main economic drivers are higher resource and cost efficiency as the manufacturers can more quickly adapt to changing market needs and also increase the lifespan of their production sites. The ability to close feedback loops fast and reliably over long distances among mobile robots, remote sensors, and human operators is a key enabler for smart manufacturing. Thus, this article provides a perspective on control and coordination over wireless networks. Based on an analysis of real-world use cases, we identify the main technical challenges that need to be solved to close the large gap between the current state of the art in industry and the vision of smart manufacturing. We discuss to what extent existing control-over-wireless solutions in the literature address those challenges, including our own approach toward a tight integration of control and wireless communication. In addition to a theoretical analysis of closed-loop stability, practical experiments on a cyber-physical testbed demonstrate that our approach supports relevant smart manufacturing scenarios. This article concludes with a discussion of open challenges and future research directions.

Closing feedback loops fast and over long distances is key to emerging cyber-physical applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless communication technology is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. Thus far, however, feedback control over multi-hop low-power wireless networks has only been demonstrated for update intervals on the order of seconds. To fill this gap, this article presents a wireless embedded system that supports dynamic mode changes and tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics in the presence of mode changes. Using experiments on a cyber-physical testbed with 20 wireless devices and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination with mode changes over multi-hop networks for update intervals of 20 to 50 milliseconds.

This abstract describes the first public demonstration of feedback control and coordination of multiple physical systems over a dynamic multi-hop low-power wireless network with update intervals of tens of milliseconds. Our running system can dynamically change between different sets of application tasks (e.g., sensing, actuation, control) executing on the spatially distributed embedded devices, while closed-loop stability is provably guaranteed even across those so-called mode changes. Moreover, any subset of the devices can move freely, which does not affect closed-loop stability and control performance as long as the wireless network remains connected.

We sketch our ongoing work toward the first design, implementation, and evaluation of a low-power embedded system providing reliable wireless feedback control of several distributed processes over multiple hops with update rates of 10 Hz or higher.

Many-to-all communication is a prerequisite for many applications and network services, including distributed control and data replication. However, current solutions do not meet the scalability and latency requirements of emerging applications. This paper presents Mixer, a many-to-all broadcast primitive for dynamic wireless mesh networks. Mixer integrates random linear network coding (RLNC) with synchronous transmissions and approaches the order-optimal scaling in the number of messages to be exchanged. To achieve an efficient operation in real networks, we design Mixer in response to the theory of RLNC and the characteristics of physical-layer capture. Our experiments demonstrate, for example, that Mixer outperforms the state of the art by up to 3.8x and provides a reliability greater than 99.99 % even at a node moving speed of 60 km/h.

Cyber-physical systems (CPS) use distributed feedback loops to control physical processes. Designing practical distributed CPS controllers often benefits from a logically centralized approach, where each node computes the control law locally based on global knowledge of the system state. We present Mixer, an all-to-all communication scheme that enables all nodes in a multi-hop low-power wireless network to exchange sizable packets with one another. Mixer's design integrates synchronous transmissions with random linear network coding, harnessing the broadcast nature of the wireless medium. Results from testbed experiments with an early Mixer prototype show that our design reduces latency by 1.1-2.6× for 16-96-byte packets compared with the state of the art, while providing a reliability above 99.9% in most settings we test.