Environmental Monitoring 2.0 | EPFL Graph Search

A sensor network data gathering and visualization infrastructure is demonstrated, comprising of Global Sensor Networks (GSN) middleware and Microsoft SensorMap. Users are invited to actively participate in the process of monitoring real-world deployments and can inspect measured data in the form of contour plots overlayed onto a high resolution map and a digital topographic model. Users can go back in time virtually to search for interesting events or simply to visualize the temporal dependencies of the data. The system presented is not only interesting and visually enticing for non-expert users but brings substantial benefits to environmental scientists. The easily installed data acquisition component as well as the powerful data sharing and visualization platform opens up new ground in collaborative data gathering and interpretation in the spirit of Web 2.0 applications.

Distributed spatiotemporal suppression for environmental data collection in real-world sensor networks

Alexander Bahr, William Christopher Evans, Alcherio Martinoli

Environmental processes are often severely over-sampled. As sensor networks become more ubiquitous for this purpose, increasing network longevity becomes ever more important. Radio transceivers in particular are a great source of energy consumption, and many networking algorithms have been proposed that seek to minimize their use. Traditionally, such approaches are often data agnostic, i.e., their performance is not dependent on the properties of the data they transport. In this paper we explore algorithms that exploit environmental relationships in order to reduce the amount of transmitted data while maintaining expected levels of accuracy. We employ a realistic testing environment for evaluating the power savings brought by such algorithms, based on Sensorscope, a commercial sensor network product for environmental monitoring. We implement and test a suppression-based data collection algorithm from literature that to our knowledge has never been implemented on a real system, and propose modifications that make it more suitable for real-world conditions. Using a custom extension board developed for in situ power monitoring, we show that while the algorithms greatly reduce the amount of energy spent on transmitting packets, they have no effect on the real system's overall power consumption due to its preexisting network architecture.

Ieee Computer Soc2013

Statistical Models for Querying and Managing Time-Series Data

Saket Sathe

In recent years we are experiencing a dramatic increase in the amount of available time-series data. Primary sources of time-series data are sensor networks, medical monitoring, financial applications, news feeds and social networking applications. Availability of large amount of time-series data calls for scalable data management techniques that enable efficient querying and analysis of such data in real-time and archival settings. Often the time-series data generated from sensors (environmental, RFID, GPS, etc.), are imprecise and uncertain in nature. Thus, it is necessary to characterize this uncertainty for producing clean answers. In this thesis we propose methods that address these important issues pertaining to time-series data. Particularly, this thesis is centered around the following three topics: Computing Statistical Measures on Large Time-Series Datasets. Computing statistical measures for large databases of time series is a fundamental primitive for querying and mining time-series data [31, 81, 97, 111, 132, 137]. This primitive is gaining importance with the increasing number and rapid growth of time-series databases. In Chapter 3, we introduce the Affinity framework for efficient computation of statistical measures by exploiting the concept of affine relationships [113, 114]. Affine relationships can be used to infer a large number of statistical measures for time series, from other related time series, instead of computing them directly; thus, reducing the overall computational cost significantly. Moreover, the Affinity framework proposes an unified approach for computing several statistical measures at once. Creating Probabilistic Databases from Imprecise Data. A large amount of time-series data produced in the real-world has an inherent element of uncertainty, arising due to the various sources of imprecision affecting its sources (like, sensor data, GPS trajectories, environmental monitoring data, etc.). The primary sources of imprecision in such data are: imprecise sensors, limited communication bandwidth, sensor failures, etc. Recently there has been an exponential rise in the number of such imprecise sensors, which has led to an explosion of imprecise data. Standard database techniques cannot be used to provide clean and consistent answers in such scenarios. Therefore, probabilistic databases that factor-in the inherent uncertainty and produce clean answers are required. An important assumption i while using probabilistic databases is that each data point has a probability distribution associated with it. This is not true in practice — the distributions are absent. As a solution to this fundamental limitation, in Chapter 4 we propose methods for inferring such probability distributions and using them for efficiently creating probabilistic databases [116]. Managing Participatory Sensing Data. Community-driven participatory sensing is a rapidly evolving paradigm in mobile geo-sensor networks. Here, sensors of various sorts (e.g., multi-sensor units monitoring air quality, cell phones, thermal watches, thermometers in vehicles, etc.) are carried by the community (public vehicles, private vehicles, or individuals) during their daily activities, collecting various types of data about their surrounding. Data generated by these devices is in large quantity, and geographically and temporally skewed. Therefore, it is important that systems designed for managing such data should be aware of these unique data characteristics. In Chapter 5, we propose the ConDense (Community-driven Sensing of the Environment) framework for managing and querying community-sensed data [5, 19, 115]. ConDense exploits spatial smoothness of environmental parameters (like, ambient pollution [5] or radiation [2]) to construct statistical models of the data. Since the number of constructed models is significantly smaller than the original data, we show that using our approach leads to dramatic increase in query processing efficiency [19, 115] and significantly reduces memory usage.

EPFL2013

Adaptive Selection Problems in Networked Systems

Runwei Zhang

Networked systems are composed of interconnected nodes that work collaboratively to maximize a given overall utility function. Typical examples of such systems are wireless sensor networks (WSNs) and participatory sensing systems: sensor nodes, either static or mobile, are deployed for monitoring a certain physical field. In these systems, there are a set of problems where we need to adaptively select a strategy to run the system, in order to enhance the efficiency of utilizing the resources available to the system. In particular, we study four adaptive selection problems as follows. We start by studying the problem of base-station (BS) selection in WSNs. Base stations are critical sensor nodes whose failures cause severe data losses. Deploying multiple fixed BSs improves the robustness, yet this scheme is not energy efficient because BSs have high energy consumptions. We propose a scheme that selects only one BS to be active at a time; other BSs are kept passive and act as regular sensor nodes. This scheme substantially reduces the energy supplies required by individual BSs. Then, we propose an algorithm for adaptively selecting the active BS so that the spatially and temporally varying energy resources are efficiently utilized. We also address implementation issues and apply the proposed algorithm on a real WSN. Field experiments have shown the effectiveness of the proposed algorithm. We generalize the BS selection problem by considering both the energy efficiency of regular sensor nodes and that of BSs. In this scheme, a subset of active BSs (instead of only one) is adaptively selected and the routing of regular sensor nodes is adjusted accordingly. Because BSs have high fixed-energy consumptions and because the number of candidate subsets of active BSs is exponential with the number of BSs, this general BS selection problem is NP-hard. We propose a polynomial-time algorithm that is guaranteed, under mild conditions, to achieve a network lifetime at least 62% of the optimal one. Through extensive numerical simulations, we verify that the lifetime achieved by the proposed algorithm is always very close to the optimum. We then study the problem of scheduling the sparse-sensing patterns in WSNs. We observe that the traditional scheme of periodically taking sensing samples is not energy efficient. Instead, we propose to adaptively schedule when and where to activate sensors for sampling a physical field, such that the energy efficiency is enhanced and the sensing precision is maintained. The schedules are learnt from the temporal signal models derived from the collected measurements. Then, using the obtained signal models and the sparse sensing-measurements, the original signal can be effectively recovered. This proposed method requires minimal on-board computation, no inter-node communications and achieves an appealing reconstruction performance. With experiments on real-world datasets, we demonstrate significant improvements over both traditional sensing schemes and the state-of-the-art sparse-sensing schemes, particularly when the measured data is characterized by a strong temporal correlation. In the last part of the thesis, we discuss the sparse-sensing framework by exploiting the spatial correlations rather than the temporal correlations among the captured measurements. In this framework, application-specific utility functions can be employed. By adaptively selecting a small subset of active sensors for sensing, a certain utility is guaranteed and the efficiency of the sensing system is enhanced. We apply this framework both in static WSNs and participatory sensing systems where sensors move in an uncoordinated manner. Through extensive simulations, we show that our proposed algorithm enhances the resource efficiency.