Model-based approximate querying in sensor networks

Abstract

Declarative queries are proving to be an attractive paradigm for interacting with networks of wireless sensors. The metaphor that "the sensornet is a database" is problematic, however, because sensors do not exhaustively represent the data in the real world. In order to map the raw sensor readings onto physical reality, a model of that reality is required to complement the readings. In this article, we enrich interactive sensor querying with statistical modeling techniques. We demonstrate that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy. Utilizing the combination of a model and live data acquisition raises the challenging optimization problem of selecting the best sensor readings to acquire, balancing the increase in the confidence of our answer against the communication and data acquisition costs in the network. We describe an exponential time algorithm for finding the optimal solution to this optimization problem, and a polynomial-time heuristic for identifying solutions that perform well in practice. We evaluate our approach on several real-world sensor-network data sets, taking into account the real measured data and communication quality, demonstrating that our model-based approach provides a high-fidelity representation of the real phenomena and leads to significant performance gains versus traditional data acquisition techniques. We thank Intel Corporation and Intel Research-Berkeley for supporting the authors' during much of this work. This article includes and extends results that were previously published in VLDB 2004 [18], and combines these techniques with the conditional planning approach published in ICDE 2005 [17].

The Challenges and Issues Facing the Deployment of RFID Technology

“…It will not however replace the missed readings. Statistical Approximations refer to the use of a Model-Based Querying system to return approximate readings found from the sensor networks (Deshpande et al, 2004;Deshplande et al, 2005). Although this method is not used primarily for RFID technology, the method is applied to wireless sensors which provide additional functionality that RFID tags do not (i.e.…”

Building ranked mashups of unstructured sources with uncertain information

“…While our main driving application is mashups, we believe that our study is relevant to other contexts as well. For example, in sensor networks [10], sensor readings can be represented as intervals with associated density functions due to the inability to continuously maintain the latest readings. An example rank join query is to find the locations with the best temperature and light settings by joining streams of sensor readings based on location.…”

Quality-aware sensor data collection

“…[Deshpande et al (2005)]. The use of approximate data allows flexibility on when to send data, creating an opportunity to decrease the amount of energy used by motes.…”

“…In the event-driven techniques, determining the frequency of updates based upon the current value and known value at the base station has been used ]. In the query driven techniques, reductions in energy can be achieved by aggregation of data in queries to reduce data sent back [Deligiannakis et al (2004)] or statistically modeling the data at the base station to reduce queries [Deshpande et al (2005)]. In contrast, this work dynamically switches between push and pull techniques based on system conditions [Hakkarinen and Han (2008)].…”