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.

In many real-world wireless IoT networks, the application dictates the location of the nodes and therefore the link characteristics are inhomogeneous. Furthermore, nodes may in many scenarios only communicate with the Internet-attached gateway via multiple hops. If an energy-efficient short-range modulation scheme is used, nodes that are reachable only via high-path-loss links cannot communicate. Using a more energy-demanding long-range modulation allows connecting more nodes but would be inefficient for nodes that are easily reachable via low-path-loss links. Combining multiple modulations is challenging, as low-power radios usually only support the use of a single modulation at a time. In this article, we present the Long-Short-Range (LSR) protocol which supports low-power multi-hop communication using multiple modulations and is suited for networks with inhomogeneous link characteristics. To reduce the inherent redundancy of long-range modulations, we present a method to determine the connectivity graph of the network during regular data communication without adding significant overhead. In simulations, we show that LSR allows for reducing power consumption significantly for many scenarios when compared to a state-of-the-art multi-hop communication protocol using a single long-range modulation. We demonstrate the applicability of LSR with an implementation on real hardware and a testbed with long-range links.

Wireless sensors form an integral part of the Internet of Things (IoT), standing at the edge between the cyber and physical domains. Ac-quiring and transmitting environmental data is an energy-intensive workload, especially when considering networks spanning large buildings or even cities. Many works aim to integrate energy har-vesting into wireless sensors, providing them with a greater level of energy autonomy. Initially deployed for energy-rich outdoor environments, recent advances have allowed wireless sensors to efficiently utilize the reduced energy harvested in indoor lighting conditions. This demo introduces a harvesting-based Dual Proces-sor Platform, the DPP3e, designed for energy harvesting in indoor environments. It features various sensors for advanced indoor en-vironmental sensing, e.g. air quality measurements, a low-power display for immediate visual feedback, and a powerful micro controller for energy-efficient inference of Tensorflow models. Fur-thermore, it has two separate RF interfaces: a 2.4 GHz Bluetooth Low Energy (BLE) radio for short-range communication, and a sub-GHz transceiver for long-range communication. Using configurable power domains and advanced power management, it can sustain sending BLE packets every 5 seconds while consuming only 37 µ W.

With the Internet of Things (IoT) large amounts of data can be gathered at the edge, centrally collected and subsequently utilized for various application domains. Efficient and reliable synchro-nous communication protocols are essential for automated data gathering, yet they typically require a stable energy supply. En-ergy harvesting enables long-term deployments, but it imposes widely varying energy budgets on each node in the network. To re-main efficient, synchronous protocols need to consider this energy variability. We propose a selective flooding protocol that employs low-energy communication rounds for all nodes and additional high-energy rounds only for nodes with high input power thus increasing their throughput. Low-energy rounds gather data with high reliability and maintain global network synchronization, while high-energy rounds use increased transmit power to overcome potentially-broken links that depend on low-power nodes. We eval-uate our proposed method on the FlockLab testbed and build a small network composed of standalone energy harvesting nodes. The average power consumption of almost 84 µW and 177 µW for low- and high-power nodes, respectively, are fully sustainable by indoor photovoltaic harvesting.

Testbeds for wireless IoT devices facilitate testing and validation of distributed target nodes. A testbed usually provides methods to control, observe, and log the execution of the software. However, most of the methods used for tracing the execution require code instrumentation and change essential properties of the observed system. Methods that are non-intrusive are typically not applicable in a distributed fashion due to a lack of time synchronization or necessary hardware/software support. In this article, we present a tracing system for validating time-critical software running on multiple distributed wireless devices that does not require code instrumentation, is non-intrusive and is designed to trace the distributed state of an entire network. For this purpose, we make use of the on-chip debug and trace hardware that is part of most modern microcontrollers. We introduce a testbed architecture as well as models and methods that accurately synchronize the timestamps of observations collected by distributed observers. In a case study, we demonstrate how the tracing system can be applied to observe the distributed state of a flooding-based low-power communication protocol for wireless sensor networks. The presented non-intrusive tracing system is implemented as a service of the publicly accessible open source FlockLab 2 testbed.

Low-power wireless sensor networks have demonstrated their potential for the detection of rare events such as rockfalls and wildfires, where rapid reporting as well as long-term energy-efficient operation is vital. However, current systems require periodic synchronization to maintain network coordination, heavily rely on node placement or use costly long-range links to infrastructure. We present STeC, a novel wireless communication design that directly exploits the spatial and temporal correlation of signals from the sensed phenomenon to orchestrate event-based communication. We leverage the locality of a co-detection, where a physical event triggers multiple sensors quasi-simultaneously, to efficiently collect, characterize and report sensor data. This eliminates the overhead of periodic network activity and centralized control, resulting in more energy-efficient communication with a lower, more consistent detection latency. In doing so, we propose a fundamentally new approach to avoid the elementary conflict between duty cycle and latency requirements immanent to synchronous protocols by exploiting correlated sensor signals for networking. Experiments using real-world traces of a natural hazard detection application show that STeC reduces the detection latency by up to 87 % compared to standard single-hop communication and outperforms traditional schedule-based methods by up to 58.4 x in energy efficiency.

In this demo abstract we present a custom-built low-power geo-phone sensor node which features on-device mountaineer classification using a convolutional neural network. The execution of such a processing-heavy algorithm on an embedded platform is enabled by optimizing the memory requirement of the neural network through advanced quantization and pipelining techniques. As a result, real-time classification with low energy consumption can be achieved.

In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn Hörnligrat at 3500 m a.s.l. since 08/2018.

The Dual Processor Platform (DPP) is a novel architecture template for networked embedded systems based on a strictly asynchronous processor interconnect that allows to minimize interference with a proven predictable behavior [7]. Contrary to traditional platforms [1, 5] DPP tries to mitigate interference by isolating different tasks and mapping them onto dedicated hardware resources. Typically, two different task sets - (i) sensing/actuation/data processing and (ii) communication - are mapped onto two different physically separated processing elements (usually low-power microcontrollers), allowing each to be optimized according to their individual requirements. Such hardware partitioning is a standard approach, frequently found in more complex sensor system implementations [2]. While the communication processor handles wireless packet transmission and reception, the application processor is dedicated to the sensor data acquisition, processing and actuation. But this strict separation of resources and function also requires a processor interconnect: BOLT [7], a stateful processor interconnect specifically designed based on this paradigm allows a strict decoupling of the power, clock and time domains of the two processing elements by allowing only asynchronous message passing between the two. The strict limitation to an asynchronous interface allows for predictable run-time behavior that, in addition to typical behavior observed from experiments, has been formally verified [7]. The most notable advantages of this approach are:

Designing and testing low-power wireless communication protocols often requires experimental deployments on real hardware in realistic settings. Infrastructure testbeds have the advantage that they allow reproducible results using different network configurations. However, most testbeds are either in- or outdoor only and do not span long and short ranges at the same time. In this work, we present an extension to the popular FlockLab testbed on a campus-scale in order to better support testing of long-range communciation, for example using the LoRa modulation. Different to existing LoRa test networks where specific protocol layers are fixed, we support custom modification above the physical hardware (above PHY) which allows the development and testing of alternative full custom MAC layers that are not based on LoRaWAN.

Event-triggered wireless sensing systems are an important class of wireless sensor network, where the detection of non-deterministic events enables the monitoring and control of processes in industries such as manufacturing, healthcare, and agriculture. The system properties of low latency, energy efficiency, and adaptability make event-triggered wireless sensing systems a key technological enabler for the Industrial Internet of Things. <?tight?>Wireless sensing systems based on periodic multi-hop communication exhibit a fundamental trade-off between latency and energy efficiency, which is unfavorable for event-triggered application scenarios. To address this technological gap, we present B litz , the first communication architecture that combines asynchronous and synchronous flooding primitives to facilitate low latency and energy-efficient multi-hop communication of non-deterministic events. B litz also incorporates a novel scheme for mitigating erroneous wake-ups, which is shown analytically and experimentally to further reduce energy consumption. We present a prototype implementation of B litz and evaluate its performance in an indoor testbed deployment. Experiments show that BLITZ supports a mean latency as low as 108.9ms for an 8-bit event packet and its associated data packet of 32 bytes through a 4-hop network, and a power dissipation of 16μW during periods of inactivity.

We present a heterogeneous system architecture for event-triggered wireless sensing capable of supporting high spatial resolution. The key differentiator between the proposed architecture and alternative state-of-the-art approaches is the ability to simultaneously maximize operational lifetime and minimize end-to-end latency of detected events. Our novel architecture takes advantage of heterogeneity with respect to the operation of the wireless communication protocol and the construction of the sensing platform. We present a two-hop proof of concept implementation, exhibiting end-to-end latencies on the order of tenths of a second, while dissipating on the order of tens of microwatts during periods of inactivity.

If visions and forecasts of industry come true then we will be soon surrounded by billions of interconnected embedded devices. We will interact with them in a cyber-human symbiosis, they will not only observe us but also our environment, and they will be part of many visible and ubiquitous objects around us. The information that is collectively gathered and analyzed is supposed to help us in our daily live, in making faithful decisions, but it will also directly be used for actuation and it will cause changes by means of local and global control loops.

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.

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.