Data Management for Sensor Networks Zachary G. Ives University of Pennsylvania CIS 650 – Database & Information Systems April 4, 2005

2 Administrivia  Please send me an updating your project status  Next readings:  Wednesday – read and summarize the Brin and Page paper

3 Today’s Trivia Question

4 Sensors and Sensor Networks  Trends:  Cameras and other sensors are very cheap  Microprocessors and microcontrollers can be very small  Wireless networks are easy to build  Why not instrument the physical world with tiny wireless sensors and networks?  Vision: “Smart dust”  Berkeley motes, RF tags, cameras, camera phones, temperature sensors, etc.  Today we already see pieces of this:  Penn buildings and SCADA system  250+ surveillance cameras through campus

5 What Can We Do with Sensor Networks?  Many “passive” monitoring applications:  Environmental monitoring:  temperature in different parts of a building  air quality  etc.  Law enforcement:  Video feeds and anomalous behavior  Research studies:  Study ocean temperature, currents  Monitor status of eggs in endangered birds’ nests  ZebraNet  Fun:  Record sporting events or performances from every angle (video & audio)  Ultimately, build reactive systems as well: robotics, Mars landers, …

6 Some Challenges  Highly distributed!  May have thousands of nodes  Know about a few nodes within proximity; may not know location  Nodes’ transmissions may interfere with one another  Power and resource constraints  Most of these devices are wireless, tiny, battery-powered  Can only transmit data every so often  Limited CPU, memory – can’t run sophisticated code  High rate of failure  Collisions, battery failures, sensor calibration, …

7 The Target Platform  Most sensor network research argues for the Berkeley mote as a target platform:  Mote: 4MHz, 8-bit CPU  128KB RAM  512KB Flash memory  40kbps radio, 100 ft range  Sensors:  Light, temperature, microphone  Accelerometer  Magnetometer

8 Sensor Net Data Acquisition First: build routing tree Second: begin sensing and aggregation

9 Sensor Net Data Acquisition (Sum) First: build routing tree Second: begin sensing and aggregation (e.g., sum)

10 Sensor Net Data Acquisition (Sum) First: build routing tree Second: begin sensing and aggregation (e.g., sum)

11 Sensor Network Research  Routing: need to aggregate and consolidate data in a power-efficient way  Ad hoc routing – generate routing tree to base station  Generally need to merge computation with routing  Robustness: need to combine info from many sensors to account for individual errors  What aggregation functions make sense?  Languages: how do we express what we want to do with sensor networks?  Many proposals here

12 A First Try: Tiny OS and nesC  TinyOS: a custom OS for sensor nets, written in nesC  Assumes low-power CPU  Very limited concurrency support: events (signaled asynchronously) and tasks (cooperatively scheduled)  Applications built from “components”  Basically, small objects without any local state  Various features in libraries that may or may not be included  interface Timer { command result_t start(char type, uint32_t interval); command result_t stop(); event result_t fired(); }

13 Drawbacks of this Approach  Need to write very low-level code for sensor net behavior  Only simple routing policies are built into TinyOS – some of the routing algorithms may have to be implemented by hand  Has required many follow-up papers to fill in some of the missing pieces, e.g., Hood (object tracking and state sharing), …

14 An Alternative  “Much” of the computation being done in sensor nets looks like what we were discussing with STREAM  Today’s sensor networks look a lot like databases, pre-Codd  Custom “access paths” to get to data  One-off custom-code  So why not look at mapping sensor network computation to SQL?  Not very many joins here, but significant aggregation  Now the challenge is in picking a distribution and routing strategy that provides appropriate guarantees and minimizes power usage

15 TinyDB and TinySQL  Treat the entire sensor network as a universal relation  Each type of sensor data is a column in a global table  Tuples are created according to a sample interval (separated by epochs)  (Implications of this model?)  SELECT nodeid, light, temp FROM sensors SAMPLE INTERVAL 1s FOR 10s

16 Storage Points and Windows  Like Aurora, STREAM, can materialize portions of the data:  CREATE STORAGE POINT recentlight SIZE 8 AS (SELECT nodeid, light FROM sensors SAMPLE INTERVAL 10s)  and we can use windowed aggregates:  SELECT WINAVG(volume, 30s, 5s) FROM sensors SAMPLE INTERVAL 1s

17 Events  ON EVENT bird-detect(loc): SELECT AVG(light), AVG(temp), event.loc FROM sensors AS s WHERE dist(s.loc, event.loc) < 10m SAMPLE INTERVAL 2s FOR 30s  How do we know about events?  Contrast to UDFs? triggers?

18 Power and TinyDB  Cost-based optimizer tries to find a query plan to yield lowest overall power consumption  Different sensors have different power usage  Try to order sampling according to selectivity (sounds familiar?)  Assumption of uniform distribution of values over range  Batching of queries (multi-query optimization)  Convert a series of events into a stream join – does this resemble anything we’ve seen recently?  Also need to consider where the query is processed…

19 Dissemination of Queries  Based on semantic routing tree idea  SRT build request is flooded first  Node n gets to choose its parent p, based on radio range from root  Parent knows its children  Maintains an interval on values for each child  Forwards requests to children as appropriate  Maintenance:  If interval changes, child notifies its parent  If a node disappears, parent learns of this when it fails to get a response to a query

20 Query Processing  Mostly consists of sleeping!  Wake briefly, sample, and compute operators, then route onwards  Nodes are time synchronized  Awake time is proportional to the neighborhood size (why?)  Computation is based on partial state records  Basically, each operation is a partial aggregate value, plus the reading from the sensor

21 Load Shedding & Approximation  What if the router queue is overflowing?  Need to prioritize tuples, drop the ones we don’t want  FIFO vs. averaging the head of the queue vs. delta-proportional weighting  Later work considers the question of using approximation for more power efficiency  If sensors in one region change less frequently, can sample less frequently (or fewer times) in that region  If sensors change less frequently, can sample readings that take less power but are correlated (e.g., battery voltage vs. temperature)  Thursday, 4:30PM, DB Group Meeting, I’ll discuss some of this work

22 The Future of Sensor Nets?  TinySQL is a nice way of formulating the problem of query processing with motes  View the sensor net as a universal relation  Can define views to abstract some concepts, e.g., an object being monitored  But:  What about when we have multiple instances of an object to be tracked? Correlations between objects?  What if we have more complex data? More CPU power?  What if we want to reason about accuracy?