Research Directions in Sensor Data Streams: Solutions and Challenges

A typical framework of sensor streams is data obtained from wireless networks of sensors, embedded in a physical space, continuously communicating a stream of data to a database. These wireless networks typically consist of large number of low-power and limited-bandwidth devices. They are primarily used for monitoring of several physical phenomenon such as, contamination, climate, building structure, etc., potentially in remote harsh environments. Research in sensor streaming has been generally focused on ultimate utilization of such devices given their limited resources and unattended deployment. This paper surveys current research directions in sensor data streams. In particular, it emphasizes existing work on storage and gathering of sensor data, architectures for querying sensor streams, and handling of erroneous sensors. It also highlights some open problems and discusses research paths to pursue in this exciting research area.

Poster abstract: Online data cleaning in wireless sensor networks

We present our ongoing work on data quality problems in sensor networks. Specifically, we deal with the problems of outliers, missing information, and noise. We propose an approach for modeling and online learning of spatio-temporal correlations in sensor networks. We utilize the learned correlations to discover outliers and recover missing information. We also propose a Bayesian approach for reducing the effect of noise on sensor data online

Context-aware sensors

Abstract. Wireless sensor networks typically consist of a large number of sensor nodes embedded in a physical space. Such sensors are low-power devices that are primarily used for monitoring several physical phenomena, potentially in remote harsh environments. Spatial and temporal dependencies between the readings at these nodes highly exist in such scenarios. Statistical contextual information encodes these spatio-temporal dependencies. It enables the sensors to locally predict their current readings based on their own past readings and the current readings of their neighbors. In this paper, we introduce context-aware sensors. Specifically, we propose a technique for modeling and learning statistical contextual information in sensor networks. Our approach is based on Bayesian classifiers; we map the problem of learning and utilizing contextual information to the problem of learning the parameters of a Bayes classifier, and then making inferences, respectively. We propose a scalable and energy-efficient procedure for online learning of these parameters in-network, in a distributed fashion. We discuss applications of our approach in discovering outliers and detection of faulty sensors, approximation of missing values, and in-network sampling. We experimentally analyze our approach in two applications, tracking and monitoring.

Cleaning and querying noisy sensors

Sensor networks have become an important source of data with numerous applications in monitoring various real-life phenomena as well as industrial applications and traffic control. Unfortunately, sensor data is subject to several sources of errors such as noise from external sources, hardware noise, inaccuracies and imprecision, and various environmental effects. Such errors may seriously impact the answer to any query posed to the sensors. In particular, they may yield imprecise or even incorrect and misleading answers which can be very significant if they result in immediate critical decisions or activation of actuators. In this paper, we present a framework for cleaning and querying noisy sensors. Specifically, we present a Bayesian approach for reducing the uncertainty associated with the data, that arise due to random noise, in an online fashion. Our approach combines prior knowledge of the true sensor reading, the noise characteristics of this sensor, and the observed noisy reading in order to obtain a more accurate estimate of the reading. This cleaning step can be performed either at the sensor level or at the base-station. Based on our proposed uncertainty models and using a statistical approach, we introduce several algorithms for answering traditional database queries over uncertain sensor readings. Finally, we present a preliminary evaluation of our proposed approach using synthetic data and highlight some exciting research directions in this area