declarative sensor networks with applications in landslide detection David Chu Computer Science Division EECS Department UC Berkeley iCAST/CMU/TRUST Joint Conference 9 January 2007

context Leach's Storm Petrel Sensor Networks 10’s – 100’s – 1000’s – 10,000’s

context Sensor Networks early experiences

motivation programming sensor networks is difficult! building entire sensor systems is even harder!!

inspiration data management network design s e n s o r n e t w o r k s

inspiration : data management declarative is widely used in data management –relational databases –spreadsheets –abstract “what” from “how” (Sensor-Network-As-Database)

inspiration : network design declarative is new idea in networking –compact –flexible –analyzable, optimizable –Internet Routing, Overlays built declaratively (the P2 project)

inspiration data management network design s e n s o r n e t w o r k s ( DSN )

what we did adapted declarative language built compiler & runtime for sensornets wrote declarative examples

P. Levis, N. Patel, D. Culler, S. Shenker. "Trickle: A Self- Regulating Algorithm for Code Propagation and Maintenance in Wireless Sensor Networks." NSDI … from original Trickle paper… DSN specification 10x6 topology 30x2 topology

agenda 1.language overview 2.declarative sensornet examples 3.system architecture 4.feasibility assessment 5.application to landslide detection

brief language overview Rule2: Fact: Rule1: join don’t care Built-ins: implies

a full example : tree D S C D Z C2 S C1

second example : multi-hop collection

and others… geographic routing* tracking localization link estimator *fallback routing not shown

arch : compiler Network support Generated nesC code store(…) :- prod(…), cons(…). … path(…) :- link(…), dest(…). … Binary Image Snlog Compiler/Optimizer Snlog Program GenericPredicateTemplate … nesC Templates nesC Backend Execution Planner Snlog Frontend nesC Compiler Built-in Predicates Type system Database Operators Runtime Components RuntimeTemplate … DSN Runtime Support

arch : runtime the network Join Proj tupleready Join Agg Proj Sel table (compiler generated) builtin (user’s library) database operators (compiler’s library) push interfaces pull interfaces thread of control event signal SelAg Proj ……… …… … … runtime daemon mac daemon tupleready sendready tupleready sendready

implementation challenges predictable execution → dynamic vs. static allocation memory constraints → memory footprint optimizations no temporary tables, join/agg operator choice asynchrony → rule-level atomicity priorities

evaluation

evaluating tree-collection messages sent hop-counts (similar performance)

evaluating trickle (similar performance)

lines of code

compiled size TelosB mote code space = 48KB, data space = 10KB

VLDB 2006 demo

application large scale and fine-grained debris flow monitoring

[Left] La Conchita, California – a small seaside community along Highway 101 south of Santa Barbara. This landslide and debris flow occurred in the spring of A reoccurrence in 2005 claimed 4 lives and resulted in 29 missing persons. [Right] Chehalis, Washington - landslides and debris flows during the winter storms of February Photographs by R.L. Schuster, U.S. Geological Survey.

[Above] The locations of the and debris flow deployment sites. [Top Right] Smoke from the Day Fire. [Middle Right] Recently burned hillside in Burbank, CA was the site of two debris flows in Winter season. [Bottom Right] Base of the channel after debris flow with remaining sediment. [Bottom Left] Burn- resilient vegetation is quickly recovering just a few months after the fires and debris flows. Harvard Burn Site Day Fire

[Above] Parshall flume used in conjunction with water level logger at the channel’s choke-point. [Top Right] Custom overland flow sensor for fine- grained detection of water runoff. [Bottom Right] Solar-powered base station for actuating and gathering data from the wireless sensor network, shown here connected to laptop during testing.

conclusion sensor networks → data + communication several examples of functional programs feasible for today’s hardware platforms preparing for landslide deployment

thanks collaborators Joe Hellerstein, Scott Shenker, Ion Stoica Arsalan Tavakoli, Lucian Popa Tsung-Te Lai Phil Levis, Jung Woo Lee, Aby John Daniel Malmon