With the appearance of wearable devices and the IoT, energy harvesting nodes are becoming more and more important. The design and evaluation of these small standalone sensors and actuators, which harvest limited amounts of energy, requires novel tools and methods. Fast and accurate measurement systems are required to capture the rapidly changing harvesting scenarios and characterize leakage currents and energy efficiencies. The need for real-world experiments creates a demand for compact and portable equipment to perform in-situ power measurements and environmental logging. This work presents the RocketLogger, a hand-held measurement device that combines both properties: portability and accuracy. The custom analog front-end allows logging at sampling rates up to 64 kSPS. The fast range switching within 1.4 μ8 guarantees continuous power measurements starting from 4pW at 1 mV up to 2.75 W at 5.5 V. The software provides remote control and manages data acquisition of up to 13Mb/ sec in real-time. We extensively characterize the RocketLogger's performance, demonstrate the need for its properties in three use-cases at different stages of the system design flow, and show its advantages in measuring and validating new harvesting-driven devices for the IoT.

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.

We demonstrate the RocketLogger, a mobile data logger designed for prototyping energy harvesting IoT devices. Novel IoT applications require new dataloggers with a highly increased dynamic range for current measurement to accommodate both ultra-low sleep currents of few nanoamperes as well as wireless communication currents in the range of hundreds of milliamperes. In parallel to ultra-low currents and high dynamic range measurements, novel applications require mobile measurements for easy in-situ characterization or wearable device testing. The RocketLogger is a solution that fulfills these requirements. While being fully mobile, it measures currents from 5 nA up to 500 mA with very fast and seamless range-switching. Using a sample energy harvesting application, we demonstrate its low-current measurement capabilities, fast, seamless auto-ranging and easy-to-use remote user interface.

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.

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.

Location information about mobile devices can reveal movement patterns of people wearing them. We present a cross-technology approach to track Wi-Fi enabled smartphones using a ZigBee sensor network. Implementing such a system on resource constrained low-power sensor nodes requires means to efficiently process signal strength measurements. We propose a new method to recognize Wi-Fi probe requests on devices that are unable to decode Wi-Fi packets. Initial experiments on a testbed show that it is possible to localize unmodified phones within a building using signal strength measurements.

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.

As the application of WSNs for long-term monitoring purposes becomes real, the issue of WSN system health monitoring grows increasingly important. Manually understanding the root causes of an observed behavior is time-consuming and difficult, often knowledge of prior behavior is necessary for understanding the potential risk on the long-term system performance. The challenges lie in the balance between the amount of system data collected and the level of detail in which state can be inferred from this data. In this paper, we propose a lightweight runtime logging and corresponding network state inference mechanism that enables scalable WSN health monitoring. Concretely, we propose that nodes only report their internal state on the occurrence of important events. Having a very low computational complexity and message overhead within the sensor network, reported events are analyzed at a less constrained network sink.

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.

In this paper, we present a performance analysis of the communication stack of the PermaDozer application. Our offline analysis is based on long-term performance data from three diverse real-world wireless sensor network deployments. Two of the deployments considered are located in the Swiss mountains at 3.500 meters a.s.l., the third deployment is located along a river in the Swiss midlands. Apart from climatic differences, the three deployments also vary in network size, network density, node placement, and type and quantity of RF interference. From September 2008 to May 2011 more than 99.6 million WSN packets have been collected serving as the dataset. All three deployments are based on the same software and hardware. This allows us to comparatively study the performance of the PermaDozer protocol under different deployment settings and environmental conditions. For the period between June 2010 and May 2011, our networks achieved a data yield of >; 99.5%. But, we can also clearly notice that achieving this performance requires varying effort in terms of radio duty cycle resulting in different power consumption and network lifetime.

A vital factor for a successful deployment of sensor nodes is testing of all system aspects in a realistic setup. This work presents a testbed architecture which allows for detailed monitoring and stimulation of a wireless sensor node. In particular, time-accurate state extraction and power measurements are provided in a distributed, yet synchronized context. The FlockLab testbed architecture provides a distributed lab instrument, where detailed observations of every sensor node enable thorough testing. Software services allow for formulating testcases and reliable test data collection.

Within the PermaSense project, two wireless sensor networks have been deployed for a long-term operation in the Swiss Alps. For enabling state-of-the-art permafrost research based on the collected data, highest possible data quality and yield have to be ensured. But, the operation of wireless sensors networks remains a hard research problem. Firstly, deployed wireless sensors networks are subject to continuous changes. Second, there are scenarios that can only be tested in the field as the capabilities of testbeds are too limited. Basically, it is not possible to test for many months before deploying in the field. In this poster, we present an analysis of our data that has been collected over nine months. In addition to describing our system design and methods, we also share our experiences from discovered severe incidences.