Matthias Woehrle

Deployment Techniques for Sensor Networks

The prominent visions of wireless sensor networks that appeared about a decade ago have spurred enormous efforts in research and development of this new class of wireless networked embedded systems. Despite the significant effort made, successful deployments and real-world applications of sensor networks are still scarce, labor-intensive and often cumbersome to achieve. In this article, we survey prominent examples of sensor network deployments, in particular for environmental monitoring applications, their interaction with the real world and classify a number of potential causes for errors and common pitfalls. In the second half of this work, we present methods and tools to be used to detect failures, identify and understand root causes. These instrumentation techniques and analysis tools are specifically designed or adapted for the analysis of distributed networked embedded systems at the level of components, sensor nodes, and networks of nodes.

ZeroCal: automatic MAC protocol calibration

Sensor network MAC protocols are typically configured for an intended deployment scenario once and for all at compile time. This approach, however, leads to suboptimal performance if the network conditions deviate from the expectations. We present ZeroCal, a distributed algorithm that allows nodes to dynamically adapt to variations in traffic volume. Using ZeroCal, each node autonomously configures its MAC protocol at runtime, thereby trying to reduce the maximum energy consumption among all nodes. While the algorithm is readily usable for any asynchronous low-power listening or low-power probing protocol, we validate and demonstrate the effectiveness of ZeroCal on X-MAC. Extensive testbed experiments and simulations indicate that ZeroCal quickly adapts to traffic variations. We further show that ZeroCal extends network lifetime by 50% compared to an optimal configuration with identical and static MAC parameters at all nodes.

Conformance testing for cyber-physical systems

Cyber-Physical Systems (CPS) require a high degree of reliability and robustness. Hence it is important to assert their correctness with respect to extra-functional properties, like power consumption, temperature, etc. In turn the physical quantities may be exploited for assessing system implementations. This article develops a methodology for utilizing measurements of physical quantities for testing the conformance of a running CPS with respect to a formal description of its required behavior allowing to uncover defects. We present foundations and implementations of this approach and demonstrate its usefulness by conformance testing power measurements of a wireless sensor node with a formal model of its power consumption.

Increasing the reliability of wireless sensor networks with a distributed testing framework

Designing Wireless Sensor Networks (WSNs) has proven to be a slow, tedious and error-prone process due to the inherent intricacies of designing a distributed, wireless, and embedded system. A systematic design approach accompanied by a test methodology supports the development of WSN software conforming to all design requirements including robustness and reliability. In this paper, we propose the fundamentals of such a test methodology. We present essential features of a framework for testing a broad range of WSN applications. We demonstrate with a case study that our test methodology is a feasible approach by integrating a number of existing design-tools for the TinyOS operating system. While we target TinyOS in the case study the proposed test methodology is general and not tailored to a specific WSN platform or operating system.

Chrysso — A multi-channel approach to mitigate external interference

When a wireless sensor network is deployed indoors, sensor nodes often need to compete for channel usage with other, more powerful devices such as 802.11 access points. To mitigate the effects of such external interference we propose Chrysso, a protocol extension specifically designed for data collection applications that leverages the channel diversity of sensor node radios. In the face of external interference Chrysso switches only the directly affected set of nodes onto a new channel. In this paper, we present the design of Chrysso as well as its implementation in Contiki. Evaluation of Chrysso on two different testbeds, and its comparison with a state-of-the-art channel hopping protocol stack show that Chrysso is effective in maintaining a stable data flow at the sink, even under extreme interference.

Think globally, act locally: on the reshaping of information landscapes

In large-scale resource-constrained systems, such as wireless sensor networks, global objectives should be ideally achieved through inexpensive local interactions. A technique satisfying these requirements is information potentials, in which distributed functions disseminate information about the process monitored by the network. Information potentials are usually computed through local aggregation or gossiping. These methods however, do not consider the topological properties of the network, such as node density, which could be exploited to enhance the performance of the system. This paper proposes a novel aggregation method with which a potential becomes sensitive to the network topology. Our method introduces the notion of affinity spaces, which allow us to uncover the deep connections between the aggregation scope (the radius of the extended neighborhood whose information is aggregated) and the networks Laplacian (which captures the topology of the connectivity graph). Our study provides two additional contributions: (i) It characterizes the convergence of information potentials for static and dynamic networks. Our analysis captures the impact of key parameters, such as node density, time-varying information, as well as of the addition (or removal) of links and nodes. (ii) It shows that information potentials are decomposed into wave-like eigenfunctions that depend on the aggregation scope. This result has important implications, for example it prevents greedy routing techniques from getting stuck by eliminating local-maxima. Simulations and experimental evaluation show that our main findings hold under realistic conditions, with unstable links and message loss.

868 MHz: A noiseless environment, but no free lunch for protocol design

Lossy links are one of the fundamental characteristics of wireless sensor networks (WSNs). A large amount of work has been performed on characterizing link properties of 802.15.4 radios, in particular in the 2.4 GHz band. Unfortunately, the 2.4 GHz band has the apparent disadvantage of a crowded spectrum and considerable external interference, e. g., from WiFi, Bluetooth or even microwave ovens. We therefore investigate the performance of radios operating on the alternative 868 MHz frequency band, which is basically noise-free as determined from extensive experiments on a large-scale indoor testbed featuring more than 100 nodes. Although the lack of external interference eases protocol design, our study reveals that - and characterizes to what extent - wireless links in the 868 MHz band still show large variations in performance that must be accounted for.

Exploiting Timed Automata for Conformance Testing of Power Measurements

For software development, testing is still the primary choice for investigating the correctness of a system. Automated testing is of utmost importance to support continuous integration and regression tests on actual hardware. For embedded systems, power consumption is a chief performance metric, which is tightly coupled to the hardware used and the software exploiting low power modes. Automated testing of power consumption requires to investigate its conformance to a specification. We employ timed automata for specifying the expected behavior of a real sensor node application, as well as for describing the power measurements obtained from its real-world implementation. Introducing computational optimizations, the presented approach allows to utilize standard model checkers for automated conformance testing of modeled systems and monitored power consumption of their implementations.

MoMi: model-based diagnosis middleware for sensor networks

Numerous deployments of Wireless Sensor Networks (WSNs) have shown that intricate problems are introduced by deploying sensor nodes in an unknown and dynamic environment. Consequently, a vital part of a WSN deployment is the supervision of the sensor nodes and detection of abnormalities in their operation. Previous approaches have devised hand-crafted solutions to detect application-specific problems. This work proposes a generic middleware component, named MoMi, to detect such problems in a systematic way. Given a description of normal system behavior MoMi uses a Model-Based Diagnosis (MBD) framework to present the likely causes of system abnormalities to an administrator. This paper demonstrates a proof of concept implementation of MBD on sensor nodes and presents examples of its applicability for typical WSN applications.

Comparative performance analysis of the PermaDozer protocol in diverse deployments

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.