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Transcript
Query Processing for Sensor Networks Yong Yao and Johannes Gehrke (Presentation: Anne Denton March 8, 2003) Outline What sensor networks are we talking about? What are the issues? What are the choices? Network issues Database issues Routing Query plans Related work What Sensor Networks are we talking about? Commercially available: Size: a few cubic inches Operating system Projected according to Moore’s law: ¼ inch available soon (not sure sure if Moore talked about batteries …) Embedded version of Linux (redhat) or Windows ce.net Wireless multi-hop RF radio Powered by batteries (LAN-attached with permanent power sources Berkeley MICA Mote http://www.xbow.com/Products/Product_pdf_files/Wireless_pdf/MICA.pdf Note related work to Gehrke’s is done at Berkeley (TinyDB) Issues Wireless Power consumption 1 year idle 1 week under full load Computation Limited QoS Latency with high variance Limited bandwith Frequently drops packets Limited memory and computing power Uncertainty in sensor readings Supported Sensors Temperature Light Magnetometers Accelerometers Microphones Example Uses Buildings Biology “Is Yong in his office” “Is there an empty seat in the meeting room” Find out about existence of specific species of bird Map bird’s trail MICA Mote developed under DARPA grant … Choices Query layer should be declarative In-Network processing Preservation of energy and bandwidth Ratio of sending 1 bit vs. executing one instruction 220 to 2900 depending on architecture Different trade-offs => job of query layer Abstract user from physical details (Why are database people interested …) Long-term, e.g., monitoring environment Short-term, e.g., battlefield Query Proxy between network and application layer (bypasses routing layer to some extent) Must be closely linked with network layer More Choices Special nodes to access network Gateway nodes Noise requires “fusing” of data Aggregation important Queries need DURATION and EVERY Event-oriented model (triggers) desirable but not implemented In-Network Aggregation Why? Partial aggregation Energy to transmit is heaviest burden Possible for algebraic aggregate operators (MAX, MIN, SUM, AVG) Impossible for holistic operator (MEDIAN) Otherwise: packet merging http://citeseer.nj.nec.com/gray97data.html Synchronization Necessary for partial aggregation and packet merging AVG and SUM are duplicate sensitive aggregate operators: Spanning tree MIN and MAX are not duplicate sensitive DAG may be sufficient Pragmatic approach to synchronization Problem: Predictions may fail due to network reorganization or query results bi-directional prediction Routing Differences to wired network Many ad-hoc routing algorithms exist Everybody has to share the routing job Network is unstable Routing layer in protocol stack Database approach requires changes to routing protocol Gehrke points out that that’s not unusual: Database file-access also bypasses operating system to some extent Changes to Routing Protocol Intercepting of packets to achieve Differences in communication pattern Packet merging Partial aggregation Communication with leader rather than point-topoint Knowledge about neighbors Route initialization and maintainance … Query Plans Example query “What is the quietest open classroom in Upson Hall” 2 levels of aggregation Query plan has Compute average value for each qualified class room Select minimum average over all class rooms Flow blocks Leader nodes Differences to traditional optimizers Focus on communication cost Flow block instead of relational operator Flow blocks Task Collect data Perform computations Parameters Set of source nodes Leader selection policy Routing structure, e.g., DAG, tree Computation Query Optimization Example SELECT D.gid, AVG(D.value) FROM SensorData D GROUP BY D.gid HAVING AVG(D.value)>Threshold Flow block for each group Good if nodes in group physically close In-Network Aggregation Single flow block for all Better if nodes in group are interspersed No In-Network Aggregation possible Packet merging more efficient Experiments Using a simulator IEEE 802.11 as MAC layer Prove energy decrease from in-Network aggregation and packet merging Extra delay overcompensated by reduced collisions … prove that the rest works too Summary Interesting database as well as network issues No data mining issues in this paper (although I could think of some …)