Embedded Networked Sensing for Environmental Monitoring Applications and Challenges Deborah Estrin Center for Embedded Networked Sensing CENS Director UCLA Computer Science Department Professor Work summarized here is largely that of students and staff at CENS Embedded Networked Sensing Potential Micro sensors onboard processing wireless interfaces feasible at very small scale can monitor phenomena up close Ecosystems Biocomplexity Marine Microorganisms Enables spatially and temporally dense environmental monitoring Embedded Networked Sensing will reveal previously unobservable phenomena Contaminant Transport Seismic Structure Response ENS enabled by Networked Sensor Node Developments LWIM III AWAIRS I UCLA 1996 UCLA RSC 1998 Geophone RFM Geophone DS SS radio PIC star Radio strongARM network Multi hop networks Sensor Mote Medusa MK 2 UCB 2000 UCLA NESL RFM radio 2002 Atmel Predecessors in DARPA Packet Radio program USC ISI Distributed Sensor Network Project DSN ENS Technology Design Themes Long lived systems that can be untethered wireless and unattended Communication will be the persistent primary consumer of scarce energy resources Mote 720nJ bit xmit 4nJ op Autonomy requires robust adaptive self configuring systems Leverage data processing inside the network Exploit computation near data to reduce communication achieve scalability Collaborative signal processing Achieve desired global behavior with localized algorithms distributed control The network is the sensor Manges Smith Oakridge Natl Labs 10 98 Requires robust distributed systems of hundreds of physically embedded unattended and often untethered devices ENS Architecture Drivers DRIVERS Varied and variable environments TECHNICAL CAPABILITIES Adaptive Self Configuring Wireless Systems Energy and scalability Distributed Signal and Information Processing Heterogeneity of devices Networked Info Mechanical Systems Smaller component size and cost Embeddable Microsensors CENS Systems under design construction Biology Biocomplexity Microclimate monitoring Triggered image capture Canopy net Wind River Canopy Crane Site Contaminant Transport County of Los Angeles Sanitation Districts CLASD wastewater recycling project Palmdale CA Seismic monitoring 50 node ad hoc wireless multi hop seismic network Structure response in USGSinstrumented Factor Building w augmented wireless sensors Ecosystem Monitoring Sensor system logical components Tasking configuration sample rates event definition triggering Data Transport Device management sample manipulation and caching with timing Duty cycling Other important examples of habitat monitoring systems Berkeley Intel GDI and Botanical gardens Extensible Sensing System ESS Software Tiered architecture components Mica2 motes 8 bit microcontrollers w TOS with Sensor Interface Board hosting in situ sensors Microservers are solar powered run linux 32 bit processors Pub sub bus over 802 11 to Databases visualization and analysis tools GUI Web interfaces Data multicast over Internet on publish and subscribe bus system called Subject Servers to databases GUIs other data analysis tools clients Osterweil Rahimi Mysore Wimbrow Long lived Self configuring Systems Localization Time Synchronization Irregular deployment and environment Dynamic network topology Hand configuration will fail Scale variability maintenance Calibration Information Transport Aggregation and Storage Common theme local adaptation and redundancy Programming Model Event Detection Network Architecture Can we adapt Internet protocols and end to end architecture Internet routes data using IP Addresses in Packets and Lookup tables in routers Humans get data by naming data to a search engine Many levels of indirection between name and IP address Works well for the Internet and for support of Person to Person communication Embedded energy constrained un tethered smallform factor unattended systems cant tolerate communication overhead of indirection Directed Diffusion Data Centric Routing Data centric approach has the right scaling properties name data not nodes with externally relevant attributes data type time location of node SNR etc diffuse requests and responses across network using application driven routing e g geo sensitive support in network aggregation and processing Not end to end data delivery Not just a database query Heidemann et al SOSP 01 Krishnamachari et al 02 Diffusion One Phase Pull Sources Sink Optimized version of general diffusion Heidemann et al Pulls data out to only one sink at a time saves energy Used in Ecosystem application over Mica 2 motes TinyDiffusion Osterweil et al Interest Gradient Routed Data Voronoi Scoping Restricted Floods from Multiple Sinks Benefits of multiple sinks Reduce average path length Equalize load over multiple trees Tiered architecture redundancy BUT Linear increase in interests flooded Voronoi clusters partition topology each subset contains nodes closest to associated sink Only fwd interests from closest sink No overlap between floods Motes receive interest from their closest sink Scalable both tiers grow load per mote remains constant With Henri Dubois Ferri re EPFL Live network emstar emview 3 sinks 55 motes color coded clusters Multi hop data extraction characteristics using Tiny Diffusion Collected basic network characteristics to verify readiness for sensor deployment Average system loss rates analyzed over fixed intervals and related to nodes of with various average minimum and maximum hop counts under 3 end to end Additional nodes deployed to augment persistent ESS topology to study effects such as loss experienced by nodes introduced with less ground clearance UCB Intel GDI deployment has good results from their fielded borrow monitoring system using same Mote platform Characterizing wireless channels Great variability over distance 50 80 of communication range vertical lines Reception rate not normally distributed around mean and standard deviation Real communication channel is not circular 5 to 30 asymmetric links Not correlated with distance or transmission power Primary cause differences in hardware calibration rx sensitivity energy levels etc Time variations correlated to mean reception rate not distance from transmitter Cerpa Busek et al Research Challenge Networked Info Mechanical Systems NIMS NIMS Architecture Robotic aerial access to full 3 D environment Enable sample acquisition Coordinated Mobility Enables self awareness of Sensing Uncertainty Sensor Diversity Diversity