Olga Saukh

FlexCup: a flexible and efficient code update mechanism for sensor networks

The ability to update the program code installed on wireless sensor nodes plays an import role in the highly dynamic environments sensor networks are often deployed in. Such code update mechanisms should support flexible reconfiguration and adaptation of the sensor nodes but should also operate in an energy and time efficient manner. In this paper, we present FlexCup, a flexible code update mechanism that minimizes the energy consumed on each sensor node for the installation of arbitrary code changes. We describe two different versions of FlexCup and show, using a precise hardware emulator, that our mechanism is able to perform updates up to 8 times faster than related code update algorithms found in the literature, while consuming only an eighth of the energy.

Sensing the air we breathe: the opensense Zurich dataset

Monitoring and managing urban air pollution is a significant challenge for the sustainability of our environment. We quickly survey the air pollution modeling problem, introduce a new dataset of mobile air quality measurements in Zurich, and discuss the challenges of making sense of these data.Molecular oxygen (O 2 )is a basic requirement for cellular growth and viability and many aspects of anatomy and physiology are dedicated to achieving reliable distribution. Recent work has identified a specific sensing and response system, centred around a transcription complex called Hypoxia-inducible Factor 1 (HIF-1), which forms the focus of this review. The HIF-system operates in all cell types and modulates a very broad range of cellular pathways, consistent with the broad importance of oxygen. It is implicated in a rapidly expanding range of developmental, physiological and pathological settings, and is potentially relevant to almost all areas of clinical medicine. Excitingly, the pathway can be activated with low molecular weight compounds which should offer therapeutic benefit, especially in diseases where oxygen supply is compromised.

On-the-Fly calibration of low-cost gas sensors

Air quality monitoring is extremely important as air pollution has a direct impact on human health. Low-cost gas sensors are used to effectively perceive the environment by mounting them on top of mobile vehicles, for example, using a public transport network. Thus, these sensors are part of a mobile network and perform from time to time measurements in each others vicinity. In this paper, we study three calibration algorithms that exploit co-located sensor measurements to enhance sensor calibration and consequently the quality of the pollution measurements on-the-fly. Forward calibration, based on a traditional approach widely used in the literature, is used as performance benchmark for two novel algorithms: backward and instant calibration. We validate all three algorithms with real ozone pollution measurements carried out in an urban setting by comparing gas sensor output to high-quality measurements from analytical instruments. We find that both backward and instant calibration reduce the average measurement error by a factor of two compared to forward calibration. Furthermore, we unveil the arising difficulties if sensor calibration is not based on reliable reference measurements but on sensor readings of low-cost gas sensors which is inevitable in a mobile scenario with only a few reliable sensors. We propose a solution and evaluate its effect on the measurement accuracy in experiments and simulation.

On boundary recognition without location information in wireless sensor networks

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This article tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. We explain the challenges of accurately defining the boundary of a wireless sensor network with and without node positions and provide a new definition of network boundary in the discrete domain. Our solution for boundary recognition approximates the boundary of the sensor network by determining the majority of inner nodes using geometric constructions, which guarantee that for a given d, a node lies inside of the construction for a d-quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We present a fully distributed algorithm for boundary recognition based on these concepts and perform a detailed complexity analysis. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.

Pushing the spatio-temporal resolution limit of urban air pollution maps

Up-to-date information on urban air pollution is of great importance for health protection agencies to assess air quality and provide advice to the general public in a timely manner. In particular, ultrafine particles (UFPs) are widely spread in urban environments and may have a severe impact on human health. However, the lack of knowledge about the spatio-temporal distribution of UFPs hampers profound evaluation of these effects. In this paper, we analyze one of the largest spatially resolved UFP data set publicly available today containing over 25 million measurements. We collected the measurements throughout more than a year using mobile sensor nodes installed on top of public transport vehicles in the city of Zurich, Switzerland. Based on these data, we develop land-use regression models to create pollution maps with a high spatial resolution of 100m × 100 m. We compare the accuracy of the derived models across various time scales and observe a rapid drop in accuracy for maps with subweekly temporal resolution. To address this problem, we propose a novel modeling approach that incorporates past measurements annotated with metadata into the modeling process. In this way, we achieve a 26% reduction in the root-mean-square error-a standard metric to evaluate the accuracy of air quality models-of pollution maps with semi-daily temporal resolution. We believe that our findings can help epidemiologists to better understand the adverse health effects related to UFPs and serve as a stepping stone towards detailed real-time pollution assessment.

On Boundary Recognition without Location Information in Wireless Sensor Networks

Boundary recognition is an important and challenging issue in wireless sensor networks when no coordinates or distances are available. The distinction between inner and boundary nodes of the network can provide valuable knowledge to a broad spectrum of algorithms. This paper tackles the challenge of providing a scalable and range-free solution for boundary recognition that does not require a high node density. Our solution approximates the boundary of the sensor network by determining the inner nodes using geometric constructions that guarantee that, for a given d, a node lies inside of the construction for a d-quasi unit disk graph model of the wireless sensor network. Moreover, such geometric constructions make it possible to compute a guaranteed distance from a node to the boundary. We provide a thorough evaluation of our approach and show that it is applicable to dense as well as sparse deployments.

Reducing multi-hop calibration errors in large-scale mobile sensor networks

Frequent sensor calibration is essential in sensor networks with low-cost sensors. We exploit the fact that temporally and spatially close measurements of different sensors measuring the same phenomenon are similar. Hence, when calibrating a sensor, we adjust its calibration parameters to minimize the differences between co-located measurements of previously calibrated sensors. In turn, freshly calibrated sensors can now be used to calibrate other sensors in the network, referred to as multi-hop calibration. We are the first to study multi-hop calibration with respect to a reference signal (micro-calibration) in detail. We show that ordinary least squares regression---commonly used to calibrate noisy sensors---suffers from significant error accumulation over multiple hops. In this paper, we propose a novel multi-hop calibration algorithm using geometric mean regression, which (i) highly reduces error propagation in the network, (ii) distinctly outperforms ordinary least squares in the multi-hop scenario, and (iii) requires considerably fewer ground truth measurements compared to existing network calibration algorithms. The proposed algorithm is especially valuable when calibrating large networks of heterogeneous sensors with different noise characteristics. We provide theoretical justifications for our claims. Then, we conduct a detailed analysis with artificial data to study calibration accuracy under various settings and to identify different error sources. Finally, we use our algorithm to accurately calibrate 13 million temperature, ground ozone (O3), and carbon monoxide (CO) measurements gathered by our mobile air pollution monitoring network.

Removing the memory limitations of sensor networks with flash-based virtual memory

Virtual memory has been successfully used in different domains to extend the amount of memory available to applications. We have adapted this mechanism to sensor networks, where, traditionally, RAM is a severely constrained resource. In this paper we show that the overhead of virtual memory can be significantly reduced with compile-time optimizations to make it usable in practice, even with the resource limitations present in sensor networks. Our approach, ViMem, creates an efficient memory layout based on variable access traces obtained from simulation tools. This layout is optimized to the memory access patterns of the application and to the specific properties of the sensor network hardware. Our implementation is based on TinyOS. It includes a pre-compiler for nesC code that translates virtual memory accesses into calls of ViMems runtime component. ViMem uses flash memory as secondary storage. In order to evaluate our system we have modified nontrivial existing applications to make use of virtual memory. We show that its runtime overhead is small even for large data sizes.

TinyLTS: Efficient network-wide Logging and Tracing System for TinyOS

Logging and tracing are important methods to gain insight into the behavior of sensor network applications. Existing generic solutions are often limited to nodes with a direct serial connection and do not provide the required efficiency for network-wide logging. Instead, this is often realized by application-specific subsystems developed for custom logging statements. In this paper, we present TinyLTS - a generic and efficient Logging and Tracing System for TinyOS. TinyLTS consists of a compiler extension that separates dynamic from static information at compile time, a declarative solution for inserting logging statements, an extensible framework for flexible storing and transmitting of logging data and a frontend for recombining dynamic and static information. Our system provides concise yet expressive programming abstractions for the developer combined with efficiency comparable to custom solutions.

Model-Driven Accuracy Bounds for Noisy Sensor Readings

Wireless sensor networks are increasingly used in application scenarios where a high data quality is inevitable, e.g., the control of industrial production areas. Nevertheless, many deployments must live with strict constraints regarding the sensing hardware and may not employ newest sensing technologies, e.g., due to limited energy budget, size, and bandwidth. Additionally, many applications would benefit from not only gathering absolute sensor readings but also knowing the quality of their low-cost sensor measurements. In this paper, we introduce a model-driven approach that (i) provides reliable accuracy bounds for individual noisy sensor readings and (ii) detects systematic and transient sensor errors. We apply our method to static and mobile real-world deployments of noisy and unstable low-cost sensors by analyzing large sets of urban temperature and ozone measurements. We find that the proposed algorithm successfully calculates precise accuracy bounds. We compare them to measurements of high-quality instruments and show that up to 96 % of the reference measurements are inside the computed accuracy bounds in the static scenario and up to 94 % in the mobile scenario. This is surprisingly high for the used low-cost sensors. By analyzing data from our static longterm deployment, we reveal that the ozone sensors reliability is dependent on seasonal weather conditions.