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.

Tiny battery-free devices running deep neural networks (DNNs) embody intermittent TinyML, a paradigm at the intersection of intermittent computing and deep learning, bringing sustainable intelligence to the extreme edge. This paper, as an overview of a special session at Embedded Systems Week (ESWEEK) 2025, presents four tales from diverse research backgrounds, sharing experiences in addressing unique challenges of efficient and reliable DNN inference despite the intermittent nature of ambient power. The first explores enhancing inference engines for efficient progress accumulation in hardware-accelerated intermittent inference and designing networks tailored for such execution. The second investigates computationally light, adaptive algorithms for faster, energy-efficient inference, and emerging computing-in-memory architectures for power failure resiliency. The third addresses battery-free networking, focusing on timely neighbor discovery and maintaining synchronization despite spatio-temporal energy dynamics across nodes. The fourth leverages modern nonvolatile memory fault behavior and DNN robustness to save energy without significant accuracy loss, with applicability to intermittent inference on nano-satellites. Collectively, these early efforts advance intermittent TinyML research and promote future cross-domain collaboration to tackle open challenges.

Public testbeds are essential for replicable experiments and meaningful comparisons on shared physical infrastructure. While many testbeds exist for battery-powered Internet of Things (IoT) systems, there is a lack of public testbeds for observing and profiling the distributed operation of energy-harvesting IoT systems, including battery-free devices. We fill this gap and present Shepherd Nova, the first public testbed designed to support experiments under repeatable energy-harvesting conditions. Shepherd Nova uses field-recorded harvesting data to supply power to devices, consistently replicating real-world spatio-temporal energy availability across multiple experiments. Its virtual power source supports diverse ambient energy sources, harvesting circuitry, and energy storage devices. Moreover, Shepherd Nova provides services like general-purpose input/output (GPIO) tracing, power profiling, and serial output logging, all of which can run synchronously and with high resolution. Sub-microsecond synchronization enables precise correlation between these observations and emulated energy-harvesting conditions, offering unprecedented insights into distributed energy-harvesting IoT systems. In this paper, we describe Shepherd Nova's design, characterize its performance, and demonstrate its capabilities through controlled experiments and an example test case. To access the testbed, documentation as well as open-source harvesting data, hardware designs, and code, visit https://testbed.nes-lab.org/.

Find My is a crowd-sourced network of hundreds of millions of Apple devices that use Bluetooth Low Energy (BLE) to detect and track the location of items. We explore the limits and opportunities of using this proprietary network for large-scale distributed sensing. The key idea is to let low-cost sensing devices emit specially crafted BLE advertisements that trick nearby Apple devices into generating location reports that carry arbitrary sensor data, which can then be retrieved from the Apple servers. This paper reports on our ongoing work to reverse engineer the Find My system and to design a protocol for the efficient and reliable collection of data from sensing devices via the Find My network. Preliminary results from real-world experiments demonstrate the feasibility of our approach and a several-fold performance improvement compared with the state of the art.

The rapidly growing Internet of Things (IoT) can avoid the high cost and environmental burden of replacing trillions of batteries by using sustainable battery-free devices that operate maintenance-free for decades. To develop battery-free IoT systems, researchers and makers require a common platform that is versatile, affordable, and easy to use. However, limited availability and lack of support have prevented widespread adoption of previous battery-free platforms. We introduce Riotee, an open-source and commercially available battery-free platform that includes multiple boards, extensive software, and comprehensive documentation. We demonstrate Riotee's capabilities through a machine-learning application and present results from a user study involving students and customers, who rated its usefulness and usability highly.

This demo uses a machine-learning-based hot word detection application to showcase the capabilities of Riotee, an open-source and commercially available hardware-software platform for the battery-free Internet of Things. We describe the Riotee hardware consisting of a base module, a debug probe for easy firmware updates, and several expansion boards that enhance functionality without the need for custom-designed printed circuit boards (PCBs). The demo features the classification of live audio recordings using TinyML deep neural network inference aboard a Riotee device. The Riotee device transmits the classification results via Bluetooth Low Energy (BLE) to a smartphone given to visitors. Visitors can also observe how the Riotee software checkpoints and restores critical state in case of power failures via the visualization of logic analyzer traces.

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.

Battery-free devices eliminate the need for batteries, which are expensive, environmentally harmful, and require frequent replacement, thus reducing waste and making devices more cost-effective. We introduce Riotee, the next-generation platform for the battery-free Internet of Things. The platform comprises a base module, a debug probe that allows to conveniently update the firmware on the base module, and a number of expansion boards that extend the capabilities of the platform without the need to design a custom printed circuit board (PCB). We provide a brief overview of Riotee, and describe a demo setup that showcases the key functionality and how to get started with the platform in less than three minutes.

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.

Low-power wireless communication is a central building block of Cyber-physical Systems and the Internet of Things. Conventional low-power wireless protocols make avoiding packet collisions a cornerstone design choice. The concept of synchronous transmissions challenges this view. As collisions are not necessarily destructive, under specific circumstances, commodity low-power wireless radios are often able to receive useful information even in the presence of superimposed signals from different transmitters. We survey the growing number of protocols that exploit synchronous transmissions for higher robustness and efficiency as well as unprecedented functionality and versatility compared to conventional designs. The illustration of protocols based on synchronous transmissions is cast in a conceptional framework we establish, with the goal of highlighting differences and similarities among the proposed solutions. We conclude the paper with a discussion on open research questions in this field.

Communication among batteryless devices is key to their success in replacing traditional battery-supported systems. However, low and unpredictable availability of ambient energy combined with limited energy storage capacity of the devices make efficient communication challenging. As a stepping stone toward addressing this challenge, we propose to leverage common patterns in harvested energy across the devices. In this abstract, we explore one possible approach that exploits a property of many fluorescent light sources used worldwide: their brightness changes with double the power line frequency. We design a circuit that transforms the corresponding changes in energy harvested with a solar panel into a digital signal that is frequency- and phase-synchronized across multiple devices. Based on our design, we build a novel batteryless node, called Flync. Using two Flync nodes, we demonstrate that the synchronized signal can be generated with less than 1 µA and a maximum measured node to node jitter of 363.24 µs.

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.

Careful energy management is a prerequisite for long-term, unattended operation of solar-harvesting sensing systems. We observe that in many applications the utility of sensed data varies over time, but current energy-management algorithms do not exploit prior knowledge of these variations for making better decisions. This paper presents PreAct, the first energy-management algorithm that exploits time-varying utility to optimize application performance. PreAct's design combines strategic long-term planning of future energy utilization with feedback control to compensate for deviations from the expected conditions. We implement PreAct on a low-power microcontroller and compare it against the state of the art on multiple years of real-world data. Our results demonstrate that PreAct is up to 53 % more effective in utilizing harvested solar energy and significantly more robust to uncertainties and inefficiencies of practical systems. These gains translate into an improvement of 28% in the end-to-end performance of a real-world application we investigate when using PreAct.

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.

Collaboration of batteryless nodes is essential to their success in replacing traditional battery-based systems. Energy-harvesting sensor nodes experience spatio-temporal fluctuations of energy availability. These fluctuations become especially critical when sensor nodes do not have sufficient energy storage to compensate for them. Understanding the challenges and opportunities of operating groups of batteryless sensor nodes requires to record and reproduce spatio-temporal characteristics of real energy environments. We thus present Shepherd, a testbed for the batteryless IoT. Shepherd allows to record synchronized energy traces with a resolution of 3 μA and 50μV at a rate of 100 kHz, and to faithfully replay these traces to any number of sensor nodes to study their behavior. We release Shepherd as an open-source tool for the community, facilitating research into time synchronization, wireless networking, and other distributed algorithms for batteryless systems.

Collaboration of batteryless nodes is essential to their success in replacing traditional battery-based systems. This abstract describes a demonstration of the recently proposed Shepherd testbed that allows to record and reproduce spatio-temporal characteristics of real energy environments. It consists of a number of spatially distributed Shepherd nodes that are tightly time-synchronized with each other and record synchronized energy traces with a resolution of 3 μA and 50 μV at a rate of 100 kHz. Additionally, Shepherd can faithfully replay these traces to any number of nodes to study their behavior, both individually and as an ensemble. Shepherd works with various sources of energy harvesting, such as kinetic or solar, is based on a modular design and provides a generic interface for sensor nodes allowing users to experiment with new platforms.

Wired fieldbuses have long been proven effective in supporting Cyber-Physical Systems (CPS). However, various domains are now striving for wireless solutions due to ease of deployment or novel functionality requiring the ability to support mobile devices. Low-power wireless protocols have been proposed in response to this need, but requirements of a large class of CPS applications can still not be satisfied. We thus propose Time-Triggered Wireless (TTW), a distributed low-power wireless system design that minimizes communication energy consumption and offers end-to-end timing predictability, runtime adaptability, reliability, and low latency. Evaluation shows a 2× reduction in communication latency and 33-40% lower radio-on time compared with DRP, the closest related work, validating the suitability of TTW for new exciting wireless CPS applications.

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.

The ability to fine-tune communication performance is key to meeting the requirements of Internet of Things applications. While years of low-power wireless research now allows developers to fully optimize the performance of applications built on top of IEEE 802.15.4, this has not yet happened with Bluetooth Low Energy (BLE), whose networking performance is still largely unexplored and whose potential is not yet fully exploited. Indeed, BLE radios are often treated as a black box, because they are meant to only execute data transfer commands and manufacturers build BLE soft devices with closed-source network stacks. As a result, developers working with BLE cannot modify the radio driver or the link-layer, and hence have no direct control over radio duty cycling and packet re-transmissions. To tackle these challenges, we analyze and model how specific BLE features can be used to fine-tune communication performance at run-time. We further present the design and implementation of BLEach, an IPv6-over-BLE stack that exposes tuning knobs for controlling the energy usage and timeliness of BLE transmissions and that allows to enforce a variety of quality-of-service (QoS) metrics. We design three exemplary modules for BLEach providing novel BLE functionality: adaptive radio duty cycling, IPv6-over-BLE traffic prioritization and multiplexing, as well as indirect link-quality monitoring. We integrate BLEach into Contiki and release its code, thus addressing the lack of a full-fledged open-source IPv6-over-BLE stack. Experiments demonstrate that BLEach is lightweight, interoperable with other standard-compliant devices, and reduces energy costs by up to 50 % while giving QoS guarantees by quickly adapting to changes in interference, traffic priority, and traffic load.

Low-power wireless technology promises greater flexibility and lower costs in cyber-physical systems. To reap these benefits, communication protocols must deliver packets reliably within real-time deadlines across resource-constrained devices , while adapting to changes in application requirements (e.g., traffic demands) and network state (e.g., link qualities). Existing protocols do not solve all these challenges simultaneously, because their operation is either localized or a function of network state, which changes unpredictably over time. By contrast, this article claims a global approach that does not use network state information as input can overcome these limitations. The Blink protocol proves this claim by providing hard guarantees on end-to-end deadlines of received packets in multi-hop low-power wireless networks, while seamlessly handling changes in application requirements and network state. We build Blink on the non-real-time Low-Power Wireless Bus (LWB) and design new scheduling algorithms based on the earliest-deadline-first policy. Using a dedicated priority queue data structure, we demonstrate a viable implementation of our algorithms on resource-constrained devices. Experiments show that Blink (i) meets all deadlines of received packets, (ii) delivers 99.97% of packets on a 94-node testbed, (iii) minimizes communication energy consumption within the limits of the underlying LWB, (iv) supports end-to-end deadlines of 100ms across four hops and nine sources, and (v) runs up to 4.1 × faster than a conventional scheduler implementation on popular microcontrollers.

In cyber-physical systems (CPS), the communication among the sensing, actuating, and computing elements is often subject to hard real-time constraints. Real-time communication among wireless network interfaces and real-time scheduling for complex, dynamic applications have been intensively studied. Despite these major efforts, there is still a significant gap to fill. In particular, the integration of several real-time components to provide end-to-end real-time guarantees between interfaces of distributed applications in wireless CPS is an unsolved problem. We thus present a distributed protocol that considers the complete transmission chain including peripheral busses, memory accesses, networking interfaces, and the wireless real-time protocol. Our protocol provably guarantees that message buffers along this chain do not overflow and that all messages received at the destination application interface meet their end-to-end deadlines. To achieve this while being adaptive to unpredictable changes in the system and the real-time traffic requirements, our protocol establishes at run-time a set of contracts among all major elements of the transmission chain based on a worst-case delay and buffer analysis of the overall system. Using simulations, we validate that our analytic bounds are both safe and tight.

Opportunistic routing protocols tackle the problem of efficient data collection in dynamic wireless sensor networks, where the radio is duty-cycled to save energy and the topology changes unpredictably due to node mobility and/or link dynamics. Unlike protocols that maintain a routing structure, in opportunistic protocols nodes forward packets to any neighbor that wakes up first, reducing latency and energy costs and increasing the resilience to network dynamics.

Experimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized benchmarks, our community still uses ad-hoc setups for its experiments and struggles to provide a fair comparison between communication protocols. Reasons for this include the diversity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental validation, we make the case for a reference benchmark to promote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmarking methodology with the goal to gather feedback from the community rather than propose a definite solution.

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.

The continuing proliferation of smartphones makes them an effective means to monitor the number of people within an area, for example, to gain insights into customer engagement in retail and to enable an intelligent traffic system in a city. However, current approaches to obtain this information are either invasive as they require to continuously run a dedicated smartphone app, or they compromise users' privacy by sniffing the MAC addresses of their smartphones. As a consequence, lawyers, authorities, and the population are very skeptical toward adopting such innovative systems. We present DevCnt, the first system that counts in real-time the number of Wi-Fi enabled smartphones in a non-invasive manner while preserving by design the privacy of the smartphone users. This paper details how DevCnt detects active Wi-Fi scans performed by smartphones on a ZigBee device, and how DevCnt uses the number of detected scans to estimate the number of Wi-Fi enabled smartphones. Results from controlled and real-world experiments show that DevCnt: (i) detects more than 99% of active Wi-Fi scans even under interference from multiple wireless technologies, (ii) achieves up to 91% accuracy in the estimated smartphone counts, and (iii) provides meaningful estimates in a real test run involving hundreds of Wi-Fi transmitters.

Resource interference is a fundamental barrier to realizing predictable wireless embedded systems. We address this problem by (i) partitioning application and communication tasks onto dedicated platforms, and (ii) designing a platform interconnect to facilitate asynchronous message exchange with predictable run-time behavior. We motivate the need for this platform interconnect, termed Bolt, and describe a prototype implementation. Evaluation results indicate that the developed platform interconnect exhibits tightly bounded run-time execution with low jitter, and a negligible resource overhead with respect to state-of-the-art application and communication platforms.

The wireless sensor network community is currently undergoing a platform paradigm shift, moving away from classical single-processor motes toward heterogeneous multi-processor architectures. These emerging platforms promise efficient concurrent processing with energy-proportional system performance. The use of shared interconnects and shared memory for inter-processor communication, however, causes interference in the time, power, and clock domains, which prevents designers from fully harnessing these benefits. We thus designed Bolt, the first ultra-low-power processor interconnect for the compositional construction of heterogeneous wireless embedded platforms. This paper presents the architectural blueprint for interconnecting two independent processors, while enabling asynchronous inter-processor communication with predictable run-time behavior. We detail a prototype implementation of Bolt, and apply formal methods to analytically derive bounds on the execution time of its message passing operations. Experiments with a custom-built dual-processor platform show that our Bolt prototype incurs a negligible power overhead relative to state-of-the-art platforms, offers predictable message passing with empirical bounds that match the analytical ones to within a few clock cycles, and achieves a high throughput of up to 3.3 Mbps.

We demonstrate the capabilities of Bolt, an ultra-low-power processor interconnect for the composable construction of new multi-processor wireless embedded platforms. Bolt provides asynchronous bidirectional communication between two processors with predictable message transfer times. In this way, Bolt solves the resource interference problem inherent in today's wireless embedded platforms, enabling simpler and more robust system designs with minimal resource overhead. Using our Bolt prototype implemented on a state-of-the-art microcontroller, we demonstrate Bolt's composability and decoupling in time, power, and clock domains.

Experiments under controlled radio interference are crucial to assess the robustness of low-power wireless protocols. While tools such as JamLab augment existing sensornet testbeds with realistic interference, it remains an error-prone and time-consuming task to manually select the set of nodes acting as jammers and their individual transmit powers. We present an automated configuration approach based on simulated annealing to overcome this problem. A preliminary evaluation based on two testbeds shows that our approach can find near-optimal solutions within at most a few hours. We believe our approach can facilitate the widespread adoption of controlled interference experiments by the sensornet community.

Testbeds are indispensable for debugging and evaluating wireless embedded systems. While existing testbeds provide ample opportunities for realistic, large-scale experiments, they are limited in their ability to closely observe and control the distributed operation of resource-constrained nodes - access to the nodes is restricted to the serial port. This paper presents FlockLab, a testbed that overcomes this limitation by allowing multiple services to run simultaneously and synchronously against all nodes under test in addition to the traditional serial port service: tracing of GPIO pins to record logical events occurring on a node, actuation of GPIO pins to trigger actions on a node, and high-resolution power profiling. FlockLab's accurate timing information in the low microsecond range enables logical events to be correlated with power samples, thus providing a previously unattained level of visibility into the distributed behavior of wireless embedded systems. In this paper, we describe FlockLab's design, benchmark its performance, and demonstrate its capabilities through several real-world test cases.

Mathematical models play a pivotal role in understanding and designing advanced low-power wireless systems. However, the distributed and uncoordinated operation of traditional multi-hop low-power wireless protocols greatly complicates their accurate modeling. This is mainly because these protocols build and maintain substantial network state to cope with the dynamics of low-power wireless links. Recent protocols depart from this design by leveraging synchronous transmissions (ST), whereby multiple nodes simultaneously transmit towards the same receiver, as opposed to pair wise link-based transmissions (LT). ST improve the one-hop packet reliability to an extent that efficient multi-hop protocols with little network state are feasible. This paper studies whether ST also enable simple yet accurate modeling of these protocols. Our contribution to this end is two-fold. First, we show, through experiments on a 139-node test bed, that characterizing packet receptions and losses as a sequence of independent and identically distributed (i.i.d.) Bernoulli trials-a common assumption in protocol modeling but often illegitimate for LT-is largely valid for ST. We then show how this finding simplifies the modeling of a recent ST-based protocol, by deriving (i) sufficient conditions for probabilistic guarantees on the end-to-end packet reliability, and (ii) a Markovian model to estimate the long-term energy consumption. Validation using test bed experiments confirms that our simple models are also highly accurate, for example, the model error in energy against real measurements is 0.25%, a figure never reported before in the related literature.

An important building block for low-power wireless systems is to efficiently share and process data among all devices in a network. However, current approaches typically split such all-to-all interactions into sequential collection, processing, and dissemination phases, thus handling them inefficiently.

We present the design of a reliable nurse call system based on wireless embedded devices and multi-hop protocols. Our work is motivated by the need for such system during annual summer camps for people with muscular dystrophy and the lack of suitable alternative solutions. We describe how our prototype meets the reliability and real-time requirements of such system, and report on results from a two-week deployment during a camp with 13 affected boys in July 2013.

Traditional low-power wireless protocols maintain distributed network state to cope with link dynamics. Modeling the protocol operation as a function of network state is difficult as the state is frequently updated in an uncoordinated fashion. Recent protocols use synchronous transmissions (ST): multiple nodes send simultaneously towards the same receiver, as opposed to pairwise link-based transmissions (LT). ST enable efficient multi-hop protocols with little network state.

By integrating computational and physical elements through feedback loops, CPSs implement a wide range of safety-critical applications, from high-confidence medical systems to critical infrastructure control. Deployed systems must therefore provide highly dependable operation against unpredictable real-world dynamics. However, common CPS hardware-comprising battery-powered and severely resource-constrained devices interconnected via low-power wireless-greatly complicates attaining the required communication guarantees. VIRTUS fills this gap by providing atomic multicast and view management atop resource-constrained devices, which together provide virtually synchronous executions that developers can leverage to apply established concepts from the dependable distributed systems literature. We build VIRTUS upon an existing best-effort communication layer, and formally prove the functional correctness of our mechanisms. We further show, through extensive real-world experiments, that VIRTUS incurs a limited performance penalty compared with best-effort communication. To the best of our knowledge, VIRTUS is the first system to provide virtual synchrony guarantees atop resource-constrained CPS hardware.

We present the low-power wireless bus (LWB), a simple yet efficient communication support for low-power wireless networks. The LWB maps different communication demands onto fast Glossy network flooding, effectively turning the wireless network into a bus-like infrastructure. The LWB requires no information of the network topology, thus drastically reducing the control overhead of common solutions such as route maintenance, and natively supports many-to-many communication and mobile nodes in addition to more traditional static, one-to-many scenarios. For instance, experiments on a 90-node testbed show that on average the LWB reduces packet loss by a factor of 231 and energy consumption due to communication by a factor of 11 compared to a state-of-the-art many-to-many routing protocol.

We present pTunes, a framework for runtime adaptation of low-power MAC protocol parameters. The MAC operating parameters bear great influence on the system performance, yet their optimal choice is a function of the current network state. Based on application requirements expressed as network lifetime, end-to-end latency, and end-to-end reliability, pTunes automatically determines optimized parameter values to adapt to link, topology, and traffic dynamics. To this end, we introduce a flexible modeling approach, separating protocol-dependent from protocol-independent aspects, which facilitates using pTunes with different MAC protocols, and design an efficient system support that integrates smoothly with the application. To demonstrate its effectiveness, we apply pTunes to X-MAC and LPP. In a 44-node testbed, pTunes achieves up to three-fold lifetime gains over static MAC parameters optimized for peak traffic, the latter being current - and almost unavoidable - practice in real deployments. pTunes promptly reacts to changes in traffic load and link quality, reducing packet loss by 80% during periods of controlled wireless interference. Moreover, pTunes helps the routing protocol recover quickly from critical network changes, reducing packet loss by 70% in a scenario where multiple core routing nodes fail.

We present the Low-Power Wireless Bus (LWB), a communication protocol that supports several traffic patterns and mobile nodes immersed in static infrastructures. LWB turns a multi-hop low-power wireless network into an infrastructure similar to a shared bus, where all nodes are potential receivers of all data. It achieves this by mapping all traffic demands on fast network floods, and by globally scheduling every flood. As a result, LWB inherently supports one-to-many, many-to-one, and many-to-many traffic. LWB also keeps no topology-dependent state, making it more resilient to link changes due to interference, node failures, and mobility than prior approaches. We compare the same LWB prototype on four testbeds with seven state-of-the-art protocols and show that: (i) LWB performs comparably or significantly better in many-to-one scenarios, and adapts efficiently to varying traffic loads; (ii) LWB outperforms our baselines in many-to-many scenarios, at times by orders of magnitude; (iii) external interference and node failures affect LWB's performance only marginally; (iv) LWB supports mobile nodes acting as sources, sinks, or both without performance loss.

Wireless control systems consist of sensing and actuating devices that are commonly driven by a central controller. Wireless communication protocols for Cyber-Physical Systems (CPS) match this design by employing a "sense → collect → process → disseminate → actuate" flow [6], where typically different protocols are employed for collecting sensor data and disseminating actuation signals.

Developing, testing, debugging, and evaluating communication protocols for low-power wireless networks is a long and cumbersome task. Simulators can be helpful in the early stages of development, but their models of hardware components and the wireless channel are often rather simplistic and hence cannot substitute experiments on real sensor node platforms. The resources available on common platforms are however very limited, and so are the possibilities for non-intrusive debugging and testing. With most existing testbeds it is only possible to collect information from the serial port, which requires adding highly intrusive logging statements that alter the timing behavior of the software running on the nodes. This is particularly detrimental to the operation of time-critical components, such as radio drivers, media access control (MAC) protocols, and certain flooding protocols [2], hindering their testbed-assisted development.

The field of Wireless Sensor Networks (WSNs) is now in a stage where serious applications of societal and economical importance are in reach. For example, it is well known that the global climate change dramatically influences the visual appearance of mountain areas like the European Alps. Very destructive geological processes may be triggered or intensified, impacting the stability of slopes, possibly inducing landslides. Unfortunately, the interactions between these complex processes is poorly understood. Therefore, one needs to develop wireless sensing technology as a new scientific instrument for environmental sensing under extreme conditions. Large variations in temperature, humidity, mechanical forces, snow coverage, and unattended operation play a crucial role in long-term deployments. We argue that, in order to significantly advance the application domain, it is inevitable that sensor networks be created as a quality scientific instrument with known and predictable properties, and not as a research toy delivering average observations at best. In this paper, key techniques for achieving highly reliable, yet resource efficient wireless sensor networks are discussed on the basis of productive wireless sensor networks measuring permafrost processes in the Swiss Alps.

Communication stacks are composed of distinct layers that, in principle, operate independently and interact through well-defined interfaces. However, resource constraints in sensor networks typically necessitate optimizations, leading to implicit assumptions and dependencies among layers (e.g., a collection protocol assumes the MAC protocol provides sufficient bandwidth). These dependencies are often tracked manually, yet become extremely complex as protocols evolve and requirements change. We propose to model assumptions and dependencies explicitly, as constraints on protocol parameters. This allows for using standard tools to generate feasible protocol configurations. We demonstrate the effectiveness of our approach using the example of FTSP running on top of a low-power listening MAC protocol.

We analyze push and pull for data collection in wireless sensor networks. Most applications to date use the traditional push approach, where nodes transmit sensed data immediately to the sink. Using a pull approach, nodes store the data in their local flash memory, and only engage in communication during dedicated collection phases. We show how one can transform an existing push-based collection protocol into a pull-based one, and compare the power consumption of both approaches on a 35-node testbed. Our results show that substantial energy gains are possible with pull, provided that the application can tolerate a long latency.

Wireless sensor networks are used for structure monitoring and border surveillance. Typical applications, such as sensors embedded in the outer surface of a pipeline or mounted along the supporting structure of a bridge, feature a linear sensor arrangement. Economical power use of sensor nodes is essential for long-lasting operation. In this paper, we present MERR (minimum energy relay routing), a novel approach to energy-efficient data routing to a single control center in a linear sensor topology. Based on an optimal transmission distance, relay paths are established that aim for minimizing the total power consumption. We study MERR by both stochastic analysis and simulation, comparing it to other possible approaches and a theoretically optimal protocol. We find that MERR consumes 80% less power than conventional approaches and performs close to the theoretical optimum for practicable sensor networks.