Flexible Decision Support in Device-Saturated Environments

摘要

The widespread distribution of small-scale sensors, actuators, and embedded processors is transforming the physical world into a computing platform. Sensor networks with nodes that combine physical sensing capabilities with networking and computation capabilities have become ubiquitous. Existing sensor networks assume that the sensors are preprogrammed and send data to a central frontend where the data is processed. This approach has two major drawbacks. First, the user cannot change the behavior of the system on the fly. Second, communication in today's networks is orders of magnitude more expensive than local computation, thus in-network processing can vastly reduce resource usage. We investigated a database approach to unite the requirements of scalability and flexibility in monitoring the physical world. We built a new distributed data management infrastructure that scales with the growth of sensor interconnectivity and computational power on the sensors over the next decades. Our system resides directly on the sensor nodes and creates the abstraction of a single processing node without centralizing data or computation. Our system provides scalable, fault-tolerant, flexible data access and intelligent data reduction, and its design involved a confluence of novel research in database query processing, data mining, networking, and distributed systems.