An Integration Framework for Sensor Networks and DSMSs

One component of this project focuses on the issues surrounding the efficient processing of declarative queries within sensor networks, looking in particular at the ways in which traditional, relational query optimizers must change to support the lossy, streaming data from sensornets. Query optimization has the potential to dramatically reduce the energy costs associated with sensor network data collection, offering dramatic increases in network lifetime bandwidth usage.

The second component of the project focusses on data integration issues. For sensor networks to become truly ubiquitous, it is important that such networks be able to integrate with data sources and share query processing with databases outside of the network, including traditional relational databases, flat files, and even other, remote sensor networks. To enable such integration, systems must be enhanced to allow them to understand each other's capabilities and offload query processing tasks when there are significant energy or computational savings to be had. This project seeks to seamlessly integrate sensor network query processing systems with other database systems, be they conventional relational DBMSes or other sensornets. To do this, we introduce a proxy to eliminate the need to make invasive changes to pre-existing software and propose techniques based upon an optimization framework that chooses to push down computation when it will decrease the power consumption of the sensor network.

DSMSs were developed to support an emerging class of applications - monitoring applications - that proved problematic for traditional DBMSs. Monitoring applications are applications that monitor continuous streams of data. Their fundamental data-active/human-passive, real-time, trigger oriented model is difficult to support in the traditional human-active/data-passive trigger-as-secondclass- citizen DBMS model. DSMSs are better suited to supporting these applications by organizing query operators in a work-flow diagram and allowing data to actively stream through these operators, which transform the input data according to a continuous query plan. The performance of a DSMS can be measured using a Quality of Service (QoS) metric in which an application can specify the utility of observed latency, throughput, or quality of result tuples that reach the application.

Sensor networks consist of multiple sensor nodes: small, battery-powered, wireless computers that can contain any number of sensors that can measure the surrounding environment (eg. temperature, light, acceleration, or position). Power is if utmost importance. If used naively, individual sensor nodes will deplete their energy supplies in only a few days. To save power, communication distances are usually reduced to below 100 feet, resulting in most real deployments making use of multi-hop communication where intermediate nodes relay information for their peers. Sensor networks can serve as a data source to monitoring applications and have limited query processing power (simple query operators can be performed on tuples as they are relayed from node to node on their way to a basestation).