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Wireless Sensor Networks for Habitat Monitoring
Alan Mainwaring1

Joseph Polastre2

Robert Szewczyk2

David Culler1,2

John Anderson3

Intel Research Laboratory, Berkeley Intel Corporation {amm,dculler}@intel-research.net

2 EECS Department University of California at Berkeley {polastre,szewczyk,culler}@cs.berkeley.edu

College of the Atlantic Bar Harbor, Mainejga@ecology.coa.edu

We provide an in-depth study of applying wireless sensor networks to real-world habitat monitoring. A set of system design requirements are developed that cover the hardware design of the nodes, the design of the sensor network, and the capabilities for remote data access and management. A system architecture is proposed to address these requirements for habitat monitoring ingeneral, and an instance of the architecture for monitoring seabird nesting environment and behavior is presented. The currently deployed network consists of 32 nodes on a small island off the coast of Maine streaming useful live data onto the web. The applicationdriven design exercise serves to identify important areas of further work in data sampling, communications, network retasking, and healthmonitoring.

Categories and Subject Descriptors
C.2.1 [Computer Communication Networks]: Network Architecture and Design; C.3 [Computer Systems Organization]: Special-Purpose and Application-based Systems; J.3 [Computer Applications]: Life and Medical Sciences

General Terms
Design, Performance, Experimentation

Habitat and environmental monitoring represent a class ofsensor network applications with enormous potential benefits for scientific communities and society as a whole. Instrumenting natural spaces with numerous networked microsensors can enable long-term data collection at scales and resolutions that are difficult, if not impossible, to obtain otherwise. The intimate connection with its immediate physical environment allows each sensor to provide localizedmeasurements and detailed information that is hard to obtain through traditional instrumentation. The integration of local processing and storage allows sensor nodes to perform

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complex filtering and triggering functions, as well as to apply application-specific or sensor-specific datacompression algorithms. The ability to communicate not only allows information and control to be communicated across the network of nodes, but nodes to cooperate in performing more complex tasks, like statistical sampling, data aggregation, and system health and status monitoring [8, 9]. Increased power efficiency gives applications flexibility in resolving fundamental design tradeoffs, e.g., betweensampling rates and battery lifetimes. Low-power radios with well-designed protocol stacks allow generalized communications among network nodes, rather than point-to-point telemetry. The computing and networking capabilities allow sensor networks to be reprogrammed or retasked after deployment in the field. Nodes have the ability to adapt their operation over time in response to changes in theenvironment, the condition of the sensor network itself, or the scientific endeavor. We are working with members of the life science community to make the potential of this emerging technology a reality. Taking an application-driven approach quickly separates actual problems from potential ones, and relevant issues from irrelevant ones. The application context helps to differentiate problems with simple,...
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